| Experimental Factor Ontology (EFO) Information | |
| Identifier | EFO_0004503 |
| Description | A measurement quantifying some blood cell, or component. | Trait category |
Hematological measurement
|
| Child trait(s) |
29 child traits
|
| Polygenic Score ID & Name | PGS Publication ID (PGP) | Reported Trait | Mapped Trait(s) (Ontology) | Number of Variants |
Ancestry distribution GWAS Dev Eval |
Scoring File (FTP Link) |
|---|---|---|---|---|---|---|
| PGS000088 (baso) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Basophil count | basophil count | 9,121 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000088/ScoringFiles/PGS000088.txt.gz | |
| PGS000089 (baso_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Basophil percentage of white cells | basophil percentage of leukocytes | 5,248 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000089/ScoringFiles/PGS000089.txt.gz | |
| PGS000090 (eo) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Eosinophil count | eosinophil count | 22,949 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000090/ScoringFiles/PGS000090.txt.gz | |
| PGS000091 (eo_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Eosinophil percentage of white cells | eosinophil percentage of leukocytes | 24,406 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000091/ScoringFiles/PGS000091.txt.gz | |
| PGS000092 (hct) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Hematocrit | hematocrit | 28,214 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000092/ScoringFiles/PGS000092.txt.gz | |
| PGS000093 (hgb) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Hemoglobin concentration | hemoglobin measurement | 25,090 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000093/ScoringFiles/PGS000093.txt.gz | |
| PGS000094 (hlr) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
High light scatter reticulocyte count | reticulocyte count | 25,493 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000094/ScoringFiles/PGS000094.txt.gz | |
| PGS000095 (hlr_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
High light scatter reticulocyte percentage of red cells | reticulocyte count | 21,957 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000095/ScoringFiles/PGS000095.txt.gz | |
| PGS000096 (irf) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Immature fraction of reticulocytes | reticulocyte count | 17,850 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000096/ScoringFiles/PGS000096.txt.gz | |
| PGS000097 (lymph) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Lymphocyte count | lymphocyte count | 24,646 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000097/ScoringFiles/PGS000097.txt.gz | |
| PGS000098 (lymph_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Lymphocyte percentage of white cells | lymphocyte percentage of leukocytes | 22,363 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000098/ScoringFiles/PGS000098.txt.gz | |
| PGS000099 (mch) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 27,081 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000099/ScoringFiles/PGS000099.txt.gz | |
| PGS000100 (mchc) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Mean corpuscular hemoglobin concentration | mean corpuscular hemoglobin concentration | 11,832 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000100/ScoringFiles/PGS000100.txt.gz | |
| PGS000101 (mcv) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Mean corpuscular volume | mean corpuscular volume | 25,001 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000101/ScoringFiles/PGS000101.txt.gz | |
| PGS000102 (mono) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Monocyte count | monocyte count | 28,162 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000102/ScoringFiles/PGS000102.txt.gz | |
| PGS000103 (mono_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Monocyte percentage of white cells | monocyte percentage of leukocytes | 22,843 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000103/ScoringFiles/PGS000103.txt.gz | |
| PGS000104 (mpv) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Mean platelet volume | mean platelet volume | 25,745 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000104/ScoringFiles/PGS000104.txt.gz | |
| PGS000105 (neut) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Neutrophil count | neutrophil count | 23,864 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000105/ScoringFiles/PGS000105.txt.gz | |
| PGS000106 (neut_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Neutrophil percentage of white cells | neutrophil percentage of leukocytes | 22,049 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000106/ScoringFiles/PGS000106.txt.gz | |
| PGS000107 (pct) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Platelet crit | platelet crit | 30,459 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000107/ScoringFiles/PGS000107.txt.gz | |
| PGS000108 (pdw) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Platelet distribution width | platelet component distribution width | 25,995 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000108/ScoringFiles/PGS000108.txt.gz | |
| PGS000109 (plt) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Platelet count | platelet count | 26,683 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000109/ScoringFiles/PGS000109.txt.gz | |
| PGS000110 (rbc) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Red blood cell count | erythrocyte count | 23,242 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000110/ScoringFiles/PGS000110.txt.gz | |
| PGS000111 (ret) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reticulocyte count | reticulocyte count | 26,077 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000111/ScoringFiles/PGS000111.txt.gz | |
| PGS000112 (ret_p) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reticulocyte fraction of red cells | reticulocyte count | 25,939 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000112/ScoringFiles/PGS000112.txt.gz | |
| PGS000113 (wbc) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
White blood cell count | leukocyte count | 28,383 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000113/ScoringFiles/PGS000113.txt.gz | |
| PGS000127 (GS-E-EUR) |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
HbA1c | HbA1c measurement | 21 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000127/ScoringFiles/PGS000127.txt.gz |
| PGS000128 (GS-E-AFR) |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
HbA1c | HbA1c measurement | 22 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000128/ScoringFiles/PGS000128.txt.gz |
| PGS000129 (GS-E-EAS) |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
HbA1c | HbA1c measurement | 17 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000129/ScoringFiles/PGS000129.txt.gz |
| PGS000130 (GS-G-EUR) |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
HbA1c | HbA1c measurement | 19 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000130/ScoringFiles/PGS000130.txt.gz |
| PGS000131 (GS-G-AFR) |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
HbA1c | HbA1c measurement | 19 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000131/ScoringFiles/PGS000131.txt.gz |
| PGS000132 (GS-G-EAS) |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
HbA1c | HbA1c measurement | 19 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000132/ScoringFiles/PGS000132.txt.gz |
| PGS000163 (baso) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Basophil count | basophil count | 185 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000163/ScoringFiles/PGS000163.txt.gz |
| PGS000164 (baso_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Basophil percentage of white cells | basophil percentage of leukocytes | 150 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000164/ScoringFiles/PGS000164.txt.gz |
| PGS000165 (eo) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Eosinophil count | eosinophil count | 607 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000165/ScoringFiles/PGS000165.txt.gz |
| PGS000166 (eo_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Eosinophil percentage of white cells | eosinophil percentage of leukocytes | 571 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000166/ScoringFiles/PGS000166.txt.gz |
| PGS000167 (hct) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Hematocrit | hematocrit | 502 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000167/ScoringFiles/PGS000167.txt.gz |
| PGS000168 (hgb) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Hemoglobin concentration | hemoglobin measurement | 515 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000168/ScoringFiles/PGS000168.txt.gz |
| PGS000169 (hlr) |
PGP000078 | Vuckovic D et al. Cell (2020) |
High light scatter reticulocyte count | reticulocyte count | 570 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000169/ScoringFiles/PGS000169.txt.gz |
| PGS000170 (hlr_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
High light scatter reticulocyte percentage of red cells | reticulocyte count | 566 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000170/ScoringFiles/PGS000170.txt.gz |
| PGS000171 (irf) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Immature fraction of reticulocytes | reticulocyte count | 372 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000171/ScoringFiles/PGS000171.txt.gz |
| PGS000172 (lymph) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Lymphocyte count | lymphocyte count | 621 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000172/ScoringFiles/PGS000172.txt.gz |
| PGS000173 (lymph_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Lymphocyte percentage of white cells | lymphocyte percentage of leukocytes | 472 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000173/ScoringFiles/PGS000173.txt.gz |
| PGS000174 (mch) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 628 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000174/ScoringFiles/PGS000174.txt.gz |
| PGS000175 (mchc) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Mean corpuscular hemoglobin concentration | mean corpuscular hemoglobin concentration | 224 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000175/ScoringFiles/PGS000175.txt.gz |
| PGS000176 (mcv) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Mean corpuscular volume | mean corpuscular volume | 685 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000176/ScoringFiles/PGS000176.txt.gz |
| PGS000177 (mono) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Monocyte count | monocyte count | 638 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000177/ScoringFiles/PGS000177.txt.gz |
| PGS000178 (mono_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Monocyte percentage of white cells | monocyte percentage of leukocytes | 549 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000178/ScoringFiles/PGS000178.txt.gz |
| PGS000179 (mpv) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Mean platelet volume | mean platelet volume | 654 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000179/ScoringFiles/PGS000179.txt.gz |
| PGS000180 (mrv) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Mean reticulocyte volume | mean reticulocyte volume | 629 | - - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000180/ScoringFiles/PGS000180.txt.gz |
| PGS000181 (mscv) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Mean sphered corpuscular volume | mean corpuscular volume | 761 | - - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000181/ScoringFiles/PGS000181.txt.gz |
| PGS000182 (neut) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Neutrophil count | neutrophil count | 492 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000182/ScoringFiles/PGS000182.txt.gz |
| PGS000183 (neut_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Neutrophil percentage of white cells | neutrophil percentage of leukocytes | 437 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000183/ScoringFiles/PGS000183.txt.gz |
| PGS000184 (pct) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Platelet crit | platelet crit | 700 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000184/ScoringFiles/PGS000184.txt.gz |
| PGS000185 (pdw) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Platelet distribution width | platelet component distribution width | 555 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000185/ScoringFiles/PGS000185.txt.gz |
| PGS000186 (plt) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Platelet count | platelet count | 739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000186/ScoringFiles/PGS000186.txt.gz |
| PGS000187 (rbc) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Red blood cell count | erythrocyte count | 678 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000187/ScoringFiles/PGS000187.txt.gz |
| PGS000188 (rdw) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Red cell distribution width | Red cell distribution width | 546 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000188/ScoringFiles/PGS000188.txt.gz |
| PGS000189 (ret) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reticulocyte count | reticulocyte count | 555 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000189/ScoringFiles/PGS000189.txt.gz |
| PGS000190 (ret_p) |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reticulocyte fraction of red cells | reticulocyte count | 537 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000190/ScoringFiles/PGS000190.txt.gz |
| PGS000191 (wbc) |
PGP000078 | Vuckovic D et al. Cell (2020) |
White blood cell count | leukocyte count | 636 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000191/ScoringFiles/PGS000191.txt.gz |
| PGS000304 (GRS43_HbA1c) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
HbA1c | HbA1c measurement | 43 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000304/ScoringFiles/PGS000304.txt.gz |
| PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
HbA1c [mmol/mol] | HbA1c measurement | 14,658 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000685/ScoringFiles/PGS000685.txt.gz |
| PGS000698 (snpnet.Total_protein) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Total protein [g/L] | total blood protein measurement | 16,420 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000698/ScoringFiles/PGS000698.txt.gz |
| PGS000987 (GBE_INI30260) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Mean reticulocyte volume | mean reticulocyte volume | 13,277 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000987/ScoringFiles/PGS000987.txt.gz |
| PGS000988 (GBE_INI30290) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
High light scatter reticulocyte percentage | reticulocyte measurement | 7,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000988/ScoringFiles/PGS000988.txt.gz |
| PGS000989 (GBE_INI30240) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reticulocyte percentage | reticulocyte measurement | 6,251 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000989/ScoringFiles/PGS000989.txt.gz |
| PGS001076 (GBE_INI30210) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Eosinophil percentage | eosinophil percentage of leukocytes | 12,563 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001076/ScoringFiles/PGS001076.txt.gz |
| PGS001077 (GBE_INI30200) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Neutrophil percentage | neutrophil percentage of leukocytes | 13,703 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001077/ScoringFiles/PGS001077.txt.gz |
| PGS001078 (GBE_INI30190) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Monocyte percentage | monocyte percentage of leukocytes | 8,762 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001078/ScoringFiles/PGS001078.txt.gz |
| PGS001079 (GBE_INI30110) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Platelet distribution width | platelet component distribution width | 18,814 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001079/ScoringFiles/PGS001079.txt.gz |
| PGS001152 (GBE_INI30070) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 10,179 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001152/ScoringFiles/PGS001152.txt.gz |
| PGS001163 (GBE_INI30130) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Monocyte count | monocyte count | 9,323 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001163/ScoringFiles/PGS001163.txt.gz |
| PGS001172 (GBE_INI30150) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Eosinophil count | eosinophil count | 12,579 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001172/ScoringFiles/PGS001172.txt.gz |
| PGS001173 (GBE_INI30140) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Neutrophil count | neutrophil count | 15,578 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001173/ScoringFiles/PGS001173.txt.gz |
| PGS001199 (GBE_INI30120) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Lymphocyte count | lymphocyte count | 4,212 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001199/ScoringFiles/PGS001199.txt.gz |
| PGS001200 (GBE_INI30100) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Mean platelet volume | mean platelet volume | 24,114 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001200/ScoringFiles/PGS001200.txt.gz |
| PGS001218 (GBE_INI30060) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Mean corpuscular hemoglobin concentration | mean corpuscular hemoglobin concentration | 2,359 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001218/ScoringFiles/PGS001218.txt.gz |
| PGS001219 (GBE_INI30050) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 13,003 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001219/ScoringFiles/PGS001219.txt.gz |
| PGS001220 (GBE_INI30040) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Mean corpuscular volume | mean corpuscular volume | 17,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001220/ScoringFiles/PGS001220.txt.gz |
| PGS001225 (GBE_INI30030) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Percentage of hematocrit | hematocrit | 15,721 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001225/ScoringFiles/PGS001225.txt.gz |
| PGS001238 (GBE_INI30080) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Platelet count | platelet count | 24,893 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001238/ScoringFiles/PGS001238.txt.gz |
| PGS001239 (GBE_INI30000) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
White blood cell count | leukocyte count | 13,785 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001239/ScoringFiles/PGS001239.txt.gz |
| PGS001240 (GBE_INI30010) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Red blood cell count | erythrocyte count | 20,480 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001240/ScoringFiles/PGS001240.txt.gz |
| PGS001352 (MAGICTA_EUR_PGS_HbA1c) |
PGP000246 | Chen J et al. Nat Genet (2021) |
Glycated haemoglobin levels (HbA1c) | HbA1c measurement | 1,018,836 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001352/ScoringFiles/PGS001352.txt.gz | |
| PGS001377 (GBE_INI30220) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Basophil percentage | basophil percentage of leukocytes | 3,205 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001377/ScoringFiles/PGS001377.txt.gz |
| PGS001378 (GBE_INI30160) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Basophil count | basophil count | 3,050 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001378/ScoringFiles/PGS001378.txt.gz |
| PGS001400 (GBE_INI30020) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Haemoglobin concentration | hemoglobin measurement | 15,602 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001400/ScoringFiles/PGS001400.txt.gz |
| PGS001406 (GBE_INI30300) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
High light scatter reticulocyte count | reticulocyte measurement | 15,856 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001406/ScoringFiles/PGS001406.txt.gz |
| PGS001408 (GBE_INI30280) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Immature reticulocyte fraction | Immature Reticulocyte Fraction Measurement | 10,871 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001408/ScoringFiles/PGS001408.txt.gz |
| PGS001414 (GBE_INI30180) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Lymphocyte % | lymphocyte percentage of leukocytes | 15,143 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001414/ScoringFiles/PGS001414.txt.gz |
| PGS001517 (GBE_INI30090) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Platelet crit | platelet crit | 20,910 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001517/ScoringFiles/PGS001517.txt.gz |
| PGS001528 (GBE_INI30250) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reticulocyte count | reticulocyte count | 6,262 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001528/ScoringFiles/PGS001528.txt.gz |
| PGS001908 (portability-PLR_erythrocyte_width) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Red blood cell (erythrocyte) distribution width | Red cell distribution width | 32,431 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001908/ScoringFiles/PGS001908.txt.gz |
| PGS001909 (portability-PLR_erythrocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Red blood cell (erythrocyte) count | erythrocyte count | 81,887 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001909/ScoringFiles/PGS001909.txt.gz |
| PGS001925 (portability-PLR_haematocrit_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Haematocrit percentage | hematocrit | 67,571 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001925/ScoringFiles/PGS001925.txt.gz |
| PGS001926 (portability-PLR_haemoglobin) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Haemoglobin concentration | hemoglobin measurement | 69,467 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001926/ScoringFiles/PGS001926.txt.gz |
| PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Immature reticulocyte fraction | Immature Reticulocyte Fraction Measurement | 39,162 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001930/ScoringFiles/PGS001930.txt.gz |
| PGS001949 (portability-PLR_log_eosinophil_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Eosinophil percentage | eosinophil percentage of leukocytes | 9,236 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001949/ScoringFiles/PGS001949.txt.gz |
| PGS001953 (portability-PLR_log_HbA1c) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Glycated haemoglobin (HbA1c) | HbA1c measurement | 46,566 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001953/ScoringFiles/PGS001953.txt.gz |
| PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
High light scatter reticulocyte count | reticulocyte measurement | 78,803 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001959/ScoringFiles/PGS001959.txt.gz |
| PGS001962 (portability-PLR_log_leukocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
White blood cell (leukocyte) count | leukocyte count | 80,228 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001962/ScoringFiles/PGS001962.txt.gz |
| PGS001965 (portability-PLR_log_lymphocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Lymphocyte count | lymphocyte count | 76,535 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001965/ScoringFiles/PGS001965.txt.gz |
| PGS001968 (portability-PLR_log_monocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Monocyte count | monocyte count | 46,673 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001968/ScoringFiles/PGS001968.txt.gz |
| PGS001969 (portability-PLR_log_neutrophil) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Neutrophil count | neutrophil count | 71,566 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001969/ScoringFiles/PGS001969.txt.gz |
| PGS001970 (portability-PLR_log_platelet_crit) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Platelet crit | platelet crit | 56,402 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001970/ScoringFiles/PGS001970.txt.gz |
| PGS001971 (portability-PLR_log_platelet_volume) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean platelet (thrombocyte) volume | mean platelet volume | 65,450 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001971/ScoringFiles/PGS001971.txt.gz |
| PGS001972 (portability-PLR_log_platelet_width) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Platelet distribution width | platelet component distribution width | 46,157 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001972/ScoringFiles/PGS001972.txt.gz |
| PGS001973 (portability-PLR_log_platelet) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Platelet count | platelet count | 60,665 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001973/ScoringFiles/PGS001973.txt.gz |
| PGS001976 (portability-PLR_log_reticulocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reticulocyte count | reticulocyte count | 75,033 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001976/ScoringFiles/PGS001976.txt.gz |
| PGS001986 (portability-PLR_lymphocyte_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Lymphocyte percentage | lymphocyte percentage of leukocytes | 66,778 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001986/ScoringFiles/PGS001986.txt.gz |
| PGS001989 (portability-PLR_MCH) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 44,174 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001989/ScoringFiles/PGS001989.txt.gz |
| PGS001990 (portability-PLR_MCV) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean corpuscular volume | mean corpuscular volume | 47,916 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001990/ScoringFiles/PGS001990.txt.gz |
| PGS001991 (portability-PLR_monocyte_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Monocyte percentage | monocyte percentage of leukocytes | 41,887 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001991/ScoringFiles/PGS001991.txt.gz |
| PGS001997 (portability-PLR_neutrophil_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Neutrophil percentage | neutrophil percentage of leukocytes | 65,022 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001997/ScoringFiles/PGS001997.txt.gz |
| PGS002001 (portability-PLR_protein) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Total protein | total blood protein measurement | 69,557 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002001/ScoringFiles/PGS002001.txt.gz |
| PGS002003 (portability-PLR_reticulocyte_volume) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean reticulocyte volume | mean reticulocyte volume | 24,536 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002003/ScoringFiles/PGS002003.txt.gz |
| PGS002008 (portability-PLR_sphered_cell_volume) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean sphered cell volume | hematological measurement | 50,360 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002008/ScoringFiles/PGS002008.txt.gz |
| PGS002122 (portability-ldpred2_erythrocyte_width) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Red blood cell (erythrocyte) distribution width | Red cell distribution width | 543,714 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002122/ScoringFiles/PGS002122.txt.gz |
| PGS002123 (portability-ldpred2_erythrocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Red blood cell (erythrocyte) count | erythrocyte count | 788,123 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002123/ScoringFiles/PGS002123.txt.gz |
| PGS002141 (portability-ldpred2_haematocrit_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Haematocrit percentage | hematocrit | 797,804 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002141/ScoringFiles/PGS002141.txt.gz |
| PGS002142 (portability-ldpred2_haemoglobin) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Haemoglobin concentration | hemoglobin measurement | 786,386 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002142/ScoringFiles/PGS002142.txt.gz |
| PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Immature reticulocyte fraction | Immature Reticulocyte Fraction Measurement | 664,696 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002147/ScoringFiles/PGS002147.txt.gz |
| PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Eosinophil percentage | eosinophil percentage of leukocytes | 593,459 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002167/ScoringFiles/PGS002167.txt.gz |
| PGS002171 (portability-ldpred2_log_HbA1c) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Glycated haemoglobin (HbA1c) | HbA1c measurement | 736,730 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002171/ScoringFiles/PGS002171.txt.gz |
| PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
High light scatter reticulocyte count | reticulocyte measurement | 780,048 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002177/ScoringFiles/PGS002177.txt.gz |
| PGS002180 (portability-ldpred2_log_leukocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
White blood cell (leukocyte) count | leukocyte count | 846,337 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002180/ScoringFiles/PGS002180.txt.gz |
| PGS002183 (portability-ldpred2_log_lymphocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Lymphocyte count | lymphocyte count | 814,921 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002183/ScoringFiles/PGS002183.txt.gz |
| PGS002186 (portability-ldpred2_log_monocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Monocyte count | monocyte count | 641,455 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002186/ScoringFiles/PGS002186.txt.gz |
| PGS002187 (portability-ldpred2_log_neutrophil) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Neutrophil count | neutrophil count | 803,767 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002187/ScoringFiles/PGS002187.txt.gz |
| PGS002188 (portability-ldpred2_log_platelet_crit) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Platelet crit | platelet crit | 703,576 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002188/ScoringFiles/PGS002188.txt.gz |
| PGS002189 (portability-ldpred2_log_platelet_volume) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean platelet (thrombocyte) volume | mean platelet volume | 462,934 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002189/ScoringFiles/PGS002189.txt.gz |
| PGS002190 (portability-ldpred2_log_platelet_width) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Platelet distribution width | platelet component distribution width | 482,649 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002190/ScoringFiles/PGS002190.txt.gz |
| PGS002191 (portability-ldpred2_log_platelet) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Platelet count | platelet count | 663,591 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002191/ScoringFiles/PGS002191.txt.gz |
| PGS002194 (portability-ldpred2_log_reticulocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reticulocyte count | reticulocyte count | 773,305 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002194/ScoringFiles/PGS002194.txt.gz |
| PGS002203 (portability-ldpred2_lymphocyte_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Lymphocyte percentage | lymphocyte percentage of leukocytes | 775,312 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002203/ScoringFiles/PGS002203.txt.gz |
| PGS002206 (portability-ldpred2_MCH) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 504,929 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002206/ScoringFiles/PGS002206.txt.gz |
| PGS002207 (portability-ldpred2_MCV) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean corpuscular volume | mean corpuscular volume | 564,228 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002207/ScoringFiles/PGS002207.txt.gz |
| PGS002208 (portability-ldpred2_monocyte_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Monocyte percentage | monocyte percentage of leukocytes | 544,905 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002208/ScoringFiles/PGS002208.txt.gz |
| PGS002214 (portability-ldpred2_neutrophil_perc) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Neutrophil percentage | neutrophil percentage of leukocytes | 769,542 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002214/ScoringFiles/PGS002214.txt.gz |
| PGS002219 (portability-ldpred2_protein) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Total protein | total blood protein measurement | 819,013 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002219/ScoringFiles/PGS002219.txt.gz |
| PGS002221 (portability-ldpred2_reticulocyte_volume) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean reticulocyte volume | mean reticulocyte volume | 523,074 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002221/ScoringFiles/PGS002221.txt.gz |
| PGS002227 (portability-ldpred2_sphered_cell_volume) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Mean sphered cell volume | hematological measurement | 630,576 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002227/ScoringFiles/PGS002227.txt.gz |
| PGS002325 (blood_EOSINOPHIL_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002325/ScoringFiles/PGS002325.txt.gz |
| PGS002331 (biochemistry_HbA1c.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002331/ScoringFiles/PGS002331.txt.gz |
| PGS002333 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002333/ScoringFiles/PGS002333.txt.gz |
| PGS002338 (blood_LYMPHOCYTE_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002338/ScoringFiles/PGS002338.txt.gz |
| PGS002339 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002339/ScoringFiles/PGS002339.txt.gz |
| PGS002340 (blood_MEAN_PLATELET_VOL.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002340/ScoringFiles/PGS002340.txt.gz |
| PGS002341 (blood_MONOCYTE_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002341/ScoringFiles/PGS002341.txt.gz |
| PGS002343 (blood_PLATELET_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002343/ScoringFiles/PGS002343.txt.gz |
| PGS002345 (blood_RED_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002345/ScoringFiles/PGS002345.txt.gz |
| PGS002346 (blood_RBC_DISTRIB_WIDTH.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002346/ScoringFiles/PGS002346.txt.gz |
| PGS002357 (blood_WHITE_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002357/ScoringFiles/PGS002357.txt.gz |
| PGS002364 (blood_EOSINOPHIL_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 920,929 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002364/ScoringFiles/PGS002364.txt.gz |
| PGS002367 (biochemistry_HbA1c.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 920,924 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002367/ScoringFiles/PGS002367.txt.gz |
| PGS002370 (blood_LYMPHOCYTE_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 920,935 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002370/ScoringFiles/PGS002370.txt.gz |
| PGS002371 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 920,923 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002371/ScoringFiles/PGS002371.txt.gz |
| PGS002372 (blood_MONOCYTE_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 920,930 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002372/ScoringFiles/PGS002372.txt.gz |
| PGS002373 (blood_PLATELET_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 920,923 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002373/ScoringFiles/PGS002373.txt.gz |
| PGS002374 (blood_RED_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 920,935 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002374/ScoringFiles/PGS002374.txt.gz |
| PGS002380 (blood_WHITE_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 920,936 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002380/ScoringFiles/PGS002380.txt.gz |
| PGS002397 (blood_EOSINOPHIL_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 15,667 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002397/ScoringFiles/PGS002397.txt.gz |
| PGS002403 (biochemistry_HbA1c.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 11,872 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002403/ScoringFiles/PGS002403.txt.gz |
| PGS002405 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 16,031 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002405/ScoringFiles/PGS002405.txt.gz |
| PGS002410 (blood_LYMPHOCYTE_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 14,889 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002410/ScoringFiles/PGS002410.txt.gz |
| PGS002411 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 22,349 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002411/ScoringFiles/PGS002411.txt.gz |
| PGS002412 (blood_MEAN_PLATELET_VOL.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 36,285 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002412/ScoringFiles/PGS002412.txt.gz |
| PGS002413 (blood_MONOCYTE_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 17,405 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002413/ScoringFiles/PGS002413.txt.gz |
| PGS002415 (blood_PLATELET_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 27,345 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002415/ScoringFiles/PGS002415.txt.gz |
| PGS002417 (blood_RED_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 18,514 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002417/ScoringFiles/PGS002417.txt.gz |
| PGS002418 (blood_RBC_DISTRIB_WIDTH.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 16,996 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002418/ScoringFiles/PGS002418.txt.gz |
| PGS002429 (blood_WHITE_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 13,898 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002429/ScoringFiles/PGS002429.txt.gz |
| PGS002446 (blood_EOSINOPHIL_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 35,512 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002446/ScoringFiles/PGS002446.txt.gz |
| PGS002452 (biochemistry_HbA1c.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 30,603 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002452/ScoringFiles/PGS002452.txt.gz |
| PGS002454 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 37,335 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002454/ScoringFiles/PGS002454.txt.gz |
| PGS002459 (blood_LYMPHOCYTE_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 36,117 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002459/ScoringFiles/PGS002459.txt.gz |
| PGS002460 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 44,827 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002460/ScoringFiles/PGS002460.txt.gz |
| PGS002461 (blood_MEAN_PLATELET_VOL.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 65,237 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002461/ScoringFiles/PGS002461.txt.gz |
| PGS002462 (blood_MONOCYTE_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 38,012 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002462/ScoringFiles/PGS002462.txt.gz |
| PGS002464 (blood_PLATELET_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 54,318 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002464/ScoringFiles/PGS002464.txt.gz |
| PGS002466 (blood_RED_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 41,471 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002466/ScoringFiles/PGS002466.txt.gz |
| PGS002467 (blood_RBC_DISTRIB_WIDTH.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 36,619 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002467/ScoringFiles/PGS002467.txt.gz |
| PGS002478 (blood_WHITE_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 35,005 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002478/ScoringFiles/PGS002478.txt.gz |
| PGS002495 (blood_EOSINOPHIL_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 135,927 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002495/ScoringFiles/PGS002495.txt.gz |
| PGS002501 (biochemistry_HbA1c.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 128,425 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002501/ScoringFiles/PGS002501.txt.gz |
| PGS002503 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 143,701 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002503/ScoringFiles/PGS002503.txt.gz |
| PGS002508 (blood_LYMPHOCYTE_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 140,957 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002508/ScoringFiles/PGS002508.txt.gz |
| PGS002509 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 151,362 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002509/ScoringFiles/PGS002509.txt.gz |
| PGS002510 (blood_MEAN_PLATELET_VOL.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 183,887 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002510/ScoringFiles/PGS002510.txt.gz |
| PGS002511 (blood_MONOCYTE_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 140,833 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002511/ScoringFiles/PGS002511.txt.gz |
| PGS002513 (blood_PLATELET_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 170,052 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002513/ScoringFiles/PGS002513.txt.gz |
| PGS002515 (blood_RED_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 150,047 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002515/ScoringFiles/PGS002515.txt.gz |
| PGS002516 (blood_RBC_DISTRIB_WIDTH.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 136,154 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002516/ScoringFiles/PGS002516.txt.gz |
| PGS002527 (blood_WHITE_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 141,866 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002527/ScoringFiles/PGS002527.txt.gz |
| PGS002544 (blood_EOSINOPHIL_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 6,683 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002544/ScoringFiles/PGS002544.txt.gz |
| PGS002550 (biochemistry_HbA1c.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 4,546 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002550/ScoringFiles/PGS002550.txt.gz |
| PGS002552 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 6,655 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002552/ScoringFiles/PGS002552.txt.gz |
| PGS002557 (blood_LYMPHOCYTE_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 5,729 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002557/ScoringFiles/PGS002557.txt.gz |
| PGS002558 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 10,888 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002558/ScoringFiles/PGS002558.txt.gz |
| PGS002559 (blood_MEAN_PLATELET_VOL.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 19,124 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002559/ScoringFiles/PGS002559.txt.gz |
| PGS002560 (blood_MONOCYTE_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 7,666 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002560/ScoringFiles/PGS002560.txt.gz |
| PGS002562 (blood_PLATELET_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 12,742 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002562/ScoringFiles/PGS002562.txt.gz |
| PGS002564 (blood_RED_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 7,590 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002564/ScoringFiles/PGS002564.txt.gz |
| PGS002565 (blood_RBC_DISTRIB_WIDTH.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 7,740 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002565/ScoringFiles/PGS002565.txt.gz |
| PGS002576 (blood_WHITE_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 4,921 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002576/ScoringFiles/PGS002576.txt.gz |
| PGS002593 (blood_EOSINOPHIL_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 4,677 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002593/ScoringFiles/PGS002593.txt.gz |
| PGS002599 (biochemistry_HbA1c.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 3,055 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002599/ScoringFiles/PGS002599.txt.gz |
| PGS002601 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 4,569 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002601/ScoringFiles/PGS002601.txt.gz |
| PGS002606 (blood_LYMPHOCYTE_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 3,808 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002606/ScoringFiles/PGS002606.txt.gz |
| PGS002607 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 8,017 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002607/ScoringFiles/PGS002607.txt.gz |
| PGS002608 (blood_MEAN_PLATELET_VOL.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 14,380 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002608/ScoringFiles/PGS002608.txt.gz |
| PGS002609 (blood_MONOCYTE_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 5,367 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002609/ScoringFiles/PGS002609.txt.gz |
| PGS002611 (blood_PLATELET_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 9,050 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002611/ScoringFiles/PGS002611.txt.gz |
| PGS002613 (blood_RED_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 5,156 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002613/ScoringFiles/PGS002613.txt.gz |
| PGS002614 (blood_RBC_DISTRIB_WIDTH.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 5,543 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002614/ScoringFiles/PGS002614.txt.gz |
| PGS002625 (blood_WHITE_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 3,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002625/ScoringFiles/PGS002625.txt.gz |
| PGS002642 (blood_EOSINOPHIL_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 346,331 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002642/ScoringFiles/PGS002642.txt.gz |
| PGS002648 (biochemistry_HbA1c.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 394,312 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002648/ScoringFiles/PGS002648.txt.gz |
| PGS002650 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 406,785 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002650/ScoringFiles/PGS002650.txt.gz |
| PGS002655 (blood_LYMPHOCYTE_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 472,203 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002655/ScoringFiles/PGS002655.txt.gz |
| PGS002656 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 351,633 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002656/ScoringFiles/PGS002656.txt.gz |
| PGS002657 (blood_MEAN_PLATELET_VOL.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 354,280 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002657/ScoringFiles/PGS002657.txt.gz |
| PGS002658 (blood_MONOCYTE_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 353,881 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002658/ScoringFiles/PGS002658.txt.gz |
| PGS002660 (blood_PLATELET_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 396,074 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002660/ScoringFiles/PGS002660.txt.gz |
| PGS002662 (blood_RED_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 473,515 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002662/ScoringFiles/PGS002662.txt.gz |
| PGS002663 (blood_RBC_DISTRIB_WIDTH.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 340,172 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002663/ScoringFiles/PGS002663.txt.gz |
| PGS002674 (blood_WHITE_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 491,764 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002674/ScoringFiles/PGS002674.txt.gz |
| PGS002691 (blood_EOSINOPHIL_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Eosinophil count | eosinophil count | 980,421 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002691/ScoringFiles/PGS002691.txt.gz |
| PGS002697 (biochemistry_HbA1c.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
HbA1c | HbA1c measurement | 989,344 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002697/ScoringFiles/PGS002697.txt.gz |
| PGS002699 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
High light scatter reticulocyte count | reticulocyte count | 983,680 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002699/ScoringFiles/PGS002699.txt.gz |
| PGS002704 (blood_LYMPHOCYTE_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Lymphocyte count | lymphocyte count | 983,350 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002704/ScoringFiles/PGS002704.txt.gz |
| PGS002705 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 979,778 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002705/ScoringFiles/PGS002705.txt.gz |
| PGS002706 (blood_MEAN_PLATELET_VOL.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Mean platelet volume | mean platelet volume | 973,986 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002706/ScoringFiles/PGS002706.txt.gz |
| PGS002707 (blood_MONOCYTE_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Monocyte count | monocyte count | 980,307 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002707/ScoringFiles/PGS002707.txt.gz |
| PGS002709 (blood_PLATELET_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Platelet count | platelet count | 981,460 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002709/ScoringFiles/PGS002709.txt.gz |
| PGS002711 (blood_RED_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell count | erythrocyte count | 982,902 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002711/ScoringFiles/PGS002711.txt.gz |
| PGS002712 (blood_RBC_DISTRIB_WIDTH.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Red blood cell distribution width | Red cell distribution width | 976,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002712/ScoringFiles/PGS002712.txt.gz |
| PGS002723 (blood_WHITE_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte count | 983,751 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002723/ScoringFiles/PGS002723.txt.gz |
| PGS003330 (PRS-F8) |
PGP000395 | Valenti L et al. JHEP Rep (2022) |
Factor VIII levels | factor VIII measurement | 10 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003330/ScoringFiles/PGS003330.txt.gz |
| PGS003337 (CVGRS_HbA1c) |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
HbA1c | HbA1c measurement | 68 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003337/ScoringFiles/PGS003337.txt.gz |
| PGS003346 (ALLGRS_HbA1c) |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
HbA1c | HbA1c measurement | 74 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003346/ScoringFiles/PGS003346.txt.gz |
| PGS003425 (prscs_hemoglobin) |
PGP000432 | Toivonen J et al. Vox Sanguinis (2023) |
Hemoglobin | hemoglobin measurement | 1,082,250 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003425/ScoringFiles/PGS003425.txt.gz | |
| PGS003464 (LDPred2_EOS) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Eosinophil count | eosinophil count | 859,056 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003464/ScoringFiles/PGS003464.txt.gz |
| PGS003467 (LDPred2_HCT) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Hematocrit | hematocrit | 860,314 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003467/ScoringFiles/PGS003467.txt.gz |
| PGS003468 (LDPred2_HGB) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Hemoglobin | hemoglobin measurement | 860,341 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003468/ScoringFiles/PGS003468.txt.gz |
| PGS003471 (LDPred2_HbA1c) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
HbA1c | HbA1c measurement | 848,979 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003471/ScoringFiles/PGS003471.txt.gz |
| PGS003475 (LDPred2_LYM) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Lymphocyte count | lymphocyte count | 859,875 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003475/ScoringFiles/PGS003475.txt.gz |
| PGS003478 (LDPred2_RBC) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Red blood cell count | erythrocyte count | 860,281 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003478/ScoringFiles/PGS003478.txt.gz |
| PGS003483 (LDPred2_WBC) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
White blood cell count | leukocyte count | 860,306 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003483/ScoringFiles/PGS003483.txt.gz |
| PGS003502 (cont-decay-erythrocyte) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Red blood cell (erythrocyte) count | erythrocyte count | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003502/ScoringFiles/PGS003502.txt.gz |
| PGS003503 (cont-decay-erythrocyte_width) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Red blood cell (erythrocyte) distribution width | Red cell distribution width | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003503/ScoringFiles/PGS003503.txt.gz |
| PGS003511 (cont-decay-haematocrit_perc) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Haematocrit percentage | hematocrit | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003511/ScoringFiles/PGS003511.txt.gz |
| PGS003512 (cont-decay-haemoglobin) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Haemoglobin concentration | hemoglobin measurement | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003512/ScoringFiles/PGS003512.txt.gz |
| PGS003515 (cont-decay-immature_reticulocyte_frac) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Immature reticulocyte fraction | reticulocyte measurement | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003515/ScoringFiles/PGS003515.txt.gz |
| PGS003530 (cont-decay-log_eosinophil_perc) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Eosinophil percentage | eosinophil percentage of leukocytes | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003530/ScoringFiles/PGS003530.txt.gz |
| PGS003533 (cont-decay-log_HbA1c) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Glycated haemoglobin (HbA1c) | HbA1c measurement | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003533/ScoringFiles/PGS003533.txt.gz |
| PGS003541 (cont-decay-log_leukocyte) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
White blood cell (leukocyte) count | leukocyte count | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003541/ScoringFiles/PGS003541.txt.gz |
| PGS003544 (cont-decay-log_monocyte) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Monocyte count | monocyte count | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003544/ScoringFiles/PGS003544.txt.gz |
| PGS003545 (cont-decay-log_neutrophil) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Neutrophil count | neutrophil count | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003545/ScoringFiles/PGS003545.txt.gz |
| PGS003546 (cont-decay-log_platelet) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Platelet count | platelet count | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003546/ScoringFiles/PGS003546.txt.gz |
| PGS003547 (cont-decay-log_platelet_crit) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Platelet crit | platelet crit | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003547/ScoringFiles/PGS003547.txt.gz |
| PGS003548 (cont-decay-log_platelet_volume) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Mean platelet (thrombocyte) volume | mean platelet volume | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003548/ScoringFiles/PGS003548.txt.gz |
| PGS003549 (cont-decay-log_platelet_width) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Platelet distribution width | platelet component distribution width | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003549/ScoringFiles/PGS003549.txt.gz |
| PGS003551 (cont-decay-log_reticulocyte) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reticulocyte count | reticulocyte count | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003551/ScoringFiles/PGS003551.txt.gz |
| PGS003557 (cont-decay-lymphocyte_perc) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Lymphocyte percentage | lymphocyte percentage of leukocytes | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003557/ScoringFiles/PGS003557.txt.gz |
| PGS003560 (cont-decay-MCH) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003560/ScoringFiles/PGS003560.txt.gz |
| PGS003561 (cont-decay-MCV) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Mean corpuscular volume | mean corpuscular volume | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003561/ScoringFiles/PGS003561.txt.gz |
| PGS003562 (cont-decay-monocyte_perc) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Monocyte percentage | monocyte percentage of leukocytes | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003562/ScoringFiles/PGS003562.txt.gz |
| PGS003566 (cont-decay-neutrophil_perc) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Neutrophil percentage | neutrophil percentage of leukocytes | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003566/ScoringFiles/PGS003566.txt.gz |
| PGS003567 (cont-decay-reticulocyte_volume) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Mean reticulocyte volume | mean reticulocyte volume | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003567/ScoringFiles/PGS003567.txt.gz |
| PGS003570 (cont-decay-sphered_cell_volume) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Mean sphered cell volume | mean corpuscular volume | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003570/ScoringFiles/PGS003570.txt.gz |
| PGS003924 (INI30000) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
White blood cell (leukocyte) count | leukocyte count | 17,890 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003924/ScoringFiles/PGS003924.txt.gz |
| PGS003925 (INI30010) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Red blood cell (erythrocyte) count | erythrocyte count | 27,293 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003925/ScoringFiles/PGS003925.txt.gz |
| PGS003926 (INI30020) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Hemoglobin concentration | hemoglobin measurement | 21,078 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003926/ScoringFiles/PGS003926.txt.gz |
| PGS003927 (INI30030) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Hematocrit percentage | hematocrit | 20,106 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003927/ScoringFiles/PGS003927.txt.gz |
| PGS003928 (INI30040) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Mean corpuscular volume | mean corpuscular volume | 21,818 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003928/ScoringFiles/PGS003928.txt.gz |
| PGS003929 (INI30050) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Mean corpuscular hemoglobin | mean corpuscular hemoglobin | 17,127 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003929/ScoringFiles/PGS003929.txt.gz |
| PGS003930 (INI30060) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Mean corpuscular hemoglobin concentration | mean corpuscular hemoglobin concentration | 4,468 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003930/ScoringFiles/PGS003930.txt.gz |
| PGS003931 (INI30070) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Red blood cell (erythrocyte) distribution width | Red cell distribution width | 12,557 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003931/ScoringFiles/PGS003931.txt.gz |
| PGS003932 (INI30080) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Platelet count | platelet count | 32,944 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003932/ScoringFiles/PGS003932.txt.gz |
| PGS003933 (INI30090) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Platelet crit | platelet crit | 27,034 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003933/ScoringFiles/PGS003933.txt.gz |
| PGS003934 (INI30100) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Mean platelet (thrombocyte) volume | mean platelet volume | 31,032 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003934/ScoringFiles/PGS003934.txt.gz |
| PGS003935 (INI30110) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Platelet distribution width | platelet component distribution width | 21,899 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003935/ScoringFiles/PGS003935.txt.gz |
| PGS003936 (INI30120) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Lymphocyte count | lymphocyte count | 7,291 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003936/ScoringFiles/PGS003936.txt.gz |
| PGS003937 (INI30130) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Monocyte count | monocyte count | 13,415 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003937/ScoringFiles/PGS003937.txt.gz |
| PGS003938 (INI30140) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Neutrophil count | neutrophil count | 18,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003938/ScoringFiles/PGS003938.txt.gz |
| PGS003939 (INI30150) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Eosinophil count | eosinophil count | 16,859 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003939/ScoringFiles/PGS003939.txt.gz |
| PGS003940 (INI30160) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Basophil count | basophil count | 4,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003940/ScoringFiles/PGS003940.txt.gz |
| PGS003941 (INI30180) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Lymphocyte percentage | lymphocyte percentage of leukocytes | 20,804 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003941/ScoringFiles/PGS003941.txt.gz |
| PGS003942 (INI30190) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Monocyte percentage | monocyte percentage of leukocytes | 10,717 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003942/ScoringFiles/PGS003942.txt.gz |
| PGS003943 (INI30200) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Neutrophil percentage | neutrophil percentage of leukocytes | 16,931 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003943/ScoringFiles/PGS003943.txt.gz |
| PGS003944 (INI30210) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Eosinophil percentage | eosinophil percentage of leukocytes | 17,227 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003944/ScoringFiles/PGS003944.txt.gz |
| PGS003945 (INI30220) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Basophil percentage | basophil percentage of leukocytes | 3,472 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003945/ScoringFiles/PGS003945.txt.gz |
| PGS003946 (INI30240) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reticulocyte percentage | reticulocyte measurement | 7,884 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003946/ScoringFiles/PGS003946.txt.gz |
| PGS003947 (INI30250) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reticulocyte count | reticulocyte count | 9,558 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003947/ScoringFiles/PGS003947.txt.gz |
| PGS003948 (INI30260) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Mean reticulocyte volume | mean reticulocyte volume | 18,832 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003948/ScoringFiles/PGS003948.txt.gz |
| PGS003949 (INI30270) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Mean sphered cell volume | mean corpuscular volume | 21,558 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003949/ScoringFiles/PGS003949.txt.gz |
| PGS003950 (INI30280) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Immature reticulocyte fraction | Immature Reticulocyte Fraction Measurement | 16,307 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003950/ScoringFiles/PGS003950.txt.gz |
| PGS003951 (INI30290) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
High light scatter reticulocyte percentage | reticulocyte measurement | 11,641 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003951/ScoringFiles/PGS003951.txt.gz |
| PGS003952 (INI30300) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
High light scatter reticulocyte count | reticulocyte count | 19,565 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003952/ScoringFiles/PGS003952.txt.gz |
| PGS003987 (dbslmm.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 1,009,642 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003987/ScoringFiles/PGS003987.txt.gz | |
| PGS004003 (lassosum.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 4,697 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004003/ScoringFiles/PGS004003.txt.gz | |
| PGS004029 (ldpred2.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 907,906 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004029/ScoringFiles/PGS004029.txt.gz | |
| PGS004044 (ldpred2.CV.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 907,906 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004044/ScoringFiles/PGS004044.txt.gz | |
| PGS004057 (megaprs.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 514,367 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004057/ScoringFiles/PGS004057.txt.gz | |
| PGS004073 (megaprs.CV.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 514,367 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004073/ScoringFiles/PGS004073.txt.gz | |
| PGS004087 (prscs.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 989,845 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004087/ScoringFiles/PGS004087.txt.gz | |
| PGS004111 (pt_clump.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 51 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004111/ScoringFiles/PGS004111.txt.gz | |
| PGS004127 (pt_clump_nested.CV.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 246 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004127/ScoringFiles/PGS004127.txt.gz | |
| PGS004141 (sbayesr.auto.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 683,029 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004141/ScoringFiles/PGS004141.txt.gz | |
| PGS004157 (UKBB_EnsPGS.GCST007954.HbA1c) |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
HbA1c | HbA1c measurement | 1,009,664 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004157/ScoringFiles/PGS004157.txt.gz | |
| PGS004337 (X30750.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Glycated haemoglobin (mmol/mol) | HbA1c measurement | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004337/ScoringFiles/PGS004337.txt.gz |
| PGS004341 (X30860.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Total protein (g/L) | total blood protein measurement | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004341/ScoringFiles/PGS004341.txt.gz |
| PGS004345 (X30000.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
White blood cell (leukocyte) count | leukocyte count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004345/ScoringFiles/PGS004345.txt.gz |
| PGS004346 (X30010.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Red blood cell (erythrocyte) count | erythrocyte count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004346/ScoringFiles/PGS004346.txt.gz |
| PGS004347 (X30020.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Haemoglobin concentration | hemoglobin measurement | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004347/ScoringFiles/PGS004347.txt.gz |
| PGS004348 (X30030.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Haematocrit percentage | hematocrit | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004348/ScoringFiles/PGS004348.txt.gz |
| PGS004349 (X30040.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Mean corpuscular volume | mean corpuscular volume | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004349/ScoringFiles/PGS004349.txt.gz |
| PGS004350 (X30050.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Mean corpuscular haemoglobin | mean corpuscular hemoglobin concentration | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004350/ScoringFiles/PGS004350.txt.gz |
| PGS004351 (X30070.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Red blood cell (erythrocyte) distribution width | Red cell distribution width | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004351/ScoringFiles/PGS004351.txt.gz |
| PGS004352 (X30080.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Platelet count | platelet count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004352/ScoringFiles/PGS004352.txt.gz |
| PGS004353 (X30090.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Platelet crit | platelet crit | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004353/ScoringFiles/PGS004353.txt.gz |
| PGS004354 (X30100.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Mean platelet (thrombocyte) volume | mean platelet volume | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004354/ScoringFiles/PGS004354.txt.gz |
| PGS004355 (X30110.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Platelet distribution width | platelet component distribution width | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004355/ScoringFiles/PGS004355.txt.gz |
| PGS004356 (X30120.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Lymphocyte count | lymphocyte count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004356/ScoringFiles/PGS004356.txt.gz |
| PGS004357 (X30130.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Monocyte count | monocyte count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004357/ScoringFiles/PGS004357.txt.gz |
| PGS004358 (X30140.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Neutrophill count | neutrophil count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004358/ScoringFiles/PGS004358.txt.gz |
| PGS004359 (X30180.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Lymphocyte percentage | lymphocyte percentage of leukocytes | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004359/ScoringFiles/PGS004359.txt.gz |
| PGS004360 (X30190.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Monocyte percentage | monocyte percentage of leukocytes | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004360/ScoringFiles/PGS004360.txt.gz |
| PGS004361 (X30200.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Neutrophill percentage | neutrophil percentage of leukocytes | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004361/ScoringFiles/PGS004361.txt.gz |
| PGS004362 (X30210.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Eosinophill percentage | eosinophil percentage of leukocytes | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004362/ScoringFiles/PGS004362.txt.gz |
| PGS004363 (X30240.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reticulocyte percentage | reticulocyte measurement | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004363/ScoringFiles/PGS004363.txt.gz |
| PGS004364 (X30250.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reticulocyte count | reticulocyte count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004364/ScoringFiles/PGS004364.txt.gz |
| PGS004365 (X30260.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Mean reticulocyte volume | mean reticulocyte volume | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004365/ScoringFiles/PGS004365.txt.gz |
| PGS004366 (X30270.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Mean sphered cell volume | mean corpuscular volume | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004366/ScoringFiles/PGS004366.txt.gz |
| PGS004367 (X30280.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Immature reticulocyte fraction | reticulocyte measurement | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004367/ScoringFiles/PGS004367.txt.gz |
| PGS004368 (X30290.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
High light scatter reticulocyte percentage | reticulocyte measurement | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004368/ScoringFiles/PGS004368.txt.gz |
| PGS004369 (X30300.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
High light scatter reticulocyte count | reticulocyte count | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004369/ScoringFiles/PGS004369.txt.gz |
| PGS004582 (PRSice_T1) |
PGP000563 | Yang Z et al. Blood (2023) |
Platelet count during the first trimester | platelet count | 407,667 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004582/ScoringFiles/PGS004582.txt.gz | |
| PGS004583 (PRSice_T2) |
PGP000563 | Yang Z et al. Blood (2023) |
Platelet count during the second trimester | platelet count | 104,759 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004583/ScoringFiles/PGS004583.txt.gz | |
| PGS004584 (PRSice_T3) |
PGP000563 | Yang Z et al. Blood (2023) |
Platelet count during the third trimester | platelet count | 5,597 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004584/ScoringFiles/PGS004584.txt.gz | |
| PGS004701 (a1c_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
HbA1c | HbA1c measurement | 1,107,927 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004701/ScoringFiles/PGS004701.txt.gz |
| PGS004702 (a1c_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
HbA1c | HbA1c measurement | 6,652,699 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004702/ScoringFiles/PGS004702.txt.gz |
| PGS004703 (a1c_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
HbA1c | HbA1c measurement | 3,872,159 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004703/ScoringFiles/PGS004703.txt.gz |
| PGS004704 (a1c_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
HbA1c | HbA1c measurement | 6,652,699 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004704/ScoringFiles/PGS004704.txt.gz |
| PGS004727 (Basophils_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Basophil count | basophil count | 6,503 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004727/ScoringFiles/PGS004727.txt.gz |
| PGS004728 (Basophils_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Basophil count | basophil count | 6,132,012 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004728/ScoringFiles/PGS004728.txt.gz |
| PGS004729 (Basophils_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Basophil count | basophil count | 1,538,675 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004729/ScoringFiles/PGS004729.txt.gz |
| PGS004730 (Basophils_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Basophil count | basophil count | 6,132,012 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004730/ScoringFiles/PGS004730.txt.gz |
| PGS004761 (Eosinophils_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Eosinophil count | eosinophil count | 1,086,541 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004761/ScoringFiles/PGS004761.txt.gz |
| PGS004762 (Eosinophils_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Eosinophil count | eosinophil count | 4,143,233 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004762/ScoringFiles/PGS004762.txt.gz |
| PGS004763 (Eosinophils_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Eosinophil count | eosinophil count | 3,749,971 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004763/ScoringFiles/PGS004763.txt.gz |
| PGS004764 (Eosinophils_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Eosinophil count | eosinophil count | 1,698,846 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004764/ScoringFiles/PGS004764.txt.gz |
| PGS004769 (Hb_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Hemoglobin | hemoglobin measurement | 584,676 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004769/ScoringFiles/PGS004769.txt.gz |
| PGS004770 (Hb_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Hemoglobin | hemoglobin measurement | 3,913,042 | - - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004770/ScoringFiles/PGS004770.txt.gz |
| PGS004771 (Hct_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Hematocrit | hematocrit | 594,293 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004771/ScoringFiles/PGS004771.txt.gz |
| PGS004772 (hct_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Hematocrit | hematocrit | 1,259,662 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004772/ScoringFiles/PGS004772.txt.gz |
| PGS004773 (Hct_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Hematocrit | hematocrit | 1,176,938 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004773/ScoringFiles/PGS004773.txt.gz |
| PGS004774 (hct_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Hematocrit | hematocrit | 1,259,662 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004774/ScoringFiles/PGS004774.txt.gz |
| PGS004801 (Monocytes_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Monocyte count | monocyte count | 1,099,478 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004801/ScoringFiles/PGS004801.txt.gz |
| PGS004802 (Monocytes_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Monocyte count | monocyte count | 2,743,298 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004802/ScoringFiles/PGS004802.txt.gz |
| PGS004803 (Monocytes_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Monocyte count | monocyte count | 3,020,361 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004803/ScoringFiles/PGS004803.txt.gz |
| PGS004804 (Monocytes_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Monocyte count | monocyte count | 2,743,298 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004804/ScoringFiles/PGS004804.txt.gz |
| PGS004805 (Neutrophils_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Neutrophil count | neutrophil count | 595,488 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004805/ScoringFiles/PGS004805.txt.gz |
| PGS004806 (Neutrophils_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Neutrophil count | neutrophil count | 4,190,918 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004806/ScoringFiles/PGS004806.txt.gz |
| PGS004807 (Neutrophils_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Neutrophil count | neutrophil count | 3,498,605 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004807/ScoringFiles/PGS004807.txt.gz |
| PGS004808 (Neutrophils_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Neutrophil count | neutrophil count | 1,877,161 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004808/ScoringFiles/PGS004808.txt.gz |
| PGS004811 (Platelets_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Platelet count | platelet count | 1,147,733 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004811/ScoringFiles/PGS004811.txt.gz |
| PGS004812 (Platelets_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Platelet count | platelet count | 6,146,883 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004812/ScoringFiles/PGS004812.txt.gz |
| PGS004813 (Platelets_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Platelet count | platelet count | 1,793,041 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004813/ScoringFiles/PGS004813.txt.gz |
| PGS004814 (Platelets_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Platelet count | platelet count | 6,146,883 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004814/ScoringFiles/PGS004814.txt.gz |
| PGS004821 (RBC_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red blood cell count | erythrocyte count | 1,160,061 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004821/ScoringFiles/PGS004821.txt.gz |
| PGS004822 (RBC_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red blood cell count | erythrocyte count | 1,874,761 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004822/ScoringFiles/PGS004822.txt.gz |
| PGS004823 (RBC_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red blood cell count | erythrocyte count | 4,047,636 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004823/ScoringFiles/PGS004823.txt.gz |
| PGS004824 (RBC_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red blood cell count | erythrocyte count | 1,542,933 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004824/ScoringFiles/PGS004824.txt.gz |
| PGS004825 (RDW_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red cell distribution width | Red cell distribution width | 1,082,665 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004825/ScoringFiles/PGS004825.txt.gz |
| PGS004826 (RDW_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red cell distribution width | Red cell distribution width | 5,175,389 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004826/ScoringFiles/PGS004826.txt.gz |
| PGS004827 (RDW_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red cell distribution width | Red cell distribution width | 4,698,860 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004827/ScoringFiles/PGS004827.txt.gz |
| PGS004828 (RDW_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Red cell distribution width | Red cell distribution width | 5,175,389 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004828/ScoringFiles/PGS004828.txt.gz |
| PGS004849 (Total_protein_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Total protein measurement | total blood protein measurement | 2,167,123 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004849/ScoringFiles/PGS004849.txt.gz |
| PGS004850 (Total_protein_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Total protein measurement | total blood protein measurement | 937,777 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004850/ScoringFiles/PGS004850.txt.gz |
| PGS004851 (TotalProtein_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Total protein measurement | total blood protein measurement | 593,738 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004851/ScoringFiles/PGS004851.txt.gz |
| PGS004852 (TotalProtein_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Total protein measurement | total blood protein measurement | 724,061 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004852/ScoringFiles/PGS004852.txt.gz |
| PGS004855 (WBC_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
White blood cell count | leukocyte count | 1,165,158 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004855/ScoringFiles/PGS004855.txt.gz |
| PGS004856 (WBC_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
White blood cell count | leukocyte count | 6,594,323 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004856/ScoringFiles/PGS004856.txt.gz |
| PGS004857 (WBC_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
White blood cell count | leukocyte count | 4,141,649 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004857/ScoringFiles/PGS004857.txt.gz |
| PGS004858 (WBC_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
White blood cell count | leukocyte count | 6,594,323 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004858/ScoringFiles/PGS004858.txt.gz |
| PGS004956 (Hemoglobin_Mean_INT_ldpred_AFRss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004956/ScoringFiles/PGS004956.txt.gz |
| PGS004957 (Hemoglobin_Mean_INT_ldpred_EURss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004957/ScoringFiles/PGS004957.txt.gz |
| PGS004958 (Hemoglobin_Mean_INT_ldpred_HISss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004958/ScoringFiles/PGS004958.txt.gz |
| PGS004959 (Hemoglobin_Mean_INT_ldpred_METAss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004959/ScoringFiles/PGS004959.txt.gz |
| PGS004960 (Hemoglobin_Mean_INT_ldpred_METAss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004960/ScoringFiles/PGS004960.txt.gz |
| PGS004961 (Hemoglobin_Mean_INT_ldpred_METAss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004961/ScoringFiles/PGS004961.txt.gz |
| PGS004962 (Hemoglobin_Mean_INT_prscs_AFRss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,273,252 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004962/ScoringFiles/PGS004962.txt.gz |
| PGS004963 (Hemoglobin_Mean_INT_prscs_EURss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,273,252 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004963/ScoringFiles/PGS004963.txt.gz |
| PGS004964 (Hemoglobin_Mean_INT_prscs_HISss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,273,252 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004964/ScoringFiles/PGS004964.txt.gz |
| PGS004965 (Hemoglobin_Mean_INT_prscs_METAss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,273,252 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004965/ScoringFiles/PGS004965.txt.gz |
| PGS004966 (Hemoglobin_Mean_INT_prscs_METAss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,273,252 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004966/ScoringFiles/PGS004966.txt.gz |
| PGS004967 (Hemoglobin_Mean_INT_prscs_METAss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,273,252 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004967/ScoringFiles/PGS004967.txt.gz |
| PGS004968 (Hemoglobin_Mean_INT_prscsx_METAweight) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Hemoglobin | hemoglobin measurement | 1,277,825 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004968/ScoringFiles/PGS004968.txt.gz |
|
PGS Performance Metric ID (PPM) |
Evaluated Score |
PGS Sample Set ID (PSS) |
Performance Source | Trait |
PGS Effect Sizes (per SD change) |
Classification Metrics | Other Metrics | Covariates Included in the Model |
PGS Performance: Other Relevant Information |
|---|---|---|---|---|---|---|---|---|---|
| PPM000208 | PGS000088 (baso) |
PSS000153| European Ancestry| 80,944 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Basophil count | — | — | Pearson correlation coefficent (r): 0.20539 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000209 | PGS000088 (baso) |
PSS000127| European Ancestry| 39,986 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Basophil count | — | — | Pearson correlation coefficent (r): 0.20489 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000210 | PGS000089 (baso_p) |
PSS000154| European Ancestry| 80,906 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Basophil percentage of white cells | — | — | Pearson correlation coefficent (r): 0.18838 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000211 | PGS000089 (baso_p) |
PSS000128| European Ancestry| 40,133 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Basophil percentage of white cells | — | — | Pearson correlation coefficent (r): 0.17123 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000212 | PGS000090 (eo) |
PSS000155| European Ancestry| 81,294 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Eosinophil count | — | — | Pearson correlation coefficent (r): 0.40991 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000213 | PGS000090 (eo) |
PSS000129| European Ancestry| 40,276 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Eosinophil count | — | — | Pearson correlation coefficent (r): 0.39315 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000214 | PGS000091 (eo_p) |
PSS000156| European Ancestry| 81,283 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Eosinophil percentage of white cells | — | — | Pearson correlation coefficent (r): 0.39099 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000215 | PGS000091 (eo_p) |
PSS000130| European Ancestry| 40,326 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Eosinophil percentage of white cells | — | — | Pearson correlation coefficent (r): 0.37643 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000216 | PGS000092 (hct) |
PSS000157| European Ancestry| 81,622 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Hematocrit | — | — | Pearson correlation coefficent (r): 0.36874 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000217 | PGS000092 (hct) |
PSS000131| European Ancestry| 40,340 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Hematocrit | — | — | Pearson correlation coefficent (r): 0.29832 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000218 | PGS000093 (hgb) |
PSS000158| European Ancestry| 81,548 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Hemoglobin concentration | — | — | Pearson correlation coefficent (r): 0.37936 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000219 | PGS000093 (hgb) |
PSS000132| European Ancestry| 40,329 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Hemoglobin concentration | — | — | Pearson correlation coefficent (r): 0.30254 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000220 | PGS000094 (hlr) |
PSS000159| European Ancestry| 80,067 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Pearson correlation coefficent (r): 0.4559 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000221 | PGS000094 (hlr) |
PSS000133| European Ancestry| 40,244 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Pearson correlation coefficent (r): 0.40097 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000222 | PGS000095 (hlr_p) |
PSS000160| European Ancestry| 80,088 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: High light scatter reticulocyte percentage of red cells | — | — | Pearson correlation coefficent (r): 0.46291 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000223 | PGS000095 (hlr_p) |
PSS000134| European Ancestry| 40,225 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: High light scatter reticulocyte percentage of red cells | — | — | Pearson correlation coefficent (r): 0.40544 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000224 | PGS000096 (irf) |
PSS000161| European Ancestry| 79,282 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Immature fraction of reticulocytes | — | — | Pearson correlation coefficent (r): 0.35972 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000225 | PGS000096 (irf) |
PSS000135| European Ancestry| 40,227 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Immature fraction of reticulocytes | — | — | Pearson correlation coefficent (r): 0.36441 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000226 | PGS000097 (lymph) |
PSS000162| European Ancestry| 81,455 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Lymphocyte count | — | — | Pearson correlation coefficent (r): 0.40707 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000227 | PGS000097 (lymph) |
PSS000136| European Ancestry| 39,191 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Lymphocyte count | — | — | Pearson correlation coefficent (r): 0.4055 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000228 | PGS000098 (lymph_p) |
PSS000163| European Ancestry| 81,464 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Lymphocyte percentage of white cells | — | — | Pearson correlation coefficent (r): 0.33396 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000229 | PGS000098 (lymph_p) |
PSS000137| European Ancestry| 39,178 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Lymphocyte percentage of white cells | — | — | Pearson correlation coefficent (r): 0.3313 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000230 | PGS000099 (mch) |
PSS000164| European Ancestry| 81,303 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean corpuscular hemoglobin | — | — | Pearson correlation coefficent (r): 0.54504 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000231 | PGS000099 (mch) |
PSS000138| European Ancestry| 40,108 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean corpuscular hemoglobin | — | — | Pearson correlation coefficent (r): 0.49689 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000232 | PGS000100 (mchc) |
PSS000165| European Ancestry| 81,570 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | Pearson correlation coefficent (r): 0.2805 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000233 | PGS000100 (mchc) |
PSS000139| European Ancestry| 40,265 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | Pearson correlation coefficent (r): 0.29105 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000234 | PGS000101 (mcv) |
PSS000166| European Ancestry| 81,431 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean corpuscular volume | — | — | Pearson correlation coefficent (r): 0.5624 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000235 | PGS000101 (mcv) |
PSS000140| European Ancestry| 40,080 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean corpuscular volume | — | — | Pearson correlation coefficent (r): 0.47754 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000236 | PGS000102 (mono) |
PSS000167| European Ancestry| 80,799 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Monocyte count | — | — | Pearson correlation coefficent (r): 0.49849 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000237 | PGS000102 (mono) |
PSS000141| European Ancestry| 39,177 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Monocyte count | — | — | Pearson correlation coefficent (r): 0.47594 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000238 | PGS000103 (mono_p) |
PSS000168| European Ancestry| 80,627 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Monocyte percentage of white cells | — | — | Pearson correlation coefficent (r): 0.46271 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000239 | PGS000103 (mono_p) |
PSS000142| European Ancestry| 39,189 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Monocyte percentage of white cells | — | — | Pearson correlation coefficent (r): 0.45879 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000240 | PGS000104 (mpv) |
PSS000169| European Ancestry| 78,320 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean platelet volume | — | — | Pearson correlation coefficent (r): 0.61214 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000241 | PGS000104 (mpv) |
PSS000143| European Ancestry| 37,224 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Mean platelet volume | — | — | Pearson correlation coefficent (r): 0.60875 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000242 | PGS000105 (neut) |
PSS000170| European Ancestry| 81,358 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Neutrophil count | — | — | Pearson correlation coefficent (r): 0.36386 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000243 | PGS000105 (neut) |
PSS000144| European Ancestry| 39,138 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Neutrophil count | — | — | Pearson correlation coefficent (r): 0.35194 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000244 | PGS000106 (neut_p) |
PSS000171| European Ancestry| 81,423 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Neutrophil percentage of white cells | — | — | Pearson correlation coefficent (r): 0.32169 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000245 | PGS000106 (neut_p) |
PSS000145| European Ancestry| 39,190 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Neutrophil percentage of white cells | — | — | Pearson correlation coefficent (r): 0.31935 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000246 | PGS000107 (pct) |
PSS000172| European Ancestry| 78,161 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Plateletcrit | — | — | Pearson correlation coefficent (r): 0.49284 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000247 | PGS000107 (pct) |
PSS000146| European Ancestry| 37,306 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Plateletcrit | — | — | Pearson correlation coefficent (r): 0.48865 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000248 | PGS000108 (pdw) |
PSS000173| European Ancestry| 78,290 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Platelet distribution width | — | — | Pearson correlation coefficent (r): 0.49624 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000249 | PGS000108 (pdw) |
PSS000147| European Ancestry| 37,262 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Platelet distribution width | — | — | Pearson correlation coefficent (r): 0.28359 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000250 | PGS000109 (plt) |
PSS000174| European Ancestry| 78,246 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Platelet count | — | — | Pearson correlation coefficent (r): 0.52039 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000251 | PGS000109 (plt) |
PSS000148| European Ancestry| 38,939 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Platelet count | — | — | Pearson correlation coefficent (r): 0.53746 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000252 | PGS000110 (rbc) |
PSS000175| European Ancestry| 81,614 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Red blood cell count | — | — | Pearson correlation coefficent (r): 0.45067 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000253 | PGS000110 (rbc) |
PSS000149| European Ancestry| 40,262 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Red blood cell count | — | — | Pearson correlation coefficent (r): 0.42574 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000254 | PGS000111 (ret) |
PSS000176| European Ancestry| 79,344 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Reticulocyte count | — | — | Pearson correlation coefficent (r): 0.45071 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000255 | PGS000111 (ret) |
PSS000150| European Ancestry| 40,253 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Reticulocyte count | — | — | Pearson correlation coefficent (r): 0.44742 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000256 | PGS000112 (ret_p) |
PSS000177| European Ancestry| 79,362 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Reticulocyte fraction of red cells | — | — | Pearson correlation coefficent (r): 0.45239 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000257 | PGS000112 (ret_p) |
PSS000151| European Ancestry| 40,286 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: Reticulocyte fraction of red cells | — | — | Pearson correlation coefficent (r): 0.45318 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000258 | PGS000113 (wbc) |
PSS000178| European Ancestry| 81,606 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: White blood cell count | — | — | Pearson correlation coefficent (r): 0.39876 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS) |
| PPM000259 | PGS000113 (wbc) |
PSS000152| European Ancestry| 40,466 individuals |
PGP000051 | Xu Y et al. Cell Genom (2022) |
Reported Trait: White blood cell count | — | — | Pearson correlation coefficent (r): 0.38866 | age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) | — |
| PPM000407 | PGS000127 (GS-E-EUR) |
PSS000236| European Ancestry| 37,357 individuals |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
Reported Trait: Incident type 2 diabetes | OR: 1.0 [0.99, 1.01] | — | — | age, sex | — |
| PPM000408 | PGS000128 (GS-E-AFR) |
PSS000234| African Ancestry| 1,906 individuals |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
Reported Trait: Incident type 2 diabetes | OR: 0.98 [0.96, 1.0] | — | — | age, sex | — |
| PPM000409 | PGS000129 (GS-E-EAS) |
PSS000235| East Asian Ancestry| 5,073 individuals |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
Reported Trait: Incident type 2 diabetes | OR: 1.02 [0.99, 1.05] | — | — | age, sex | — |
| PPM000410 | PGS000130 (GS-G-EUR) |
PSS000236| European Ancestry| 37,357 individuals |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
Reported Trait: Incident type 2 diabetes | OR: 1.05 [1.04, 1.06] | — | — | age, sex | — |
| PPM000411 | PGS000131 (GS-G-AFR) |
PSS000234| African Ancestry| 1,906 individuals |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
Reported Trait: Incident type 2 diabetes | OR: 1.0 [0.95, 1.05] | — | — | age, sex | — |
| PPM000412 | PGS000132 (GS-G-EAS) |
PSS000235| East Asian Ancestry| 5,073 individuals |
PGP000064 | Wheeler E et al. PLoS Med (2017) |
Reported Trait: Incident type 2 diabetes | OR: 1.05 [1.02, 1.07] | — | — | age, sex | — |
| PPM000546 | PGS000163 (baso) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Basophil count | — | — | R²: 0.0089 | sex, age, 10 genetic PCs | — |
| PPM000519 | PGS000163 (baso) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Basophil count | — | — | R²: 0.02691 | sex, age, 10 genetic PCs | — |
| PPM000520 | PGS000164 (baso_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Basophil percentage of white cells | — | — | R²: 0.02544 | sex, age, 10 genetic PCs | — |
| PPM000547 | PGS000165 (eo) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Eosinophil count | — | — | R²: 0.06931 | sex, age, 10 genetic PCs | — |
| PPM000521 | PGS000165 (eo) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Eosinophil count | — | — | R²: 0.11155 | sex, age, 10 genetic PCs | — |
| PPM000522 | PGS000166 (eo_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Eosinophil percentage of white cells | — | — | R²: 0.0979 | sex, age, 10 genetic PCs | — |
| PPM001777 | PGS000167 (hct) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Hematocrit | — | — | Pearson correlation coefficent (r): 0.15 | — | — |
| PPM000548 | PGS000167 (hct) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Hematocrit | — | — | R²: 0.05147 | sex, age, 10 genetic PCs | — |
| PPM000523 | PGS000167 (hct) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Hematocrit | — | — | R²: 0.04931 | sex, age, 10 genetic PCs | — |
| PPM001778 | PGS000168 (hgb) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Hemoglobin | — | — | Pearson correlation coefficent (r): 0.14 | — | — |
| PPM000549 | PGS000168 (hgb) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.0646 | sex, age, 10 genetic PCs | — |
| PPM000524 | PGS000168 (hgb) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.05697 | sex, age, 10 genetic PCs | — |
| PPM000525 | PGS000169 (hlr) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.11896 | sex, age, 10 genetic PCs | — |
| PPM000526 | PGS000170 (hlr_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: High light scatter reticulocyte percentage of red cells | — | — | R²: 0.12799 | sex, age, 10 genetic PCs | — |
| PPM000527 | PGS000171 (irf) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Immature fraction of reticulocytes | — | — | R²: 0.09164 | sex, age, 10 genetic PCs | — |
| PPM000550 | PGS000172 (lymph) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Lymphocyte count | — | — | R²: 0.03217 | sex, age, 10 genetic PCs | — |
| PPM000528 | PGS000172 (lymph) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Lymphocyte count | — | — | R²: 0.10555 | sex, age, 10 genetic PCs | — |
| PPM000529 | PGS000173 (lymph_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Lymphocyte percentage of white cells | — | — | R²: 0.06965 | sex, age, 10 genetic PCs | — |
| PPM000552 | PGS000174 (mch) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.17483 | sex, age, 10 genetic PCs | — |
| PPM000530 | PGS000174 (mch) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.18282 | sex, age, 10 genetic PCs | — |
| PPM001773 | PGS000174 (mch) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Mean corpuscular hemoglobin | — | — | Pearson correlation coefficent (r): 0.22 | — | — |
| PPM000551 | PGS000175 (mchc) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.04796 | sex, age, 10 genetic PCs | — |
| PPM000531 | PGS000175 (mchc) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.05159 | sex, age, 10 genetic PCs | — |
| PPM001772 | PGS000175 (mchc) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Mean cell hemoglobin concentration | — | — | Pearson correlation coefficent (r): 0.12 | — | — |
| PPM000553 | PGS000176 (mcv) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.1665 | sex, age, 10 genetic PCs | — |
| PPM000532 | PGS000176 (mcv) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.15931 | sex, age, 10 genetic PCs | — |
| PPM001774 | PGS000176 (mcv) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Mean corpuscular volume | — | — | Pearson correlation coefficent (r): 0.23 | — | — |
| PPM000554 | PGS000177 (mono) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Monocyte count | — | — | R²: 0.09474 | sex, age, 10 genetic PCs | — |
| PPM000533 | PGS000177 (mono) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Monocyte count | — | — | R²: 0.15859 | sex, age, 10 genetic PCs | — |
| PPM000534 | PGS000178 (mono_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Monocyte percentage of white cells | — | — | R²: 0.14461 | sex, age, 10 genetic PCs | — |
| PPM000555 | PGS000179 (mpv) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean platelet volume | — | — | R²: 0.19379 | sex, age, 10 genetic PCs | — |
| PPM000535 | PGS000179 (mpv) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Mean platelet volume | — | — | R²: 0.27306 | sex, age, 10 genetic PCs | — |
| PPM001776 | PGS000179 (mpv) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Mean platelet volume | — | — | Pearson correlation coefficent (r): 0.51 | — | — |
| PPM000556 | PGS000182 (neut) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Neutrophil count | — | — | R²: 0.06859 | sex, age, 10 genetic PCs | — |
| PPM000536 | PGS000182 (neut) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Neutrophil count | — | — | R²: 0.08009 | sex, age, 10 genetic PCs | — |
| PPM000537 | PGS000183 (neut_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Neutrophil percentage of white cells | — | — | R²: 0.06432 | sex, age, 10 genetic PCs | — |
| PPM000538 | PGS000184 (pct) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Plateletcrit | — | — | R²: 0.1555 | sex, age, 10 genetic PCs | — |
| PPM000539 | PGS000185 (pdw) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Platelet distribution width | — | — | R²: 0.05996 | sex, age, 10 genetic PCs | — |
| PPM001775 | PGS000186 (plt) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Platelet count | — | — | Pearson correlation coefficent (r): 0.35 | — | — |
| PPM000557 | PGS000186 (plt) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Platelet count | — | — | R²: 0.16049 | sex, age, 10 genetic PCs | — |
| PPM000540 | PGS000186 (plt) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Platelet count | — | — | R²: 0.19195 | sex, age, 10 genetic PCs | — |
| PPM000558 | PGS000187 (rbc) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Red blood cell count | — | — | R²: 0.06798 | sex, age, 10 genetic PCs | — |
| PPM000541 | PGS000187 (rbc) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Red blood cell count | — | — | R²: 0.11765 | sex, age, 10 genetic PCs | — |
| PPM001771 | PGS000187 (rbc) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: Red blood cell count | — | — | Pearson correlation coefficent (r): 0.22 | — | — |
| PPM000559 | PGS000188 (rdw) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Red cell distribution width | — | — | R²: 0.07409 | sex, age, 10 genetic PCs | — |
| PPM000542 | PGS000188 (rdw) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Red cell distribution width | — | — | R²: 0.09519 | sex, age, 10 genetic PCs | — |
| PPM000543 | PGS000189 (ret) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Reticulocyte count | — | — | R²: 0.14142 | sex, age, 10 genetic PCs | — |
| PPM000544 | PGS000190 (ret_p) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: Reticulocyte fraction of red cells | — | — | R²: 0.15022 | sex, age, 10 genetic PCs | — |
| PPM000560 | PGS000191 (wbc) |
PSS000290| European Ancestry| 2,314 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: White blood cell count | — | — | R²: 0.06336 | sex, age, 10 genetic PCs | — |
| PPM000545 | PGS000191 (wbc) |
PSS000291| European Ancestry| 39,260 individuals |
PGP000078 | Vuckovic D et al. Cell (2020) |
Reported Trait: White blood cell count | — | — | R²: 0.08672 | sex, age, 10 genetic PCs | — |
| PPM001770 | PGS000191 (wbc) |
PSS000911| Greater Middle Eastern Ancestry| 13,989 individuals |
PGP000147 | Thareja G et al. Nat Commun (2021) |Ext. |
Reported Trait: White blood cell count | — | — | Pearson correlation coefficent (r): 0.19 | — | — |
| PPM000774 | PGS000304 (GRS43_HbA1c) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: HbA1c (%) | — | — | R²: 0.0283 | Sex, age, age^2 | — |
| PPM000803 | PGS000304 (GRS43_HbA1c) |
PSS000371| European Ancestry| 288 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: HbA1c (%) | — | — | R²: 0.0277 | Sex, age, age^2 | — |
| PPM001413 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS000704| African Ancestry| 4,847 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: HbA1c [mmol/mol] | — | — | R²: 0.07974 Spearman's ρ: 0.132 |
Age, sex, PCs(1-40) | — |
| PPM001448 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS000705| East Asian Ancestry| 1,032 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: HbA1c [mmol/mol] | — | — | R²: 0.16979 Spearman's ρ: 0.285 |
Age, sex, PCs(1-40) | — |
| PPM001483 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS000706| European Ancestry| 22,518 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: HbA1c [mmol/mol] | — | — | R²: 0.17335 Spearman's ρ: 0.367 |
Age, sex, PCs(1-40) | — |
| PPM001518 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS000707| South Asian Ancestry| 6,895 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: HbA1c [mmol/mol] | — | — | R²: 0.12743 Spearman's ρ: 0.268 |
Age, sex, PCs(1-40) | — |
| PPM001553 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS000708| European Ancestry| 60,920 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: HbA1c [mmol/mol] | — | — | R²: 0.16381 Spearman's ρ: 0.375 |
Age, sex, PCs(1-40) | — |
| PPM001585 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS000817| European Ancestry| 1,995 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: HbA1c [mmol/mol] | — | — | Spearman's ρ: 0.217 | Age, sex | — |
| PPM007350 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS007156| African Ancestry| 5,219 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | R²: 0.05396 [0.04328, 0.06464] Incremental R2 (full-covars): 0.00054 PGS R2 (no covariates): 0.00619 [0.00239, 0.00998] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007351 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS007157| East Asian Ancestry| 1,622 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | R²: 0.1264 [0.09696, 0.15583] Incremental R2 (full-covars): 0.00207 PGS R2 (no covariates): 0.04145 [0.02295, 0.05994] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007352 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS007158| European Ancestry| 23,762 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | R²: 0.06205 [0.05625, 0.06785] Incremental R2 (full-covars): 0.00348 PGS R2 (no covariates): 0.06567 [0.05972, 0.07162] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007353 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS007159| South Asian Ancestry| 7,345 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | R²: 0.05344 [0.04375, 0.06313] Incremental R2 (full-covars): 0.00135 PGS R2 (no covariates): 0.03576 [0.02769, 0.04383] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007354 | PGS000685 (snpnet.Glycated_haemoglobin_HbA1c) |
PSS007160| European Ancestry| 64,432 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | R²: 0.05264 [0.04936, 0.05592] Incremental R2 (full-covars): 0.00369 PGS R2 (no covariates): 0.06933 [0.06563, 0.07303] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001426 | PGS000698 (snpnet.Total_protein) |
PSS000767| African Ancestry| 5,573 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Total protein [g/L] | — | — | R²: 0.04363 Spearman's ρ: 0.144 |
Age, sex, PCs(1-40) | — |
| PPM001531 | PGS000698 (snpnet.Total_protein) |
PSS000770| South Asian Ancestry| 6,687 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Total protein [g/L] | — | — | R²: 0.10522 Spearman's ρ: 0.258 |
Age, sex, PCs(1-40) | — |
| PPM001566 | PGS000698 (snpnet.Total_protein) |
PSS000771| European Ancestry| 58,196 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Total protein [g/L] | — | — | R²: 0.11218 Spearman's ρ: 0.284 |
Age, sex, PCs(1-40) | — |
| PPM001461 | PGS000698 (snpnet.Total_protein) |
PSS000768| East Asian Ancestry| 984 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Total protein [g/L] | — | — | R²: 0.09275 Spearman's ρ: 0.247 |
Age, sex, PCs(1-40) | — |
| PPM001496 | PGS000698 (snpnet.Total_protein) |
PSS000769| European Ancestry| 21,516 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Total protein [g/L] | — | — | R²: 0.11654 Spearman's ρ: 0.282 |
Age, sex, PCs(1-40) | — |
| PPM007415 | PGS000698 (snpnet.Total_protein) |
PSS007201| African Ancestry| 5,659 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Total protein | — | — | R²: 0.01038 [0.00548, 0.01528] Incremental R2 (full-covars): 0.00061 PGS R2 (no covariates): 0.02117 [0.01425, 0.0281] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007416 | PGS000698 (snpnet.Total_protein) |
PSS007202| East Asian Ancestry| 1,474 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Total protein | — | — | R²: 0.04549 [0.0262, 0.06478] Incremental R2 (full-covars): 0.00107 PGS R2 (no covariates): 0.05677 [0.03548, 0.07807] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007417 | PGS000698 (snpnet.Total_protein) |
PSS007203| European Ancestry| 21,730 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Total protein | — | — | R²: 0.01493 [0.01194, 0.01792] Incremental R2 (full-covars): 0.00167 PGS R2 (no covariates): 0.07937 [0.07293, 0.08581] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007418 | PGS000698 (snpnet.Total_protein) |
PSS007204| South Asian Ancestry| 6,788 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Total protein | — | — | R²: 0.01363 [0.00853, 0.01873] Incremental R2 (full-covars): 0.00134 PGS R2 (no covariates): 0.0679 [0.05715, 0.07866] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007419 | PGS000698 (snpnet.Total_protein) |
PSS007205| European Ancestry| 59,024 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Total protein | — | — | R²: 0.00668 [0.00545, 0.0079] Incremental R2 (full-covars): 0.00176 PGS R2 (no covariates): 0.08081 [0.07687, 0.08476] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007703 | PGS000987 (GBE_INI30260) |
PSS007026| African Ancestry| 5,973 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.03718 [0.02815, 0.0462] Incremental R2 (full-covars): 0.02777 PGS R2 (no covariates): 0.02867 [0.02068, 0.03667] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007704 | PGS000987 (GBE_INI30260) |
PSS007027| East Asian Ancestry| 1,623 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.08812 [0.06247, 0.11377] Incremental R2 (full-covars): 0.07173 PGS R2 (no covariates): 0.07447 [0.05053, 0.0984] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007705 | PGS000987 (GBE_INI30260) |
PSS007028| European Ancestry| 23,687 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.1616 [0.15323, 0.16997] Incremental R2 (full-covars): 0.14435 PGS R2 (no covariates): 0.1455 [0.1374, 0.15359] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007706 | PGS000987 (GBE_INI30260) |
PSS007029| South Asian Ancestry| 7,323 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.13294 [0.11894, 0.14694] Incremental R2 (full-covars): 0.09343 PGS R2 (no covariates): 0.09786 [0.08537, 0.11036] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007707 | PGS000987 (GBE_INI30260) |
PSS007030| European Ancestry| 64,570 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.15675 [0.15171, 0.16179] Incremental R2 (full-covars): 0.14554 PGS R2 (no covariates): 0.14631 [0.14138, 0.15124] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007708 | PGS000988 (GBE_INI30290) |
PSS007036| African Ancestry| 5,974 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte % | — | — | R²: 0.02767 [0.0198, 0.03553] Incremental R2 (full-covars): 0.01965 PGS R2 (no covariates): 0.01997 [0.01324, 0.0267] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007709 | PGS000988 (GBE_INI30290) |
PSS007037| East Asian Ancestry| 1,623 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte % | — | — | R²: 0.05671 [0.03542, 0.07799] Incremental R2 (full-covars): 0.04573 PGS R2 (no covariates): 0.04876 [0.02885, 0.06866] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007710 | PGS000988 (GBE_INI30290) |
PSS007038| European Ancestry| 23,681 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte % | — | — | R²: 0.08594 [0.07929, 0.0926] Incremental R2 (full-covars): 0.07798 PGS R2 (no covariates): 0.07863 [0.07222, 0.08505] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007711 | PGS000988 (GBE_INI30290) |
PSS007039| South Asian Ancestry| 7,321 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte % | — | — | R²: 0.07357 [0.06245, 0.0847] Incremental R2 (full-covars): 0.06049 PGS R2 (no covariates): 0.06609 [0.05546, 0.07672] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007712 | PGS000988 (GBE_INI30290) |
PSS007040| European Ancestry| 64,524 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte % | — | — | R²: 0.01671 [0.0148, 0.01863] Incremental R2 (full-covars): 0.01605 PGS R2 (no covariates): 0.016 [0.01412, 0.01788] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007715 | PGS000989 (GBE_INI30240) |
PSS007018| European Ancestry| 23,688 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte % | — | — | R²: 0.0318 [0.02751, 0.03609] Incremental R2 (full-covars): 0.02883 PGS R2 (no covariates): 0.02905 [0.02494, 0.03316] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007713 | PGS000989 (GBE_INI30240) |
PSS007016| African Ancestry| 5,974 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte % | — | — | R²: 0.01993 [0.01321, 0.02666] Incremental R2 (full-covars): 0.01309 PGS R2 (no covariates): 0.01384 [0.0082, 0.01948] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007714 | PGS000989 (GBE_INI30240) |
PSS007017| East Asian Ancestry| 1,623 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte % | — | — | R²: 0.05032 [0.03013, 0.0705] Incremental R2 (full-covars): 0.03871 PGS R2 (no covariates): 0.03842 [0.02056, 0.05628] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007716 | PGS000989 (GBE_INI30240) |
PSS007019| South Asian Ancestry| 7,323 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte % | — | — | R²: 0.03312 [0.02533, 0.04091] Incremental R2 (full-covars): 0.02678 PGS R2 (no covariates): 0.02784 [0.02066, 0.03502] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007717 | PGS000989 (GBE_INI30240) |
PSS007020| European Ancestry| 64,569 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte % | — | — | R²: 0.02928 [0.02677, 0.03179] Incremental R2 (full-covars): 0.02823 PGS R2 (no covariates): 0.02826 [0.0258, 0.03073] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008138 | PGS001076 (GBE_INI30210) |
PSS007006| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill % | — | — | R²: 0.02749 [0.01965, 0.03533] Incremental R2 (full-covars): 0.01358 PGS R2 (no covariates): 0.01502 [0.00915, 0.02088] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008139 | PGS001076 (GBE_INI30210) |
PSS007007| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill % | — | — | R²: 0.0678 [0.0448, 0.0908] Incremental R2 (full-covars): 0.03179 PGS R2 (no covariates): 0.03326 [0.01655, 0.04996] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008140 | PGS001076 (GBE_INI30210) |
PSS007008| European Ancestry| 24,130 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill % | — | — | R²: 0.10724 [0.09998, 0.1145] Incremental R2 (full-covars): 0.09158 PGS R2 (no covariates): 0.09579 [0.08884, 0.10274] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008141 | PGS001076 (GBE_INI30210) |
PSS007009| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill % | — | — | R²: 0.08004 [0.06852, 0.09157] Incremental R2 (full-covars): 0.0618 PGS R2 (no covariates): 0.06258 [0.05219, 0.07296] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008142 | PGS001076 (GBE_INI30210) |
PSS007010| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill % | — | — | R²: 0.10364 [0.09929, 0.108] Incremental R2 (full-covars): 0.09566 PGS R2 (no covariates): 0.09594 [0.09171, 0.10016] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008143 | PGS001077 (GBE_INI30200) |
PSS007001| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill % | — | — | Incremental R2 (full-covars): 0.01344 R²: 0.04156 [0.03206, 0.05106] PGS R2 (no covariates): 0.01383 [0.00819, 0.01946] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008144 | PGS001077 (GBE_INI30200) |
PSS007002| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill % | — | — | R²: 0.07662 [0.0524, 0.10084] Incremental R2 (full-covars): 0.04345 PGS R2 (no covariates): 0.04564 [0.02632, 0.06496] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008145 | PGS001077 (GBE_INI30200) |
PSS007003| European Ancestry| 24,130 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill % | — | — | R²: 0.07418 [0.06792, 0.08044] Incremental R2 (full-covars): 0.07151 PGS R2 (no covariates): 0.07289 [0.06667, 0.0791] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008146 | PGS001077 (GBE_INI30200) |
PSS007004| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill % | — | — | R²: 0.05036 [0.04093, 0.0598] Incremental R2 (full-covars): 0.04554 PGS R2 (no covariates): 0.04441 [0.03549, 0.05333] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008147 | PGS001077 (GBE_INI30200) |
PSS007005| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill % | — | — | R²: 0.08175 [0.07779, 0.08572] Incremental R2 (full-covars): 0.07935 PGS R2 (no covariates): 0.07934 [0.07542, 0.08325] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008148 | PGS001078 (GBE_INI30190) |
PSS006996| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte % | — | — | R²: 0.03107 [0.02277, 0.03938] Incremental R2 (full-covars): 0.01068 PGS R2 (no covariates): 0.01162 [0.00644, 0.0168] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008149 | PGS001078 (GBE_INI30190) |
PSS006997| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte % | — | — | R²: 0.10068 [0.07364, 0.12772] Incremental R2 (full-covars): 0.04628 PGS R2 (no covariates): 0.04808 [0.0283, 0.06786] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008150 | PGS001078 (GBE_INI30190) |
PSS006998| European Ancestry| 24,130 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte % | — | — | R²: 0.12442 [0.11675, 0.13209] Incremental R2 (full-covars): 0.08036 PGS R2 (no covariates): 0.08169 [0.07518, 0.08821] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008151 | PGS001078 (GBE_INI30190) |
PSS006999| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte % | — | — | R²: 0.12434 [0.11066, 0.13801] Incremental R2 (full-covars): 0.05529 PGS R2 (no covariates): 0.05423 [0.04448, 0.06398] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008152 | PGS001078 (GBE_INI30190) |
PSS007000| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte % | — | — | R²: 0.12757 [0.12287, 0.13228] Incremental R2 (full-covars): 0.08527 PGS R2 (no covariates): 0.08574 [0.0817, 0.08978] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008153 | PGS001079 (GBE_INI30110) |
PSS006961| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet distribution width | — | — | R²: 0.08809 [0.07494, 0.10125] Incremental R2 (full-covars): 0.05017 PGS R2 (no covariates): 0.05279 [0.04222, 0.06337] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008154 | PGS001079 (GBE_INI30110) |
PSS006962| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet distribution width | — | — | R²: 0.1275 [0.09798, 0.15703] Incremental R2 (full-covars): 0.09522 PGS R2 (no covariates): 0.09877 [0.07193, 0.12561] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008155 | PGS001079 (GBE_INI30110) |
PSS006963| European Ancestry| 24,171 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet distribution width | — | — | R²: 0.2279 [0.21875, 0.23705] Incremental R2 (full-covars): 0.20117 PGS R2 (no covariates): 0.2053 [0.19636, 0.21424] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008156 | PGS001079 (GBE_INI30110) |
PSS006964| South Asian Ancestry| 7,519 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet distribution width | — | — | R²: 0.18488 [0.16936, 0.2004] Incremental R2 (full-covars): 0.16662 PGS R2 (no covariates): 0.16657 [0.15151, 0.18163] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008157 | PGS001079 (GBE_INI30110) |
PSS006965| European Ancestry| 65,601 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet distribution width | — | — | R²: 0.22746 [0.2219, 0.23302] Incremental R2 (full-covars): 0.20559 PGS R2 (no covariates): 0.20528 [0.19985, 0.21072] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008482 | PGS001152 (GBE_INI30070) |
PSS006941| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell distribution width | — | — | R²: 0.02633 [0.01865, 0.03401] Incremental R2 (full-covars): 0.00466 PGS R2 (no covariates): 0.00617 [0.00238, 0.00997] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008483 | PGS001152 (GBE_INI30070) |
PSS006942| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell distribution width | — | — | R²: 0.06947 [0.04623, 0.09271] Incremental R2 (full-covars): 0.04028 PGS R2 (no covariates): 0.04714 [0.02754, 0.06675] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008484 | PGS001152 (GBE_INI30070) |
PSS006943| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell distribution width | — | — | R²: 0.08241 [0.07587, 0.08895] Incremental R2 (full-covars): 0.07409 PGS R2 (no covariates): 0.0754 [0.06909, 0.0817] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008485 | PGS001152 (GBE_INI30070) |
PSS006944| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell distribution width | — | — | R²: 0.06357 [0.05312, 0.07403] Incremental R2 (full-covars): 0.04077 PGS R2 (no covariates): 0.04137 [0.03274, 0.05] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008486 | PGS001152 (GBE_INI30070) |
PSS006945| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell distribution width | — | — | R²: 0.0852 [0.08117, 0.08923] Incremental R2 (full-covars): 0.07926 PGS R2 (no covariates): 0.07945 [0.07553, 0.08337] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008537 | PGS001163 (GBE_INI30130) |
PSS006971| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte count | — | — | R²: 0.02543 [0.01787, 0.03298] Incremental R2 (full-covars): 0.01339 PGS R2 (no covariates): 0.01659 [0.01043, 0.02274] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008538 | PGS001163 (GBE_INI30130) |
PSS006972| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte count | — | — | R²: 0.10427 [0.07686, 0.13168] Incremental R2 (full-covars): 0.03555 PGS R2 (no covariates): 0.04025 [0.022, 0.0585] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008539 | PGS001163 (GBE_INI30130) |
PSS006973| European Ancestry| 24,129 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte count | — | — | R²: 0.12792 [0.12017, 0.13567] Incremental R2 (full-covars): 0.0805 PGS R2 (no covariates): 0.08361 [0.07703, 0.09019] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008540 | PGS001163 (GBE_INI30130) |
PSS006974| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte count | — | — | R²: 0.09808 [0.08557, 0.11059] Incremental R2 (full-covars): 0.05583 PGS R2 (no covariates): 0.05525 [0.04542, 0.06508] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008541 | PGS001163 (GBE_INI30130) |
PSS006975| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Monocyte count | — | — | R²: 0.11973 [0.11513, 0.12432] Incremental R2 (full-covars): 0.081 PGS R2 (no covariates): 0.08138 [0.07742, 0.08533] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008570 | PGS001172 (GBE_INI30150) |
PSS006981| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill count | — | — | R²: 0.02165 [0.01465, 0.02865] Incremental R2 (full-covars): 0.01273 PGS R2 (no covariates): 0.01483 [0.009, 0.02067] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008571 | PGS001172 (GBE_INI30150) |
PSS006982| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill count | — | — | R²: 0.07017 [0.04683, 0.09352] Incremental R2 (full-covars): 0.02406 PGS R2 (no covariates): 0.02686 [0.01175, 0.04197] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008572 | PGS001172 (GBE_INI30150) |
PSS006983| European Ancestry| 24,129 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill count | — | — | R²: 0.10592 [0.09869, 0.11315] Incremental R2 (full-covars): 0.08944 PGS R2 (no covariates): 0.0939 [0.08701, 0.1008] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008573 | PGS001172 (GBE_INI30150) |
PSS006984| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill count | — | — | R²: 0.06486 [0.05432, 0.07541] Incremental R2 (full-covars): 0.05229 PGS R2 (no covariates): 0.05348 [0.04379, 0.06318] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008574 | PGS001172 (GBE_INI30150) |
PSS006985| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Eosinophill count | — | — | R²: 0.10264 [0.0983, 0.10698] Incremental R2 (full-covars): 0.09343 PGS R2 (no covariates): 0.09398 [0.08978, 0.09817] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008575 | PGS001173 (GBE_INI30140) |
PSS006976| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill count | — | — | R²: 0.06347 [0.052, 0.07494] Incremental R2 (full-covars): 0.00644 PGS R2 (no covariates): 0.01221 [0.00691, 0.01752] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008576 | PGS001173 (GBE_INI30140) |
PSS006977| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill count | — | — | R²: 0.05754 [0.03611, 0.07896] Incremental R2 (full-covars): 0.037 PGS R2 (no covariates): 0.0354 [0.0182, 0.0526] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008577 | PGS001173 (GBE_INI30140) |
PSS006978| European Ancestry| 24,129 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill count | — | — | R²: 0.08794 [0.08123, 0.09466] Incremental R2 (full-covars): 0.07899 PGS R2 (no covariates): 0.08282 [0.07627, 0.08938] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008578 | PGS001173 (GBE_INI30140) |
PSS006979| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill count | — | — | R²: 0.07553 [0.06428, 0.08677] Incremental R2 (full-covars): 0.0671 PGS R2 (no covariates): 0.06762 [0.05688, 0.07835] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008579 | PGS001173 (GBE_INI30140) |
PSS006980| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Neutrophill count | — | — | R²: 0.09385 [0.08966, 0.09804] Incremental R2 (full-covars): 0.08871 PGS R2 (no covariates): 0.08935 [0.08524, 0.09346] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008616 | PGS001199 (GBE_INI30120) |
PSS006968| European Ancestry| 24,129 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte count | — | — | R²: 0.04047 [0.03568, 0.04526] Incremental R2 (full-covars): 0.03437 PGS R2 (no covariates): 0.03502 [0.03053, 0.0395] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008614 | PGS001199 (GBE_INI30120) |
PSS006966| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte count | — | — | R²: 0.01509 [0.00921, 0.02097] Incremental R2 (full-covars): 0.0048 PGS R2 (no covariates): 0.00655 [0.00264, 0.01046] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008615 | PGS001199 (GBE_INI30120) |
PSS006967| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte count | — | — | R²: 0.07751 [0.05317, 0.10185] Incremental R2 (full-covars): 0.02783 PGS R2 (no covariates): 0.0308 [0.01468, 0.04692] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008617 | PGS001199 (GBE_INI30120) |
PSS006969| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte count | — | — | R²: 0.04041 [0.03187, 0.04896] Incremental R2 (full-covars): 0.02891 PGS R2 (no covariates): 0.02928 [0.02192, 0.03663] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008618 | PGS001199 (GBE_INI30120) |
PSS006970| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte count | — | — | R²: 0.02566 [0.0233, 0.02801] Incremental R2 (full-covars): 0.02051 PGS R2 (no covariates): 0.02072 [0.01859, 0.02285] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008619 | PGS001200 (GBE_INI30100) |
PSS006956| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean platelet volume | — | — | R²: 0.11523 [0.10064, 0.12983] Incremental R2 (full-covars): 0.10713 PGS R2 (no covariates): 0.10764 [0.09341, 0.12187] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008620 | PGS001200 (GBE_INI30100) |
PSS006957| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean platelet volume | — | — | R²: 0.22982 [0.19483, 0.26481] Incremental R2 (full-covars): 0.1912 PGS R2 (no covariates): 0.20572 [0.17158, 0.23986] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008621 | PGS001200 (GBE_INI30100) |
PSS006958| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean platelet volume | — | — | R²: 0.36048 [0.35094, 0.37001] Incremental R2 (full-covars): 0.35332 PGS R2 (no covariates): 0.3577 [0.34816, 0.36724] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008622 | PGS001200 (GBE_INI30100) |
PSS006959| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean platelet volume | — | — | R²: 0.26489 [0.24813, 0.28164] Incremental R2 (full-covars): 0.25606 PGS R2 (no covariates): 0.25974 [0.24303, 0.27644] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008623 | PGS001200 (GBE_INI30100) |
PSS006960| European Ancestry| 65,636 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean platelet volume | — | — | R²: 0.36177 [0.35598, 0.36757] Incremental R2 (full-covars): 0.35994 PGS R2 (no covariates): 0.36024 [0.35444, 0.36603] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008624 | PGS001218 (GBE_INI30060) |
PSS006936| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin concentration | — | — | R²: 0.01334 [0.0078, 0.01888] Incremental R2 (full-covars): 0.0007 PGS R2 (no covariates): 0.00244 [0.00004, 0.00483] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008625 | PGS001218 (GBE_INI30060) |
PSS006937| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin concentration | — | — | R²: 0.05415 [0.03329, 0.07501] Incremental R2 (full-covars): 0.01409 PGS R2 (no covariates): 0.01986 [0.00677, 0.03295] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008626 | PGS001218 (GBE_INI30060) |
PSS006938| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin concentration | — | — | R²: 0.02769 [0.02367, 0.03171] Incremental R2 (full-covars): 0.01601 PGS R2 (no covariates): 0.01701 [0.01382, 0.02019] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008627 | PGS001218 (GBE_INI30060) |
PSS006939| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin concentration | — | — | R²: 0.04592 [0.03687, 0.05498] Incremental R2 (full-covars): 0.01608 PGS R2 (no covariates): 0.01906 [0.01306, 0.02505] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008628 | PGS001218 (GBE_INI30060) |
PSS006940| European Ancestry| 65,635 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin concentration | — | — | R²: 0.03753 [0.03472, 0.04035] Incremental R2 (full-covars): 0.02447 PGS R2 (no covariates): 0.02534 [0.023, 0.02768] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008629 | PGS001219 (GBE_INI30050) |
PSS006931| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | R²: 0.03354 [0.02494, 0.04215] Incremental R2 (full-covars): 0.00833 PGS R2 (no covariates): 0.01254 [0.00717, 0.01792] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008630 | PGS001219 (GBE_INI30050) |
PSS006932| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | R²: 0.0892 [0.06342, 0.11498] Incremental R2 (full-covars): 0.03143 PGS R2 (no covariates): 0.0524 [0.03184, 0.07296] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008631 | PGS001219 (GBE_INI30050) |
PSS006933| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | R²: 0.16096 [0.1526, 0.16932] Incremental R2 (full-covars): 0.12049 PGS R2 (no covariates): 0.13012 [0.12233, 0.13791] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008632 | PGS001219 (GBE_INI30050) |
PSS006934| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | R²: 0.11958 [0.1061, 0.13306] Incremental R2 (full-covars): 0.06684 PGS R2 (no covariates): 0.07101 [0.06005, 0.08197] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008633 | PGS001219 (GBE_INI30050) |
PSS006935| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | R²: 0.18797 [0.18266, 0.19328] Incremental R2 (full-covars): 0.1678 PGS R2 (no covariates): 0.1703 [0.16513, 0.17547] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008634 | PGS001220 (GBE_INI30040) |
PSS006926| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.03909 [0.02986, 0.04833] Incremental R2 (full-covars): 0.01317 PGS R2 (no covariates): 0.01523 [0.00932, 0.02114] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008635 | PGS001220 (GBE_INI30040) |
PSS006927| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.09134 [0.06532, 0.11737] Incremental R2 (full-covars): 0.03736 PGS R2 (no covariates): 0.05987 [0.03807, 0.08167] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008636 | PGS001220 (GBE_INI30040) |
PSS006928| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.20688 [0.19792, 0.21584] Incremental R2 (full-covars): 0.15861 PGS R2 (no covariates): 0.17414 [0.16558, 0.1827] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008637 | PGS001220 (GBE_INI30040) |
PSS006929| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.12834 [0.11451, 0.14216] Incremental R2 (full-covars): 0.07837 PGS R2 (no covariates): 0.08395 [0.0722, 0.0957] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008638 | PGS001220 (GBE_INI30040) |
PSS006930| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.21614 [0.21064, 0.22164] Incremental R2 (full-covars): 0.19948 PGS R2 (no covariates): 0.20189 [0.19648, 0.2073] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008639 | PGS001225 (GBE_INI30030) |
PSS006921| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haematocrit % | — | — | Incremental R2 (full-covars): 0.01035 R²: 0.39181 [0.37331, 0.41032] PGS R2 (no covariates): 0.01326 [0.00774, 0.01879] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008640 | PGS001225 (GBE_INI30030) |
PSS006922| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haematocrit % | — | — | R²: 0.4128 [0.37705, 0.44856] Incremental R2 (full-covars): 0.02196 PGS R2 (no covariates): 0.02063 [0.0073, 0.03396] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008641 | PGS001225 (GBE_INI30030) |
PSS006923| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haematocrit % | — | — | R²: 0.396 [0.38656, 0.40544] Incremental R2 (full-covars): 0.05878 PGS R2 (no covariates): 0.06263 [0.0568, 0.06846] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008642 | PGS001225 (GBE_INI30030) |
PSS006924| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haematocrit % | — | — | R²: 0.4249 [0.4083, 0.4415] Incremental R2 (full-covars): 0.02482 PGS R2 (no covariates): 0.0222 [0.01575, 0.02865] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008643 | PGS001225 (GBE_INI30030) |
PSS006925| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haematocrit % | — | — | R²: 0.39123 [0.38548, 0.39698] Incremental R2 (full-covars): 0.06504 PGS R2 (no covariates): 0.06544 [0.06183, 0.06904] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008699 | PGS001238 (GBE_INI30080) |
PSS006946| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet count | — | — | R²: 0.13129 [0.11599, 0.14659] Incremental R2 (full-covars): 0.04423 PGS R2 (no covariates): 0.04881 [0.03859, 0.05902] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008700 | PGS001238 (GBE_INI30080) |
PSS006947| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet count | — | — | R²: 0.14398 [0.1132, 0.17476] Incremental R2 (full-covars): 0.09414 PGS R2 (no covariates): 0.09846 [0.07165, 0.12527] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008701 | PGS001238 (GBE_INI30080) |
PSS006948| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet count | — | — | R²: 0.26403 [0.25464, 0.27343] Incremental R2 (full-covars): 0.21574 PGS R2 (no covariates): 0.21973 [0.21064, 0.22881] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008702 | PGS001238 (GBE_INI30080) |
PSS006949| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet count | — | — | R²: 0.24088 [0.22438, 0.25738] Incremental R2 (full-covars): 0.15169 PGS R2 (no covariates): 0.153 [0.13833, 0.16767] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008703 | PGS001238 (GBE_INI30080) |
PSS006950| European Ancestry| 65,637 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet count | — | — | R²: 0.26678 [0.26106, 0.2725] Incremental R2 (full-covars): 0.20845 PGS R2 (no covariates): 0.20987 [0.20441, 0.21533] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008704 | PGS001239 (GBE_INI30000) |
PSS006891| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: White blood cell count | — | — | R²: 0.05282 [0.04224, 0.0634] Incremental R2 (full-covars): 0.00963 PGS R2 (no covariates): 0.01451 [0.00874, 0.02028] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008705 | PGS001239 (GBE_INI30000) |
PSS006892| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: White blood cell count | — | — | R²: 0.07596 [0.05182, 0.10009] Incremental R2 (full-covars): 0.0496 PGS R2 (no covariates): 0.05069 [0.03044, 0.07095] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008706 | PGS001239 (GBE_INI30000) |
PSS006893| European Ancestry| 24,174 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: White blood cell count | — | — | R²: 0.08839 [0.08166, 0.09513] Incremental R2 (full-covars): 0.07763 PGS R2 (no covariates): 0.08212 [0.07559, 0.08865] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008707 | PGS001239 (GBE_INI30000) |
PSS006894| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: White blood cell count | — | — | R²: 0.07166 [0.06066, 0.08267] Incremental R2 (full-covars): 0.06404 PGS R2 (no covariates): 0.06438 [0.05387, 0.07489] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008708 | PGS001239 (GBE_INI30000) |
PSS006895| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: White blood cell count | — | — | R²: 0.0777 [0.07382, 0.08158] Incremental R2 (full-covars): 0.07177 PGS R2 (no covariates): 0.07264 [0.06887, 0.07641] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008709 | PGS001240 (GBE_INI30010) |
PSS006896| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell count | — | — | R²: 0.26906 [0.25063, 0.28749] Incremental R2 (full-covars): 0.01652 PGS R2 (no covariates): 0.01819 [0.01175, 0.02463] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008710 | PGS001240 (GBE_INI30010) |
PSS006897| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell count | — | — | R²: 0.32546 [0.289, 0.36193] Incremental R2 (full-covars): 0.04519 PGS R2 (no covariates): 0.05665 [0.03538, 0.07793] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008711 | PGS001240 (GBE_INI30010) |
PSS006898| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell count | — | — | R²: 0.37954 [0.37005, 0.38904] Incremental R2 (full-covars): 0.10462 PGS R2 (no covariates): 0.11155 [0.10418, 0.11892] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008712 | PGS001240 (GBE_INI30010) |
PSS006899| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell count | — | — | R²: 0.33598 [0.31894, 0.35302] Incremental R2 (full-covars): 0.05745 PGS R2 (no covariates): 0.05295 [0.0433, 0.0626] |
age, sex, UKB array type, Genotype PCs | — |
| PPM008713 | PGS001240 (GBE_INI30010) |
PSS006900| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Red blood cell count | — | — | R²: 0.37344 [0.36766, 0.37922] Incremental R2 (full-covars): 0.11681 PGS R2 (no covariates): 0.11784 [0.11327, 0.12241] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005139 | PGS001352 (MAGICTA_EUR_PGS_HbA1c) |
PSS003594| European Ancestry| 61,820 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Glycated haemoglobin levels (HbA1c) | — | — | R²: 0.178 | — | — |
| PPM005142 | PGS001352 (MAGICTA_EUR_PGS_HbA1c) |
PSS003591| African Ancestry| 6,647 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Glycated haemoglobin levels (HbA1c) | — | — | R²: 0.012 | — | — |
| PPM005145 | PGS001352 (MAGICTA_EUR_PGS_HbA1c) |
PSS003593| East Asian Ancestry| 31,236 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Glycated haemoglobin levels (HbA1c) | — | — | R²: 0.026 | — | — |
| PPM005146 | PGS001352 (MAGICTA_EUR_PGS_HbA1c) |
PSS003592| African Ancestry| 4,441 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Glycated haemoglobin levels (HbA1c) | — | — | R²: 0.006 | — | — |
| PPM007058 | PGS001377 (GBE_INI30220) |
PSS007014| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill % | — | — | R²: 0.01668 [0.01106, 0.0223] Incremental R2 (full-covars): 0.01178 PGS R2 (no covariates): 0.01253 [0.00763, 0.01742] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007059 | PGS001377 (GBE_INI30220) |
PSS007015| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill % | — | — | R²: 0.01674 [0.01482, 0.01866] Incremental R2 (full-covars): 0.01485 PGS R2 (no covariates): 0.01504 [0.01321, 0.01686] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007055 | PGS001377 (GBE_INI30220) |
PSS007011| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill % | — | — | R²: 0.00639 [0.00253, 0.01025] Incremental R2 (full-covars): 0.00238 PGS R2 (no covariates): 0.00249 [0.00007, 0.00491] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007056 | PGS001377 (GBE_INI30220) |
PSS007012| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill % | — | — | R²: 0.01523 [0.00371, 0.02675] Incremental R2 (full-covars): 0.00867 PGS R2 (no covariates): 0.00896 [0.00007, 0.01786] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007057 | PGS001377 (GBE_INI30220) |
PSS007013| European Ancestry| 24,130 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill % | — | — | R²: 0.01922 [0.01584, 0.0226] Incremental R2 (full-covars): 0.01406 PGS R2 (no covariates): 0.0152 [0.01219, 0.01822] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007045 | PGS001378 (GBE_INI30160) |
PSS006986| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill count | — | — | R²: 0.0091 [0.0045, 0.01369] Incremental R2 (full-covars): 0.00051 PGS R2 (no covariates): 0.00175 [-0.00028, 0.00379] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007046 | PGS001378 (GBE_INI30160) |
PSS006987| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill count | — | — | R²: 0.01746 [0.00516, 0.02976] Incremental R2 (full-covars): 0.00946 PGS R2 (no covariates): 0.00963 [0.00042, 0.01883] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007047 | PGS001378 (GBE_INI30160) |
PSS006988| European Ancestry| 24,129 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill count | — | — | R²: 0.01903 [0.01567, 0.0224] Incremental R2 (full-covars): 0.01496 PGS R2 (no covariates): 0.01602 [0.01293, 0.01911] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007048 | PGS001378 (GBE_INI30160) |
PSS006989| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill count | — | — | R²: 0.01814 [0.01228, 0.02399] Incremental R2 (full-covars): 0.01206 PGS R2 (no covariates): 0.01266 [0.00775, 0.01758] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007049 | PGS001378 (GBE_INI30160) |
PSS006990| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Basophill count | — | — | R²: 0.01717 [0.01523, 0.01911] Incremental R2 (full-covars): 0.01451 PGS R2 (no covariates): 0.01475 [0.01294, 0.01655] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007035 | PGS001400 (GBE_INI30020) |
PSS006901| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | R²: 0.38602 [0.36748, 0.40456] Incremental R2 (full-covars): 0.00491 PGS R2 (no covariates): 0.01221 [0.00691, 0.01752] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007036 | PGS001400 (GBE_INI30020) |
PSS006902| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | R²: 0.40783 [0.37199, 0.44367] Incremental R2 (full-covars): 0.02084 PGS R2 (no covariates): 0.01558 [0.00394, 0.02723] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007037 | PGS001400 (GBE_INI30020) |
PSS006903| European Ancestry| 24,175 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | R²: 0.43532 [0.42607, 0.44457] Incremental R2 (full-covars): 0.06361 PGS R2 (no covariates): 0.06382 [0.05795, 0.06969] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007038 | PGS001400 (GBE_INI30020) |
PSS006904| South Asian Ancestry| 7,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | R²: 0.4337 [0.41719, 0.45022] Incremental R2 (full-covars): 0.02482 PGS R2 (no covariates): 0.01823 [0.01236, 0.0241] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007039 | PGS001400 (GBE_INI30020) |
PSS006905| European Ancestry| 65,638 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | R²: 0.42918 [0.42353, 0.43482] Incremental R2 (full-covars): 0.06776 PGS R2 (no covariates): 0.06681 [0.06316, 0.07045] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007070 | PGS001406 (GBE_INI30300) |
PSS007041| African Ancestry| 5,974 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.03376 [0.02514, 0.04239] Incremental R2 (full-covars): 0.02097 PGS R2 (no covariates): 0.02166 [0.01466, 0.02866] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007071 | PGS001406 (GBE_INI30300) |
PSS007042| East Asian Ancestry| 1,623 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.08784 [0.06222, 0.11346] Incremental R2 (full-covars): 0.06019 PGS R2 (no covariates): 0.06306 [0.04077, 0.08536] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007072 | PGS001406 (GBE_INI30300) |
PSS007043| European Ancestry| 23,681 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.116 [0.10853, 0.12348] Incremental R2 (full-covars): 0.09413 PGS R2 (no covariates): 0.09553 [0.08859, 0.10247] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007073 | PGS001406 (GBE_INI30300) |
PSS007044| South Asian Ancestry| 7,321 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.09799 [0.08549, 0.1105] Incremental R2 (full-covars): 0.07113 PGS R2 (no covariates): 0.07659 [0.06528, 0.08791] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007074 | PGS001406 (GBE_INI30300) |
PSS007045| European Ancestry| 64,524 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.12733 [0.12263, 0.13203] Incremental R2 (full-covars): 0.11178 PGS R2 (no covariates): 0.1113 [0.10683, 0.11578] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007065 | PGS001408 (GBE_INI30280) |
PSS007031| African Ancestry| 5,973 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.03195 [0.02354, 0.04036] Incremental R2 (full-covars): 0.01989 PGS R2 (no covariates): 0.01953 [0.01287, 0.02619] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007066 | PGS001408 (GBE_INI30280) |
PSS007032| East Asian Ancestry| 1,623 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.04983 [0.02973, 0.06992] Incremental R2 (full-covars): 0.02998 PGS R2 (no covariates): 0.03394 [0.01708, 0.05081] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007067 | PGS001408 (GBE_INI30280) |
PSS007033| European Ancestry| 23,680 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.10073 [0.09364, 0.10782] Incremental R2 (full-covars): 0.08989 PGS R2 (no covariates): 0.09045 [0.08366, 0.09725] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007068 | PGS001408 (GBE_INI30280) |
PSS007034| South Asian Ancestry| 7,321 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.07122 [0.06025, 0.0822] Incremental R2 (full-covars): 0.05964 PGS R2 (no covariates): 0.06252 [0.05214, 0.0729] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007069 | PGS001408 (GBE_INI30280) |
PSS007035| European Ancestry| 64,524 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.09976 [0.09547, 0.10405] Incremental R2 (full-covars): 0.0902 PGS R2 (no covariates): 0.09063 [0.0865, 0.09477] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007054 | PGS001414 (GBE_INI30180) |
PSS006995| European Ancestry| 65,520 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte % | — | — | R²: 0.10838 [0.10395, 0.11281] Incremental R2 (full-covars): 0.08842 PGS R2 (no covariates): 0.08835 [0.08426, 0.09245] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007050 | PGS001414 (GBE_INI30180) |
PSS006991| African Ancestry| 6,120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte % | — | — | R²: 0.03982 [0.03051, 0.04913] Incremental R2 (full-covars): 0.01769 PGS R2 (no covariates): 0.02018 [0.01341, 0.02694] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007051 | PGS001414 (GBE_INI30180) |
PSS006992| East Asian Ancestry| 1,654 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte % | — | — | R²: 0.08102 [0.05624, 0.10581] Incremental R2 (full-covars): 0.04011 PGS R2 (no covariates): 0.03886 [0.02091, 0.05682] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007052 | PGS001414 (GBE_INI30180) |
PSS006993| European Ancestry| 24,130 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte % | — | — | R²: 0.09934 [0.09229, 0.10639] Incremental R2 (full-covars): 0.08221 PGS R2 (no covariates): 0.084 [0.0774, 0.09059] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007053 | PGS001414 (GBE_INI30180) |
PSS006994| South Asian Ancestry| 7,491 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Lymphocyte % | — | — | R²: 0.05905 [0.04893, 0.06918] Incremental R2 (full-covars): 0.05259 PGS R2 (no covariates): 0.05171 [0.04216, 0.06126] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007040 | PGS001517 (GBE_INI30090) |
PSS006951| African Ancestry| 6,139 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet crit | — | — | R²: 0.1382 [0.12263, 0.15377] Incremental R2 (full-covars): 0.02064 PGS R2 (no covariates): 0.0286 [0.02062, 0.03659] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007041 | PGS001517 (GBE_INI30090) |
PSS006952| East Asian Ancestry| 1,655 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet crit | — | — | R²: 0.15738 [0.1257, 0.18906] Incremental R2 (full-covars): 0.08366 PGS R2 (no covariates): 0.07725 [0.05295, 0.10155] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007042 | PGS001517 (GBE_INI30090) |
PSS006953| European Ancestry| 24,171 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet crit | — | — | R²: 0.23874 [0.2295, 0.24797] Incremental R2 (full-covars): 0.16467 PGS R2 (no covariates): 0.16697 [0.15852, 0.17543] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007043 | PGS001517 (GBE_INI30090) |
PSS006954| South Asian Ancestry| 7,519 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet crit | — | — | R²: 0.23898 [0.2225, 0.25545] Incremental R2 (full-covars): 0.10472 PGS R2 (no covariates): 0.10873 [0.09572, 0.12174] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007044 | PGS001517 (GBE_INI30090) |
PSS006955| European Ancestry| 65,601 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Platelet crit | — | — | R²: 0.24748 [0.24183, 0.25313] Incremental R2 (full-covars): 0.16016 PGS R2 (no covariates): 0.16141 [0.15632, 0.16649] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007061 | PGS001528 (GBE_INI30250) |
PSS007022| East Asian Ancestry| 1,623 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte count | — | — | R²: 0.07592 [0.05179, 0.10005] Incremental R2 (full-covars): 0.04581 PGS R2 (no covariates): 0.04535 [0.02608, 0.06461] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007062 | PGS001528 (GBE_INI30250) |
PSS007023| European Ancestry| 23,688 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte count | — | — | R²: 0.04398 [0.039, 0.04896] Incremental R2 (full-covars): 0.0298 PGS R2 (no covariates): 0.03007 [0.02589, 0.03425] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007063 | PGS001528 (GBE_INI30250) |
PSS007024| South Asian Ancestry| 7,323 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte count | — | — | R²: 0.05148 [0.04195, 0.06101] Incremental R2 (full-covars): 0.03077 PGS R2 (no covariates): 0.03212 [0.02444, 0.0398] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007060 | PGS001528 (GBE_INI30250) |
PSS007021| African Ancestry| 5,974 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte count | — | — | R²: 0.03089 [0.02261, 0.03917] Incremental R2 (full-covars): 0.01205 PGS R2 (no covariates): 0.01388 [0.00823, 0.01953] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007064 | PGS001528 (GBE_INI30250) |
PSS007025| European Ancestry| 64,570 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Reticulocyte count | — | — | R²: 0.0518 [0.04854, 0.05506] Incremental R2 (full-covars): 0.03968 PGS R2 (no covariates): 0.03966 [0.03677, 0.04254] |
age, sex, UKB array type, Genotype PCs | — |
| PPM010147 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS009400| European Ancestry| 19,392 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.3342 [0.3217, 0.3467] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010148 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS009174| European Ancestry| 3,996 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.3297 [0.3017, 0.3571] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010149 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS008728| European Ancestry| 6,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2987 [0.2762, 0.3208] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010150 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS008502| Greater Middle Eastern Ancestry| 1,144 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2615 [0.2062, 0.3152] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010151 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS008280| South Asian Ancestry| 6,035 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2435 [0.2196, 0.2671] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010152 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS008057| East Asian Ancestry| 1,757 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2371 [0.1922, 0.281] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010154 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS008948| African Ancestry| 3,680 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.1241 [0.092, 0.1558] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010153 | PGS001908 (portability-PLR_erythrocyte_width) |
PSS007844| African Ancestry| 2,328 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.1157 [0.0753, 0.1558] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010155 | PGS001909 (portability-PLR_erythrocyte) |
PSS009399| European Ancestry| 19,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.3987 [0.3868, 0.4105] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010156 | PGS001909 (portability-PLR_erythrocyte) |
PSS009173| European Ancestry| 4,001 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.404 [0.3776, 0.4296] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010157 | PGS001909 (portability-PLR_erythrocyte) |
PSS008727| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.3387 [0.3169, 0.3602] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010158 | PGS001909 (portability-PLR_erythrocyte) |
PSS008501| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.3306 [0.2777, 0.3815] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010159 | PGS001909 (portability-PLR_erythrocyte) |
PSS008279| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.2916 [0.2684, 0.3145] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010160 | PGS001909 (portability-PLR_erythrocyte) |
PSS008056| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.2689 [0.2247, 0.3119] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010161 | PGS001909 (portability-PLR_erythrocyte) |
PSS007843| African Ancestry| 2,342 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.1886 [0.149, 0.2275] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010162 | PGS001909 (portability-PLR_erythrocyte) |
PSS008947| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.1298 [0.098, 0.1614] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010275 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS009411| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.3256 [0.3129, 0.3381] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010276 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS009185| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.3126 [0.2843, 0.3404] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010277 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS008739| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.2824 [0.2597, 0.3047] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010278 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS008513| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.2516 [0.1962, 0.3053] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010281 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS007855| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.13 [0.0898, 0.1698] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010282 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS008959| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.1286 [0.0967, 0.1602] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010279 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS008291| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.2255 [0.2015, 0.2493] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010280 | PGS001925 (portability-PLR_haematocrit_perc) |
PSS008068| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.2014 [0.1559, 0.2461] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010284 | PGS001926 (portability-PLR_haemoglobin) |
PSS009186| European Ancestry| 4,001 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.3146 [0.2864, 0.3423] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010285 | PGS001926 (portability-PLR_haemoglobin) |
PSS008740| European Ancestry| 6,436 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.2911 [0.2686, 0.3134] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010286 | PGS001926 (portability-PLR_haemoglobin) |
PSS008514| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.2493 [0.1938, 0.3031] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010287 | PGS001926 (portability-PLR_haemoglobin) |
PSS008292| South Asian Ancestry| 6,077 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.2104 [0.1863, 0.2344] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010288 | PGS001926 (portability-PLR_haemoglobin) |
PSS008069| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.208 [0.1627, 0.2525] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010289 | PGS001926 (portability-PLR_haemoglobin) |
PSS007856| African Ancestry| 2,340 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.1224 [0.0822, 0.1623] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010290 | PGS001926 (portability-PLR_haemoglobin) |
PSS008960| African Ancestry| 3,709 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.1163 [0.0844, 0.148] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010283 | PGS001926 (portability-PLR_haemoglobin) |
PSS009412| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.3431 [0.3306, 0.3555] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010315 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS009417| European Ancestry| 19,121 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.3146 [0.3017, 0.3273] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010316 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS009191| European Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.3213 [0.2928, 0.3491] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010317 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS008745| European Ancestry| 6,298 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.3224 [0.3, 0.3444] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010318 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS008519| Greater Middle Eastern Ancestry| 1,127 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.2696 [0.214, 0.3234] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010319 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS008297| South Asian Ancestry| 5,937 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.2631 [0.2392, 0.2867] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010320 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS008074| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.2154 [0.1696, 0.2602] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010321 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS007861| African Ancestry| 2,294 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.1811 [0.141, 0.2205] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010322 | PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PSS008965| African Ancestry| 3,602 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.1665 [0.1345, 0.1982] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010467 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS009442| European Ancestry| 11,529 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.2072 [0.1897, 0.2246] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010468 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS009216| European Ancestry| 2,282 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.2342 [0.1949, 0.2728] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010469 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS008770| European Ancestry| 3,365 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1724 [0.1393, 0.2051] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010470 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS008544| Greater Middle Eastern Ancestry| 676 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1901 [0.1152, 0.2628] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010471 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS008322| South Asian Ancestry| 4,224 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1678 [0.1382, 0.197] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010472 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS008099| East Asian Ancestry| 983 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1291 [0.0665, 0.1907] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010473 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS007886| African Ancestry| 1,291 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.0617 [0.0067, 0.1162] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010474 | PGS001949 (portability-PLR_log_eosinophil_perc) |
PSS008990| African Ancestry| 2,100 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.0967 [0.054, 0.1391] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010500 | PGS001953 (portability-PLR_log_HbA1c) |
PSS009208| European Ancestry| 3,930 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3374 [0.3093, 0.3649] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010501 | PGS001953 (portability-PLR_log_HbA1c) |
PSS008762| European Ancestry| 6,326 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3092 [0.2867, 0.3314] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010502 | PGS001953 (portability-PLR_log_HbA1c) |
PSS008536| Greater Middle Eastern Ancestry| 1,128 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.329 [0.2755, 0.3806] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010503 | PGS001953 (portability-PLR_log_HbA1c) |
PSS008314| South Asian Ancestry| 5,936 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.2406 [0.2164, 0.2644] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010504 | PGS001953 (portability-PLR_log_HbA1c) |
PSS008091| East Asian Ancestry| 1,731 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.2506 [0.2057, 0.2945] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010505 | PGS001953 (portability-PLR_log_HbA1c) |
PSS007878| African Ancestry| 2,100 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.1217 [0.0792, 0.1639] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010506 | PGS001953 (portability-PLR_log_HbA1c) |
PSS008982| African Ancestry| 2,976 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.087 [0.0511, 0.1226] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010499 | PGS001953 (portability-PLR_log_HbA1c) |
PSS009434| European Ancestry| 19,060 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3405 [0.3279, 0.353] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010548 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS009207| European Ancestry| 3,919 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.4054 [0.3788, 0.4313] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010549 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS008761| European Ancestry| 6,289 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3962 [0.3751, 0.4169] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010550 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS008535| Greater Middle Eastern Ancestry| 1,124 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3255 [0.2717, 0.3772] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010551 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS008313| South Asian Ancestry| 5,923 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3191 [0.296, 0.3418] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010552 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS008090| East Asian Ancestry| 1,715 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.2778 [0.2333, 0.3212] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010553 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS007877| African Ancestry| 2,293 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.2182 [0.1787, 0.257] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010554 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS008981| African Ancestry| 3,600 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.2136 [0.1822, 0.2447] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010547 | PGS001959 (portability-PLR_log_HLR_reticulocyte) |
PSS009433| European Ancestry| 19,088 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.4069 [0.395, 0.4186] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010571 | PGS001962 (portability-PLR_log_leukocyte) |
PSS009451| European Ancestry| 19,419 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3641 [0.3518, 0.3762] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010572 | PGS001962 (portability-PLR_log_leukocyte) |
PSS009225| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3304 [0.3024, 0.3578] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010574 | PGS001962 (portability-PLR_log_leukocyte) |
PSS008553| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.2724 [0.2177, 0.3255] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010575 | PGS001962 (portability-PLR_log_leukocyte) |
PSS008331| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3155 [0.2926, 0.338] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010576 | PGS001962 (portability-PLR_log_leukocyte) |
PSS008108| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.2467 [0.2021, 0.2903] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010577 | PGS001962 (portability-PLR_log_leukocyte) |
PSS007895| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.1956 [0.1562, 0.2344] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010578 | PGS001962 (portability-PLR_log_leukocyte) |
PSS008999| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.1532 [0.1215, 0.1845] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010573 | PGS001962 (portability-PLR_log_leukocyte) |
PSS008779| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3374 [0.3155, 0.3589] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010595 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS009454| European Ancestry| 19,387 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3636 [0.3514, 0.3758] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010596 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS009228| European Ancestry| 3,993 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3456 [0.3179, 0.3727] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010597 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS008782| European Ancestry| 6,421 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3335 [0.3115, 0.3551] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010598 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS008556| Greater Middle Eastern Ancestry| 1,150 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3153 [0.2618, 0.3669] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010599 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS008334| South Asian Ancestry| 6,058 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.301 [0.2779, 0.3238] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010600 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS008111| East Asian Ancestry| 1,761 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.2717 [0.2277, 0.3147] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010602 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS009002| African Ancestry| 3,699 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.1702 [0.1386, 0.2014] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010601 | PGS001965 (portability-PLR_log_lymphocyte) |
PSS007898| African Ancestry| 2,337 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.167 [0.1271, 0.2063] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010619 | PGS001968 (portability-PLR_log_monocyte) |
PSS009457| European Ancestry| 19,384 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.4056 [0.3937, 0.4173] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010620 | PGS001968 (portability-PLR_log_monocyte) |
PSS009231| European Ancestry| 3,993 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3829 [0.3561, 0.4091] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010621 | PGS001968 (portability-PLR_log_monocyte) |
PSS008785| European Ancestry| 6,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3731 [0.3518, 0.394] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010622 | PGS001968 (portability-PLR_log_monocyte) |
PSS008559| Greater Middle Eastern Ancestry| 1,151 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3791 [0.328, 0.4279] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010623 | PGS001968 (portability-PLR_log_monocyte) |
PSS008337| South Asian Ancestry| 6,060 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3366 [0.314, 0.3588] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010624 | PGS001968 (portability-PLR_log_monocyte) |
PSS008114| East Asian Ancestry| 1,760 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.2771 [0.2332, 0.3199] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010625 | PGS001968 (portability-PLR_log_monocyte) |
PSS007901| African Ancestry| 2,336 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.2104 [0.1711, 0.249] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010626 | PGS001968 (portability-PLR_log_monocyte) |
PSS009005| African Ancestry| 3,699 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.1892 [0.1579, 0.2202] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010629 | PGS001969 (portability-PLR_log_neutrophil) |
PSS008786| European Ancestry| 6,419 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3157 [0.2934, 0.3376] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010627 | PGS001969 (portability-PLR_log_neutrophil) |
PSS009458| European Ancestry| 19,382 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3305 [0.3179, 0.343] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010628 | PGS001969 (portability-PLR_log_neutrophil) |
PSS009232| European Ancestry| 3,992 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3137 [0.2853, 0.3414] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010631 | PGS001969 (portability-PLR_log_neutrophil) |
PSS008338| South Asian Ancestry| 6,060 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.2983 [0.2751, 0.3211] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010632 | PGS001969 (portability-PLR_log_neutrophil) |
PSS008115| East Asian Ancestry| 1,761 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.2374 [0.1926, 0.2813] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010633 | PGS001969 (portability-PLR_log_neutrophil) |
PSS007902| African Ancestry| 2,336 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.1856 [0.146, 0.2246] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010634 | PGS001969 (portability-PLR_log_neutrophil) |
PSS009006| African Ancestry| 3,697 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.1384 [0.1066, 0.17] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010630 | PGS001969 (portability-PLR_log_neutrophil) |
PSS008560| Greater Middle Eastern Ancestry| 1,149 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.267 [0.212, 0.3204] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010635 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS009460| European Ancestry| 19,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.4299 [0.4184, 0.4413] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010636 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS009234| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.4361 [0.4106, 0.4609] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010637 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS008788| European Ancestry| 6,436 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.4086 [0.388, 0.4288] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010639 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS008340| South Asian Ancestry| 6,076 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.3614 [0.3393, 0.3831] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010640 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS008117| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.3104 [0.2674, 0.3523] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010641 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS007904| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.1795 [0.1398, 0.2185] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010642 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS009008| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.1907 [0.1594, 0.2216] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010638 | PGS001970 (portability-PLR_log_platelet_crit) |
PSS008562| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.3976 [0.3474, 0.4455] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010643 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS009461| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.6032 [0.5942, 0.6121] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010644 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS009235| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.6067 [0.5867, 0.626] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010645 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS008789| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5713 [0.5546, 0.5875] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010646 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS008563| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5283 [0.485, 0.5691] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010647 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS008341| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5282 [0.5098, 0.5461] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010648 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS008118| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.4452 [0.4067, 0.4821] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010649 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS007905| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.3353 [0.2987, 0.3709] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010650 | PGS001971 (portability-PLR_log_platelet_volume) |
PSS009009| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.3125 [0.2831, 0.3414] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010652 | PGS001972 (portability-PLR_log_platelet_width) |
PSS009236| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4717 [0.4471, 0.4955] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010653 | PGS001972 (portability-PLR_log_platelet_width) |
PSS008790| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4481 [0.4283, 0.4674] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010654 | PGS001972 (portability-PLR_log_platelet_width) |
PSS008564| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4507 [0.403, 0.4959] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010655 | PGS001972 (portability-PLR_log_platelet_width) |
PSS008342| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4365 [0.4158, 0.4566] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010656 | PGS001972 (portability-PLR_log_platelet_width) |
PSS008119| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.3413 [0.2991, 0.3821] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010657 | PGS001972 (portability-PLR_log_platelet_width) |
PSS007906| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.227 [0.188, 0.2652] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010658 | PGS001972 (portability-PLR_log_platelet_width) |
PSS009010| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.2247 [0.1938, 0.2551] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010651 | PGS001972 (portability-PLR_log_platelet_width) |
PSS009462| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4623 [0.4511, 0.4733] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010665 | PGS001973 (portability-PLR_log_platelet) |
PSS007903| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.2414 [0.2028, 0.2794] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010659 | PGS001973 (portability-PLR_log_platelet) |
PSS009459| European Ancestry| 19,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4839 [0.473, 0.4946] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010660 | PGS001973 (portability-PLR_log_platelet) |
PSS009233| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4742 [0.4498, 0.498] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010661 | PGS001973 (portability-PLR_log_platelet) |
PSS008787| European Ancestry| 6,436 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4739 [0.4547, 0.4927] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010662 | PGS001973 (portability-PLR_log_platelet) |
PSS008561| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4562 [0.4088, 0.5011] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010663 | PGS001973 (portability-PLR_log_platelet) |
PSS008339| South Asian Ancestry| 6,076 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4242 [0.4034, 0.4447] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010664 | PGS001973 (portability-PLR_log_platelet) |
PSS008116| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.3503 [0.3084, 0.3908] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010666 | PGS001973 (portability-PLR_log_platelet) |
PSS009007| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.2443 [0.2137, 0.2744] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010683 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS009465| European Ancestry| 19,117 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3967 [0.3847, 0.4086] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010684 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS009239| European Ancestry| 3,923 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3757 [0.3485, 0.4024] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010685 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS008793| European Ancestry| 6,297 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3753 [0.3538, 0.3963] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010686 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS008567| Greater Middle Eastern Ancestry| 1,127 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3116 [0.2574, 0.3638] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010688 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS008122| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.2708 [0.2261, 0.3142] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010690 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS009013| African Ancestry| 3,602 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.1736 [0.1416, 0.2052] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010687 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS008345| South Asian Ancestry| 5,935 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.2981 [0.2747, 0.3211] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010689 | PGS001976 (portability-PLR_log_reticulocyte) |
PSS007909| African Ancestry| 2,294 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.1931 [0.1532, 0.2323] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010763 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS009473| European Ancestry| 19,383 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.314 [0.3012, 0.3266] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010764 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS009247| European Ancestry| 3,992 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2972 [0.2686, 0.3253] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010765 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS008801| European Ancestry| 6,417 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.3022 [0.2798, 0.3243] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010766 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS008575| Greater Middle Eastern Ancestry| 1,150 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2825 [0.228, 0.3353] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010767 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS008353| South Asian Ancestry| 6,056 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2722 [0.2487, 0.2954] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010768 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS008130| East Asian Ancestry| 1,761 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2475 [0.2028, 0.291] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010769 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS007917| African Ancestry| 2,337 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.1708 [0.131, 0.21] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010770 | PGS001986 (portability-PLR_lymphocyte_perc) |
PSS009021| African Ancestry| 3,695 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.1726 [0.1411, 0.2038] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010787 | PGS001989 (portability-PLR_MCH) |
PSS009377| European Ancestry| 19,421 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.4309 [0.4194, 0.4423] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010788 | PGS001989 (portability-PLR_MCH) |
PSS009151| European Ancestry| 4,000 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.384 [0.3572, 0.4102] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010789 | PGS001989 (portability-PLR_MCH) |
PSS008705| European Ancestry| 6,435 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.317 [0.2948, 0.3388] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010790 | PGS001989 (portability-PLR_MCH) |
PSS008479| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.2578 [0.2026, 0.3114] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010791 | PGS001989 (portability-PLR_MCH) |
PSS008257| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.2698 [0.2462, 0.2929] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010792 | PGS001989 (portability-PLR_MCH) |
PSS008035| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.2299 [0.1849, 0.2739] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010793 | PGS001989 (portability-PLR_MCH) |
PSS007821| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.1586 [0.1187, 0.198] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010794 | PGS001989 (portability-PLR_MCH) |
PSS008925| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.1189 [0.087, 0.1506] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010795 | PGS001990 (portability-PLR_MCV) |
PSS009378| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.4447 [0.4333, 0.4559] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010796 | PGS001990 (portability-PLR_MCV) |
PSS009152| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.3969 [0.3704, 0.4228] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010797 | PGS001990 (portability-PLR_MCV) |
PSS008706| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.3414 [0.3196, 0.3628] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010799 | PGS001990 (portability-PLR_MCV) |
PSS008258| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2987 [0.2756, 0.3214] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010800 | PGS001990 (portability-PLR_MCV) |
PSS008036| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2426 [0.1979, 0.2863] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010801 | PGS001990 (portability-PLR_MCV) |
PSS007822| African Ancestry| 2,342 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2067 [0.1674, 0.2453] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010802 | PGS001990 (portability-PLR_MCV) |
PSS008926| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.1227 [0.0908, 0.1543] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010798 | PGS001990 (portability-PLR_MCV) |
PSS008480| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2898 [0.2355, 0.3422] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010803 | PGS001991 (portability-PLR_monocyte_perc) |
PSS009474| European Ancestry| 19,244 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.4126 [0.4008, 0.4243] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010804 | PGS001991 (portability-PLR_monocyte_perc) |
PSS009248| European Ancestry| 3,963 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.4017 [0.3752, 0.4276] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010805 | PGS001991 (portability-PLR_monocyte_perc) |
PSS008802| European Ancestry| 6,368 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.4021 [0.3813, 0.4225] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010806 | PGS001991 (portability-PLR_monocyte_perc) |
PSS008576| Greater Middle Eastern Ancestry| 1,144 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.4032 [0.3531, 0.4511] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010807 | PGS001991 (portability-PLR_monocyte_perc) |
PSS008354| South Asian Ancestry| 6,010 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.3324 [0.3096, 0.3547] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010808 | PGS001991 (portability-PLR_monocyte_perc) |
PSS008131| East Asian Ancestry| 1,744 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.3106 [0.2673, 0.3526] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010809 | PGS001991 (portability-PLR_monocyte_perc) |
PSS007918| African Ancestry| 2,298 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.184 [0.1441, 0.2234] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010810 | PGS001991 (portability-PLR_monocyte_perc) |
PSS009022| African Ancestry| 3,663 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.1696 [0.1379, 0.201] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010852 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS009254| European Ancestry| 3,988 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2971 [0.2684, 0.3252] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010853 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS008808| European Ancestry| 6,413 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2972 [0.2747, 0.3194] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010854 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS008582| Greater Middle Eastern Ancestry| 1,147 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2921 [0.2377, 0.3446] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010855 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS008360| South Asian Ancestry| 6,055 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2672 [0.2436, 0.2904] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010856 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS008137| East Asian Ancestry| 1,760 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2332 [0.1882, 0.2771] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010857 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS007924| African Ancestry| 2,334 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.166 [0.1261, 0.2054] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010858 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS009028| African Ancestry| 3,681 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.1587 [0.1269, 0.1901] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010851 | PGS001997 (portability-PLR_neutrophil_perc) |
PSS009480| European Ancestry| 19,359 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.3059 [0.293, 0.3186] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010883 | PGS002001 (portability-PLR_protein) |
PSS009485| European Ancestry| 17,433 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.3216 [0.3082, 0.3348] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010884 | PGS002001 (portability-PLR_protein) |
PSS009259| European Ancestry| 3,593 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.3209 [0.2911, 0.35] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010885 | PGS002001 (portability-PLR_protein) |
PSS008813| European Ancestry| 5,794 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.3023 [0.2787, 0.3256] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010886 | PGS002001 (portability-PLR_protein) |
PSS008587| Greater Middle Eastern Ancestry| 1,036 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.2629 [0.2047, 0.3192] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010887 | PGS002001 (portability-PLR_protein) |
PSS008365| South Asian Ancestry| 5,483 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.2907 [0.2662, 0.3148] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010888 | PGS002001 (portability-PLR_protein) |
PSS008142| East Asian Ancestry| 1,562 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.2431 [0.1955, 0.2895] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010889 | PGS002001 (portability-PLR_protein) |
PSS007929| African Ancestry| 2,161 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.1497 [0.108, 0.1909] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010890 | PGS002001 (portability-PLR_protein) |
PSS009033| African Ancestry| 3,404 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.1462 [0.113, 0.179] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010898 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS009487| European Ancestry| 19,120 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3934 [0.3814, 0.4054] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010899 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS009261| European Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3882 [0.3612, 0.4145] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010900 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS008815| European Ancestry| 6,298 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3774 [0.356, 0.3984] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010901 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS008589| Greater Middle Eastern Ancestry| 1,127 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3348 [0.2815, 0.3861] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010902 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS008367| South Asian Ancestry| 5,935 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3217 [0.2986, 0.3443] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010903 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS008143| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.2638 [0.219, 0.3074] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010904 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS007931| African Ancestry| 2,293 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.2125 [0.1729, 0.2514] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010905 | PGS002003 (portability-PLR_reticulocyte_volume) |
PSS009035| African Ancestry| 3,602 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.1616 [0.1296, 0.1934] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010938 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS009493| European Ancestry| 19,116 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.4104 [0.3985, 0.4221] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010939 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS009267| European Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.387 [0.36, 0.4134] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010940 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS008821| European Ancestry| 6,293 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.3684 [0.3468, 0.3896] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010942 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS008373| South Asian Ancestry| 5,934 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.3247 [0.3018, 0.3474] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010943 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS008149| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.2632 [0.2185, 0.3069] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010944 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS007937| African Ancestry| 2,292 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.2172 [0.1777, 0.256] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010945 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS009041| African Ancestry| 3,601 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.1513 [0.1191, 0.1832] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010941 | PGS002008 (portability-PLR_sphered_cell_volume) |
PSS008595| Greater Middle Eastern Ancestry| 1,126 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.361 [0.3086, 0.4112] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011832 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS009174| European Ancestry| 3,996 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.3227 [0.2946, 0.3503] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011831 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS009400| European Ancestry| 19,392 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.3341 [0.3215, 0.3465] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011833 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS008728| European Ancestry| 6,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2938 [0.2713, 0.3161] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011834 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS008502| Greater Middle Eastern Ancestry| 1,144 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2528 [0.1972, 0.3067] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011835 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS008280| South Asian Ancestry| 6,035 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2424 [0.2185, 0.266] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011836 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS008057| East Asian Ancestry| 1,757 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.2289 [0.1838, 0.273] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011837 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS007844| African Ancestry| 2,328 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.0986 [0.058, 0.1388] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011838 | PGS002122 (portability-ldpred2_erythrocyte_width) |
PSS008948| African Ancestry| 3,680 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | Partial Correlation (partial-r): 0.1434 [0.1115, 0.175] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011839 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS009399| European Ancestry| 19,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.39 [0.3781, 0.4019] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011840 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS009173| European Ancestry| 4,001 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.398 [0.3716, 0.4239] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011841 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS008727| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.3326 [0.3107, 0.3542] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011842 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS008501| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.3308 [0.2779, 0.3816] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011843 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS008279| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.2888 [0.2656, 0.3117] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011844 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS008056| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.271 [0.2269, 0.314] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011845 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS007843| African Ancestry| 2,342 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.1984 [0.159, 0.2371] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011846 | PGS002123 (portability-ldpred2_erythrocyte) |
PSS008947| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | Partial Correlation (partial-r): 0.1336 [0.1018, 0.1651] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011981 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS007855| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.1376 [0.0974, 0.1772] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011975 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS009411| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.3167 [0.304, 0.3293] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011976 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS009185| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.3059 [0.2775, 0.3338] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011977 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS008739| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.2833 [0.2607, 0.3057] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011978 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS008513| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.2387 [0.183, 0.2929] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011979 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS008291| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.224 [0.1999, 0.2478] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011980 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS008068| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.1992 [0.1536, 0.2438] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011982 | PGS002141 (portability-ldpred2_haematocrit_perc) |
PSS008959| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haematocrit percentage | — | — | Partial Correlation (partial-r): 0.1335 [0.1016, 0.165] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011983 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS009412| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.3358 [0.3233, 0.3482] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011984 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS009186| European Ancestry| 4,001 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.3107 [0.2824, 0.3385] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011985 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS008740| European Ancestry| 6,436 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.2912 [0.2686, 0.3134] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011986 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS008514| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.2407 [0.185, 0.2948] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011987 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS008292| South Asian Ancestry| 6,077 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.2123 [0.1881, 0.2362] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011988 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS008069| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.1969 [0.1513, 0.2416] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011989 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS007856| African Ancestry| 2,340 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.1241 [0.0838, 0.164] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011990 | PGS002142 (portability-ldpred2_haemoglobin) |
PSS008960| African Ancestry| 3,709 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Haemoglobin concentration | — | — | Partial Correlation (partial-r): 0.1255 [0.0936, 0.1572] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012023 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS009417| European Ancestry| 19,121 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.3135 [0.3007, 0.3263] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012024 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS009191| European Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.3215 [0.2931, 0.3494] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012025 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS008745| European Ancestry| 6,298 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.3195 [0.2971, 0.3416] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012026 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS008519| Greater Middle Eastern Ancestry| 1,127 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.2889 [0.234, 0.342] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012027 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS008297| South Asian Ancestry| 5,937 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.2624 [0.2385, 0.286] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012028 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS008074| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.2089 [0.163, 0.2539] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012029 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS007861| African Ancestry| 2,294 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.1842 [0.1442, 0.2236] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012030 | PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PSS008965| African Ancestry| 3,602 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Immature reticulocyte fraction | — | — | Partial Correlation (partial-r): 0.174 [0.1421, 0.2056] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012184 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS009216| European Ancestry| 2,282 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.2199 [0.1803, 0.2587] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012185 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS008770| European Ancestry| 3,365 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1751 [0.142, 0.2077] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012186 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS008544| Greater Middle Eastern Ancestry| 676 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.18 [0.1049, 0.253] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012187 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS008322| South Asian Ancestry| 4,224 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1683 [0.1388, 0.1976] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012188 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS008099| East Asian Ancestry| 983 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.1159 [0.0531, 0.1778] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012189 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS007886| African Ancestry| 1,291 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.0644 [0.0095, 0.119] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012190 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS008990| African Ancestry| 2,100 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.091 [0.0482, 0.1335] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012183 | PGS002167 (portability-ldpred2_log_eosinophil_perc) |
PSS009442| European Ancestry| 11,529 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Eosinophil percentage | — | — | Partial Correlation (partial-r): 0.2043 [0.1867, 0.2217] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012215 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS009434| European Ancestry| 19,060 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3359 [0.3233, 0.3485] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012216 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS009208| European Ancestry| 3,930 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3366 [0.3085, 0.3641] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012217 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS008762| European Ancestry| 6,326 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3096 [0.2871, 0.3317] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012219 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS008314| South Asian Ancestry| 5,936 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.2393 [0.2152, 0.2632] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012220 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS008091| East Asian Ancestry| 1,731 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.2571 [0.2123, 0.3008] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012221 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS007878| African Ancestry| 2,100 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.1245 [0.082, 0.1666] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012222 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS008982| African Ancestry| 2,976 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.0934 [0.0575, 0.129] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012218 | PGS002171 (portability-ldpred2_log_HbA1c) |
PSS008536| Greater Middle Eastern Ancestry| 1,128 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | Partial Correlation (partial-r): 0.3222 [0.2684, 0.374] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012263 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS009433| European Ancestry| 19,088 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3974 [0.3854, 0.4093] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012264 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS009207| European Ancestry| 3,919 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3985 [0.3718, 0.4246] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012265 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS008761| European Ancestry| 6,289 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3888 [0.3676, 0.4096] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012266 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS008535| Greater Middle Eastern Ancestry| 1,124 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3261 [0.2723, 0.3778] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012267 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS008313| South Asian Ancestry| 5,923 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.3041 [0.2808, 0.3271] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012268 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS008090| East Asian Ancestry| 1,715 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.2632 [0.2183, 0.307] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012269 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS007877| African Ancestry| 2,293 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.2241 [0.1847, 0.2628] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012270 | PGS002177 (portability-ldpred2_log_HLR_reticulocyte) |
PSS008981| African Ancestry| 3,600 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: High light scatter reticulocyte count | — | — | Partial Correlation (partial-r): 0.1964 [0.1647, 0.2277] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012287 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS009451| European Ancestry| 19,419 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3596 [0.3473, 0.3718] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012288 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS009225| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3332 [0.3053, 0.3605] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012289 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS008779| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3368 [0.3149, 0.3583] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012290 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS008553| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.2706 [0.2158, 0.3237] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012291 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS008331| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.3147 [0.2919, 0.3372] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012292 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS008108| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.2454 [0.2007, 0.289] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012293 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS007895| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.1863 [0.1467, 0.2253] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012294 | PGS002180 (portability-ldpred2_log_leukocyte) |
PSS008999| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: White blood cell (leukocyte) count | — | — | Partial Correlation (partial-r): 0.1563 [0.1247, 0.1876] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012311 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS009454| European Ancestry| 19,387 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3574 [0.3451, 0.3697] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012312 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS009228| European Ancestry| 3,993 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3369 [0.3091, 0.3642] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012314 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS008556| Greater Middle Eastern Ancestry| 1,150 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.2968 [0.2427, 0.3491] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012315 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS008334| South Asian Ancestry| 6,058 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.2945 [0.2713, 0.3173] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012316 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS008111| East Asian Ancestry| 1,761 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.2745 [0.2305, 0.3174] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012317 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS007898| African Ancestry| 2,337 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.1609 [0.121, 0.2003] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012318 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS009002| African Ancestry| 3,699 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.1618 [0.1302, 0.1931] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012313 | PGS002183 (portability-ldpred2_log_lymphocyte) |
PSS008782| European Ancestry| 6,421 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte count | — | — | Partial Correlation (partial-r): 0.3319 [0.31, 0.3536] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012335 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS009457| European Ancestry| 19,384 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.4013 [0.3894, 0.4131] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012336 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS009231| European Ancestry| 3,993 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3828 [0.356, 0.4091] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012338 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS008559| Greater Middle Eastern Ancestry| 1,151 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3913 [0.3408, 0.4395] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012339 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS008337| South Asian Ancestry| 6,060 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.326 [0.3033, 0.3484] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012340 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS008114| East Asian Ancestry| 1,760 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.2727 [0.2287, 0.3157] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012341 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS007901| African Ancestry| 2,336 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.2009 [0.1615, 0.2396] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012342 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS009005| African Ancestry| 3,699 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.1818 [0.1503, 0.2128] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012337 | PGS002186 (portability-ldpred2_log_monocyte) |
PSS008785| European Ancestry| 6,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte count | — | — | Partial Correlation (partial-r): 0.3692 [0.3478, 0.3901] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012343 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS009458| European Ancestry| 19,382 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3263 [0.3137, 0.3388] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012344 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS009232| European Ancestry| 3,992 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3117 [0.2834, 0.3395] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012345 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS008786| European Ancestry| 6,419 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3147 [0.2924, 0.3366] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012346 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS008560| Greater Middle Eastern Ancestry| 1,149 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.2709 [0.2159, 0.3241] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012347 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS008338| South Asian Ancestry| 6,060 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.3015 [0.2784, 0.3242] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012348 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS008115| East Asian Ancestry| 1,761 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.2401 [0.1953, 0.2839] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012349 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS007902| African Ancestry| 2,336 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.1884 [0.1488, 0.2274] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012350 | PGS002187 (portability-ldpred2_log_neutrophil) |
PSS009006| African Ancestry| 3,697 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil count | — | — | Partial Correlation (partial-r): 0.1552 [0.1235, 0.1866] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012351 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS009460| European Ancestry| 19,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.4219 [0.4103, 0.4334] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012352 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS009234| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.4319 [0.4063, 0.4568] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012353 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS008788| European Ancestry| 6,436 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.4045 [0.3839, 0.4248] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012355 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS008340| South Asian Ancestry| 6,076 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.357 [0.3348, 0.3788] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012356 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS008117| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.3181 [0.2753, 0.3597] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012357 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS007904| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.2035 [0.1642, 0.2422] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012358 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS009008| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.1849 [0.1536, 0.2159] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012354 | PGS002188 (portability-ldpred2_log_platelet_crit) |
PSS008562| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet crit | — | — | Partial Correlation (partial-r): 0.3907 [0.3402, 0.439] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012359 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS009461| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5923 [0.5831, 0.6013] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012360 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS009235| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5914 [0.5709, 0.6113] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012361 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS008789| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5603 [0.5433, 0.5769] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012362 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS008563| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5196 [0.4758, 0.5609] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012363 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS008341| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.5166 [0.4979, 0.5348] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012364 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS008118| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.444 [0.4054, 0.4809] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012365 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS007905| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.3231 [0.2862, 0.3591] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012366 | PGS002189 (portability-ldpred2_log_platelet_volume) |
PSS009009| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | Partial Correlation (partial-r): 0.2907 [0.2609, 0.3199] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012368 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS009236| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4638 [0.439, 0.4878] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012369 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS008790| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4435 [0.4237, 0.463] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012370 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS008564| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.433 [0.3844, 0.4791] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012371 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS008342| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4272 [0.4064, 0.4476] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012372 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS008119| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.3414 [0.2992, 0.3822] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012373 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS007906| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.2196 [0.1806, 0.258] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012374 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS009010| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.2233 [0.1924, 0.2538] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012367 | PGS002190 (portability-ldpred2_log_platelet_width) |
PSS009462| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet distribution width | — | — | Partial Correlation (partial-r): 0.4539 [0.4426, 0.465] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012375 | PGS002191 (portability-ldpred2_log_platelet) |
PSS009459| European Ancestry| 19,422 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4757 [0.4648, 0.4866] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012376 | PGS002191 (portability-ldpred2_log_platelet) |
PSS009233| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.469 [0.4444, 0.4929] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012377 | PGS002191 (portability-ldpred2_log_platelet) |
PSS008787| European Ancestry| 6,436 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.468 [0.4487, 0.4869] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012378 | PGS002191 (portability-ldpred2_log_platelet) |
PSS008561| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4476 [0.3998, 0.493] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012379 | PGS002191 (portability-ldpred2_log_platelet) |
PSS008339| South Asian Ancestry| 6,076 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.4155 [0.3944, 0.4361] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012380 | PGS002191 (portability-ldpred2_log_platelet) |
PSS008116| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.3472 [0.3052, 0.3878] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012381 | PGS002191 (portability-ldpred2_log_platelet) |
PSS007903| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.2554 [0.217, 0.293] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012382 | PGS002191 (portability-ldpred2_log_platelet) |
PSS009007| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Platelet count | — | — | Partial Correlation (partial-r): 0.2355 [0.2048, 0.2658] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012399 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS009465| European Ancestry| 19,117 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.391 [0.3789, 0.403] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012400 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS009239| European Ancestry| 3,923 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3672 [0.3398, 0.3941] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012401 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS008793| European Ancestry| 6,297 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3718 [0.3503, 0.393] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012402 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS008567| Greater Middle Eastern Ancestry| 1,127 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.3104 [0.2562, 0.3627] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012403 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS008345| South Asian Ancestry| 5,935 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.2814 [0.2577, 0.3047] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012404 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS008122| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.2574 [0.2125, 0.3013] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012405 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS007909| African Ancestry| 2,294 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.21 [0.1704, 0.249] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012406 | PGS002194 (portability-ldpred2_log_reticulocyte) |
PSS009013| African Ancestry| 3,602 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Reticulocyte count | — | — | Partial Correlation (partial-r): 0.1782 [0.1463, 0.2097] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012471 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS009473| European Ancestry| 19,383 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.3116 [0.2989, 0.3243] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012472 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS009247| European Ancestry| 3,992 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2932 [0.2645, 0.3213] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012473 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS008801| European Ancestry| 6,417 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2976 [0.2751, 0.3198] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012474 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS008575| Greater Middle Eastern Ancestry| 1,150 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2842 [0.2297, 0.3369] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012475 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS008353| South Asian Ancestry| 6,056 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2664 [0.2428, 0.2897] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012476 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS008130| East Asian Ancestry| 1,761 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.2567 [0.2122, 0.3] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012477 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS007917| African Ancestry| 2,337 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.1725 [0.1327, 0.2117] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012478 | PGS002203 (portability-ldpred2_lymphocyte_perc) |
PSS009021| African Ancestry| 3,695 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Lymphocyte percentage | — | — | Partial Correlation (partial-r): 0.164 [0.1324, 0.1953] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012495 | PGS002206 (portability-ldpred2_MCH) |
PSS009377| European Ancestry| 19,421 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.427 [0.4154, 0.4384] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012496 | PGS002206 (portability-ldpred2_MCH) |
PSS009151| European Ancestry| 4,000 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.3723 [0.3453, 0.3988] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012497 | PGS002206 (portability-ldpred2_MCH) |
PSS008705| European Ancestry| 6,435 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.3119 [0.2897, 0.3338] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012498 | PGS002206 (portability-ldpred2_MCH) |
PSS008479| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.2511 [0.1957, 0.3049] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012499 | PGS002206 (portability-ldpred2_MCH) |
PSS008257| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.2691 [0.2455, 0.2923] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012500 | PGS002206 (portability-ldpred2_MCH) |
PSS008035| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.2349 [0.19, 0.2787] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012501 | PGS002206 (portability-ldpred2_MCH) |
PSS007821| African Ancestry| 2,343 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.1483 [0.1083, 0.1879] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012502 | PGS002206 (portability-ldpred2_MCH) |
PSS008925| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular haemoglobin | — | — | Partial Correlation (partial-r): 0.1335 [0.1017, 0.1651] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012503 | PGS002207 (portability-ldpred2_MCV) |
PSS009378| European Ancestry| 19,423 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.4393 [0.4279, 0.4506] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012504 | PGS002207 (portability-ldpred2_MCV) |
PSS009152| European Ancestry| 4,002 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.3883 [0.3616, 0.4144] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012506 | PGS002207 (portability-ldpred2_MCV) |
PSS008480| Greater Middle Eastern Ancestry| 1,153 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2809 [0.2264, 0.3337] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012507 | PGS002207 (portability-ldpred2_MCV) |
PSS008258| South Asian Ancestry| 6,078 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2928 [0.2696, 0.3156] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012508 | PGS002207 (portability-ldpred2_MCV) |
PSS008036| East Asian Ancestry| 1,762 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2554 [0.211, 0.2988] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012509 | PGS002207 (portability-ldpred2_MCV) |
PSS007822| African Ancestry| 2,342 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.2029 [0.1636, 0.2416] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012505 | PGS002207 (portability-ldpred2_MCV) |
PSS008706| European Ancestry| 6,437 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.3311 [0.3091, 0.3527] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012510 | PGS002207 (portability-ldpred2_MCV) |
PSS008926| African Ancestry| 3,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean corpuscular volume | — | — | Partial Correlation (partial-r): 0.139 [0.1072, 0.1705] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012511 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS009474| European Ancestry| 19,244 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.4079 [0.396, 0.4196] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012512 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS009248| European Ancestry| 3,963 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.3935 [0.3668, 0.4196] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012513 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS008802| European Ancestry| 6,368 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.3978 [0.3769, 0.4183] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012514 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS008576| Greater Middle Eastern Ancestry| 1,144 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.4047 [0.3546, 0.4524] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012516 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS008131| East Asian Ancestry| 1,744 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.3023 [0.2587, 0.3445] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012517 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS007918| African Ancestry| 2,298 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.1887 [0.1488, 0.228] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012518 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS009022| African Ancestry| 3,663 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.1635 [0.1317, 0.1949] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012515 | PGS002208 (portability-ldpred2_monocyte_perc) |
PSS008354| South Asian Ancestry| 6,010 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Monocyte percentage | — | — | Partial Correlation (partial-r): 0.3313 [0.3085, 0.3536] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012559 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS009480| European Ancestry| 19,359 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.3042 [0.2913, 0.3169] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012560 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS009254| European Ancestry| 3,988 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2993 [0.2707, 0.3274] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012561 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS008808| European Ancestry| 6,413 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2906 [0.268, 0.3129] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012562 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS008582| Greater Middle Eastern Ancestry| 1,147 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2984 [0.2443, 0.3507] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012563 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS008360| South Asian Ancestry| 6,055 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2641 [0.2405, 0.2874] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012564 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS008137| East Asian Ancestry| 1,760 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.2471 [0.2025, 0.2907] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012565 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS007924| African Ancestry| 2,334 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.1602 [0.1202, 0.1996] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012566 | PGS002214 (portability-ldpred2_neutrophil_perc) |
PSS009028| African Ancestry| 3,681 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Neutrophil percentage | — | — | Partial Correlation (partial-r): 0.1568 [0.125, 0.1882] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012599 | PGS002219 (portability-ldpred2_protein) |
PSS009485| European Ancestry| 17,433 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.3191 [0.3057, 0.3324] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012600 | PGS002219 (portability-ldpred2_protein) |
PSS009259| European Ancestry| 3,593 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.3196 [0.2899, 0.3488] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012601 | PGS002219 (portability-ldpred2_protein) |
PSS008813| European Ancestry| 5,794 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.2989 [0.2752, 0.3222] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012602 | PGS002219 (portability-ldpred2_protein) |
PSS008587| Greater Middle Eastern Ancestry| 1,036 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.243 [0.1842, 0.3] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012603 | PGS002219 (portability-ldpred2_protein) |
PSS008365| South Asian Ancestry| 5,483 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.2818 [0.2572, 0.306] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012604 | PGS002219 (portability-ldpred2_protein) |
PSS008142| East Asian Ancestry| 1,562 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.244 [0.1965, 0.2904] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012606 | PGS002219 (portability-ldpred2_protein) |
PSS009033| African Ancestry| 3,404 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.138 [0.1048, 0.1709] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012605 | PGS002219 (portability-ldpred2_protein) |
PSS007929| African Ancestry| 2,161 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Total protein | — | — | Partial Correlation (partial-r): 0.1313 [0.0894, 0.1727] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012614 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS009487| European Ancestry| 19,120 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3894 [0.3773, 0.4014] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012615 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS009261| European Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3877 [0.3608, 0.4141] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012616 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS008815| European Ancestry| 6,298 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3736 [0.3521, 0.3947] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012617 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS008589| Greater Middle Eastern Ancestry| 1,127 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3308 [0.2773, 0.3823] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012618 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS008367| South Asian Ancestry| 5,935 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.3152 [0.2921, 0.338] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012619 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS008143| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.2541 [0.2091, 0.298] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012620 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS007931| African Ancestry| 2,293 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.2118 [0.1722, 0.2507] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012621 | PGS002221 (portability-ldpred2_reticulocyte_volume) |
PSS009035| African Ancestry| 3,602 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean reticulocyte volume | — | — | Partial Correlation (partial-r): 0.1834 [0.1516, 0.2149] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012662 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS009493| European Ancestry| 19,116 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.404 [0.392, 0.4158] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012663 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS009267| European Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.3849 [0.3579, 0.4113] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012664 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS008821| European Ancestry| 6,293 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.364 [0.3423, 0.3853] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012665 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS008595| Greater Middle Eastern Ancestry| 1,126 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.3564 [0.3038, 0.4068] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012666 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS008373| South Asian Ancestry| 5,934 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.3214 [0.2984, 0.3441] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012668 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS007937| African Ancestry| 2,292 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.2143 [0.1747, 0.2532] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012669 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS009041| African Ancestry| 3,601 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.1551 [0.123, 0.1869] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012667 | PGS002227 (portability-ldpred2_sphered_cell_volume) |
PSS008149| East Asian Ancestry| 1,722 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Mean sphered cell volume | — | — | Partial Correlation (partial-r): 0.2626 [0.2178, 0.3063] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM013090 | PGS002325 (blood_EOSINOPHIL_COUNT.BOLT-LMM) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0299 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013139 | PGS002325 (blood_EOSINOPHIL_COUNT.BOLT-LMM) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0682 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013188 | PGS002325 (blood_EOSINOPHIL_COUNT.BOLT-LMM) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.1407 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013237 | PGS002325 (blood_EOSINOPHIL_COUNT.BOLT-LMM) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.09 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013243 | PGS002331 (biochemistry_HbA1c.BOLT-LMM) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0576 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013096 | PGS002331 (biochemistry_HbA1c.BOLT-LMM) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0201 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013145 | PGS002331 (biochemistry_HbA1c.BOLT-LMM) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0596 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013194 | PGS002331 (biochemistry_HbA1c.BOLT-LMM) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.1142 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013147 | PGS002333 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.BOLT-LMM) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0931 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013196 | PGS002333 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.BOLT-LMM) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1501 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013098 | PGS002333 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.BOLT-LMM) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0378 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013245 | PGS002333 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.BOLT-LMM) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0872 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013103 | PGS002338 (blood_LYMPHOCYTE_COUNT.BOLT-LMM) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0305 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013152 | PGS002338 (blood_LYMPHOCYTE_COUNT.BOLT-LMM) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0585 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013201 | PGS002338 (blood_LYMPHOCYTE_COUNT.BOLT-LMM) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1307 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013250 | PGS002338 (blood_LYMPHOCYTE_COUNT.BOLT-LMM) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0755 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013104 | PGS002339 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0196 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013153 | PGS002339 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1122 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013202 | PGS002339 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1998 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013251 | PGS002339 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1238 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013105 | PGS002340 (blood_MEAN_PLATELET_VOL.BOLT-LMM) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1049 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013154 | PGS002340 (blood_MEAN_PLATELET_VOL.BOLT-LMM) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1872 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013203 | PGS002340 (blood_MEAN_PLATELET_VOL.BOLT-LMM) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.3575 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013252 | PGS002340 (blood_MEAN_PLATELET_VOL.BOLT-LMM) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.259 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013106 | PGS002341 (blood_MONOCYTE_COUNT.BOLT-LMM) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0391 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013155 | PGS002341 (blood_MONOCYTE_COUNT.BOLT-LMM) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0668 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013204 | PGS002341 (blood_MONOCYTE_COUNT.BOLT-LMM) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1716 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013253 | PGS002341 (blood_MONOCYTE_COUNT.BOLT-LMM) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1221 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013108 | PGS002343 (blood_PLATELET_COUNT.BOLT-LMM) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0682 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013157 | PGS002343 (blood_PLATELET_COUNT.BOLT-LMM) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0998 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013206 | PGS002343 (blood_PLATELET_COUNT.BOLT-LMM) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.2489 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013255 | PGS002343 (blood_PLATELET_COUNT.BOLT-LMM) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1747 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013208 | PGS002345 (blood_RED_COUNT.BOLT-LMM) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1566 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013257 | PGS002345 (blood_RED_COUNT.BOLT-LMM) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0917 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013110 | PGS002345 (blood_RED_COUNT.BOLT-LMM) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0248 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013159 | PGS002345 (blood_RED_COUNT.BOLT-LMM) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0807 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013111 | PGS002346 (blood_RBC_DISTRIB_WIDTH.BOLT-LMM) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0257 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013160 | PGS002346 (blood_RBC_DISTRIB_WIDTH.BOLT-LMM) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0933 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013209 | PGS002346 (blood_RBC_DISTRIB_WIDTH.BOLT-LMM) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.1557 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013258 | PGS002346 (blood_RBC_DISTRIB_WIDTH.BOLT-LMM) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0998 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013122 | PGS002357 (blood_WHITE_COUNT.BOLT-LMM) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0249 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013171 | PGS002357 (blood_WHITE_COUNT.BOLT-LMM) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0616 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013220 | PGS002357 (blood_WHITE_COUNT.BOLT-LMM) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.123 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013269 | PGS002357 (blood_WHITE_COUNT.BOLT-LMM) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0945 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013276 | PGS002364 (blood_EOSINOPHIL_COUNT.BOLT-LMM-BBJ) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0016 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013299 | PGS002364 (blood_EOSINOPHIL_COUNT.BOLT-LMM-BBJ) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0497 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013322 | PGS002364 (blood_EOSINOPHIL_COUNT.BOLT-LMM-BBJ) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0044 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013345 | PGS002364 (blood_EOSINOPHIL_COUNT.BOLT-LMM-BBJ) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0111 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013279 | PGS002367 (biochemistry_HbA1c.BOLT-LMM-BBJ) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013302 | PGS002367 (biochemistry_HbA1c.BOLT-LMM-BBJ) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0153 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013325 | PGS002367 (biochemistry_HbA1c.BOLT-LMM-BBJ) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0014 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013348 | PGS002367 (biochemistry_HbA1c.BOLT-LMM-BBJ) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0018 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013351 | PGS002370 (blood_LYMPHOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0045 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013282 | PGS002370 (blood_LYMPHOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0008 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013328 | PGS002370 (blood_LYMPHOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0034 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013305 | PGS002370 (blood_LYMPHOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0363 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013283 | PGS002371 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM-BBJ) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0005 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013306 | PGS002371 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM-BBJ) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1055 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013329 | PGS002371 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM-BBJ) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0115 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013352 | PGS002371 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.BOLT-LMM-BBJ) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.01 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013284 | PGS002372 (blood_MONOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0059 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013307 | PGS002372 (blood_MONOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0379 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013330 | PGS002372 (blood_MONOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0073 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013353 | PGS002372 (blood_MONOCYTE_COUNT.BOLT-LMM-BBJ) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0068 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013285 | PGS002373 (blood_PLATELET_COUNT.BOLT-LMM-BBJ) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0054 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013308 | PGS002373 (blood_PLATELET_COUNT.BOLT-LMM-BBJ) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.091 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013331 | PGS002373 (blood_PLATELET_COUNT.BOLT-LMM-BBJ) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0111 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013354 | PGS002373 (blood_PLATELET_COUNT.BOLT-LMM-BBJ) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0186 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013286 | PGS002374 (blood_RED_COUNT.BOLT-LMM-BBJ) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0026 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013309 | PGS002374 (blood_RED_COUNT.BOLT-LMM-BBJ) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0611 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013332 | PGS002374 (blood_RED_COUNT.BOLT-LMM-BBJ) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0146 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013355 | PGS002374 (blood_RED_COUNT.BOLT-LMM-BBJ) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0119 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013315 | PGS002380 (blood_WHITE_COUNT.BOLT-LMM-BBJ) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0507 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013338 | PGS002380 (blood_WHITE_COUNT.BOLT-LMM-BBJ) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0029 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013361 | PGS002380 (blood_WHITE_COUNT.BOLT-LMM-BBJ) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0025 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013292 | PGS002380 (blood_WHITE_COUNT.BOLT-LMM-BBJ) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0018 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013378 | PGS002397 (blood_EOSINOPHIL_COUNT.P+T.0.0001) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0027 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013427 | PGS002397 (blood_EOSINOPHIL_COUNT.P+T.0.0001) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0358 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013476 | PGS002397 (blood_EOSINOPHIL_COUNT.P+T.0.0001) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0771 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013525 | PGS002397 (blood_EOSINOPHIL_COUNT.P+T.0.0001) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0491 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013384 | PGS002403 (biochemistry_HbA1c.P+T.0.0001) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013433 | PGS002403 (biochemistry_HbA1c.P+T.0.0001) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.024 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013482 | PGS002403 (biochemistry_HbA1c.P+T.0.0001) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.033 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013531 | PGS002403 (biochemistry_HbA1c.P+T.0.0001) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0108 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013435 | PGS002405 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.0001) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.055 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013484 | PGS002405 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.0001) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0785 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013533 | PGS002405 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.0001) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.05 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013386 | PGS002405 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.0001) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0037 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013391 | PGS002410 (blood_LYMPHOCYTE_COUNT.P+T.0.0001) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0004 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013440 | PGS002410 (blood_LYMPHOCYTE_COUNT.P+T.0.0001) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0566 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013489 | PGS002410 (blood_LYMPHOCYTE_COUNT.P+T.0.0001) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0706 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013538 | PGS002410 (blood_LYMPHOCYTE_COUNT.P+T.0.0001) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0387 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013392 | PGS002411 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.0001) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0029 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013441 | PGS002411 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.0001) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0926 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013490 | PGS002411 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.0001) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1367 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013539 | PGS002411 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.0001) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0845 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013393 | PGS002412 (blood_MEAN_PLATELET_VOL.P+T.0.0001) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0202 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013442 | PGS002412 (blood_MEAN_PLATELET_VOL.P+T.0.0001) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1256 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013491 | PGS002412 (blood_MEAN_PLATELET_VOL.P+T.0.0001) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.2484 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013540 | PGS002412 (blood_MEAN_PLATELET_VOL.P+T.0.0001) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1677 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013394 | PGS002413 (blood_MONOCYTE_COUNT.P+T.0.0001) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0017 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013443 | PGS002413 (blood_MONOCYTE_COUNT.P+T.0.0001) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.072 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013492 | PGS002413 (blood_MONOCYTE_COUNT.P+T.0.0001) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1167 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013541 | PGS002413 (blood_MONOCYTE_COUNT.P+T.0.0001) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0813 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013396 | PGS002415 (blood_PLATELET_COUNT.P+T.0.0001) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013494 | PGS002415 (blood_PLATELET_COUNT.P+T.0.0001) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1524 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013543 | PGS002415 (blood_PLATELET_COUNT.P+T.0.0001) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0979 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013445 | PGS002415 (blood_PLATELET_COUNT.P+T.0.0001) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0672 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013398 | PGS002417 (blood_RED_COUNT.P+T.0.0001) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0031 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013447 | PGS002417 (blood_RED_COUNT.P+T.0.0001) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0331 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013545 | PGS002417 (blood_RED_COUNT.P+T.0.0001) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.037 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013496 | PGS002417 (blood_RED_COUNT.P+T.0.0001) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0916 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013399 | PGS002418 (blood_RBC_DISTRIB_WIDTH.P+T.0.0001) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013448 | PGS002418 (blood_RBC_DISTRIB_WIDTH.P+T.0.0001) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0576 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013497 | PGS002418 (blood_RBC_DISTRIB_WIDTH.P+T.0.0001) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0994 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013546 | PGS002418 (blood_RBC_DISTRIB_WIDTH.P+T.0.0001) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0649 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013410 | PGS002429 (blood_WHITE_COUNT.P+T.0.0001) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.004 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013459 | PGS002429 (blood_WHITE_COUNT.P+T.0.0001) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0423 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013508 | PGS002429 (blood_WHITE_COUNT.P+T.0.0001) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0662 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013557 | PGS002429 (blood_WHITE_COUNT.P+T.0.0001) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0458 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013574 | PGS002446 (blood_EOSINOPHIL_COUNT.P+T.0.001) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0015 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013623 | PGS002446 (blood_EOSINOPHIL_COUNT.P+T.0.001) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0309 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013672 | PGS002446 (blood_EOSINOPHIL_COUNT.P+T.0.001) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0744 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013721 | PGS002446 (blood_EOSINOPHIL_COUNT.P+T.0.001) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0298 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013580 | PGS002452 (biochemistry_HbA1c.P+T.0.001) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013629 | PGS002452 (biochemistry_HbA1c.P+T.0.001) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0058 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013678 | PGS002452 (biochemistry_HbA1c.P+T.0.001) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0272 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013727 | PGS002452 (biochemistry_HbA1c.P+T.0.001) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0056 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013582 | PGS002454 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.001) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0011 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013631 | PGS002454 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.001) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0141 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013680 | PGS002454 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.001) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0817 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013729 | PGS002454 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.001) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0228 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013587 | PGS002459 (blood_LYMPHOCYTE_COUNT.P+T.0.001) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0003 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013636 | PGS002459 (blood_LYMPHOCYTE_COUNT.P+T.0.001) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0358 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013685 | PGS002459 (blood_LYMPHOCYTE_COUNT.P+T.0.001) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.058 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013734 | PGS002459 (blood_LYMPHOCYTE_COUNT.P+T.0.001) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0294 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013588 | PGS002460 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.001) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0002 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013637 | PGS002460 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.001) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0709 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013686 | PGS002460 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.001) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1354 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013735 | PGS002460 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.001) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0791 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013638 | PGS002461 (blood_MEAN_PLATELET_VOL.P+T.0.001) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1171 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013687 | PGS002461 (blood_MEAN_PLATELET_VOL.P+T.0.001) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.2478 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013736 | PGS002461 (blood_MEAN_PLATELET_VOL.P+T.0.001) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1579 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013589 | PGS002461 (blood_MEAN_PLATELET_VOL.P+T.0.001) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0039 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013590 | PGS002462 (blood_MONOCYTE_COUNT.P+T.0.001) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0011 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013639 | PGS002462 (blood_MONOCYTE_COUNT.P+T.0.001) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0556 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013688 | PGS002462 (blood_MONOCYTE_COUNT.P+T.0.001) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1119 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013737 | PGS002462 (blood_MONOCYTE_COUNT.P+T.0.001) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.072 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013592 | PGS002464 (blood_PLATELET_COUNT.P+T.0.001) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0009 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013641 | PGS002464 (blood_PLATELET_COUNT.P+T.0.001) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0596 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013690 | PGS002464 (blood_PLATELET_COUNT.P+T.0.001) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1455 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013739 | PGS002464 (blood_PLATELET_COUNT.P+T.0.001) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0778 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013594 | PGS002466 (blood_RED_COUNT.P+T.0.001) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0006 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013643 | PGS002466 (blood_RED_COUNT.P+T.0.001) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0089 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013741 | PGS002466 (blood_RED_COUNT.P+T.0.001) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.027 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013692 | PGS002466 (blood_RED_COUNT.P+T.0.001) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0939 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013595 | PGS002467 (blood_RBC_DISTRIB_WIDTH.P+T.0.001) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013644 | PGS002467 (blood_RBC_DISTRIB_WIDTH.P+T.0.001) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0099 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013693 | PGS002467 (blood_RBC_DISTRIB_WIDTH.P+T.0.001) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0856 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013742 | PGS002467 (blood_RBC_DISTRIB_WIDTH.P+T.0.001) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0084 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013606 | PGS002478 (blood_WHITE_COUNT.P+T.0.001) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013655 | PGS002478 (blood_WHITE_COUNT.P+T.0.001) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.036 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013704 | PGS002478 (blood_WHITE_COUNT.P+T.0.001) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.069 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013753 | PGS002478 (blood_WHITE_COUNT.P+T.0.001) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0441 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013770 | PGS002495 (blood_EOSINOPHIL_COUNT.P+T.0.01) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0012 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013819 | PGS002495 (blood_EOSINOPHIL_COUNT.P+T.0.01) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0108 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013868 | PGS002495 (blood_EOSINOPHIL_COUNT.P+T.0.01) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.053 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013917 | PGS002495 (blood_EOSINOPHIL_COUNT.P+T.0.01) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0191 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013776 | PGS002501 (biochemistry_HbA1c.P+T.0.01) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0002 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013825 | PGS002501 (biochemistry_HbA1c.P+T.0.01) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0071 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013874 | PGS002501 (biochemistry_HbA1c.P+T.0.01) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0215 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013923 | PGS002501 (biochemistry_HbA1c.P+T.0.01) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0025 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013778 | PGS002503 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.01) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0055 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013876 | PGS002503 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.01) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0492 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013925 | PGS002503 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.01) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0066 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013827 | PGS002503 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.0.01) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0057 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013881 | PGS002508 (blood_LYMPHOCYTE_COUNT.P+T.0.01) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0456 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013783 | PGS002508 (blood_LYMPHOCYTE_COUNT.P+T.0.01) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013832 | PGS002508 (blood_LYMPHOCYTE_COUNT.P+T.0.01) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0123 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013930 | PGS002508 (blood_LYMPHOCYTE_COUNT.P+T.0.01) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0138 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013784 | PGS002509 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.01) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013833 | PGS002509 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.01) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0213 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013882 | PGS002509 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.01) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0946 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013931 | PGS002509 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.0.01) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0334 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013785 | PGS002510 (blood_MEAN_PLATELET_VOL.P+T.0.01) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0014 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013834 | PGS002510 (blood_MEAN_PLATELET_VOL.P+T.0.01) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0417 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013883 | PGS002510 (blood_MEAN_PLATELET_VOL.P+T.0.01) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1998 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013932 | PGS002510 (blood_MEAN_PLATELET_VOL.P+T.0.01) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0686 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013835 | PGS002511 (blood_MONOCYTE_COUNT.P+T.0.01) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0163 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013884 | PGS002511 (blood_MONOCYTE_COUNT.P+T.0.01) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0568 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013786 | PGS002511 (blood_MONOCYTE_COUNT.P+T.0.01) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0012 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013933 | PGS002511 (blood_MONOCYTE_COUNT.P+T.0.01) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0235 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013788 | PGS002513 (blood_PLATELET_COUNT.P+T.0.01) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0006 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013886 | PGS002513 (blood_PLATELET_COUNT.P+T.0.01) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1163 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013935 | PGS002513 (blood_PLATELET_COUNT.P+T.0.01) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0301 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013837 | PGS002513 (blood_PLATELET_COUNT.P+T.0.01) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0374 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013790 | PGS002515 (blood_RED_COUNT.P+T.0.01) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013839 | PGS002515 (blood_RED_COUNT.P+T.0.01) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0033 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013937 | PGS002515 (blood_RED_COUNT.P+T.0.01) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.018 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013888 | PGS002515 (blood_RED_COUNT.P+T.0.01) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0705 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013791 | PGS002516 (blood_RBC_DISTRIB_WIDTH.P+T.0.01) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013840 | PGS002516 (blood_RBC_DISTRIB_WIDTH.P+T.0.01) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0008 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013889 | PGS002516 (blood_RBC_DISTRIB_WIDTH.P+T.0.01) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0643 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013938 | PGS002516 (blood_RBC_DISTRIB_WIDTH.P+T.0.01) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0048 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013802 | PGS002527 (blood_WHITE_COUNT.P+T.0.01) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013851 | PGS002527 (blood_WHITE_COUNT.P+T.0.01) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0109 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013900 | PGS002527 (blood_WHITE_COUNT.P+T.0.01) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0384 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013949 | PGS002527 (blood_WHITE_COUNT.P+T.0.01) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0128 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013966 | PGS002544 (blood_EOSINOPHIL_COUNT.P+T.1e-06) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0094 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014015 | PGS002544 (blood_EOSINOPHIL_COUNT.P+T.1e-06) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0334 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014064 | PGS002544 (blood_EOSINOPHIL_COUNT.P+T.1e-06) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0633 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014113 | PGS002544 (blood_EOSINOPHIL_COUNT.P+T.1e-06) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0426 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013972 | PGS002550 (biochemistry_HbA1c.P+T.1e-06) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014021 | PGS002550 (biochemistry_HbA1c.P+T.1e-06) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0268 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014070 | PGS002550 (biochemistry_HbA1c.P+T.1e-06) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0444 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014119 | PGS002550 (biochemistry_HbA1c.P+T.1e-06) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0172 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013974 | PGS002552 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.1e-06) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0149 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014072 | PGS002552 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.1e-06) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0653 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014121 | PGS002552 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.1e-06) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0442 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014023 | PGS002552 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.1e-06) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0649 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013979 | PGS002557 (blood_LYMPHOCYTE_COUNT.P+T.1e-06) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0087 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014028 | PGS002557 (blood_LYMPHOCYTE_COUNT.P+T.1e-06) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.057 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014077 | PGS002557 (blood_LYMPHOCYTE_COUNT.P+T.1e-06) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0561 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014126 | PGS002557 (blood_LYMPHOCYTE_COUNT.P+T.1e-06) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0302 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013980 | PGS002558 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.1e-06) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.013 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014029 | PGS002558 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.1e-06) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0871 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014078 | PGS002558 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.1e-06) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1257 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014127 | PGS002558 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.1e-06) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0825 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013981 | PGS002559 (blood_MEAN_PLATELET_VOL.P+T.1e-06) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0539 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014030 | PGS002559 (blood_MEAN_PLATELET_VOL.P+T.1e-06) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.114 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014079 | PGS002559 (blood_MEAN_PLATELET_VOL.P+T.1e-06) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.2375 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014128 | PGS002559 (blood_MEAN_PLATELET_VOL.P+T.1e-06) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1648 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013982 | PGS002560 (blood_MONOCYTE_COUNT.P+T.1e-06) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0238 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014031 | PGS002560 (blood_MONOCYTE_COUNT.P+T.1e-06) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0645 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014080 | PGS002560 (blood_MONOCYTE_COUNT.P+T.1e-06) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1053 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014129 | PGS002560 (blood_MONOCYTE_COUNT.P+T.1e-06) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.077 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013984 | PGS002562 (blood_PLATELET_COUNT.P+T.1e-06) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.035 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014033 | PGS002562 (blood_PLATELET_COUNT.P+T.1e-06) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0664 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014131 | PGS002562 (blood_PLATELET_COUNT.P+T.1e-06) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1092 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014082 | PGS002562 (blood_PLATELET_COUNT.P+T.1e-06) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1496 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014035 | PGS002564 (blood_RED_COUNT.P+T.1e-06) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0602 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014084 | PGS002564 (blood_RED_COUNT.P+T.1e-06) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0762 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013986 | PGS002564 (blood_RED_COUNT.P+T.1e-06) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0079 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014133 | PGS002564 (blood_RED_COUNT.P+T.1e-06) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0496 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013987 | PGS002565 (blood_RBC_DISTRIB_WIDTH.P+T.1e-06) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.014 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014036 | PGS002565 (blood_RBC_DISTRIB_WIDTH.P+T.1e-06) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0554 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014085 | PGS002565 (blood_RBC_DISTRIB_WIDTH.P+T.1e-06) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0938 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014134 | PGS002565 (blood_RBC_DISTRIB_WIDTH.P+T.1e-06) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0644 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013998 | PGS002576 (blood_WHITE_COUNT.P+T.1e-06) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0151 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014047 | PGS002576 (blood_WHITE_COUNT.P+T.1e-06) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0323 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014096 | PGS002576 (blood_WHITE_COUNT.P+T.1e-06) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0515 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014145 | PGS002576 (blood_WHITE_COUNT.P+T.1e-06) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0369 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014162 | PGS002593 (blood_EOSINOPHIL_COUNT.P+T.5e-08) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.008 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014211 | PGS002593 (blood_EOSINOPHIL_COUNT.P+T.5e-08) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0318 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014260 | PGS002593 (blood_EOSINOPHIL_COUNT.P+T.5e-08) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0573 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014309 | PGS002593 (blood_EOSINOPHIL_COUNT.P+T.5e-08) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0387 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014168 | PGS002599 (biochemistry_HbA1c.P+T.5e-08) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014217 | PGS002599 (biochemistry_HbA1c.P+T.5e-08) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0234 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014266 | PGS002599 (biochemistry_HbA1c.P+T.5e-08) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0488 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014315 | PGS002599 (biochemistry_HbA1c.P+T.5e-08) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0179 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014219 | PGS002601 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.5e-08) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0643 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014268 | PGS002601 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.5e-08) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0598 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014170 | PGS002601 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.5e-08) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0141 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014317 | PGS002601 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.P+T.5e-08) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0413 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014175 | PGS002606 (blood_LYMPHOCYTE_COUNT.P+T.5e-08) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0085 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014224 | PGS002606 (blood_LYMPHOCYTE_COUNT.P+T.5e-08) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0542 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014273 | PGS002606 (blood_LYMPHOCYTE_COUNT.P+T.5e-08) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0498 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014322 | PGS002606 (blood_LYMPHOCYTE_COUNT.P+T.5e-08) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0265 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014176 | PGS002607 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.5e-08) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0125 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014274 | PGS002607 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.5e-08) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.12 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014323 | PGS002607 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.5e-08) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0807 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014225 | PGS002607 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.P+T.5e-08) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0847 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014177 | PGS002608 (blood_MEAN_PLATELET_VOL.P+T.5e-08) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.0528 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014226 | PGS002608 (blood_MEAN_PLATELET_VOL.P+T.5e-08) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1077 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014275 | PGS002608 (blood_MEAN_PLATELET_VOL.P+T.5e-08) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.2311 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014324 | PGS002608 (blood_MEAN_PLATELET_VOL.P+T.5e-08) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1619 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014178 | PGS002609 (blood_MONOCYTE_COUNT.P+T.5e-08) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0222 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014227 | PGS002609 (blood_MONOCYTE_COUNT.P+T.5e-08) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0591 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014276 | PGS002609 (blood_MONOCYTE_COUNT.P+T.5e-08) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0991 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014325 | PGS002609 (blood_MONOCYTE_COUNT.P+T.5e-08) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0716 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014180 | PGS002611 (blood_PLATELET_COUNT.P+T.5e-08) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0343 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014229 | PGS002611 (blood_PLATELET_COUNT.P+T.5e-08) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0598 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014278 | PGS002611 (blood_PLATELET_COUNT.P+T.5e-08) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1408 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014327 | PGS002611 (blood_PLATELET_COUNT.P+T.5e-08) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1049 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014182 | PGS002613 (blood_RED_COUNT.P+T.5e-08) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0114 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014231 | PGS002613 (blood_RED_COUNT.P+T.5e-08) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0594 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014280 | PGS002613 (blood_RED_COUNT.P+T.5e-08) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0684 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014329 | PGS002613 (blood_RED_COUNT.P+T.5e-08) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0445 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014183 | PGS002614 (blood_RBC_DISTRIB_WIDTH.P+T.5e-08) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0136 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014232 | PGS002614 (blood_RBC_DISTRIB_WIDTH.P+T.5e-08) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0506 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014281 | PGS002614 (blood_RBC_DISTRIB_WIDTH.P+T.5e-08) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0887 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014330 | PGS002614 (blood_RBC_DISTRIB_WIDTH.P+T.5e-08) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0604 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014194 | PGS002625 (blood_WHITE_COUNT.P+T.5e-08) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0181 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014292 | PGS002625 (blood_WHITE_COUNT.P+T.5e-08) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0445 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014341 | PGS002625 (blood_WHITE_COUNT.P+T.5e-08) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.033 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014243 | PGS002625 (blood_WHITE_COUNT.P+T.5e-08) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.027 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014407 | PGS002642 (blood_EOSINOPHIL_COUNT.PolyFun-pred) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0799 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_EOSINOPHIL_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014358 | PGS002642 (blood_EOSINOPHIL_COUNT.PolyFun-pred) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.037 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_EOSINOPHIL_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014456 | PGS002642 (blood_EOSINOPHIL_COUNT.PolyFun-pred) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1552 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_EOSINOPHIL_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014505 | PGS002642 (blood_EOSINOPHIL_COUNT.PolyFun-pred) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1011 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_EOSINOPHIL_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014364 | PGS002648 (biochemistry_HbA1c.PolyFun-pred) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0239 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_HbA1c.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014413 | PGS002648 (biochemistry_HbA1c.PolyFun-pred) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0785 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_HbA1c.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014511 | PGS002648 (biochemistry_HbA1c.PolyFun-pred) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.064 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_HbA1c.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014462 | PGS002648 (biochemistry_HbA1c.PolyFun-pred) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1252 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_HbA1c.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014513 | PGS002650 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.PolyFun-pred) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1032 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014366 | PGS002650 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.PolyFun-pred) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0563 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014415 | PGS002650 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.PolyFun-pred) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1068 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014464 | PGS002650 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.PolyFun-pred) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1606 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014371 | PGS002655 (blood_LYMPHOCYTE_COUNT.PolyFun-pred) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0339 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_LYMPHOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014420 | PGS002655 (blood_LYMPHOCYTE_COUNT.PolyFun-pred) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0735 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_LYMPHOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014469 | PGS002655 (blood_LYMPHOCYTE_COUNT.PolyFun-pred) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1393 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_LYMPHOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014518 | PGS002655 (blood_LYMPHOCYTE_COUNT.PolyFun-pred) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0834 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_LYMPHOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014372 | PGS002656 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.PolyFun-pred) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0234 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_CORPUSCULAR_HEMOGLOBIN.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014421 | PGS002656 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.PolyFun-pred) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1192 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_CORPUSCULAR_HEMOGLOBIN.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014470 | PGS002656 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.PolyFun-pred) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.2162 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_CORPUSCULAR_HEMOGLOBIN.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014519 | PGS002656 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.PolyFun-pred) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1375 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_CORPUSCULAR_HEMOGLOBIN.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014373 | PGS002657 (blood_MEAN_PLATELET_VOL.PolyFun-pred) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1611 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_PLATELET_VOL.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014422 | PGS002657 (blood_MEAN_PLATELET_VOL.PolyFun-pred) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.2269 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_PLATELET_VOL.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014471 | PGS002657 (blood_MEAN_PLATELET_VOL.PolyFun-pred) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.3972 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_PLATELET_VOL.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014520 | PGS002657 (blood_MEAN_PLATELET_VOL.PolyFun-pred) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.2957 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MEAN_PLATELET_VOL.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014521 | PGS002658 (blood_MONOCYTE_COUNT.PolyFun-pred) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1406 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MONOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014374 | PGS002658 (blood_MONOCYTE_COUNT.PolyFun-pred) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0526 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MONOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014423 | PGS002658 (blood_MONOCYTE_COUNT.PolyFun-pred) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0939 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MONOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014472 | PGS002658 (blood_MONOCYTE_COUNT.PolyFun-pred) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1972 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_MONOCYTE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014376 | PGS002660 (blood_PLATELET_COUNT.PolyFun-pred) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1041 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_PLATELET_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014425 | PGS002660 (blood_PLATELET_COUNT.PolyFun-pred) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1227 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_PLATELET_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014474 | PGS002660 (blood_PLATELET_COUNT.PolyFun-pred) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.2716 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_PLATELET_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014523 | PGS002660 (blood_PLATELET_COUNT.PolyFun-pred) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.2014 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_PLATELET_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014427 | PGS002662 (blood_RED_COUNT.PolyFun-pred) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0976 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RED_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014476 | PGS002662 (blood_RED_COUNT.PolyFun-pred) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1679 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RED_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014525 | PGS002662 (blood_RED_COUNT.PolyFun-pred) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1044 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RED_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014378 | PGS002662 (blood_RED_COUNT.PolyFun-pred) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0299 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RED_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014379 | PGS002663 (blood_RBC_DISTRIB_WIDTH.PolyFun-pred) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0343 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RBC_DISTRIB_WIDTH.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014428 | PGS002663 (blood_RBC_DISTRIB_WIDTH.PolyFun-pred) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1048 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RBC_DISTRIB_WIDTH.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014477 | PGS002663 (blood_RBC_DISTRIB_WIDTH.PolyFun-pred) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1682 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RBC_DISTRIB_WIDTH.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014526 | PGS002663 (blood_RBC_DISTRIB_WIDTH.PolyFun-pred) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1097 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_RBC_DISTRIB_WIDTH.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014390 | PGS002674 (blood_WHITE_COUNT.PolyFun-pred) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.047 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_WHITE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014439 | PGS002674 (blood_WHITE_COUNT.PolyFun-pred) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0632 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_WHITE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014488 | PGS002674 (blood_WHITE_COUNT.PolyFun-pred) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1299 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_WHITE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014537 | PGS002674 (blood_WHITE_COUNT.PolyFun-pred) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1008 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See blood_WHITE_COUNT.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014603 | PGS002691 (blood_EOSINOPHIL_COUNT.SBayesR) |
PSS009744| East Asian Ancestry| 880 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0679 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014554 | PGS002691 (blood_EOSINOPHIL_COUNT.SBayesR) |
PSS009743| African Ancestry| 6,017 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0323 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014652 | PGS002691 (blood_EOSINOPHIL_COUNT.SBayesR) |
PSS009745| European Ancestry| 41,583 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.1288 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014701 | PGS002691 (blood_EOSINOPHIL_COUNT.SBayesR) |
PSS009746| South Asian Ancestry| 7,446 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (full model vs. covariates alone): 0.0858 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014560 | PGS002697 (biochemistry_HbA1c.SBayesR) |
PSS009767| African Ancestry| 4,803 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0181 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014609 | PGS002697 (biochemistry_HbA1c.SBayesR) |
PSS009768| East Asian Ancestry| 841 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0677 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014658 | PGS002697 (biochemistry_HbA1c.SBayesR) |
PSS009769| European Ancestry| 38,991 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.112 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014707 | PGS002697 (biochemistry_HbA1c.SBayesR) |
PSS009770| South Asian Ancestry| 6,861 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: HbA1c | — | — | Incremental R2 (full model vs. covariates alone): 0.0608 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014562 | PGS002699 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.SBayesR) |
PSS009775| African Ancestry| 5,976 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0423 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014611 | PGS002699 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.SBayesR) |
PSS009776| East Asian Ancestry| 881 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0835 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014660 | PGS002699 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.SBayesR) |
PSS009777| European Ancestry| 41,231 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1452 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014709 | PGS002699 (blood_HIGH_LIGHT_SCATTER_RETICULOCYTE_COUNT.SBayesR) |
PSS009778| South Asian Ancestry| 7,560 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: High Light Scatter Reticulocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0888 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014616 | PGS002704 (blood_LYMPHOCYTE_COUNT.SBayesR) |
PSS009796| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0673 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014567 | PGS002704 (blood_LYMPHOCYTE_COUNT.SBayesR) |
PSS009795| African Ancestry| 6,135 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0268 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014665 | PGS002704 (blood_LYMPHOCYTE_COUNT.SBayesR) |
PSS009797| European Ancestry| 41,973 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1222 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014714 | PGS002704 (blood_LYMPHOCYTE_COUNT.SBayesR) |
PSS009798| South Asian Ancestry| 7,737 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Lymphocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0722 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014568 | PGS002705 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.SBayesR) |
PSS009799| African Ancestry| 5,952 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.0316 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014617 | PGS002705 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.SBayesR) |
PSS009800| East Asian Ancestry| 845 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1188 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014666 | PGS002705 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.SBayesR) |
PSS009801| European Ancestry| 41,826 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1825 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014715 | PGS002705 (blood_MEAN_CORPUSCULAR_HEMOGLOBIN.SBayesR) |
PSS009802| South Asian Ancestry| 7,485 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Corpuscular Hemoglobin | — | — | Incremental R2 (full model vs. covariates alone): 0.1216 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014569 | PGS002706 (blood_MEAN_PLATELET_VOL.SBayesR) |
PSS009803| African Ancestry| 6,153 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1127 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014618 | PGS002706 (blood_MEAN_PLATELET_VOL.SBayesR) |
PSS009804| East Asian Ancestry| 892 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.1723 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014667 | PGS002706 (blood_MEAN_PLATELET_VOL.SBayesR) |
PSS009805| European Ancestry| 42,078 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.3475 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014716 | PGS002706 (blood_MEAN_PLATELET_VOL.SBayesR) |
PSS009806| South Asian Ancestry| 7,770 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Mean Platelet Volume | — | — | Incremental R2 (full model vs. covariates alone): 0.2571 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014570 | PGS002707 (blood_MONOCYTE_COUNT.SBayesR) |
PSS009807| African Ancestry| 6,119 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0361 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014619 | PGS002707 (blood_MONOCYTE_COUNT.SBayesR) |
PSS009808| East Asian Ancestry| 894 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0896 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014717 | PGS002707 (blood_MONOCYTE_COUNT.SBayesR) |
PSS009810| South Asian Ancestry| 7,718 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1212 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014668 | PGS002707 (blood_MONOCYTE_COUNT.SBayesR) |
PSS009809| European Ancestry| 41,863 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Monocyte Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1669 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014572 | PGS002709 (blood_PLATELET_COUNT.SBayesR) |
PSS009815| African Ancestry| 6,143 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0667 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014621 | PGS002709 (blood_PLATELET_COUNT.SBayesR) |
PSS009816| East Asian Ancestry| 895 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.0811 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014670 | PGS002709 (blood_PLATELET_COUNT.SBayesR) |
PSS009817| European Ancestry| 42,007 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.24 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014719 | PGS002709 (blood_PLATELET_COUNT.SBayesR) |
PSS009818| South Asian Ancestry| 7,730 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Platelet count | — | — | Incremental R2 (full model vs. covariates alone): 0.1748 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014574 | PGS002711 (blood_RED_COUNT.SBayesR) |
PSS009823| African Ancestry| 6,120 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.027 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014672 | PGS002711 (blood_RED_COUNT.SBayesR) |
PSS009825| European Ancestry| 42,065 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1523 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014623 | PGS002711 (blood_RED_COUNT.SBayesR) |
PSS009824| East Asian Ancestry| 885 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0804 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014721 | PGS002711 (blood_RED_COUNT.SBayesR) |
PSS009826| South Asian Ancestry| 7,707 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0944 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014575 | PGS002712 (blood_RBC_DISTRIB_WIDTH.SBayesR) |
PSS009827| African Ancestry| 5,990 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0314 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014624 | PGS002712 (blood_RBC_DISTRIB_WIDTH.SBayesR) |
PSS009828| East Asian Ancestry| 888 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0864 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014673 | PGS002712 (blood_RBC_DISTRIB_WIDTH.SBayesR) |
PSS009829| European Ancestry| 41,837 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.1461 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014722 | PGS002712 (blood_RBC_DISTRIB_WIDTH.SBayesR) |
PSS009830| South Asian Ancestry| 7,623 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Red Blood Cell Distribution Width | — | — | Incremental R2 (full model vs. covariates alone): 0.0986 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014586 | PGS002723 (blood_WHITE_COUNT.SBayesR) |
PSS009871| African Ancestry| 6,149 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0218 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014635 | PGS002723 (blood_WHITE_COUNT.SBayesR) |
PSS009872| East Asian Ancestry| 893 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0624 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014684 | PGS002723 (blood_WHITE_COUNT.SBayesR) |
PSS009873| European Ancestry| 42,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.1169 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014733 | PGS002723 (blood_WHITE_COUNT.SBayesR) |
PSS009874| South Asian Ancestry| 7,769 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: White Blood Count | — | — | Incremental R2 (full model vs. covariates alone): 0.0902 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM016128 | PGS003330 (PRS-F8) |
PSS010041| European Ancestry| 581 individuals |
PGP000395 | Valenti L et al. JHEP Rep (2022) |
Reported Trait: von Willebrand factor | β: 152.57 (14.24) | — | — | — | — |
| PPM016129 | PGS003330 (PRS-F8) |
PSS010041| European Ancestry| 581 individuals |
PGP000395 | Valenti L et al. JHEP Rep (2022) |
Reported Trait: Factor VIII levels | β: 92.45 (10.2) | — | — | — | — |
| PPM016130 | PGS003330 (PRS-F8) |
PSS010041| European Ancestry| 581 individuals |
PGP000395 | Valenti L et al. JHEP Rep (2022) |
Reported Trait: Factor VIII/protein ratio | β: 0.88 (0.11) | — | — | — | — |
| PPM016176 | PGS003337 (CVGRS_HbA1c) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Hemoglobin A1c level | β: 0.27528 | — | — | — | — |
| PPM016193 | PGS003337 (CVGRS_HbA1c) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Type 2 diabetes | OR: 1.35007 | — | — | — | — |
| PPM016185 | PGS003346 (ALLGRS_HbA1c) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Hemoglobin A1c level | β: 0.28295 | — | — | — | — |
| PPM017080 | PGS003425 (prscs_hemoglobin) |
PSS010117| European Ancestry| 13,780 individuals |
PGP000432 | Toivonen J et al. Vox Sanguinis (2023) |
Reported Trait: Hemoglobin levels in postmenopausal females | — | — | R²: 0.04 | — | — |
| PPM017078 | PGS003425 (prscs_hemoglobin) |
PSS010118| European Ancestry| 21,577 individuals |
PGP000432 | Toivonen J et al. Vox Sanguinis (2023) |
Reported Trait: Hemoglobin levels in males | — | — | R²: 0.07 | — | — |
| PPM017079 | PGS003425 (prscs_hemoglobin) |
PSS010116| European Ancestry| 5,403 individuals |
PGP000432 | Toivonen J et al. Vox Sanguinis (2023) |
Reported Trait: Hemoglobin levels in postmenopausal females | — | — | R²: 0.06 | — | — |
| PPM017274 | PGS003464 (LDPred2_EOS) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: 0.002 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017297 | PGS003464 (LDPred2_EOS) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.014 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017278 | PGS003467 (LDPred2_HCT) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: -0.005 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017301 | PGS003467 (LDPred2_HCT) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.008 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017279 | PGS003468 (LDPred2_HGB) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: -0.012 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017302 | PGS003468 (LDPred2_HGB) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.0 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017277 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: 0.023 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017300 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.045 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017346 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index in obsese | β: 0.027 (0.017) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017366 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea in obsese | β: 0.056 (0.034) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017367 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea in non-obsese | β: 0.037 (0.034) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017395 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index x obesity interaction | β: 0.008 (1.008) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017347 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index in non-obsese | β: 0.025 (0.012) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017405 | PGS003471 (LDPred2_HbA1c) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea x obesity interaction | β: 1.042 (0.049) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017285 | PGS003475 (LDPred2_LYM) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: 0.009 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017308 | PGS003475 (LDPred2_LYM) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.007 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017288 | PGS003478 (LDPred2_RBC) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: -0.001 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017311 | PGS003478 (LDPred2_RBC) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.008 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017292 | PGS003483 (LDPred2_WBC) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: -0.007 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017315 | PGS003483 (LDPred2_WBC) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: -0.023 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017598 | PGS003502 (cont-decay-erythrocyte) |
PSS010611| European Ancestry| 6,278 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017682 | PGS003502 (cont-decay-erythrocyte) |
PSS010527| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017766 | PGS003502 (cont-decay-erythrocyte) |
PSS010191| European Ancestry| 2,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017934 | PGS003502 (cont-decay-erythrocyte) |
PSS010359| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM018018 | PGS003502 (cont-decay-erythrocyte) |
PSS010275| African Ancestry| 2,329 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018102 | PGS003502 (cont-decay-erythrocyte) |
PSS010695| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017430 | PGS003502 (cont-decay-erythrocyte) |
PSS010863| European Ancestry| 19,416 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017514 | PGS003502 (cont-decay-erythrocyte) |
PSS010779| European Ancestry| 3,990 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017850 | PGS003502 (cont-decay-erythrocyte) |
PSS010443| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) count | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017431 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010864| European Ancestry| 19,389 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017515 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010780| European Ancestry| 3,985 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017599 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010612| European Ancestry| 6,263 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017683 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010528| Greater Middle Eastern Ancestry| 1,114 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017851 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010444| South Asian Ancestry| 5,983 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM017935 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010360| East Asian Ancestry| 1,744 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM018019 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010276| African Ancestry| 2,315 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017767 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010192| European Ancestry| 2,258 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM018103 | PGS003503 (cont-decay-erythrocyte_width) |
PSS010696| African Ancestry| 3,651 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017439 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010873| European Ancestry| 19,418 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017523 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010789| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017607 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010621| European Ancestry| 6,278 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017691 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010537| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM017775 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010201| European Ancestry| 2,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017859 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010453| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017943 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010369| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018111 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010705| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM018027 | PGS003511 (cont-decay-haematocrit_perc) |
PSS010285| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haematocrit percentage | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017440 | PGS003512 (cont-decay-haemoglobin) |
PSS010874| European Ancestry| 19,417 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017524 | PGS003512 (cont-decay-haemoglobin) |
PSS010790| European Ancestry| 3,990 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017608 | PGS003512 (cont-decay-haemoglobin) |
PSS010622| European Ancestry| 6,277 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017692 | PGS003512 (cont-decay-haemoglobin) |
PSS010538| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM017776 | PGS003512 (cont-decay-haemoglobin) |
PSS010202| European Ancestry| 2,263 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017860 | PGS003512 (cont-decay-haemoglobin) |
PSS010454| South Asian Ancestry| 6,025 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM018028 | PGS003512 (cont-decay-haemoglobin) |
PSS010286| African Ancestry| 2,327 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM018112 | PGS003512 (cont-decay-haemoglobin) |
PSS010706| African Ancestry| 3,680 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017944 | PGS003512 (cont-decay-haemoglobin) |
PSS010370| East Asian Ancestry| 1,749 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Haemoglobin concentration | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017443 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010877| European Ancestry| 19,116 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017527 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010793| European Ancestry| 3,913 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017611 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010625| European Ancestry| 6,144 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017695 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010541| Greater Middle Eastern Ancestry| 1,097 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017779 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010205| European Ancestry| 2,221 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017863 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010457| South Asian Ancestry| 5,888 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017947 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010373| East Asian Ancestry| 1,711 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018031 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010289| African Ancestry| 2,282 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018115 | PGS003515 (cont-decay-immature_reticulocyte_frac) |
PSS010709| African Ancestry| 3,574 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Immature reticulocyte fraction | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017458 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010894| European Ancestry| 11,449 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017542 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010810| European Ancestry| 2,277 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017710 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010558| Greater Middle Eastern Ancestry| 659 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017794 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010222| European Ancestry| 1,249 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017878 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010474| South Asian Ancestry| 4,192 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018046 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010306| African Ancestry| 1,285 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.0 | sex, age, deprivation index, PC1-16 | — |
| PPM018130 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010726| African Ancestry| 2,085 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017626 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010642| European Ancestry| 3,283 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017962 | PGS003530 (cont-decay-log_eosinophil_perc) |
PSS010390| East Asian Ancestry| 973 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Eosinophil percentage | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017461 | PGS003533 (cont-decay-log_HbA1c) |
PSS010897| European Ancestry| 19,078 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017545 | PGS003533 (cont-decay-log_HbA1c) |
PSS010813| European Ancestry| 3,920 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017629 | PGS003533 (cont-decay-log_HbA1c) |
PSS010645| European Ancestry| 6,172 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017713 | PGS003533 (cont-decay-log_HbA1c) |
PSS010561| Greater Middle Eastern Ancestry| 1,100 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017797 | PGS003533 (cont-decay-log_HbA1c) |
PSS010225| European Ancestry| 2,233 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017881 | PGS003533 (cont-decay-log_HbA1c) |
PSS010477| South Asian Ancestry| 5,884 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017965 | PGS003533 (cont-decay-log_HbA1c) |
PSS010393| East Asian Ancestry| 1,718 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM018049 | PGS003533 (cont-decay-log_HbA1c) |
PSS010309| African Ancestry| 2,089 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM018133 | PGS003533 (cont-decay-log_HbA1c) |
PSS010729| African Ancestry| 2,950 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Glycated haemoglobin (HbA1c) | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017469 | PGS003541 (cont-decay-log_leukocyte) |
PSS010906| European Ancestry| 19,415 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017553 | PGS003541 (cont-decay-log_leukocyte) |
PSS010822| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017721 | PGS003541 (cont-decay-log_leukocyte) |
PSS010570| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017805 | PGS003541 (cont-decay-log_leukocyte) |
PSS010234| European Ancestry| 2,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017889 | PGS003541 (cont-decay-log_leukocyte) |
PSS010486| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM018057 | PGS003541 (cont-decay-log_leukocyte) |
PSS010318| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018141 | PGS003541 (cont-decay-log_leukocyte) |
PSS010738| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017637 | PGS003541 (cont-decay-log_leukocyte) |
PSS010654| European Ancestry| 6,278 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017973 | PGS003541 (cont-decay-log_leukocyte) |
PSS010402| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: White blood cell (leukocyte) count | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017472 | PGS003544 (cont-decay-log_monocyte) |
PSS010909| European Ancestry| 19,376 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017640 | PGS003544 (cont-decay-log_monocyte) |
PSS010657| European Ancestry| 6,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.13 | sex, age, deprivation index, PC1-16 | — |
| PPM017724 | PGS003544 (cont-decay-log_monocyte) |
PSS010573| Greater Middle Eastern Ancestry| 1,121 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.14 | sex, age, deprivation index, PC1-16 | — |
| PPM017808 | PGS003544 (cont-decay-log_monocyte) |
PSS010237| European Ancestry| 2,256 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.14 | sex, age, deprivation index, PC1-16 | — |
| PPM017892 | PGS003544 (cont-decay-log_monocyte) |
PSS010489| South Asian Ancestry| 6,008 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017976 | PGS003544 (cont-decay-log_monocyte) |
PSS010405| East Asian Ancestry| 1,748 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM018060 | PGS003544 (cont-decay-log_monocyte) |
PSS010321| African Ancestry| 2,323 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018144 | PGS003544 (cont-decay-log_monocyte) |
PSS010741| African Ancestry| 3,670 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017556 | PGS003544 (cont-decay-log_monocyte) |
PSS010825| European Ancestry| 3,982 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte count | — | — | partial-R2: 0.14 | sex, age, deprivation index, PC1-16 | — |
| PPM017473 | PGS003545 (cont-decay-log_neutrophil) |
PSS010910| European Ancestry| 19,374 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017557 | PGS003545 (cont-decay-log_neutrophil) |
PSS010826| European Ancestry| 3,981 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017641 | PGS003545 (cont-decay-log_neutrophil) |
PSS010658| European Ancestry| 6,260 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017725 | PGS003545 (cont-decay-log_neutrophil) |
PSS010574| Greater Middle Eastern Ancestry| 1,119 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017809 | PGS003545 (cont-decay-log_neutrophil) |
PSS010238| European Ancestry| 2,259 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017893 | PGS003545 (cont-decay-log_neutrophil) |
PSS010490| South Asian Ancestry| 6,008 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017977 | PGS003545 (cont-decay-log_neutrophil) |
PSS010406| East Asian Ancestry| 1,749 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018061 | PGS003545 (cont-decay-log_neutrophil) |
PSS010322| African Ancestry| 2,323 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018145 | PGS003545 (cont-decay-log_neutrophil) |
PSS010742| African Ancestry| 3,668 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil count | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017474 | PGS003546 (cont-decay-log_platelet) |
PSS010911| European Ancestry| 19,418 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.23 | sex, age, deprivation index, PC1-16 | — |
| PPM017558 | PGS003546 (cont-decay-log_platelet) |
PSS010827| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.22 | sex, age, deprivation index, PC1-16 | — |
| PPM017642 | PGS003546 (cont-decay-log_platelet) |
PSS010659| European Ancestry| 6,277 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.21 | sex, age, deprivation index, PC1-16 | — |
| PPM017726 | PGS003546 (cont-decay-log_platelet) |
PSS010575| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.22 | sex, age, deprivation index, PC1-16 | — |
| PPM017810 | PGS003546 (cont-decay-log_platelet) |
PSS010239| European Ancestry| 2,263 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.17 | sex, age, deprivation index, PC1-16 | — |
| PPM017894 | PGS003546 (cont-decay-log_platelet) |
PSS010491| South Asian Ancestry| 6,024 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.17 | sex, age, deprivation index, PC1-16 | — |
| PPM017978 | PGS003546 (cont-decay-log_platelet) |
PSS010407| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM018062 | PGS003546 (cont-decay-log_platelet) |
PSS010323| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM018146 | PGS003546 (cont-decay-log_platelet) |
PSS010743| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet count | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM017475 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010912| European Ancestry| 19,417 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.18 | sex, age, deprivation index, PC1-16 | — |
| PPM017559 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010828| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.19 | sex, age, deprivation index, PC1-16 | — |
| PPM017643 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010660| European Ancestry| 6,277 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017727 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010576| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.17 | sex, age, deprivation index, PC1-16 | — |
| PPM017811 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010240| European Ancestry| 2,263 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.13 | sex, age, deprivation index, PC1-16 | — |
| PPM017895 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010492| South Asian Ancestry| 6,024 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017979 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010408| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM018063 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010324| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018147 | PGS003547 (cont-decay-log_platelet_crit) |
PSS010744| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet crit | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017560 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010829| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.35 | sex, age, deprivation index, PC1-16 | — |
| PPM017644 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010661| European Ancestry| 6,278 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.31 | sex, age, deprivation index, PC1-16 | — |
| PPM017812 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010241| European Ancestry| 2,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.28 | sex, age, deprivation index, PC1-16 | — |
| PPM017896 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010493| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.26 | sex, age, deprivation index, PC1-16 | — |
| PPM017980 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010409| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.19 | sex, age, deprivation index, PC1-16 | — |
| PPM018064 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010325| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM018148 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010745| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017476 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010913| European Ancestry| 19,418 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.34 | sex, age, deprivation index, PC1-16 | — |
| PPM017728 | PGS003548 (cont-decay-log_platelet_volume) |
PSS010577| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | partial-R2: 0.26 | sex, age, deprivation index, PC1-16 | — |
| PPM017477 | PGS003549 (cont-decay-log_platelet_width) |
PSS010915| European Ancestry| 19,418 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.21 | sex, age, deprivation index, PC1-16 | — |
| PPM017561 | PGS003549 (cont-decay-log_platelet_width) |
PSS010831| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.21 | sex, age, deprivation index, PC1-16 | — |
| PPM017645 | PGS003549 (cont-decay-log_platelet_width) |
PSS010663| European Ancestry| 6,278 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.19 | sex, age, deprivation index, PC1-16 | — |
| PPM017729 | PGS003549 (cont-decay-log_platelet_width) |
PSS010579| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.18 | sex, age, deprivation index, PC1-16 | — |
| PPM017897 | PGS003549 (cont-decay-log_platelet_width) |
PSS010495| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.18 | sex, age, deprivation index, PC1-16 | — |
| PPM017981 | PGS003549 (cont-decay-log_platelet_width) |
PSS010411| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM018065 | PGS003549 (cont-decay-log_platelet_width) |
PSS010327| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM018149 | PGS003549 (cont-decay-log_platelet_width) |
PSS010747| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017813 | PGS003549 (cont-decay-log_platelet_width) |
PSS010243| European Ancestry| 2,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Platelet distribution width | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017479 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010917| European Ancestry| 19,111 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017563 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010833| European Ancestry| 3,912 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.14 | sex, age, deprivation index, PC1-16 | — |
| PPM017647 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010665| European Ancestry| 6,143 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.14 | sex, age, deprivation index, PC1-16 | — |
| PPM017731 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010581| Greater Middle Eastern Ancestry| 1,097 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017815 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010245| European Ancestry| 2,221 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.13 | sex, age, deprivation index, PC1-16 | — |
| PPM017899 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010497| South Asian Ancestry| 5,886 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017983 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010413| East Asian Ancestry| 1,711 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM018067 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010329| African Ancestry| 2,282 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018151 | PGS003551 (cont-decay-log_reticulocyte) |
PSS010749| African Ancestry| 3,574 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Reticulocyte count | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017905 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010503| South Asian Ancestry| 6,004 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017485 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010923| European Ancestry| 19,370 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017569 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010839| European Ancestry| 3,981 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017737 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010587| Greater Middle Eastern Ancestry| 1,120 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017821 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010251| European Ancestry| 2,258 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017989 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010419| East Asian Ancestry| 1,749 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM018073 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010335| African Ancestry| 2,324 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018157 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010755| African Ancestry| 3,666 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017653 | PGS003557 (cont-decay-lymphocyte_perc) |
PSS010671| European Ancestry| 6,258 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Lymphocyte percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017572 | PGS003560 (cont-decay-MCH) |
PSS010843| European Ancestry| 3,989 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017656 | PGS003560 (cont-decay-MCH) |
PSS010675| European Ancestry| 6,276 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017740 | PGS003560 (cont-decay-MCH) |
PSS010591| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM017908 | PGS003560 (cont-decay-MCH) |
PSS010507| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017992 | PGS003560 (cont-decay-MCH) |
PSS010423| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM018076 | PGS003560 (cont-decay-MCH) |
PSS010339| African Ancestry| 2,330 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM018160 | PGS003560 (cont-decay-MCH) |
PSS010759| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017488 | PGS003560 (cont-decay-MCH) |
PSS010927| European Ancestry| 19,414 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.19 | sex, age, deprivation index, PC1-16 | — |
| PPM017824 | PGS003560 (cont-decay-MCH) |
PSS010255| European Ancestry| 2,263 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular haemoglobin | — | — | partial-R2: 0.14 | sex, age, deprivation index, PC1-16 | — |
| PPM017573 | PGS003561 (cont-decay-MCV) |
PSS010844| European Ancestry| 3,991 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017657 | PGS003561 (cont-decay-MCV) |
PSS010676| European Ancestry| 6,278 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017741 | PGS003561 (cont-decay-MCV) |
PSS010592| Greater Middle Eastern Ancestry| 1,123 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017825 | PGS003561 (cont-decay-MCV) |
PSS010256| European Ancestry| 2,264 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017909 | PGS003561 (cont-decay-MCV) |
PSS010508| South Asian Ancestry| 6,026 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017993 | PGS003561 (cont-decay-MCV) |
PSS010424| East Asian Ancestry| 1,750 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM018077 | PGS003561 (cont-decay-MCV) |
PSS010340| African Ancestry| 2,329 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018161 | PGS003561 (cont-decay-MCV) |
PSS010760| African Ancestry| 3,682 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017489 | PGS003561 (cont-decay-MCV) |
PSS010928| European Ancestry| 19,418 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean corpuscular volume | — | — | partial-R2: 0.2 | sex, age, deprivation index, PC1-16 | — |
| PPM017574 | PGS003562 (cont-decay-monocyte_perc) |
PSS010845| European Ancestry| 3,952 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017658 | PGS003562 (cont-decay-monocyte_perc) |
PSS010677| European Ancestry| 6,213 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017826 | PGS003562 (cont-decay-monocyte_perc) |
PSS010257| European Ancestry| 2,241 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017910 | PGS003562 (cont-decay-monocyte_perc) |
PSS010509| South Asian Ancestry| 5,958 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017994 | PGS003562 (cont-decay-monocyte_perc) |
PSS010425| East Asian Ancestry| 1,732 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM018078 | PGS003562 (cont-decay-monocyte_perc) |
PSS010341| African Ancestry| 2,285 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018162 | PGS003562 (cont-decay-monocyte_perc) |
PSS010761| African Ancestry| 3,634 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017490 | PGS003562 (cont-decay-monocyte_perc) |
PSS010929| European Ancestry| 19,237 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017742 | PGS003562 (cont-decay-monocyte_perc) |
PSS010593| Greater Middle Eastern Ancestry| 1,114 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Monocyte percentage | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017494 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010933| European Ancestry| 19,342 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017662 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010681| European Ancestry| 6,254 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017746 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010597| Greater Middle Eastern Ancestry| 1,117 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017830 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010261| European Ancestry| 2,256 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017914 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010513| South Asian Ancestry| 6,003 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017998 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010429| East Asian Ancestry| 1,748 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM018082 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010345| African Ancestry| 2,321 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM018166 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010765| African Ancestry| 3,652 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017578 | PGS003566 (cont-decay-neutrophil_perc) |
PSS010849| European Ancestry| 3,977 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Neutrophil percentage | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017495 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010934| European Ancestry| 19,112 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017579 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010850| European Ancestry| 3,913 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017663 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010682| European Ancestry| 6,144 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.13 | sex, age, deprivation index, PC1-16 | — |
| PPM017747 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010598| Greater Middle Eastern Ancestry| 1,097 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017831 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010262| European Ancestry| 2,221 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017915 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010514| South Asian Ancestry| 5,886 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM017999 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010430| East Asian Ancestry| 1,711 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM018083 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010346| African Ancestry| 2,281 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM018167 | PGS003567 (cont-decay-reticulocyte_volume) |
PSS010766| African Ancestry| 3,574 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean reticulocyte volume | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017834 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010266| European Ancestry| 2,220 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017498 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010938| European Ancestry| 19,109 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.16 | sex, age, deprivation index, PC1-16 | — |
| PPM017582 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010854| European Ancestry| 3,913 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.15 | sex, age, deprivation index, PC1-16 | — |
| PPM017750 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010602| Greater Middle Eastern Ancestry| 1,096 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017918 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010518| South Asian Ancestry| 5,885 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.1 | sex, age, deprivation index, PC1-16 | — |
| PPM018002 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010434| East Asian Ancestry| 1,711 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM018086 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010350| African Ancestry| 2,280 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017666 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010686| European Ancestry| 6,139 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.13 | sex, age, deprivation index, PC1-16 | — |
| PPM018170 | PGS003570 (cont-decay-sphered_cell_volume) |
PSS010770| African Ancestry| 3,573 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Mean sphered cell volume | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM018940 | PGS003924 (INI30000) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: White blood cell (leukocyte) count | — | — | R²: 0.09098 [0.07103, 0.11094] PGS R2 (no covariates): 0.07615 [0.05759, 0.0947] Incremental R2 (full-covars): 0.07328 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018941 | PGS003924 (INI30000) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: White blood cell (leukocyte) count | — | — | R²: 0.09373 [0.06559, 0.12186] PGS R2 (no covariates): 0.08974 [0.06208, 0.11739] Incremental R2 (full-covars): 0.08665 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018942 | PGS003924 (INI30000) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: White blood cell (leukocyte) count | — | — | R²: 0.05083 [0.02681, 0.07484] PGS R2 (no covariates): 0.04407 [0.02155, 0.06659] Incremental R2 (full-covars): 0.04309 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018943 | PGS003924 (INI30000) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: White blood cell (leukocyte) count | — | — | R²: 0.09843 [0.08604, 0.11081] PGS R2 (no covariates): 0.08243 [0.0709, 0.09397] Incremental R2 (full-covars): 0.07062 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018939 | PGS003924 (INI30000) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: White blood cell (leukocyte) count | — | — | R²: 0.08905 [0.08496, 0.09315] PGS R2 (no covariates): 0.07643 [0.07259, 0.08028] Incremental R2 (full-covars): 0.07561 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018944 | PGS003925 (INI30010) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | R²: 0.38181 [0.37606, 0.38756] PGS R2 (no covariates): 0.1179 [0.11333, 0.12246] Incremental R2 (full-covars): 0.1164 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018945 | PGS003925 (INI30010) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | R²: 0.38936 [0.36163, 0.41709] PGS R2 (no covariates): 0.09281 [0.0727, 0.11293] Incremental R2 (full-covars): 0.09305 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018946 | PGS003925 (INI30010) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | R²: 0.35286 [0.31388, 0.39185] PGS R2 (no covariates): 0.07492 [0.04924, 0.1006] Incremental R2 (full-covars): 0.0761 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018947 | PGS003925 (INI30010) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | R²: 0.29476 [0.25179, 0.33773] PGS R2 (no covariates): 0.04144 [0.01954, 0.06333] Incremental R2 (full-covars): 0.03544 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018948 | PGS003925 (INI30010) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | R²: 0.38816 [0.37147, 0.40486] PGS R2 (no covariates): 0.10146 [0.08893, 0.114] Incremental R2 (full-covars): 0.07754 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018949 | PGS003926 (INI30020) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.4378 [0.43219, 0.4434] PGS R2 (no covariates): 0.06821 [0.06455, 0.07188] Incremental R2 (full-covars): 0.06859 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018950 | PGS003926 (INI30020) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.46462 [0.43806, 0.49117] PGS R2 (no covariates): 0.06272 [0.04564, 0.0798] Incremental R2 (full-covars): 0.06567 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018951 | PGS003926 (INI30020) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.43017 [0.39227, 0.46807] PGS R2 (no covariates): 0.02868 [0.012, 0.04536] Incremental R2 (full-covars): 0.03286 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018952 | PGS003926 (INI30020) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.40118 [0.35861, 0.44374] PGS R2 (no covariates): 0.01753 [0.00293, 0.03213] Incremental R2 (full-covars): 0.00674 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018953 | PGS003926 (INI30020) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hemoglobin concentration | — | — | R²: 0.43523 [0.41892, 0.45155] PGS R2 (no covariates): 0.04824 [0.03908, 0.05739] Incremental R2 (full-covars): 0.0422 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018954 | PGS003927 (INI30030) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hematocrit percentage | — | — | R²: 0.39954 [0.39382, 0.40526] PGS R2 (no covariates): 0.06736 [0.06371, 0.071] Incremental R2 (full-covars): 0.0668 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018955 | PGS003927 (INI30030) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hematocrit percentage | — | — | R²: 0.42556 [0.39828, 0.45283] PGS R2 (no covariates): 0.05792 [0.04142, 0.07443] Incremental R2 (full-covars): 0.0582 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018957 | PGS003927 (INI30030) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hematocrit percentage | — | — | R²: 0.4128 [0.37046, 0.45514] PGS R2 (no covariates): 0.0161 [0.00209, 0.0301] Incremental R2 (full-covars): 0.00702 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018958 | PGS003927 (INI30030) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hematocrit percentage | — | — | R²: 0.40359 [0.387, 0.42018] PGS R2 (no covariates): 0.04862 [0.03943, 0.05781] Incremental R2 (full-covars): 0.04508 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018956 | PGS003927 (INI30030) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Hematocrit percentage | — | — | R²: 0.42574 [0.38774, 0.46374] PGS R2 (no covariates): 0.03312 [0.01528, 0.05097] Incremental R2 (full-covars): 0.03573 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018959 | PGS003928 (INI30040) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.21483 [0.20935, 0.22031] PGS R2 (no covariates): 0.1996 [0.19422, 0.20499] Incremental R2 (full-covars): 0.19669 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018960 | PGS003928 (INI30040) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.21868 [0.19209, 0.24527] PGS R2 (no covariates): 0.19629 [0.17038, 0.22221] Incremental R2 (full-covars): 0.19227 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018961 | PGS003928 (INI30040) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.13118 [0.09927, 0.1631] PGS R2 (no covariates): 0.10078 [0.07183, 0.12973] Incremental R2 (full-covars): 0.09425 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018962 | PGS003928 (INI30040) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.07522 [0.04675, 0.10368] PGS R2 (no covariates): 0.05806 [0.03259, 0.08353] Incremental R2 (full-covars): 0.05248 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018963 | PGS003928 (INI30040) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular volume | — | — | R²: 0.18827 [0.17284, 0.20369] PGS R2 (no covariates): 0.14096 [0.12683, 0.15508] Incremental R2 (full-covars): 0.11206 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018964 | PGS003929 (INI30050) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.1843 [0.17903, 0.18957] PGS R2 (no covariates): 0.16693 [0.1618, 0.17205] Incremental R2 (full-covars): 0.16424 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018965 | PGS003929 (INI30050) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.20494 [0.17875, 0.23114] PGS R2 (no covariates): 0.18314 [0.1577, 0.20858] Incremental R2 (full-covars): 0.17698 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018966 | PGS003929 (INI30050) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.11596 [0.08543, 0.14649] PGS R2 (no covariates): 0.08633 [0.05911, 0.11356] Incremental R2 (full-covars): 0.08195 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018967 | PGS003929 (INI30050) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.07189 [0.04397, 0.09982] PGS R2 (no covariates): 0.05454 [0.02976, 0.07932] Incremental R2 (full-covars): 0.05029 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018968 | PGS003929 (INI30050) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin | — | — | R²: 0.17414 [0.15905, 0.18923] PGS R2 (no covariates): 0.1276 [0.11395, 0.14124] Incremental R2 (full-covars): 0.0916 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018969 | PGS003930 (INI30060) |
PSS011137| European Ancestry| 65,929 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.03988 [0.03699, 0.04277] PGS R2 (no covariates): 0.0253 [0.02296, 0.02763] Incremental R2 (full-covars): 0.02492 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018970 | PGS003930 (INI30060) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.0486 [0.03333, 0.06386] PGS R2 (no covariates): 0.03186 [0.01929, 0.04444] Incremental R2 (full-covars): 0.02908 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018971 | PGS003930 (INI30060) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.0252 [0.0095, 0.04089] PGS R2 (no covariates): 0.01719 [0.00412, 0.03026] Incremental R2 (full-covars): 0.01588 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018973 | PGS003930 (INI30060) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.04959 [0.04032, 0.05886] PGS R2 (no covariates): 0.02383 [0.01723, 0.03043] Incremental R2 (full-covars): 0.01421 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018972 | PGS003930 (INI30060) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean corpuscular hemoglobin concentration | — | — | R²: 0.01616 [0.00212, 0.03019] PGS R2 (no covariates): 0.00861 [-0.00171, 0.01894] Incremental R2 (full-covars): 0.00736 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018974 | PGS003931 (INI30070) |
PSS011136| European Ancestry| 65,932 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | R²: 0.09215 [0.088, 0.0963] PGS R2 (no covariates): 0.07947 [0.07556, 0.08338] Incremental R2 (full-covars): 0.07934 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018975 | PGS003931 (INI30070) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | R²: 0.10616 [0.08496, 0.12736] PGS R2 (no covariates): 0.08744 [0.06781, 0.10708] Incremental R2 (full-covars): 0.08479 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018976 | PGS003931 (INI30070) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | R²: 0.06648 [0.04207, 0.09088] PGS R2 (no covariates): 0.05249 [0.03048, 0.07451] Incremental R2 (full-covars): 0.05172 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018977 | PGS003931 (INI30070) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | R²: 0.0264 [0.00865, 0.04415] PGS R2 (no covariates): 0.01807 [0.00326, 0.03288] Incremental R2 (full-covars): 0.01826 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018978 | PGS003931 (INI30070) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | R²: 0.08776 [0.07593, 0.09959] PGS R2 (no covariates): 0.06743 [0.05683, 0.07804] Incremental R2 (full-covars): 0.06076 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018979 | PGS003932 (INI30080) |
PSS011145| European Ancestry| 65,931 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet count | — | — | R²: 0.26848 [0.26277, 0.27419] PGS R2 (no covariates): 0.20939 [0.20394, 0.21484] Incremental R2 (full-covars): 0.20822 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018980 | PGS003932 (INI30080) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet count | — | — | R²: 0.26562 [0.23807, 0.29316] PGS R2 (no covariates): 0.22463 [0.19789, 0.25138] Incremental R2 (full-covars): 0.22027 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018981 | PGS003932 (INI30080) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet count | — | — | R²: 0.23486 [0.19726, 0.27247] PGS R2 (no covariates): 0.15582 [0.12203, 0.18962] Incremental R2 (full-covars): 0.15006 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018982 | PGS003932 (INI30080) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet count | — | — | R²: 0.13941 [0.10335, 0.17547] PGS R2 (no covariates): 0.06653 [0.03951, 0.09355] Incremental R2 (full-covars): 0.0492 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018983 | PGS003932 (INI30080) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet count | — | — | R²: 0.24864 [0.23223, 0.26504] PGS R2 (no covariates): 0.19334 [0.17781, 0.20888] Incremental R2 (full-covars): 0.18573 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018984 | PGS003933 (INI30090) |
PSS011138| European Ancestry| 65,896 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet crit | — | — | R²: 0.25161 [0.24596, 0.25727] PGS R2 (no covariates): 0.16315 [0.15806, 0.16824] Incremental R2 (full-covars): 0.16228 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018985 | PGS003933 (INI30090) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet crit | — | — | R²: 0.24843 [0.22117, 0.27569] PGS R2 (no covariates): 0.17673 [0.15154, 0.20191] Incremental R2 (full-covars): 0.17323 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018986 | PGS003933 (INI30090) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet crit | — | — | R²: 0.24781 [0.20983, 0.28578] PGS R2 (no covariates): 0.11999 [0.08908, 0.1509] Incremental R2 (full-covars): 0.10886 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018987 | PGS003933 (INI30090) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet crit | — | — | R²: 0.1523 [0.11518, 0.18943] PGS R2 (no covariates): 0.04384 [0.02137, 0.06631] Incremental R2 (full-covars): 0.02917 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018988 | PGS003933 (INI30090) |
PSS011167| Multi-ancestry (excluding European)| 7,743 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet crit | — | — | R²: 0.22916 [0.213, 0.24532] PGS R2 (no covariates): 0.13828 [0.12425, 0.15231] Incremental R2 (full-covars): 0.13431 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018989 | PGS003934 (INI30100) |
PSS011144| European Ancestry| 65,930 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | R²: 0.36086 [0.35507, 0.36664] PGS R2 (no covariates): 0.35829 [0.35251, 0.36408] Incremental R2 (full-covars): 0.35783 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018990 | PGS003934 (INI30100) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | R²: 0.36427 [0.33635, 0.3922] PGS R2 (no covariates): 0.35942 [0.33147, 0.38737] Incremental R2 (full-covars): 0.35587 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018991 | PGS003934 (INI30100) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | R²: 0.2322 [0.19468, 0.26972] PGS R2 (no covariates): 0.23172 [0.19421, 0.26922] Incremental R2 (full-covars): 0.22951 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018993 | PGS003934 (INI30100) |
PSS011166| Multi-ancestry (excluding European)| 7,746 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | R²: 0.30944 [0.29261, 0.32626] PGS R2 (no covariates): 0.31539 [0.29855, 0.33222] Incremental R2 (full-covars): 0.2903 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018992 | PGS003934 (INI30100) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | R²: 0.10997 [0.07685, 0.1431] PGS R2 (no covariates): 0.10936 [0.0763, 0.14241] Incremental R2 (full-covars): 0.10916 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018994 | PGS003935 (INI30110) |
PSS011138| European Ancestry| 65,896 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet distribution width | — | — | R²: 0.23112 [0.22556, 0.23669] PGS R2 (no covariates): 0.20884 [0.20339, 0.21428] Incremental R2 (full-covars): 0.20956 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018996 | PGS003935 (INI30110) |
PSS011113| South Asian Ancestry| 1,433 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet distribution width | — | — | R²: 0.18373 [0.14825, 0.21922] PGS R2 (no covariates): 0.16615 [0.13168, 0.20061] Incremental R2 (full-covars): 0.17042 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018998 | PGS003935 (INI30110) |
PSS011167| Multi-ancestry (excluding European)| 7,743 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet distribution width | — | — | R²: 0.22441 [0.20832, 0.2405] PGS R2 (no covariates): 0.19806 [0.18243, 0.21368] Incremental R2 (full-covars): 0.17896 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018995 | PGS003935 (INI30110) |
PSS011109| European Ancestry| 2,813 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet distribution width | — | — | R²: 0.20802 [0.18174, 0.23431] PGS R2 (no covariates): 0.19878 [0.17279, 0.22478] Incremental R2 (full-covars): 0.19816 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018997 | PGS003935 (INI30110) |
PSS011155| African Ancestry| 1,157 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Platelet distribution width | — | — | R²: 0.0902 [0.05954, 0.12087] PGS R2 (no covariates): 0.06578 [0.03889, 0.09267] Incremental R2 (full-covars): 0.05799 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM018999 | PGS003936 (INI30120) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte count | — | — | R²: 0.02749 [0.02507, 0.02992] PGS R2 (no covariates): 0.02217 [0.01997, 0.02436] Incremental R2 (full-covars): 0.02189 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019000 | PGS003936 (INI30120) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte count | — | — | R²: 0.03382 [0.02089, 0.04675] PGS R2 (no covariates): 0.02699 [0.01535, 0.03862] Incremental R2 (full-covars): 0.02644 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019001 | PGS003936 (INI30120) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte count | — | — | R²: 0.05901 [0.03583, 0.08219] PGS R2 (no covariates): 0.04232 [0.02234, 0.0623] Incremental R2 (full-covars): 0.03929 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019002 | PGS003936 (INI30120) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte count | — | — | R²: 0.01861 [0.00358, 0.03363] PGS R2 (no covariates): 0.00551 [-0.00278, 0.01379] Incremental R2 (full-covars): -0.00495 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019003 | PGS003936 (INI30120) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte count | — | — | R²: 0.04696 [0.03792, 0.05601] PGS R2 (no covariates): 0.02897 [0.02173, 0.0362] Incremental R2 (full-covars): 0.02614 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019004 | PGS003937 (INI30130) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte count | — | — | R²: 0.12426 [0.11961, 0.12891] PGS R2 (no covariates): 0.08189 [0.07793, 0.08585] Incremental R2 (full-covars): 0.08198 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019005 | PGS003937 (INI30130) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte count | — | — | R²: 0.14509 [0.12139, 0.16879] PGS R2 (no covariates): 0.0898 [0.06995, 0.10965] Incremental R2 (full-covars): 0.08851 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019006 | PGS003937 (INI30130) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte count | — | — | R²: 0.11251 [0.08232, 0.1427] PGS R2 (no covariates): 0.07028 [0.04528, 0.09527] Incremental R2 (full-covars): 0.06806 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019007 | PGS003937 (INI30130) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte count | — | — | R²: 0.0334 [0.01357, 0.05322] PGS R2 (no covariates): 0.03342 [0.01359, 0.05325] Incremental R2 (full-covars): 0.02807 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019008 | PGS003937 (INI30130) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte count | — | — | R²: 0.11068 [0.09772, 0.12363] PGS R2 (no covariates): 0.07932 [0.06797, 0.09067] Incremental R2 (full-covars): 0.06798 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019009 | PGS003938 (INI30140) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil count | — | — | R²: 0.10887 [0.10444, 0.1133] PGS R2 (no covariates): 0.09021 [0.0861, 0.09433] Incremental R2 (full-covars): 0.08972 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019010 | PGS003938 (INI30140) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil count | — | — | R²: 0.11328 [0.09156, 0.13501] PGS R2 (no covariates): 0.09258 [0.07249, 0.11268] Incremental R2 (full-covars): 0.09167 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019011 | PGS003938 (INI30140) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil count | — | — | R²: 0.0886 [0.06109, 0.11611] PGS R2 (no covariates): 0.08009 [0.05369, 0.10649] Incremental R2 (full-covars): 0.07779 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019012 | PGS003938 (INI30140) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil count | — | — | R²: 0.07013 [0.0425, 0.09777] PGS R2 (no covariates): 0.05672 [0.03151, 0.08193] Incremental R2 (full-covars): 0.05374 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019013 | PGS003938 (INI30140) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil count | — | — | R²: 0.10909 [0.0962, 0.12197] PGS R2 (no covariates): 0.09632 [0.08404, 0.1086] Incremental R2 (full-covars): 0.07245 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019014 | PGS003939 (INI30150) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil count | — | — | R²: 0.10132 [0.09701, 0.10563] PGS R2 (no covariates): 0.09268 [0.08852, 0.09684] Incremental R2 (full-covars): 0.0925 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019015 | PGS003939 (INI30150) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil count | — | — | R²: 0.08417 [0.06484, 0.10351] PGS R2 (no covariates): 0.0719 [0.05379, 0.09001] Incremental R2 (full-covars): 0.07156 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019016 | PGS003939 (INI30150) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil count | — | — | R²: 0.05308 [0.03095, 0.0752] PGS R2 (no covariates): 0.048 [0.02685, 0.06916] Incremental R2 (full-covars): 0.04773 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019017 | PGS003939 (INI30150) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil count | — | — | R²: 0.01311 [0.00043, 0.0258] PGS R2 (no covariates): 0.00652 [-0.00248, 0.01551] Incremental R2 (full-covars): 0.00319 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019018 | PGS003939 (INI30150) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil count | — | — | R²: 0.09274 [0.08064, 0.10484] PGS R2 (no covariates): 0.06898 [0.05827, 0.07969] Incremental R2 (full-covars): 0.06441 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019019 | PGS003940 (INI30160) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil count | — | — | R²: 0.01943 [0.01737, 0.02149] PGS R2 (no covariates): 0.01516 [0.01334, 0.01699] Incremental R2 (full-covars): 0.01498 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019021 | PGS003940 (INI30160) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil count | — | — | R²: 0.00683 [-0.00149, 0.01515] PGS R2 (no covariates): 0.00551 [-0.00198, 0.013] Incremental R2 (full-covars): 0.00504 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019020 | PGS003940 (INI30160) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil count | — | — | R²: 0.02167 [0.01119, 0.03216] PGS R2 (no covariates): 0.01847 [0.00876, 0.02818] Incremental R2 (full-covars): 0.01766 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019022 | PGS003940 (INI30160) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil count | — | — | R²: 0.00981 [-0.00119, 0.02082] PGS R2 (no covariates): 0.00446 [-0.003, 0.01192] Incremental R2 (full-covars): 0.00447 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019023 | PGS003940 (INI30160) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil count | — | — | R²: 0.02131 [0.01506, 0.02757] PGS R2 (no covariates): 0.01354 [0.00851, 0.01856] Incremental R2 (full-covars): 0.01229 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019024 | PGS003941 (INI30180) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte percentage | — | — | R²: 0.12323 [0.1186, 0.12787] PGS R2 (no covariates): 0.09003 [0.08592, 0.09414] Incremental R2 (full-covars): 0.09003 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019025 | PGS003941 (INI30180) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte percentage | — | — | R²: 0.11201 [0.09038, 0.13364] PGS R2 (no covariates): 0.08186 [0.06274, 0.10098] Incremental R2 (full-covars): 0.08335 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019026 | PGS003941 (INI30180) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte percentage | — | — | R²: 0.07411 [0.04855, 0.09967] PGS R2 (no covariates): 0.05351 [0.03131, 0.07571] Incremental R2 (full-covars): 0.05293 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019027 | PGS003941 (INI30180) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte percentage | — | — | R²: 0.06881 [0.0414, 0.09623] PGS R2 (no covariates): 0.0468 [0.02366, 0.06995] Incremental R2 (full-covars): 0.04285 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019028 | PGS003941 (INI30180) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Lymphocyte percentage | — | — | R²: 0.15346 [0.13894, 0.16798] PGS R2 (no covariates): 0.11132 [0.09834, 0.12431] Incremental R2 (full-covars): 0.07958 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019029 | PGS003942 (INI30190) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte percentage | — | — | R²: 0.1281 [0.1234, 0.1328] PGS R2 (no covariates): 0.08549 [0.08146, 0.08952] Incremental R2 (full-covars): 0.0852 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019030 | PGS003942 (INI30190) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte percentage | — | — | R²: 0.1222 [0.09987, 0.14453] PGS R2 (no covariates): 0.08548 [0.06603, 0.10494] Incremental R2 (full-covars): 0.08222 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019031 | PGS003942 (INI30190) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte percentage | — | — | R²: 0.15234 [0.11879, 0.18589] PGS R2 (no covariates): 0.07059 [0.04555, 0.09563] Incremental R2 (full-covars): 0.06921 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019032 | PGS003942 (INI30190) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte percentage | — | — | R²: 0.03382 [0.01388, 0.05376] PGS R2 (no covariates): 0.01755 [0.00294, 0.03215] Incremental R2 (full-covars): 0.01822 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019033 | PGS003942 (INI30190) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Monocyte percentage | — | — | R²: 0.08833 [0.07647, 0.1002] PGS R2 (no covariates): 0.0562 [0.0464, 0.06599] Incremental R2 (full-covars): 0.04903 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019034 | PGS003943 (INI30200) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil percentage | — | — | R²: 0.0952 [0.091, 0.09941] PGS R2 (no covariates): 0.08263 [0.07866, 0.0866] Incremental R2 (full-covars): 0.08285 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019035 | PGS003943 (INI30200) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil percentage | — | — | R²: 0.08975 [0.0699, 0.10959] PGS R2 (no covariates): 0.08096 [0.06193, 0.09999] Incremental R2 (full-covars): 0.08129 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019037 | PGS003943 (INI30200) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil percentage | — | — | R²: 0.06709 [0.03997, 0.09421] PGS R2 (no covariates): 0.04252 [0.02036, 0.06467] Incremental R2 (full-covars): 0.03641 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019038 | PGS003943 (INI30200) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil percentage | — | — | R²: 0.1212 [0.1078, 0.1346] PGS R2 (no covariates): 0.09346 [0.08132, 0.10559] Incremental R2 (full-covars): 0.06663 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019036 | PGS003943 (INI30200) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Neutrophil percentage | — | — | R²: 0.05999 [0.03664, 0.08334] PGS R2 (no covariates): 0.04709 [0.02612, 0.06807] Incremental R2 (full-covars): 0.04754 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019039 | PGS003944 (INI30210) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil percentage | — | — | R²: 0.10224 [0.09792, 0.10657] PGS R2 (no covariates): 0.09455 [0.09036, 0.09874] Incremental R2 (full-covars): 0.09429 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019040 | PGS003944 (INI30210) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil percentage | — | — | R²: 0.06945 [0.0516, 0.0873] PGS R2 (no covariates): 0.05892 [0.04229, 0.07554] Incremental R2 (full-covars): 0.05918 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019041 | PGS003944 (INI30210) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil percentage | — | — | R²: 0.07575 [0.04995, 0.10155] PGS R2 (no covariates): 0.06543 [0.04119, 0.08968] Incremental R2 (full-covars): 0.06438 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019042 | PGS003944 (INI30210) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil percentage | — | — | R²: 0.01699 [0.00261, 0.03136] PGS R2 (no covariates): 0.00576 [-0.00271, 0.01422] Incremental R2 (full-covars): -0.00348 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019043 | PGS003944 (INI30210) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Eosinophil percentage | — | — | R²: 0.09676 [0.08445, 0.10906] PGS R2 (no covariates): 0.07585 [0.0647, 0.08699] Incremental R2 (full-covars): 0.07466 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019044 | PGS003945 (INI30220) |
PSS011139| European Ancestry| 65,814 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil percentage | — | — | R²: 0.01773 [0.01576, 0.01971] PGS R2 (no covariates): 0.01574 [0.01388, 0.0176] Incremental R2 (full-covars): 0.01546 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019046 | PGS003945 (INI30220) |
PSS011112| South Asian Ancestry| 1,428 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil percentage | — | — | R²: 0.00464 [-0.00224, 0.01152] PGS R2 (no covariates): 0.00472 [-0.00221, 0.01166] Incremental R2 (full-covars): 0.00423 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019047 | PGS003945 (INI30220) |
PSS011154| African Ancestry| 1,154 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil percentage | — | — | R²: 0.00436 [-0.00302, 0.01174] PGS R2 (no covariates): 0.002 [-0.00301, 0.007] Incremental R2 (full-covars): 0.00188 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019048 | PGS003945 (INI30220) |
PSS011168| Multi-ancestry (excluding European)| 7,732 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil percentage | — | — | R²: 0.02293 [0.01645, 0.02941] PGS R2 (no covariates): 0.01249 [0.00766, 0.01732] Incremental R2 (full-covars): 0.01169 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019045 | PGS003945 (INI30220) |
PSS011110| European Ancestry| 2,810 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Basophil percentage | — | — | R²: 0.02094 [0.01063, 0.03125] PGS R2 (no covariates): 0.01757 [0.00809, 0.02705] Incremental R2 (full-covars): 0.01716 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019049 | PGS003946 (INI30240) |
PSS011141| European Ancestry| 64,860 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte percentage | — | — | R²: 0.0298 [0.02728, 0.03233] PGS R2 (no covariates): 0.02838 [0.02592, 0.03085] Incremental R2 (full-covars): 0.02829 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019050 | PGS003946 (INI30240) |
PSS011099| European Ancestry| 2,770 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte percentage | — | — | R²: 0.07465 [0.05625, 0.09305] PGS R2 (no covariates): 0.06854 [0.05079, 0.08629] Incremental R2 (full-covars): 0.06923 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019051 | PGS003946 (INI30240) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte percentage | — | — | R²: 0.08898 [0.06142, 0.11654] PGS R2 (no covariates): 0.08201 [0.05535, 0.10867] Incremental R2 (full-covars): 0.07867 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019052 | PGS003946 (INI30240) |
PSS011153| African Ancestry| 1,125 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte percentage | — | — | R²: 0.01247 [0.00009, 0.02484] PGS R2 (no covariates): 0.01579 [0.00191, 0.02967] Incremental R2 (full-covars): 0.012 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019053 | PGS003946 (INI30240) |
PSS011169| Multi-ancestry (excluding European)| 7,578 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte percentage | — | — | R²: 0.04153 [0.03298, 0.05009] PGS R2 (no covariates): 0.03618 [0.02815, 0.04421] Incremental R2 (full-covars): 0.03446 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019054 | PGS003947 (INI30250) |
PSS011140| European Ancestry| 64,861 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte count | — | — | R²: 0.0537 [0.0504, 0.057] PGS R2 (no covariates): 0.04073 [0.03782, 0.04365] Incremental R2 (full-covars): 0.04072 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019056 | PGS003947 (INI30250) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte count | — | — | R²: 0.09735 [0.06879, 0.12592] PGS R2 (no covariates): 0.07127 [0.04612, 0.09641] Incremental R2 (full-covars): 0.06906 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019057 | PGS003947 (INI30250) |
PSS011153| African Ancestry| 1,125 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte count | — | — | R²: 0.02713 [0.00914, 0.04511] PGS R2 (no covariates): 0.01461 [0.00124, 0.02797] Incremental R2 (full-covars): 0.01527 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019058 | PGS003947 (INI30250) |
PSS011169| Multi-ancestry (excluding European)| 7,578 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte count | — | — | R²: 0.05468 [0.045, 0.06436] PGS R2 (no covariates): 0.03553 [0.02757, 0.04349] Incremental R2 (full-covars): 0.03303 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019055 | PGS003947 (INI30250) |
PSS011099| European Ancestry| 2,770 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Reticulocyte count | — | — | R²: 0.10144 [0.08061, 0.12226] PGS R2 (no covariates): 0.07084 [0.05284, 0.08884] Incremental R2 (full-covars): 0.07225 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019059 | PGS003948 (INI30260) |
PSS011140| European Ancestry| 64,861 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.16087 [0.1558, 0.16594] PGS R2 (no covariates): 0.14603 [0.14111, 0.15095] Incremental R2 (full-covars): 0.14515 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019060 | PGS003948 (INI30260) |
PSS011099| European Ancestry| 2,770 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.17217 [0.14717, 0.19717] PGS R2 (no covariates): 0.14414 [0.1205, 0.16779] Incremental R2 (full-covars): 0.14281 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019061 | PGS003948 (INI30260) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.13781 [0.10535, 0.17027] PGS R2 (no covariates): 0.11019 [0.08024, 0.14015] Incremental R2 (full-covars): 0.10391 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019062 | PGS003948 (INI30260) |
PSS011152| African Ancestry| 1,124 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.03874 [0.01751, 0.05997] PGS R2 (no covariates): 0.03349 [0.01364, 0.05333] Incremental R2 (full-covars): 0.033 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019063 | PGS003948 (INI30260) |
PSS011169| Multi-ancestry (excluding European)| 7,578 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean reticulocyte volume | — | — | R²: 0.15509 [0.14052, 0.16966] PGS R2 (no covariates): 0.13046 [0.11671, 0.14422] Incremental R2 (full-covars): 0.11887 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019064 | PGS003949 (INI30270) |
PSS011143| European Ancestry| 64,816 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean sphered cell volume | — | — | R²: 0.16403 [0.15893, 0.16913] PGS R2 (no covariates): 0.15331 [0.14832, 0.15831] Incremental R2 (full-covars): 0.15179 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019065 | PGS003949 (INI30270) |
PSS011100| European Ancestry| 2,768 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean sphered cell volume | — | — | R²: 0.17891 [0.15363, 0.20418] PGS R2 (no covariates): 0.15547 [0.13123, 0.1797] Incremental R2 (full-covars): 0.15478 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019066 | PGS003949 (INI30270) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean sphered cell volume | — | — | R²: 0.11613 [0.08558, 0.14667] PGS R2 (no covariates): 0.10275 [0.07358, 0.13192] Incremental R2 (full-covars): 0.09928 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019067 | PGS003949 (INI30270) |
PSS011153| African Ancestry| 1,125 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean sphered cell volume | — | — | R²: 0.03373 [0.01382, 0.05365] PGS R2 (no covariates): 0.03281 [0.01315, 0.05248] Incremental R2 (full-covars): 0.03191 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019068 | PGS003949 (INI30270) |
PSS011170| Multi-ancestry (excluding European)| 7,574 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Mean sphered cell volume | — | — | R²: 0.16365 [0.14884, 0.17847] PGS R2 (no covariates): 0.13716 [0.12317, 0.15116] Incremental R2 (full-covars): 0.12494 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019069 | PGS003950 (INI30280) |
PSS011143| European Ancestry| 64,816 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.10325 [0.09891, 0.10759] PGS R2 (no covariates): 0.09279 [0.08863, 0.09695] Incremental R2 (full-covars): 0.09226 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019070 | PGS003950 (INI30280) |
PSS011100| European Ancestry| 2,768 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.09801 [0.07746, 0.11857] PGS R2 (no covariates): 0.09403 [0.07381, 0.11425] Incremental R2 (full-covars): 0.09203 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019071 | PGS003950 (INI30280) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.06095 [0.03744, 0.08446] PGS R2 (no covariates): 0.05417 [0.03184, 0.07649] Incremental R2 (full-covars): 0.05046 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019072 | PGS003950 (INI30280) |
PSS011152| African Ancestry| 1,124 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.04778 [0.02442, 0.07113] PGS R2 (no covariates): 0.03953 [0.0181, 0.06096] Incremental R2 (full-covars): 0.0407 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019073 | PGS003950 (INI30280) |
PSS011170| Multi-ancestry (excluding European)| 7,574 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: Immature reticulocyte fraction | — | — | R²: 0.10912 [0.09624, 0.12201] PGS R2 (no covariates): 0.08182 [0.07032, 0.09332] Incremental R2 (full-covars): 0.08036 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019075 | PGS003951 (INI30290) |
PSS011100| European Ancestry| 2,768 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte percentage | — | — | R²: 0.10452 [0.08345, 0.12558] PGS R2 (no covariates): 0.09885 [0.07823, 0.11947] Incremental R2 (full-covars): 0.09847 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019076 | PGS003951 (INI30290) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte percentage | — | — | R²: 0.09744 [0.06887, 0.12601] PGS R2 (no covariates): 0.08757 [0.06019, 0.11496] Incremental R2 (full-covars): 0.08129 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019077 | PGS003951 (INI30290) |
PSS011153| African Ancestry| 1,125 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte percentage | — | — | R²: 0.02997 [0.01112, 0.04881] PGS R2 (no covariates): 0.03101 [0.01186, 0.05016] Incremental R2 (full-covars): 0.0295 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019078 | PGS003951 (INI30290) |
PSS011170| Multi-ancestry (excluding European)| 7,574 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte percentage | — | — | R²: 0.09491 [0.0827, 0.10712] PGS R2 (no covariates): 0.08288 [0.07132, 0.09444] Incremental R2 (full-covars): 0.07762 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019074 | PGS003951 (INI30290) |
PSS011143| European Ancestry| 64,816 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte percentage | — | — | R²: 0.01834 [0.01634, 0.02034] PGS R2 (no covariates): 0.01717 [0.01523, 0.01911] Incremental R2 (full-covars): 0.01712 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019079 | PGS003952 (INI30300) |
PSS011143| European Ancestry| 64,816 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.13075 [0.12601, 0.13548] PGS R2 (no covariates): 0.11335 [0.10885, 0.11784] Incremental R2 (full-covars): 0.11327 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019080 | PGS003952 (INI30300) |
PSS011100| European Ancestry| 2,768 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.14122 [0.11773, 0.16471] PGS R2 (no covariates): 0.12176 [0.09946, 0.14407] Incremental R2 (full-covars): 0.12235 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019081 | PGS003952 (INI30300) |
PSS011111| South Asian Ancestry| 1,393 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.11255 [0.08236, 0.14275] PGS R2 (no covariates): 0.0909 [0.0631, 0.1187] Incremental R2 (full-covars): 0.08654 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019082 | PGS003952 (INI30300) |
PSS011153| African Ancestry| 1,125 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.04777 [0.02441, 0.07112] PGS R2 (no covariates): 0.04026 [0.01865, 0.06188] Incremental R2 (full-covars): 0.04027 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019083 | PGS003952 (INI30300) |
PSS011170| Multi-ancestry (excluding European)| 7,574 individuals |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Reported Trait: High light scatter reticulocyte count | — | — | R²: 0.12479 [0.11125, 0.13833] PGS R2 (no covariates): 0.09343 [0.0813, 0.10557] Incremental R2 (full-covars): 0.08525 |
age, sex, age^2, age*sex, Townsend deprivation index, genotype PCs (PC1-PC18) | — |
| PPM019653 | PGS003987 (dbslmm.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.0202 [0.00297807, 0.03742271] | — | R²: 0.00041 [0.00000887, 0.00140046] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019654 | PGS003987 (dbslmm.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.1522 [0.14172414, 0.1626768] | — | R²: 0.02316 [0.02008573, 0.02646374] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019655 | PGS003987 (dbslmm.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.14804 [0.12730836, 0.16876192] | — | R²: 0.02191 [0.01620742, 0.02848058] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019656 | PGS003987 (dbslmm.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.18031 [0.17393503, 0.18668242] | — | R²: 0.0345 [0.03209972, 0.0369772] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019681 | PGS004003 (lassosum.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.01939 [0.00216552, 0.03661071] | — | R²: 0.00038 [0.00000469, 0.00134034] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019682 | PGS004003 (lassosum.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.15659 [0.14612563, 0.16706373] | — | R²: 0.02452 [0.0213527, 0.02791029] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019683 | PGS004003 (lassosum.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.1469 [0.12616468, 0.16762536] | — | R²: 0.02158 [0.01591753, 0.02809826] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019684 | PGS004003 (lassosum.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.18516 [0.17877662, 0.19153954] | — | R²: 0.03622 [0.03376864, 0.03876225] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019665 | PGS004029 (ldpred2.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.03102 [0.0137992, 0.0482343] | — | R²: 0.00096 [0.00019, 0.00232655] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019666 | PGS004029 (ldpred2.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.11223 [0.10170135, 0.12276705] | — | R²: 0.0126 [0.01034316, 0.01507175] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019667 | PGS004029 (ldpred2.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.10588 [0.08503512, 0.12671491] | — | R²: 0.01121 [0.00723097, 0.01605667] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019668 | PGS004029 (ldpred2.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.13529 [0.12882048, 0.14176166] | — | R²: 0.01914 [0.01735574, 0.02101798] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019641 | PGS004044 (ldpred2.CV.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.02666 [0.00943679, 0.04387621] | — | R²: 0.00071 [0.0000891, 0.00192512] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019642 | PGS004044 (ldpred2.CV.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.16248 [0.1520189, 0.17293684] | — | R²: 0.0264 [0.02310974, 0.02990715] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019643 | PGS004044 (ldpred2.CV.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.15314 [0.13242664, 0.17384762] | — | R²: 0.02345 [0.01753681, 0.030223] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019644 | PGS004044 (ldpred2.CV.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.19178 [0.18540806, 0.19815508] | — | R²: 0.03885 [0.03631164, 0.04147621] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019673 | PGS004057 (megaprs.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.02167 [0.00444446, 0.03888804] | — | R²: 0.00047 [0.0000198, 0.00151228] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019674 | PGS004057 (megaprs.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.16342 [0.15296397, 0.17387862] | — | R²: 0.02671 [0.02339798, 0.03023377] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019675 | PGS004057 (megaprs.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.16331 [0.1426289, 0.18398159] | — | R²: 0.02667 [0.020343, 0.03384922] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019676 | PGS004057 (megaprs.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.18961 [0.18324065, 0.1959698] | — | R²: 0.03811 [0.03559471, 0.04071179] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019669 | PGS004073 (megaprs.CV.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.02487 [0.00764872, 0.04208973] | — | R²: 0.00062 [0.0000585, 0.00177155] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019670 | PGS004073 (megaprs.CV.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.16593 [0.15548016, 0.17638591] | — | R²: 0.02753 [0.02417408, 0.03111199] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019671 | PGS004073 (megaprs.CV.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.16274 [0.14205739, 0.18341402] | — | R²: 0.02648 [0.0201803, 0.0336407] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019672 | PGS004073 (megaprs.CV.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.19138 [0.18502304, 0.19774227] | — | R²: 0.03886 [0.03631892, 0.04148397] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019677 | PGS004087 (prscs.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.02279 [0.00556903, 0.04001175] | — | R²: 0.00052 [0.000031, 0.00160094] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019678 | PGS004087 (prscs.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.1563 [0.14582935, 0.16676845] | — | R²: 0.02443 [0.0212662, 0.02781172] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019679 | PGS004087 (prscs.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.15808 [0.13738112, 0.17876949] | — | R²: 0.02499 [0.01887357, 0.03195853] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019680 | PGS004087 (prscs.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.17698 [0.17058817, 0.18336594] | — | R²: 0.03312 [0.03077336, 0.03555613] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019645 | PGS004111 (pt_clump.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.00455 [-0.0126804, 0.0217709] | — | R²: 2e-05 [0.0, 0.000474] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019646 | PGS004111 (pt_clump.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.1122 [0.10166953, 0.12273531] | — | R²: 0.01259 [0.01033669, 0.01506396] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019647 | PGS004111 (pt_clump.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.07796 [0.05706538, 0.09885319] | — | R²: 0.00608 [0.00325646, 0.00977195] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019648 | PGS004111 (pt_clump.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.13494 [0.12849111, 0.14139339] | — | R²: 0.01916 [0.01737109, 0.02103483] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019649 | PGS004127 (pt_clump_nested.CV.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.02027 [0.00304789, 0.03749248] | — | R²: 0.00041 [0.00000929, 0.00140569] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019650 | PGS004127 (pt_clump_nested.CV.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.13414 [0.12363329, 0.14464134] | — | R²: 0.01799 [0.01528519, 0.02092112] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019652 | PGS004127 (pt_clump_nested.CV.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.14619 [0.13975924, 0.15261188] | — | R²: 0.02258 [0.02063827, 0.02460871] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019651 | PGS004127 (pt_clump_nested.CV.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.12036 [0.09955043, 0.14116111] | — | R²: 0.01449 [0.00991029, 0.01992646] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019661 | PGS004141 (sbayesr.auto.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.02812 [0.01089907, 0.04533712] | — | R²: 0.00079 [0.000119, 0.00205545] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019662 | PGS004141 (sbayesr.auto.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.15673 [0.14625874, 0.16719639] | — | R²: 0.02456 [0.02139162, 0.02795463] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019663 | PGS004141 (sbayesr.auto.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.14236 [0.12161776, 0.16310622] | — | R²: 0.02027 [0.01479088, 0.02660364] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019664 | PGS004141 (sbayesr.auto.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.18408 [0.17769293, 0.19046359] | — | R²: 0.03577 [0.03333553, 0.03829932] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019657 | PGS004157 (UKBB_EnsPGS.GCST007954.HbA1c) |
PSS011242| South Asian Ancestry| 12,948 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.035 [0.01778701, 0.05221757] | — | R²: 0.00123 [0.000316, 0.00272667] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019658 | PGS004157 (UKBB_EnsPGS.GCST007954.HbA1c) |
PSS011258| European Ancestry| 34,192 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.17514 [0.16470577, 0.18557774] | — | R²: 0.03067 [0.02712799, 0.0344391] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019659 | PGS004157 (UKBB_EnsPGS.GCST007954.HbA1c) |
PSS011286| South Asian Ancestry| 8,748 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.16507 [0.14440242, 0.18574277] | — | R²: 0.02725 [0.02085206, 0.03450038] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM019660 | PGS004157 (UKBB_EnsPGS.GCST007954.HbA1c) |
PSS011272| European Ancestry| 86,050 individuals |
PGP000517 | Monti R et al. Am J Hum Genet (2024) |
Reported Trait: Haemoglobin A1C | β: 0.20758 [0.20123038, 0.21391987] | — | R²: 0.0456 [0.04285908, 0.04843486] | 0 | beta = sd_trait/sd_pgs = pearson correlation |
| PPM020452 | PGS004337 (X30750.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Glycated haemoglobin (mmol/mol) | — | — | PGS R2 (no covariates): 0.26245 | — | — |
| PPM020456 | PGS004341 (X30860.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Total protein (g/L) | — | — | PGS R2 (no covariates): 0.21416 | — | — |
| PPM020460 | PGS004345 (X30000.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: White blood cell (leukocyte) count | — | — | PGS R2 (no covariates): 0.24739 | — | — |
| PPM020461 | PGS004346 (X30010.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Red blood cell (erythrocyte) count | — | — | PGS R2 (no covariates): 0.24747 | — | — |
| PPM020462 | PGS004347 (X30020.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Haemoglobin concentration | — | — | PGS R2 (no covariates): 0.19005 | — | — |
| PPM020463 | PGS004348 (X30030.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Haematocrit percentage | — | — | PGS R2 (no covariates): 0.18557 | — | — |
| PPM020464 | PGS004349 (X30040.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Mean corpuscular volume | — | — | PGS R2 (no covariates): 0.31845 | — | — |
| PPM020465 | PGS004350 (X30050.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Mean corpuscular haemoglobin | — | — | PGS R2 (no covariates): 0.31917 | — | — |
| PPM020466 | PGS004351 (X30070.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Red blood cell (erythrocyte) distribution width | — | — | PGS R2 (no covariates): 0.26452 | — | — |
| PPM020467 | PGS004352 (X30080.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Platelet count | — | — | PGS R2 (no covariates): 0.32753 | — | — |
| PPM020468 | PGS004353 (X30090.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Platelet crit | — | — | PGS R2 (no covariates): 0.29084 | — | — |
| PPM020469 | PGS004354 (X30100.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Mean platelet (thrombocyte) volume | — | — | PGS R2 (no covariates): 0.39954 | — | — |
| PPM020470 | PGS004355 (X30110.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Platelet distribution width | — | — | PGS R2 (no covariates): 0.28434 | — | — |
| PPM020471 | PGS004356 (X30120.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Lymphocyte count | — | — | PGS R2 (no covariates): 0.25007 | — | — |
| PPM020472 | PGS004357 (X30130.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Monocyte count | — | — | PGS R2 (no covariates): 0.26078 | — | — |
| PPM020473 | PGS004358 (X30140.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Neutrophill count | — | — | PGS R2 (no covariates): 0.22972 | — | — |
| PPM020474 | PGS004359 (X30180.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Lymphocyte percentage | — | — | PGS R2 (no covariates): 0.22005 | — | — |
| PPM020475 | PGS004360 (X30190.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Monocyte percentage | — | — | PGS R2 (no covariates): 0.24644 | — | — |
| PPM020476 | PGS004361 (X30200.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Neutrophill percentage | — | — | PGS R2 (no covariates): 0.20984 | — | — |
| PPM020477 | PGS004362 (X30210.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Eosinophill percentage | — | — | PGS R2 (no covariates): 0.25942 | — | — |
| PPM020478 | PGS004363 (X30240.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Reticulocyte percentage | — | — | PGS R2 (no covariates): 0.26292 | — | — |
| PPM020479 | PGS004364 (X30250.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Reticulocyte count | — | — | PGS R2 (no covariates): 0.26266 | — | — |
| PPM020480 | PGS004365 (X30260.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Mean reticulocyte volume | — | — | PGS R2 (no covariates): 0.26343 | — | — |
| PPM020481 | PGS004366 (X30270.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Mean sphered cell volume | — | — | PGS R2 (no covariates): 0.27321 | — | — |
| PPM020482 | PGS004367 (X30280.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Immature reticulocyte fraction | — | — | PGS R2 (no covariates): 0.20473 | — | — |
| PPM020483 | PGS004368 (X30290.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: High light scatter reticulocyte percentage | — | — | PGS R2 (no covariates): 0.27287 | — | — |
| PPM020484 | PGS004369 (X30300.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: High light scatter reticulocyte count | — | — | PGS R2 (no covariates): 0.27177 | — | — |
| PPM020697 | PGS004582 (PRSice_T1) |
PSS011369| East Asian Ancestry| 818 individuals |
PGP000563 | Yang Z et al. Blood (2023) |
Reported Trait: Gestational Thrombocytopenia | — | AUROC: 0.617 | R²: 0.02 | — | — |
| PPM020698 | PGS004583 (PRSice_T2) |
PSS011370| East Asian Ancestry| 878 individuals |
PGP000563 | Yang Z et al. Blood (2023) |
Reported Trait: Gestational Thrombocytopenia | — | AUROC: 0.637 | R²: 0.035 | — | — |
| PPM020699 | PGS004584 (PRSice_T3) |
PSS011371| East Asian Ancestry| 615 individuals |
PGP000563 | Yang Z et al. Blood (2023) |
Reported Trait: Gestational Thrombocytopenia | — | AUROC: 0.637 | R²: 0.057 | — | — |
| PPM020926 | PGS004701 (a1c_PRSmix_eur) |
PSS011478| European Ancestry| 1,543 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: A1c | — | — | Incremental R2 (Full model versus model with only covariates): 0.068 [0.044, 0.093] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020927 | PGS004702 (a1c_PRSmix_sas) |
PSS011479| South Asian Ancestry| 6,414 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: A1c | — | — | Incremental R2 (Full model versus model with only covariates): 0.053 [0.042, 0.063] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020928 | PGS004703 (a1c_PRSmixPlus_eur) |
PSS011478| European Ancestry| 1,543 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: A1c | — | — | Incremental R2 (Full model versus model with only covariates): 0.1 [0.071, 0.129] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020929 | PGS004704 (a1c_PRSmixPlus_sas) |
PSS011479| South Asian Ancestry| 6,414 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: A1c | — | — | Incremental R2 (Full model versus model with only covariates): 0.071 [0.059, 0.084] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020952 | PGS004727 (Basophils_PRSmix_eur) |
PSS011482| European Ancestry| 3,385 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Basophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.014 [0.006, 0.022] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020953 | PGS004728 (Basophils_PRSmix_sas) |
PSS011463| South Asian Ancestry| 7,096 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Basophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.018 [0.012, 0.024] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020954 | PGS004729 (Basophils_PRSmixPlus_eur) |
PSS011482| European Ancestry| 3,385 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Basophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.023 [0.013, 0.033] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020955 | PGS004730 (Basophils_PRSmixPlus_sas) |
PSS011463| South Asian Ancestry| 7,096 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Basophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.03 [0.022, 0.038] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020986 | PGS004761 (Eosinophils_PRSmix_eur) |
PSS011492| European Ancestry| 3,464 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.097 [0.078, 0.116] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020987 | PGS004762 (Eosinophils_PRSmix_sas) |
PSS011464| South Asian Ancestry| 7,056 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.091 [0.078, 0.104] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020988 | PGS004763 (Eosinophils_PRSmixPlus_eur) |
PSS011492| European Ancestry| 3,464 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.103 [0.083, 0.122] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020989 | PGS004764 (Eosinophils_PRSmixPlus_sas) |
PSS011464| South Asian Ancestry| 7,056 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Eosinophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.098 [0.085, 0.112] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020994 | PGS004769 (Hb_PRSmix_eur) |
PSS011466| European Ancestry| 5,325 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Hemoglobin | — | — | Incremental R2 (Full model versus model with only covariates): 0.041 [0.03, 0.051] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020995 | PGS004770 (Hb_PRSmixPlus_eur) |
PSS011466| European Ancestry| 5,325 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Hemoglobin | — | — | Incremental R2 (Full model versus model with only covariates): 0.044 [0.033, 0.055] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020996 | PGS004771 (Hct_PRSmix_eur) |
PSS011493| European Ancestry| 5,174 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Hematocrit | — | — | Incremental R2 (Full model versus model with only covariates): 0.03 [0.021, 0.039] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020997 | PGS004772 (hct_PRSmix_sas) |
PSS011494| South Asian Ancestry| 5,766 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Hematocrit | — | — | Incremental R2 (Full model versus model with only covariates): 0.016 [0.009, 0.022] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020998 | PGS004773 (Hct_PRSmixPlus_eur) |
PSS011493| European Ancestry| 5,174 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Hematocrit | — | — | Incremental R2 (Full model versus model with only covariates): 0.033 [0.023, 0.042] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020999 | PGS004774 (hct_PRSmixPlus_sas) |
PSS011494| South Asian Ancestry| 5,766 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Hematocrit | — | — | Incremental R2 (Full model versus model with only covariates): 0.017 [0.011, 0.024] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021026 | PGS004801 (Monocytes_PRSmix_eur) |
PSS011501| European Ancestry| 3,587 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Monocyte count | — | — | Incremental R2 (Full model versus model with only covariates): 0.109 [0.09, 0.129] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021027 | PGS004802 (Monocytes_PRSmix_sas) |
PSS011467| South Asian Ancestry| 7,098 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Monocyte count | — | — | Incremental R2 (Full model versus model with only covariates): 0.13 [0.115, 0.144] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021028 | PGS004803 (Monocytes_PRSmixPlus_eur) |
PSS011501| European Ancestry| 3,587 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Monocyte count | — | — | Incremental R2 (Full model versus model with only covariates): 0.117 [0.097, 0.137] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021029 | PGS004804 (Monocytes_PRSmixPlus_sas) |
PSS011467| South Asian Ancestry| 7,098 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Monocyte count | — | — | Incremental R2 (Full model versus model with only covariates): 0.138 [0.123, 0.153] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021030 | PGS004805 (Neutrophils_PRSmix_eur) |
PSS011502| European Ancestry| 3,495 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Neutrophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.057 [0.042, 0.072] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021031 | PGS004806 (Neutrophils_PRSmix_sas) |
PSS011468| South Asian Ancestry| 7,099 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Neutrophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.05 [0.04, 0.06] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021032 | PGS004807 (Neutrophils_PRSmixPlus_eur) |
PSS011502| European Ancestry| 3,495 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Neutrophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.065 [0.049, 0.081] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021033 | PGS004808 (Neutrophils_PRSmixPlus_sas) |
PSS011468| South Asian Ancestry| 7,099 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Neutrophil count | — | — | Incremental R2 (Full model versus model with only covariates): 0.055 [0.045, 0.066] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021036 | PGS004811 (Platelets_PRSmix_eur) |
PSS011470| European Ancestry| 5,341 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Platelet count | — | — | Incremental R2 (Full model versus model with only covariates): 0.135 [0.118, 0.152] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021037 | PGS004812 (Platelets_PRSmix_sas) |
PSS011471| South Asian Ancestry| 7,072 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Platelet count | — | — | Incremental R2 (Full model versus model with only covariates): 0.13 [0.115, 0.144] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021038 | PGS004813 (Platelets_PRSmixPlus_eur) |
PSS011470| European Ancestry| 5,341 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Platelet count | — | — | Incremental R2 (Full model versus model with only covariates): 0.139 [0.121, 0.156] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021039 | PGS004814 (Platelets_PRSmixPlus_sas) |
PSS011471| South Asian Ancestry| 7,072 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Platelet count | — | — | Incremental R2 (Full model versus model with only covariates): 0.135 [0.12, 0.15] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021046 | PGS004821 (RBC_PRSmix_eur) |
PSS011503| European Ancestry| 5,163 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.08 [0.066, 0.094] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021047 | PGS004822 (RBC_PRSmix_sas) |
PSS011472| South Asian Ancestry| 7,055 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.039 [0.031, 0.048] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021048 | PGS004823 (RBC_PRSmixPlus_eur) |
PSS011503| European Ancestry| 5,163 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.083 [0.068, 0.097] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021049 | PGS004824 (RBC_PRSmixPlus_sas) |
PSS011472| South Asian Ancestry| 7,055 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.041 [0.031, 0.05] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021050 | PGS004825 (RDW_PRSmix_eur) |
PSS011504| European Ancestry| 5,060 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red cell distribution width | — | — | Incremental R2 (Full model versus model with only covariates): 0.076 [0.062, 0.09] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021051 | PGS004826 (RDW_PRSmix_sas) |
PSS011473| South Asian Ancestry| 6,847 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red cell distribution width | — | — | Incremental R2 (Full model versus model with only covariates): 0.04 [0.031, 0.049] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021052 | PGS004827 (RDW_PRSmixPlus_eur) |
PSS011504| European Ancestry| 5,060 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red cell distribution width | — | — | Incremental R2 (Full model versus model with only covariates): 0.093 [0.078, 0.108] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021053 | PGS004828 (RDW_PRSmixPlus_sas) |
PSS011473| South Asian Ancestry| 6,847 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Red cell distribution width | — | — | Incremental R2 (Full model versus model with only covariates): 0.047 [0.037, 0.056] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021074 | PGS004849 (Total_protein_PRSmix_sas) |
PSS011476| South Asian Ancestry| 6,486 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Total protein measurement | — | — | Incremental R2 (Full model versus model with only covariates): 0.058 [0.047, 0.069] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021075 | PGS004850 (Total_protein_PRSmixPlus_sas) |
PSS011476| South Asian Ancestry| 6,486 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Total protein measurement | — | — | Incremental R2 (Full model versus model with only covariates): 0.059 [0.048, 0.07] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021076 | PGS004851 (TotalProtein_PRSmix_eur) |
PSS011475| European Ancestry| 5,330 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Total protein measurement | — | — | Incremental R2 (Full model versus model with only covariates): 0.04 [0.03, 0.051] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021077 | PGS004852 (TotalProtein_PRSmixPlus_eur) |
PSS011475| European Ancestry| 5,330 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Total protein measurement | — | — | Incremental R2 (Full model versus model with only covariates): 0.041 [0.03, 0.051] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021080 | PGS004855 (WBC_PRSmix_eur) |
PSS011512| European Ancestry| 5,118 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: White blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.075 [0.061, 0.089] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021081 | PGS004856 (WBC_PRSmix_sas) |
PSS011477| South Asian Ancestry| 7,058 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: White blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.084 [0.072, 0.097] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021082 | PGS004857 (WBC_PRSmixPlus_eur) |
PSS011512| European Ancestry| 5,118 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: White blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.085 [0.071, 0.1] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021083 | PGS004858 (WBC_PRSmixPlus_sas) |
PSS011477| South Asian Ancestry| 7,058 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: White blood count | — | — | Incremental R2 (Full model versus model with only covariates): 0.089 [0.076, 0.102] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM022079 | PGS004956 (Hemoglobin_Mean_INT_ldpred_AFRss_afrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.053 [0.04, 0.06] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022091 | PGS004956 (Hemoglobin_Mean_INT_ldpred_AFRss_afrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.049 [0.03, 0.07] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022085 | PGS004956 (Hemoglobin_Mean_INT_ldpred_AFRss_afrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.086 [0.07, 0.11] | — | R²: 0.008 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022081 | PGS004957 (Hemoglobin_Mean_INT_ldpred_EURss_eurld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.102 [0.09, 0.11] | — | R²: 0.012 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022087 | PGS004957 (Hemoglobin_Mean_INT_ldpred_EURss_eurld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.031 [0.01, 0.05] | — | R²: 0.001 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022093 | PGS004957 (Hemoglobin_Mean_INT_ldpred_EURss_eurld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.07 [0.05, 0.09] | — | R²: 0.006 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022080 | PGS004958 (Hemoglobin_Mean_INT_ldpred_HISss_amrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.052 [0.04, 0.06] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022086 | PGS004958 (Hemoglobin_Mean_INT_ldpred_HISss_amrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.036 [0.02, 0.06] | — | R²: 0.001 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022092 | PGS004958 (Hemoglobin_Mean_INT_ldpred_HISss_amrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.055 [0.03, 0.08] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022076 | PGS004959 (Hemoglobin_Mean_INT_ldpred_METAss_afrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.114 [0.1, 0.12] | — | R²: 0.015 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022082 | PGS004959 (Hemoglobin_Mean_INT_ldpred_METAss_afrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.074 [0.05, 0.09] | — | R²: 0.006 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022088 | PGS004959 (Hemoglobin_Mean_INT_ldpred_METAss_afrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.085 [0.06, 0.11] | — | R²: 0.008 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022077 | PGS004960 (Hemoglobin_Mean_INT_ldpred_METAss_amrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.117 [0.11, 0.13] | — | R²: 0.016 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022083 | PGS004960 (Hemoglobin_Mean_INT_ldpred_METAss_amrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.076 [0.06, 0.1] | — | R²: 0.007 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022089 | PGS004960 (Hemoglobin_Mean_INT_ldpred_METAss_amrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.086 [0.06, 0.11] | — | R²: 0.009 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022078 | PGS004961 (Hemoglobin_Mean_INT_ldpred_METAss_eurld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.129 [0.12, 0.14] | — | R²: 0.019 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022084 | PGS004961 (Hemoglobin_Mean_INT_ldpred_METAss_eurld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.087 [0.07, 0.11] | — | R²: 0.009 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022090 | PGS004961 (Hemoglobin_Mean_INT_ldpred_METAss_eurld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.082 [0.06, 0.11] | — | R²: 0.008 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021975 | PGS004962 (Hemoglobin_Mean_INT_prscs_AFRss_afrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.044 [0.03, 0.05] | — | R²: 0.002 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021982 | PGS004962 (Hemoglobin_Mean_INT_prscs_AFRss_afrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.065 [0.05, 0.08] | — | R²: 0.005 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021989 | PGS004962 (Hemoglobin_Mean_INT_prscs_AFRss_afrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.048 [0.02, 0.07] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021977 | PGS004963 (Hemoglobin_Mean_INT_prscs_EURss_eurld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.134 [0.12, 0.14] | — | R²: 0.02 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021984 | PGS004963 (Hemoglobin_Mean_INT_prscs_EURss_eurld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.04 [0.02, 0.06] | — | R²: 0.002 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021991 | PGS004963 (Hemoglobin_Mean_INT_prscs_EURss_eurld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.093 [0.07, 0.12] | — | R²: 0.01 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021976 | PGS004964 (Hemoglobin_Mean_INT_prscs_HISss_amrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.053 [0.04, 0.06] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021983 | PGS004964 (Hemoglobin_Mean_INT_prscs_HISss_amrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.039 [0.02, 0.06] | — | R²: 0.002 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021990 | PGS004964 (Hemoglobin_Mean_INT_prscs_HISss_amrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.05 [0.03, 0.07] | — | R²: 0.003 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021972 | PGS004965 (Hemoglobin_Mean_INT_prscs_METAss_afrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.114 [0.1, 0.12] | — | R²: 0.015 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021986 | PGS004965 (Hemoglobin_Mean_INT_prscs_METAss_afrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.089 [0.07, 0.11] | — | R²: 0.009 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021979 | PGS004965 (Hemoglobin_Mean_INT_prscs_METAss_afrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.067 [0.05, 0.09] | — | R²: 0.005 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021973 | PGS004966 (Hemoglobin_Mean_INT_prscs_METAss_amrld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.131 [0.12, 0.14] | — | R²: 0.019 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021980 | PGS004966 (Hemoglobin_Mean_INT_prscs_METAss_amrld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.096 [0.08, 0.12] | — | R²: 0.01 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021987 | PGS004966 (Hemoglobin_Mean_INT_prscs_METAss_amrld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.096 [0.07, 0.12] | — | R²: 0.011 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021974 | PGS004967 (Hemoglobin_Mean_INT_prscs_METAss_eurld) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.135 [0.12, 0.15] | — | R²: 0.021 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021981 | PGS004967 (Hemoglobin_Mean_INT_prscs_METAss_eurld) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.081 [0.06, 0.1] | — | R²: 0.007 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021988 | PGS004967 (Hemoglobin_Mean_INT_prscs_METAss_eurld) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.086 [0.06, 0.11] | — | R²: 0.009 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021971 | PGS004968 (Hemoglobin_Mean_INT_prscsx_METAweight) |
PSS011815| European Ancestry| 28,828 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.139 [0.13, 0.15] | — | R²: 0.022 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021978 | PGS004968 (Hemoglobin_Mean_INT_prscsx_METAweight) |
PSS011795| African Ancestry| 8,793 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.088 [0.07, 0.11] | — | R²: 0.009 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021985 | PGS004968 (Hemoglobin_Mean_INT_prscsx_METAweight) |
PSS011805| Hispanic or Latin American Ancestry| 6,376 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: Hemoglobin | β: 0.098 [0.07, 0.12] | — | R²: 0.011 | score previously adjusted for age, sex, 20 PCs | — |
|
PGS Sample Set ID (PSS) |
Phenotype Definitions and Methods | Participant Follow-up Time | Sample Numbers | Age of Study Participants | Sample Ancestry | Additional Ancestry Description | Cohort(s) | Additional Sample/Cohort Information |
|---|---|---|---|---|---|---|---|---|
| PSS009173 | — | — | 4,001 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009174 | — | — | 3,996 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009185 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009186 | — | — | 4,001 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009191 | — | — | 3,924 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010191 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010192 | — | — | 2,258 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009207 | — | — | 3,919 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009208 | — | — | 3,930 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010201 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009216 | — | — | 2,282 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010202 | — | — | 2,263 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010205 | — | — | 2,221 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009225 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009228 | — | — | 3,993 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009231 | — | — | 3,993 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009232 | — | — | 3,992 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009233 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009234 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009235 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009236 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010222 | — | — | 1,249 individuals, 48.0 % Male samples |
Mean = 58.2 years Sd = 6.9 years |
European | Ashkenazi | UKB | — |
| PSS009239 | — | — | 3,923 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010225 | — | — | 2,233 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009247 | — | — | 3,992 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009248 | — | — | 3,963 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010234 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010237 | — | — | 2,256 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010238 | — | — | 2,259 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010239 | — | — | 2,263 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009254 | — | — | 3,988 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010240 | — | — | 2,263 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010241 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010243 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009259 | — | — | 3,593 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010245 | — | — | 2,221 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009261 | — | — | 3,924 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010251 | — | — | 2,258 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS009267 | — | — | 3,924 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010255 | — | — | 2,263 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010256 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010257 | — | — | 2,241 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010261 | — | — | 2,256 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010262 | — | — | 2,221 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010266 | — | — | 2,220 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010275 | — | — | 2,329 individuals, 36.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010276 | — | — | 2,315 individuals, 37.0 % Male samples |
Mean = 52.5 years Sd = 8.1 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010285 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010286 | — | — | 2,327 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010289 | — | — | 2,282 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011463 | — | — | 7,096 individuals | — | South Asian | — | G&H | — |
| PSS011464 | — | — | 7,056 individuals | — | South Asian | — | G&H | — |
| PSS011466 | — | — | 5,325 individuals | — | European | — | AllofUs | — |
| PSS011467 | — | — | 7,098 individuals | — | South Asian | — | G&H | — |
| PSS011468 | — | — | 7,099 individuals | — | South Asian | — | G&H | — |
| PSS011470 | — | — | 5,341 individuals | — | European | — | AllofUs | — |
| PSS011471 | — | — | 7,072 individuals | — | South Asian | — | G&H | — |
| PSS011472 | — | — | 7,055 individuals | — | South Asian | — | G&H | — |
| PSS011473 | — | — | 6,847 individuals | — | South Asian | — | G&H | — |
| PSS011475 | — | — | 5,330 individuals | — | European | — | AllofUs | — |
| PSS011476 | — | — | 6,486 individuals | — | South Asian | — | G&H | — |
| PSS011477 | — | — | 7,058 individuals | — | South Asian | — | G&H | — |
| PSS011478 | — | — | 1,543 individuals | — | European | — | AllofUs | — |
| PSS010306 | — | — | 1,285 individuals, 40.0 % Male samples |
Mean = 52.3 years Sd = 7.9 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011479 | — | — | 6,414 individuals | — | South Asian | — | G&H | — |
| PSS010309 | — | — | 2,089 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011482 | — | — | 3,385 individuals | — | European | — | AllofUs | — |
| PSS010318 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011492 | — | — | 3,464 individuals | — | European | — | AllofUs | — |
| PSS011493 | — | — | 5,174 individuals | — | European | — | AllofUs | — |
| PSS010321 | — | — | 2,323 individuals, 36.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010322 | — | — | 2,323 individuals, 36.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010323 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010324 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010325 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011494 | — | — | 5,766 individuals | — | South Asian | — | G&H | — |
| PSS010327 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011501 | — | — | 3,587 individuals | — | European | — | AllofUs | — |
| PSS010329 | — | — | 2,282 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011502 | — | — | 3,495 individuals | — | European | — | AllofUs | — |
| PSS011503 | — | — | 5,163 individuals | — | European | — | AllofUs | — |
| PSS011504 | — | — | 5,060 individuals | — | European | — | AllofUs | — |
| PSS010335 | — | — | 2,324 individuals, 36.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010339 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010340 | — | — | 2,329 individuals, 36.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010341 | — | — | 2,285 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011512 | — | — | 5,118 individuals | — | European | — | AllofUs | — |
| PSS010345 | — | — | 2,321 individuals, 36.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010346 | — | — | 2,281 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010350 | — | — | 2,280 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010359 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010360 | — | — | 1,744 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009377 | — | — | 19,421 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009378 | — | — | 19,423 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010369 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010370 | — | — | 1,749 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010373 | — | — | 1,711 individuals, 33.0 % Male samples |
Mean = 52.4 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009399 | — | — | 19,422 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009400 | — | — | 19,392 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010390 | — | — | 973 individuals, 38.0 % Male samples |
Mean = 52.5 years Sd = 7.7 years |
East Asian | Chinese | UKB | — |
| PSS010393 | — | — | 1,718 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009411 | — | — | 19,423 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009412 | — | — | 19,423 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010402 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009417 | — | — | 19,121 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010405 | — | — | 1,748 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010406 | — | — | 1,749 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010407 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010408 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010409 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010411 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010413 | — | — | 1,711 individuals, 33.0 % Male samples |
Mean = 52.4 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009433 | — | — | 19,088 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009434 | — | — | 19,060 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010419 | — | — | 1,749 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010423 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010424 | — | — | 1,750 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010425 | — | — | 1,732 individuals, 34.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009442 | — | — | 11,529 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010429 | — | — | 1,748 individuals, 33.0 % Male samples |
Mean = 52.5 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010430 | — | — | 1,711 individuals, 33.0 % Male samples |
Mean = 52.4 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010434 | — | — | 1,711 individuals, 33.0 % Male samples |
Mean = 52.4 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS009451 | — | — | 19,419 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009454 | — | — | 19,387 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009457 | — | — | 19,384 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009458 | — | — | 19,382 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009459 | — | — | 19,422 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009460 | — | — | 19,422 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009461 | — | — | 19,423 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009462 | — | — | 19,423 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010443 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010444 | — | — | 5,983 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS009465 | — | — | 19,117 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010453 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010454 | — | — | 6,025 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010457 | — | — | 5,888 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS009473 | — | — | 19,383 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009474 | — | — | 19,244 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009480 | — | — | 19,359 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009485 | — | — | 17,433 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009487 | — | — | 19,120 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010474 | — | — | 4,192 individuals, 58.0 % Male samples |
Mean = 53.4 years Sd = 8.5 years |
South Asian | Indian | UKB | — |
| PSS010477 | — | — | 5,884 individuals, 54.0 % Male samples |
Mean = 53.4 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS009493 | — | — | 19,116 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010486 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010489 | — | — | 6,008 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010490 | — | — | 6,008 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010491 | — | — | 6,024 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010492 | — | — | 6,024 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010493 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010495 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010497 | — | — | 5,886 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010503 | — | — | 6,004 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010507 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010508 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010509 | — | — | 5,958 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010513 | — | — | 6,003 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010514 | — | — | 5,886 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS010518 | — | — | 5,885 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS003591 | — | — | 6,647 individuals | — | African American or Afro-Caribbean | — | ARIC, BioMe, HANDLS, JHS, MESA | — |
| PSS003592 | — | — | 4,441 individuals | — | Sub-Saharan African (Ugandan) |
— | NR | — |
| PSS003593 | — | — | 31,236 individuals | — | East Asian (Japanese, Chinese, Malay, Han Chinese) |
— | 15 cohorts
|
— |
| PSS003594 | — | — | 61,820 individuals | — | European | — | EPIC, InterAct, LifeLines, METSIM, WGHS | — |
| PSS010527 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010528 | — | — | 1,114 individuals, 60.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010537 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010538 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010541 | — | — | 1,097 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010558 | — | — | 659 individuals, 65.0 % Male samples |
Mean = 52.0 years Sd = 7.9 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010561 | — | — | 1,100 individuals, 60.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010570 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010573 | — | — | 1,121 individuals, 60.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010574 | — | — | 1,119 individuals, 60.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010575 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010576 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010577 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010579 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010581 | — | — | 1,097 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010587 | — | — | 1,120 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010591 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010592 | — | — | 1,123 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010593 | — | — | 1,114 individuals, 60.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010597 | — | — | 1,117 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010598 | — | — | 1,097 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010602 | — | — | 1,096 individuals, 59.0 % Male samples |
Mean = 52.0 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010611 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010612 | — | — | 6,263 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010621 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010622 | — | — | 6,277 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010625 | — | — | 6,144 individuals, 45.0 % Male samples |
Mean = 54.4 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010642 | — | — | 3,283 individuals, 51.0 % Male samples |
Mean = 54.8 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010645 | — | — | 6,172 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010654 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010657 | — | — | 6,264 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010658 | — | — | 6,260 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010659 | — | — | 6,277 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010660 | — | — | 6,277 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010661 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010663 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010665 | — | — | 6,143 individuals, 45.0 % Male samples |
Mean = 54.4 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010671 | — | — | 6,258 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010675 | — | — | 6,276 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010676 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010677 | — | — | 6,213 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS000704 | — | — | 4,847 individuals | — | African unspecified | — | UKB | — |
| PSS000705 | — | — | 1,032 individuals | — | East Asian | — | UKB | — |
| PSS000706 | — | — | 22,518 individuals | — | European | Non-British White | UKB | — |
| PSS000707 | — | — | 6,895 individuals | — | South Asian | — | UKB | — |
| PSS000708 | — | — | 60,920 individuals | — | European (British) |
— | UKB | — |
| PSS010681 | — | — | 6,254 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010682 | — | — | 6,144 individuals, 45.0 % Male samples |
Mean = 54.4 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010686 | — | — | 6,139 individuals, 45.0 % Male samples |
Mean = 54.4 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010695 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010696 | — | — | 3,651 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010705 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010706 | — | — | 3,680 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010709 | — | — | 3,574 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS000127 | — | — | 39,986 individuals, 50.0 % Male samples |
Mean = 43.87 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000128 | — | — | 40,133 individuals, 50.0 % Male samples |
Mean = 43.92 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000129 | — | — | 40,276 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000130 | — | — | 40,326 individuals, 49.0 % Male samples |
Mean = 43.83 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000131 | — | — | 40,340 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000132 | — | — | 40,329 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000133 | — | — | 40,244 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000134 | — | — | 40,225 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000135 | — | — | 40,227 individuals, 49.0 % Male samples |
Mean = 43.85 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000136 | — | — | 39,191 individuals, 50.0 % Male samples |
Mean = 43.82 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000137 | — | — | 39,178 individuals, 50.0 % Male samples |
Mean = 43.82 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000138 | — | — | 40,108 individuals, 50.0 % Male samples |
Mean = 43.85 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000139 | — | — | 40,265 individuals, 50.0 % Male samples |
Mean = 43.83 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000140 | — | — | 40,080 individuals, 50.0 % Male samples |
Mean = 43.82 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000141 | — | — | 39,177 individuals, 50.0 % Male samples |
Mean = 43.82 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000142 | — | — | 39,189 individuals, 50.0 % Male samples |
Mean = 43.83 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000143 | — | — | 37,224 individuals, 50.0 % Male samples |
Mean = 43.8 years Range = [18.0, 75.7] years |
European | — | INTERVAL | — |
| PSS000144 | — | — | 39,138 individuals, 50.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000145 | — | — | 39,190 individuals, 50.0 % Male samples |
Mean = 43.83 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000146 | — | — | 37,306 individuals, 49.0 % Male samples |
Mean = 43.82 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000147 | — | — | 37,262 individuals, 50.0 % Male samples |
Mean = 43.81 years Range = [18.0, 75.8] years |
European | — | INTERVAL | — |
| PSS000148 | — | — | 38,939 individuals, 49.0 % Male samples |
Mean = 43.75 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000149 | — | — | 40,262 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000150 | — | — | 40,253 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000151 | — | — | 40,286 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000152 | — | — | 40,466 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS000153 | — | — | 80,944 individuals, 46.0 % Male samples |
Mean = 57.21 years Range = [40.05, 70.91] years |
European | — | UKB | — |
| PSS000154 | — | — | 80,906 individuals, 46.0 % Male samples |
Mean = 57.2 years Range = [40.18, 72.91] years |
European | — | UKB | — |
| PSS000155 | — | — | 81,294 individuals, 46.0 % Male samples |
Mean = 57.2 years Range = [39.98, 72.44] years |
European | — | UKB | — |
| PSS000156 | — | — | 81,283 individuals, 45.0 % Male samples |
Mean = 57.19 years Range = [38.87, 71.1] years |
European | — | UKB | — |
| PSS000768 | — | — | 984 individuals | — | East Asian | — | UKB | — |
| PSS000769 | — | — | 21,516 individuals | — | European | Non-British White | UKB | — |
| PSS000157 | — | — | 81,622 individuals, 46.0 % Male samples |
Mean = 57.26 years Range = [40.11, 72.44] years |
European | — | UKB | — |
| PSS000158 | — | — | 81,548 individuals, 46.0 % Male samples |
Mean = 57.23 years Range = [40.16, 72.91] years |
European | — | UKB | — |
| PSS000159 | — | — | 80,067 individuals, 46.0 % Male samples |
Mean = 57.2 years Range = [39.91, 70.52] years |
European | — | UKB | — |
| PSS000160 | — | — | 80,088 individuals, 46.0 % Male samples |
Mean = 57.19 years Range = [39.66, 72.91] years |
European | — | UKB | — |
| PSS000161 | — | — | 79,282 individuals, 46.0 % Male samples |
Mean = 57.28 years Range = [39.91, 70.99] years |
European | — | UKB | — |
| PSS000162 | — | — | 81,455 individuals, 45.0 % Male samples |
Mean = 57.23 years Range = [40.05, 71.07] years |
European | — | UKB | — |
| PSS000163 | — | — | 81,464 individuals, 46.0 % Male samples |
Mean = 57.22 years Range = [40.17, 71.1] years |
European | — | UKB | — |
| PSS000164 | — | — | 81,303 individuals, 46.0 % Male samples |
Mean = 57.27 years Range = [39.99, 70.44] years |
European | — | UKB | — |
| PSS000165 | — | — | 81,570 individuals, 46.0 % Male samples |
Mean = 57.27 years Range = [39.98, 70.7] years |
European | — | UKB | — |
| PSS000166 | — | — | 81,431 individuals, 46.0 % Male samples |
Mean = 57.26 years Range = [40.15, 71.1] years |
European | — | UKB | — |
| PSS000167 | — | — | 80,799 individuals, 46.0 % Male samples |
Mean = 57.23 years Range = [39.66, 70.7] years |
European | — | UKB | — |
| PSS000168 | — | — | 80,627 individuals, 46.0 % Male samples |
Mean = 57.23 years Range = [40.19, 71.1] years |
European | — | UKB | — |
| PSS000169 | — | — | 78,320 individuals, 46.0 % Male samples |
Mean = 57.26 years Range = [38.87, 70.99] years |
European | — | UKB | — |
| PSS000170 | — | — | 81,358 individuals, 45.0 % Male samples |
Mean = 57.19 years Range = [40.19, 72.44] years |
European | — | UKB | — |
| PSS000171 | — | — | 81,423 individuals, 46.0 % Male samples |
Mean = 57.25 years Range = [39.99, 70.5] years |
European | — | UKB | — |
| PSS000172 | — | — | 78,161 individuals, 46.0 % Male samples |
Mean = 57.2 years Range = [39.66, 72.91] years |
European | — | UKB | — |
| PSS000173 | — | — | 78,290 individuals, 46.0 % Male samples |
Mean = 57.2 years Range = [39.99, 70.53] years |
European | — | UKB | — |
| PSS000174 | — | — | 78,246 individuals, 46.0 % Male samples |
Mean = 57.23 years Range = [39.98, 70.43] years |
European | — | UKB | — |
| PSS000175 | — | — | 81,614 individuals, 45.0 % Male samples |
Mean = 57.26 years Range = [39.99, 70.99] years |
European | — | UKB | — |
| PSS000176 | — | — | 79,344 individuals, 46.0 % Male samples |
Mean = 57.29 years Range = [39.66, 72.91] years |
European | — | UKB | — |
| PSS000177 | — | — | 79,362 individuals, 46.0 % Male samples |
Mean = 57.22 years Range = [39.66, 72.91] years |
European | — | UKB | — |
| PSS000178 | — | — | 81,606 individuals, 46.0 % Male samples |
Mean = 57.23 years Range = [38.87, 70.97] years |
European | — | UKB | — |
| PSS010759 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010760 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010761 | — | — | 3,634 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010765 | — | — | 3,652 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010766 | — | — | 3,574 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010770 | — | — | 3,573 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010779 | — | — | 3,990 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010780 | — | — | 3,985 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010789 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010790 | — | — | 3,990 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS000817 | — | — | 1,995 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS010793 | — | — | 3,913 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010810 | — | — | 2,277 individuals, 42.0 % Male samples |
Mean = 54.5 years Sd = 7.4 years |
European | Polish | UKB | — |
| PSS010813 | — | — | 3,920 individuals, 38.0 % Male samples |
Mean = 54.4 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010822 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010825 | — | — | 3,982 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010826 | — | — | 3,981 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010827 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010828 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010829 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010831 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010833 | — | — | 3,912 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010839 | — | — | 3,981 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010843 | — | — | 3,989 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010844 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010845 | — | — | 3,952 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010849 | — | — | 3,977 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010850 | — | — | 3,913 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS010854 | — | — | 3,913 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS007821 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007822 | — | — | 2,342 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010863 | — | — | 19,416 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010864 | — | — | 19,389 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS000234 | Incident type 2 diabtes defined as fasting glucose >= 7 mmol/L, 2 hour glucose >= 11.1 mmol/L, antidiabetic medication use, or a physician diagnosis of type 2 diabetes | Range = [10.0, 15.0] years | [ ,
37.93 % Male samples |
Mean = 57.4 years Sd = 7.43 years |
African unspecified | — | ARIC, MESA | Partial overlap with discovery GWAS |
| PSS000235 | Incident type 2 diabtes defined as fasting glucose >= 7 mmol/L, 2 hour glucose >= 11.1 mmol/L, antidiabetic medication use, or a physician diagnosis of type 2 diabetes | Range = [10.0, 15.0] years | [ ,
47.45 % Male samples |
Mean = 55.6 years Sd = 7.37 years |
East Asian | — | MESA, SCHS | Partial overlap with discovery GWAS |
| PSS000236 | Incident type 2 diabtes defined as fasting glucose >= 7 mmol/L, 2 hour glucose >= 11.1 mmol/L, antidiabetic medication use, or a physician diagnosis of type 2 diabetes | Range = [10.0, 15.0] years | [ ,
43.47 % Male samples |
Mean = 54.63 years Sd = 8.27 years |
European | — | ARIC, FHS, InterAct, MESA | Partial overlap with discovery GWAS |
| PSS010873 | — | — | 19,418 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010874 | — | — | 19,417 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007843 | — | — | 2,342 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007844 | — | — | 2,328 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010877 | — | — | 19,116 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007855 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007856 | — | — | 2,340 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010894 | — | — | 11,449 individuals, 50.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007861 | — | — | 2,294 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010897 | — | — | 19,078 individuals, 46.0 % Male samples |
Mean = 56.8 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010906 | — | — | 19,415 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010909 | — | — | 19,376 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010910 | — | — | 19,374 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007877 | — | — | 2,293 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007878 | — | — | 2,100 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010911 | — | — | 19,418 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010912 | — | — | 19,417 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010913 | — | — | 19,418 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010915 | — | — | 19,418 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010917 | — | — | 19,111 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007886 | — | — | 1,291 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010923 | — | — | 19,370 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010927 | — | — | 19,414 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010928 | — | — | 19,418 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007895 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010929 | — | — | 19,237 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007898 | — | — | 2,337 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010933 | — | — | 19,342 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS010934 | — | — | 19,112 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007901 | — | — | 2,336 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007902 | — | — | 2,336 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007903 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007904 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007905 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007906 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010938 | — | — | 19,109 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS007909 | — | — | 2,294 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007917 | — | — | 2,337 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007918 | — | — | 2,298 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS008821 | — | — | 6,293 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS000911 | — | — | 13,989 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) (Qatari) |
— | QBB | — |
| PSS007924 | — | — | 2,334 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007929 | — | — | 2,161 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007931 | — | — | 2,293 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007937 | — | — | 2,292 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS011795 | — | — | 8,793 individuals | — | African American or Afro-Caribbean | — | AllofUs | — |
| PSS011805 | — | — | 6,376 individuals | — | Hispanic or Latin American | — | AllofUs | — |
| PSS011815 | — | — | 28,828 individuals | — | European | — | AllofUs | — |
| PSS009743 | — | — | 6,017 individuals | — | African unspecified | — | UKB | — |
| PSS009744 | — | — | 880 individuals | — | East Asian | — | UKB | — |
| PSS009745 | — | — | 41,583 individuals | — | European | Non-British European | UKB | — |
| PSS009746 | — | — | 7,446 individuals | — | South Asian | — | UKB | — |
| PSS009767 | — | — | 4,803 individuals | — | African unspecified | — | UKB | — |
| PSS009768 | — | — | 841 individuals | — | East Asian | — | UKB | — |
| PSS009769 | — | — | 38,991 individuals | — | European | Non-British European | UKB | — |
| PSS009770 | — | — | 6,861 individuals | — | South Asian | — | UKB | — |
| PSS008035 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008036 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009775 | — | — | 5,976 individuals | — | African unspecified | — | UKB | — |
| PSS009776 | — | — | 881 individuals | — | East Asian | — | UKB | — |
| PSS009777 | — | — | 41,231 individuals | — | European | Non-British European | UKB | — |
| PSS009778 | — | — | 7,560 individuals | — | South Asian | — | UKB | — |
| PSS008056 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008057 | — | — | 1,757 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009795 | — | — | 6,135 individuals | — | African unspecified | — | UKB | — |
| PSS009796 | — | — | 895 individuals | — | East Asian | — | UKB | — |
| PSS009797 | — | — | 41,973 individuals | — | European | Non-British European | UKB | — |
| PSS009798 | — | — | 7,737 individuals | — | South Asian | — | UKB | — |
| PSS009799 | — | — | 5,952 individuals | — | African unspecified | — | UKB | — |
| PSS009800 | — | — | 845 individuals | — | East Asian | — | UKB | — |
| PSS009801 | — | — | 41,826 individuals | — | European | Non-British European | UKB | — |
| PSS009802 | — | — | 7,485 individuals | — | South Asian | — | UKB | — |
| PSS009803 | — | — | 6,153 individuals | — | African unspecified | — | UKB | — |
| PSS008068 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008069 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009804 | — | — | 892 individuals | — | East Asian | — | UKB | — |
| PSS009805 | — | — | 42,078 individuals | — | European | Non-British European | UKB | — |
| PSS009806 | — | — | 7,770 individuals | — | South Asian | — | UKB | — |
| PSS009807 | — | — | 6,119 individuals | — | African unspecified | — | UKB | — |
| PSS008074 | — | — | 1,722 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009808 | — | — | 894 individuals | — | East Asian | — | UKB | — |
| PSS009809 | — | — | 41,863 individuals | — | European | Non-British European | UKB | — |
| PSS009810 | — | — | 7,718 individuals | — | South Asian | — | UKB | — |
| PSS009815 | — | — | 6,143 individuals | — | African unspecified | — | UKB | — |
| PSS009816 | — | — | 895 individuals | — | East Asian | — | UKB | — |
| PSS009817 | — | — | 42,007 individuals | — | European | Non-British European | UKB | — |
| PSS009818 | — | — | 7,730 individuals | — | South Asian | — | UKB | — |
| PSS009823 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS009824 | — | — | 885 individuals | — | East Asian | — | UKB | — |
| PSS009825 | — | — | 42,065 individuals | — | European | Non-British European | UKB | — |
| PSS008090 | — | — | 1,715 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008091 | — | — | 1,731 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009826 | — | — | 7,707 individuals | — | South Asian | — | UKB | — |
| PSS009827 | — | — | 5,990 individuals | — | African unspecified | — | UKB | — |
| PSS009828 | — | — | 888 individuals | — | East Asian | — | UKB | — |
| PSS009829 | — | — | 41,837 individuals | — | European | Non-British European | UKB | — |
| PSS009830 | — | — | 7,623 individuals | — | South Asian | — | UKB | — |
| PSS008099 | — | — | 983 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008108 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008111 | — | — | 1,761 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008114 | — | — | 1,760 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008115 | — | — | 1,761 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008116 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008117 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008118 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008119 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008122 | — | — | 1,722 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008130 | — | — | 1,761 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008131 | — | — | 1,744 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009871 | — | — | 6,149 individuals | — | African unspecified | — | UKB | — |
| PSS009872 | — | — | 893 individuals | — | East Asian | — | UKB | — |
| PSS008137 | — | — | 1,760 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS009873 | — | — | 42,026 individuals | — | European | Non-British European | UKB | — |
| PSS009874 | — | — | 7,769 individuals | — | South Asian | — | UKB | — |
| PSS008142 | — | — | 1,562 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008143 | — | — | 1,722 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008149 | — | — | 1,722 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS000290 | — | — | 2,314 individuals | — | European (French Canadian) |
— | CARTaGENE | — |
| PSS000291 | — | — | 39,260 individuals | — | European | — | INTERVAL | — |
| PSS000767 | — | — | 5,573 individuals | — | African unspecified | — | UKB | — |
| PSS000770 | — | — | 6,687 individuals | — | South Asian | — | UKB | — |
| PSS000771 | — | — | 58,196 individuals | — | European (British) |
— | UKB | — |
| PSS008257 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008258 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008279 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008280 | — | — | 6,035 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008291 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008292 | — | — | 6,077 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008297 | — | — | 5,937 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008313 | — | — | 5,923 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008314 | — | — | 5,936 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008322 | — | — | 4,224 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008331 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008334 | — | — | 6,058 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008337 | — | — | 6,060 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008338 | — | — | 6,060 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008339 | — | — | 6,076 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008340 | — | — | 6,076 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008341 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008342 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008345 | — | — | 5,935 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS011099 | — | — | 2,770 individuals | — | European (non-white British ancestry) |
— | UKB | — |
| PSS011100 | — | — | 2,768 individuals | — | European (non-white British ancestry) |
— | UKB | — |
| PSS008353 | — | — | 6,056 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008354 | — | — | 6,010 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS011109 | — | — | 2,813 individuals | — | European (non-white British ancestry) |
— | UKB | — |
| PSS011110 | — | — | 2,810 individuals | — | European (non-white British ancestry) |
— | UKB | — |
| PSS011111 | — | — | 1,393 individuals | — | South Asian | — | UKB | — |
| PSS008360 | — | — | 6,055 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS011113 | — | — | 1,433 individuals | — | South Asian | — | UKB | — |
| PSS011112 | — | — | 1,428 individuals | — | South Asian | — | UKB | — |
| PSS008365 | — | — | 5,483 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008367 | — | — | 5,935 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008373 | — | — | 5,934 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS011136 | — | — | 65,932 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011137 | — | — | 65,929 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011138 | — | — | 65,896 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011139 | — | — | 65,814 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011140 | — | — | 64,861 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011141 | — | — | 64,860 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011143 | — | — | 64,816 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011144 | — | — | 65,930 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011145 | — | — | 65,931 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011152 | — | — | 1,124 individuals | — | African unspecified | — | UKB | — |
| PSS011153 | — | — | 1,125 individuals | — | African unspecified | — | UKB | — |
| PSS011154 | — | — | 1,154 individuals | — | African unspecified | — | UKB | — |
| PSS011155 | — | — | 1,157 individuals | — | African unspecified | — | UKB | — |
| PSS011166 | — | — | 7,746 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS011167 | — | — | 7,743 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS011168 | — | — | 7,732 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS011169 | — | — | 7,578 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS011170 | — | — | 7,574 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS008925 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008926 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008479 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008480 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008501 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008502 | — | — | 1,144 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010726 | — | — | 2,085 individuals, 50.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008513 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008514 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008519 | — | — | 1,127 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010729 | — | — | 2,950 individuals, 46.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008535 | — | — | 1,124 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008536 | — | — | 1,128 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008544 | — | — | 676 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010116 | — | — | 5,403 individuals, 0.0 % Male samples |
— | European (British) |
— | INTERVAL | — |
| PSS010117 | — | — | 13,780 individuals, 0.0 % Male samples |
— | European (British) |
— | INTERVAL | — |
| PSS008947 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008553 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008556 | — | — | 1,150 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010118 | — | — | 21,577 individuals, 100.0 % Male samples |
— | European (British) |
— | INTERVAL | — |
| PSS008948 | — | — | 3,680 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008559 | — | — | 1,151 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008560 | — | — | 1,149 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008561 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008562 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS000371 | Individuals who have been referred to a child psychiatric outpatient clinic in the Northern Netherlands before the age of 11. We measured weight and height using regularly calibrated equipment (scales and stadiometer models 770 and 214, respectively; Seca, Hamburg, Germany). Body mass index (BMI; in kg/m2) was also calculated. We measured waist circumference at the midpoint between the lower costal margin and the iliac crest. The hip circumference was measured over both trochanter majores (tangible bone on the outside of the hip joint). Waist to hip ratio was also calculated. We performed all measurements in duplicate, and, if the difference between these measurements exceeded a predefined value, a third measurement was performed. All available measurements were used to calculate means. Heart rate, systolic (SBP) and diastolic (DBP) blood pressure were measured in duplicate with a Dinamap Critikon 1846SX (Critikon Inc, Tampa, FL), from which we calculated means. At the third visit, fasting blood sample of participants were drawn for the measurement of glucose (Roche Diagnostics, Basel, Switzerland), insulin (Diagnostic Systems Laboratories Inc, Webster, TX), HbA1c (high performance liquid chromatography, Variant, Bio-Rad), triglycerides, total cholesterol, HDL cholesterol (Roche Diagnostics) and LDL cholesterol (calculated according to Friedewald’s equation5), as well as alanine transaminase (Photometric determination according to the reference method of the International Federation of Clinical Chemistry (IFCC)6) and lipoprotein(a) (Nephelometric method, BN2, DadeBehring). Serum creatinine was measured by photometric determination with the Jaffé method without deproteinisation (Ecoline® MEGA, DiaSys Diagnostic Systems GmbH. Merck). eGFR for adolescents who were younger than 18 years old was calculated using the Schwartz formula.7 High‐sensitivity C‐reactive protein (hsCRP) was determined using an immunonephelometric method, BN2 (CardioPhase hsCRP, Siemens) with a lower detection limit of 0.175 mg/L. Total IgE measurements were performed using the Phadia Immunocap 100 system with fluoroenzyme immunoassay (FEIA). | — | 288 individuals, 69.2 % Male samples |
Mean = 15.83 years Sd = 0.6 years |
European | — | TRAILS, TRAILSCC | TRAILS Clinical Cohort |
| PSS008563 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008564 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008567 | — | — | 1,127 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS000376 | We measured weight and height using regularly calibrated equipment (scales and stadiometer models 770 and 214, respectively; Seca, Hamburg, Germany). Body mass index (BMI; in kg/m2) was also calculated. We measured waist circumference at the midpoint between the lower costal margin and the iliac crest. The hip circumference was measured over both trochanter majores (tangible bone on the outside of the hip joint). Waist to hip ratio was also calculated. We performed all measurements in duplicate, and, if the difference between these measurements exceeded a predefined value, a third measurement was performed. All available measurements were used to calculate means. Heart rate, systolic (SBP) and diastolic (DBP) blood pressure were measured in duplicate with a Dinamap Critikon 1846SX (Critikon Inc, Tampa, FL), from which we calculated means. At the third visit, fasting blood sample of participants were drawn for the measurement of glucose (Roche Diagnostics, Basel, Switzerland), insulin (Diagnostic Systems Laboratories Inc, Webster, TX), HbA1c (high performance liquid chromatography, Variant, Bio-Rad), triglycerides, total cholesterol, HDL cholesterol (Roche Diagnostics) and LDL cholesterol (calculated according to Friedewald’s equation5), as well as alanine transaminase (Photometric determination according to the reference method of the International Federation of Clinical Chemistry (IFCC)6) and lipoprotein(a) (Nephelometric method, BN2, DadeBehring). Serum creatinine was measured by photometric determination with the Jaffé method without deproteinisation (Ecoline® MEGA, DiaSys Diagnostic Systems GmbH. Merck). eGFR for adolescents who were younger than 18 years old was calculated using the Schwartz formula.7 High‐sensitivity C‐reactive protein (hsCRP) was determined using an immunonephelometric method, BN2 (CardioPhase hsCRP, Siemens) with a lower detection limit of 0.175 mg/L. Total IgE measurements were performed using the Phadia Immunocap 100 system with fluoroenzyme immunoassay (FEIA). | — | 1,354 individuals, 47.56 % Male samples |
Mean = 16.22 years Sd = 0.66 years |
European | — | TRAILS | — |
| PSS010738 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008575 | — | — | 1,150 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008576 | — | — | 1,144 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008582 | — | — | 1,147 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010741 | — | — | 3,670 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008587 | — | — | 1,036 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008589 | — | — | 1,127 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010742 | — | — | 3,668 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008595 | — | — | 1,126 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010743 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010744 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010745 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008959 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS010747 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008960 | — | — | 3,709 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS010749 | — | — | 3,574 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010755 | — | — | 3,666 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS008705 | — | — | 6,435 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008706 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008727 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008728 | — | — | 6,422 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008739 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008740 | — | — | 6,436 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS010041 | — | — | 581 individuals | — | European (majority European) |
— | NR | LIVER-BIBLE |
| PSS011242 | — | — | 12,948 individuals | — | South Asian | — | G&H | — |
| PSS008745 | — | — | 6,298 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS011258 | — | — | 34,192 individuals | — | European | — | HUNT | — |
| PSS008761 | — | — | 6,289 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008762 | — | — | 6,326 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008770 | — | — | 3,365 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS011272 | — | — | 86,050 individuals | — | European | — | UKB | — |
| PSS008779 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008782 | — | — | 6,421 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008785 | — | — | 6,423 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008786 | — | — | 6,419 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008787 | — | — | 6,436 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008788 | — | — | 6,436 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008789 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008790 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS010055 | — | — | 22,608 individuals | — | East Asian | — | KBA, KoGES | — |
| PSS011286 | — | — | 8,748 individuals | — | South Asian | — | UKB | — |
| PSS008793 | — | — | 6,297 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008801 | — | — | 6,417 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008802 | — | — | 6,368 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008808 | — | — | 6,413 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008813 | — | — | 5,794 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008815 | — | — | 6,298 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS006891 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006892 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006893 | — | — | 24,174 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006894 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006895 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006896 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006897 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006898 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006899 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006900 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006901 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006902 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006903 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006904 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006905 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006921 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006922 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006923 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006924 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006925 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006926 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006927 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006928 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006929 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006930 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006931 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006932 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006933 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006934 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006935 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006936 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006937 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006938 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006939 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006940 | — | — | 65,635 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006941 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006942 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006943 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006944 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006945 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006946 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006947 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006948 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006949 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006950 | — | — | 65,637 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006951 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006952 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006953 | — | — | 24,171 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006954 | — | — | 7,519 individuals | — | South Asian | — | UKB | — |
| PSS006955 | — | — | 65,601 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006956 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006957 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006958 | — | — | 24,175 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006959 | — | — | 7,520 individuals | — | South Asian | — | UKB | — |
| PSS006960 | — | — | 65,636 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006961 | — | — | 6,139 individuals | — | African unspecified | — | UKB | — |
| PSS006962 | — | — | 1,655 individuals | — | East Asian | — | UKB | — |
| PSS006963 | — | — | 24,171 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006964 | — | — | 7,519 individuals | — | South Asian | — | UKB | — |
| PSS006965 | — | — | 65,601 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006966 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006967 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006968 | — | — | 24,129 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006969 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS006970 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006971 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006972 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006973 | — | — | 24,129 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006974 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS006975 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006976 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006977 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006978 | — | — | 24,129 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006979 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS006980 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006981 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006982 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006983 | — | — | 24,129 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006984 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS006985 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006986 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006987 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006988 | — | — | 24,129 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006989 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS006990 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006991 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006992 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006993 | — | — | 24,130 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006994 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS006995 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS006996 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS006997 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS006998 | — | — | 24,130 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006999 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS007000 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007001 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS007002 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS007003 | — | — | 24,130 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007004 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS007005 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007006 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS007007 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS007008 | — | — | 24,130 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007009 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS007010 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007011 | — | — | 6,120 individuals | — | African unspecified | — | UKB | — |
| PSS007012 | — | — | 1,654 individuals | — | East Asian | — | UKB | — |
| PSS007013 | — | — | 24,130 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007014 | — | — | 7,491 individuals | — | South Asian | — | UKB | — |
| PSS007015 | — | — | 65,520 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007016 | — | — | 5,974 individuals | — | African unspecified | — | UKB | — |
| PSS007017 | — | — | 1,623 individuals | — | East Asian | — | UKB | — |
| PSS007018 | — | — | 23,688 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007019 | — | — | 7,323 individuals | — | South Asian | — | UKB | — |
| PSS007020 | — | — | 64,569 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007021 | — | — | 5,974 individuals | — | African unspecified | — | UKB | — |
| PSS007022 | — | — | 1,623 individuals | — | East Asian | — | UKB | — |
| PSS007023 | — | — | 23,688 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007024 | — | — | 7,323 individuals | — | South Asian | — | UKB | — |
| PSS007025 | — | — | 64,570 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007026 | — | — | 5,973 individuals | — | African unspecified | — | UKB | — |
| PSS007027 | — | — | 1,623 individuals | — | East Asian | — | UKB | — |
| PSS007028 | — | — | 23,687 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007029 | — | — | 7,323 individuals | — | South Asian | — | UKB | — |
| PSS007030 | — | — | 64,570 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007031 | — | — | 5,973 individuals | — | African unspecified | — | UKB | — |
| PSS007032 | — | — | 1,623 individuals | — | East Asian | — | UKB | — |
| PSS007033 | — | — | 23,680 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007034 | — | — | 7,321 individuals | — | South Asian | — | UKB | — |
| PSS007035 | — | — | 64,524 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007036 | — | — | 5,974 individuals | — | African unspecified | — | UKB | — |
| PSS007037 | — | — | 1,623 individuals | — | East Asian | — | UKB | — |
| PSS007038 | — | — | 23,681 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007039 | — | — | 7,321 individuals | — | South Asian | — | UKB | — |
| PSS007040 | — | — | 64,524 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007041 | — | — | 5,974 individuals | — | African unspecified | — | UKB | — |
| PSS007042 | — | — | 1,623 individuals | — | East Asian | — | UKB | — |
| PSS007043 | — | — | 23,681 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007044 | — | — | 7,321 individuals | — | South Asian | — | UKB | — |
| PSS007045 | — | — | 64,524 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008965 | — | — | 3,602 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008981 | — | — | 3,600 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008982 | — | — | 2,976 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008990 | — | — | 2,100 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS011364 | — | — | 56,192 individuals | — | European | — | UKB | — |
| PSS008999 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009002 | — | — | 3,699 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009005 | — | — | 3,699 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009006 | — | — | 3,697 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009007 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009008 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009009 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009010 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS011369 | Pregnancy with at least one platelet count measurements in each trimester during pregnancy. If multiple platelet count measurements were available, the earilest platelet count measurements within each trimester was chosen. | — | 818 individuals, 0.0 % Male samples |
— | East Asian (Chinese) |
— | NIPT PLUS | 5,733 Chinese pregnant women underwent non-invasive prenatal PLUS testing. |
| PSS011370 | Pregnancy with at least one platelet count measurements in each trimester during pregnancy. If multiple platelet count measurements were available, the earilest platelet count measurements within each trimester was chosen. | — | 878 individuals, 0.0 % Male samples |
— | East Asian (Chinese) |
— | NIPT PLUS | 5,733 Chinese pregnant women underwent non-invasive prenatal PLUS testing. |
| PSS009013 | — | — | 3,602 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS011371 | Pregnancy with at least one platelet count measurements in each trimester during pregnancy. If multiple platelet count measurements were available, the earilest platelet count measurements within each trimester was chosen. | — | 615 individuals, 0.0 % Male samples |
— | East Asian (Chinese) |
— | NIPT PLUS | 5,733 Chinese pregnant women underwent non-invasive prenatal PLUS testing. |
| PSS009021 | — | — | 3,695 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009022 | — | — | 3,663 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009028 | — | — | 3,681 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009033 | — | — | 3,404 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009035 | — | — | 3,602 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009041 | — | — | 3,601 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS010185 | — | — | 1,115 individuals, 41.1 % Male samples |
Mean = 46.18 years | Hispanic or Latin American | — | HCHS, SOL | — |
| PSS007156 | — | — | 5,219 individuals | — | African unspecified | — | UKB | — |
| PSS007157 | — | — | 1,622 individuals | — | East Asian | — | UKB | — |
| PSS007158 | — | — | 23,762 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007159 | — | — | 7,345 individuals | — | South Asian | — | UKB | — |
| PSS007160 | — | — | 64,432 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007201 | — | — | 5,659 individuals | — | African unspecified | — | UKB | — |
| PSS007202 | — | — | 1,474 individuals | — | East Asian | — | UKB | — |
| PSS007203 | — | — | 21,730 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007204 | — | — | 6,788 individuals | — | South Asian | — | UKB | — |
| PSS007205 | — | — | 59,024 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS009151 | — | — | 4,000 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009152 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |