| Experimental Factor Ontology (EFO) Information | |
| Identifier | EFO_0004343 |
| Description | The waist circumference measurement divided by the hip circumference measurement. For both men and women, a waist-to-hip ratio (WHR) of 1.0 or higher is considered "at risk" for undesirable health consequences, such as heart disease and ailments associated with OVERWEIGHT. A healthy WHR is 0.90 or less for men, and 0.80 or less for women. (National Center for Chronic Disease Prevention and Health Promotion, 2004) | Trait category |
Body measurement
|
| Mapped terms |
2 mapped terms
|
| Child trait(s) | BMI-adjusted waist-hip ratio |
| 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) |
|---|---|---|---|---|---|---|
| PGS000299 (GRS462_WHRadjBMI) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Waist-to-hip ratio (body mass index adjusted) | BMI-adjusted waist-hip ratio | 462 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000299/ScoringFiles/PGS000299.txt.gz |
| PGS000842 (WHR) |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Waist-hip ratio | waist-hip ratio | 39 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000842/ScoringFiles/PGS000842.txt.gz |
| PGS000843 (WHRadjBMI) |
PGP000211 | Aly DM et al. Nat Genet (2021) |
WHR adjusted for BMI | BMI-adjusted waist-hip ratio | 63 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000843/ScoringFiles/PGS000843.txt.gz |
| PGS002356 (body_WHRadjBMIz.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002356/ScoringFiles/PGS002356.txt.gz |
| PGS002428 (body_WHRadjBMIz.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 9,477 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002428/ScoringFiles/PGS002428.txt.gz |
| PGS002477 (body_WHRadjBMIz.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 27,033 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002477/ScoringFiles/PGS002477.txt.gz |
| PGS002526 (body_WHRadjBMIz.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 126,268 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002526/ScoringFiles/PGS002526.txt.gz |
| PGS002575 (body_WHRadjBMIz.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 2,912 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002575/ScoringFiles/PGS002575.txt.gz |
| PGS002624 (body_WHRadjBMIz.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 1,829 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002624/ScoringFiles/PGS002624.txt.gz |
| PGS002673 (body_WHRadjBMIz.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 325,206 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002673/ScoringFiles/PGS002673.txt.gz |
| PGS002722 (body_WHRadjBMIz.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Waist-hip ratio | waist-hip ratio | 984,160 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002722/ScoringFiles/PGS002722.txt.gz |
| PGS003484 (LDPred2_WHR) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Waist-hip ratio | waist-hip ratio | 855,882 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003484/ScoringFiles/PGS003484.txt.gz |
| PGS003485 (LDPred2_WHRadjBMI) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
BMI-adjusted waist-hip ratio | BMI-adjusted waist-hip ratio | 785,126 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003485/ScoringFiles/PGS003485.txt.gz |
| PGS005095 (WHRadjBMI_GRS) |
PGP000686 | Christiansen MR et al. Diabetes (2023) |
WHR adjusted for BMI | BMI-adjusted waist-hip ratio | 481 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005095/ScoringFiles/PGS005095.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 |
|---|---|---|---|---|---|---|---|---|---|
| PPM000766 | PGS000299 (GRS462_WHRadjBMI) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Waist-to-hip ratio | — | — | R²: 0.0138 | Sex, age, age^2, BMI | — |
| PPM000767 | PGS000299 (GRS462_WHRadjBMI) |
PSS000377| European Ancestry| 1,174 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Waist-to-hip ratio | — | — | R²: 0.0141 | Sex, age, age^2, BMI | — |
| PPM000768 | PGS000299 (GRS462_WHRadjBMI) |
PSS000378| European Ancestry| 1,095 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Waist-to-hip ratio | — | — | R²: 0.0195 | Sex, age, age^2, BMI | — |
| PPM000796 | PGS000299 (GRS462_WHRadjBMI) |
PSS000371| European Ancestry| 288 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Waist-to-hip ratio | — | — | R²: 0.01 | Sex, age, age^2, BMI | — |
| PPM000797 | PGS000299 (GRS462_WHRadjBMI) |
PSS000372| European Ancestry| 265 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Waist-to-hip ratio | — | — | R²: 0.017 | Sex, age, age^2, BMI | — |
| PPM000798 | PGS000299 (GRS462_WHRadjBMI) |
PSS000373| European Ancestry| 245 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Waist-to-hip ratio | — | — | R²: 0.0156 | Sex, age, age^2, BMI | — |
| PPM002293 | PGS000842 (WHR) |
PSS001086| European Ancestry| 3,194 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Autoimmune Diabetes | OR: 1.13 [1.02, 1.25] | — | — | PC1-10 | — |
| PPM002295 | PGS000842 (WHR) |
PSS001088| European Ancestry| 3,869 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Resistant Diabetes | OR: 1.14 [1.06, 1.22] | — | — | PC1-10 | — |
| PPM002296 | PGS000842 (WHR) |
PSS001085| European Ancestry| 4,116 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Obesity-related Diabetes | OR: 1.1 [1.03, 1.18] | — | — | PC1-10 | — |
| PPM002297 | PGS000842 (WHR) |
PSS001084| European Ancestry| 5,597 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Age-Related Diabetes | OR: 1.03 [0.98, 1.08] | — | — | PC1-10 | — |
| PPM002294 | PGS000842 (WHR) |
PSS001087| European Ancestry| 3,930 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Deficient Diabetes | OR: 1.04 [0.97, 1.11] | — | — | PC1-10 | — |
| PPM020166 | PGS000842 (WHR) |
PSS011301| South Asian Ancestry| 1,282 individuals |
PGP000525 | Yajnik CS et al. Lancet Reg Health Southeast Asia (2023) |Ext. |
Reported Trait: Type 2 diabetes | OR: 1.17 [1.04, 1.32] | — | — | sex | — |
| PPM020181 | PGS000842 (WHR) |
PSS011301| South Asian Ancestry| 1,282 individuals |
PGP000525 | Yajnik CS et al. Lancet Reg Health Southeast Asia (2023) |Ext. |
Reported Trait: BMI | β: 0.05687 (0.027539) | — | — | — | — |
| PPM020186 | PGS000842 (WHR) |
PSS011302| South Asian Ancestry| 830 individuals |
PGP000525 | Yajnik CS et al. Lancet Reg Health Southeast Asia (2023) |Ext. |
Reported Trait: Severe insulin deficiency diabetes | OR: 1.22 [1.06, 1.41] | — | — | sex | — |
| PPM002298 | PGS000843 (WHRadjBMI) |
PSS001086| European Ancestry| 3,194 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Autoimmune Diabetes | OR: 1.19 [1.08, 1.31] | — | — | PC1-10 | — |
| PPM002299 | PGS000843 (WHRadjBMI) |
PSS001087| European Ancestry| 3,930 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Deficient Diabetes | OR: 1.09 [1.02, 1.17] | — | — | PC1-10 | — |
| PPM002300 | PGS000843 (WHRadjBMI) |
PSS001088| European Ancestry| 3,869 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Resistant Diabetes | OR: 1.15 [1.07, 1.24] | — | — | PC1-10 | — |
| PPM002301 | PGS000843 (WHRadjBMI) |
PSS001085| European Ancestry| 4,116 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Obesity-related Diabetes | OR: 1.03 [0.96, 1.1] | — | — | PC1-10 | — |
| PPM002302 | PGS000843 (WHRadjBMI) |
PSS001084| European Ancestry| 5,597 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Age-Related Diabetes | OR: 1.05 [0.99, 1.1] | — | — | PC1-10 | — |
| PPM013121 | PGS002356 (body_WHRadjBMIz.BOLT-LMM) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0167 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013170 | PGS002356 (body_WHRadjBMIz.BOLT-LMM) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0412 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013219 | PGS002356 (body_WHRadjBMIz.BOLT-LMM) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0764 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013268 | PGS002356 (body_WHRadjBMIz.BOLT-LMM) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0414 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013409 | PGS002428 (body_WHRadjBMIz.P+T.0.0001) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0036 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013458 | PGS002428 (body_WHRadjBMIz.P+T.0.0001) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.008 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013507 | PGS002428 (body_WHRadjBMIz.P+T.0.0001) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0406 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013556 | PGS002428 (body_WHRadjBMIz.P+T.0.0001) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0201 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013605 | PGS002477 (body_WHRadjBMIz.P+T.0.001) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0008 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013654 | PGS002477 (body_WHRadjBMIz.P+T.0.001) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0067 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013703 | PGS002477 (body_WHRadjBMIz.P+T.0.001) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0413 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013752 | PGS002477 (body_WHRadjBMIz.P+T.0.001) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0164 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013801 | PGS002526 (body_WHRadjBMIz.P+T.0.01) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0003 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013850 | PGS002526 (body_WHRadjBMIz.P+T.0.01) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0077 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013899 | PGS002526 (body_WHRadjBMIz.P+T.0.01) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0224 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013948 | PGS002526 (body_WHRadjBMIz.P+T.0.01) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0048 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013997 | PGS002575 (body_WHRadjBMIz.P+T.1e-06) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0117 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014046 | PGS002575 (body_WHRadjBMIz.P+T.1e-06) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0221 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014095 | PGS002575 (body_WHRadjBMIz.P+T.1e-06) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0313 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014144 | PGS002575 (body_WHRadjBMIz.P+T.1e-06) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0178 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014193 | PGS002624 (body_WHRadjBMIz.P+T.5e-08) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0117 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014242 | PGS002624 (body_WHRadjBMIz.P+T.5e-08) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.016 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014291 | PGS002624 (body_WHRadjBMIz.P+T.5e-08) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0273 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014340 | PGS002624 (body_WHRadjBMIz.P+T.5e-08) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.016 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014389 | PGS002673 (body_WHRadjBMIz.PolyFun-pred) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0289 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See body_WHRadjBMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014438 | PGS002673 (body_WHRadjBMIz.PolyFun-pred) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0456 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See body_WHRadjBMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014487 | PGS002673 (body_WHRadjBMIz.PolyFun-pred) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | 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 body_WHRadjBMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014536 | PGS002673 (body_WHRadjBMIz.PolyFun-pred) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0442 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See body_WHRadjBMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014585 | PGS002722 (body_WHRadjBMIz.SBayesR) |
PSS009867| African Ancestry| 6,423 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0214 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014634 | PGS002722 (body_WHRadjBMIz.SBayesR) |
PSS009868| East Asian Ancestry| 917 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0413 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014683 | PGS002722 (body_WHRadjBMIz.SBayesR) |
PSS009869| European Ancestry| 43,384 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0746 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014732 | PGS002722 (body_WHRadjBMIz.SBayesR) |
PSS009870| South Asian Ancestry| 8,026 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: Waist-Hip Ratio | — | — | Incremental R2 (full model vs. covariates alone): 0.0416 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM017316 | PGS003484 (LDPred2_WHR) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.009 (0.025) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017356 | PGS003484 (LDPred2_WHR) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index in obsese | β: 0.025 (0.017) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017357 | PGS003484 (LDPred2_WHR) |
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: 0.015 (0.013) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017377 | PGS003484 (LDPred2_WHR) |
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: -0.002 (0.036) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017293 | PGS003484 (LDPred2_WHR) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: 0.019 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017376 | PGS003484 (LDPred2_WHR) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea in obsese | β: 0.012 (0.035) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017400 | PGS003484 (LDPred2_WHR) |
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: -0.002 (0.998) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017410 | PGS003484 (LDPred2_WHR) |
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: 1.015 (0.05) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017294 | PGS003485 (LDPred2_WHRadjBMI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: 0.025 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017358 | PGS003485 (LDPred2_WHRadjBMI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index in obsese | β: 0.028 (0.017) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017359 | PGS003485 (LDPred2_WHRadjBMI) |
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: 0.029 (0.013) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017378 | PGS003485 (LDPred2_WHRadjBMI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea in obsese | β: 0.034 (0.035) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017379 | PGS003485 (LDPred2_WHRadjBMI) |
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: 0.042 (0.035) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017317 | PGS003485 (LDPred2_WHRadjBMI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: 0.038 (0.025) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017401 | PGS003485 (LDPred2_WHRadjBMI) |
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: -0.016 (0.984) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017411 | PGS003485 (LDPred2_WHRadjBMI) |
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: 0.993 (0.05) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM022186 | PGS005095 (WHRadjBMI_GRS) |
PSS011834| Multi-ancestry (including European)| 1,415 individuals |
PGP000686 | Christiansen MR et al. Diabetes (2023) |
Reported Trait: Waist circumference change following weight loss (year 1 to year 2) | β: 0.033 (0.01) | — | — | Intervention arm, sex, baseline age, 4 PCs, baseline weight, baseline height, baseline WC, year-1 weight and waist circumference, year-2 weight | — |
| PPM022187 | PGS005095 (WHRadjBMI_GRS) |
PSS011834| Multi-ancestry (including European)| 1,415 individuals |
PGP000686 | Christiansen MR et al. Diabetes (2023) |
Reported Trait: Waist circumference change following weight loss (year 1 to year 4) | β: 0.026 (0.01) | — | — | Intervention arm, sex, baseline age, 4 PCs, baseline weight, baseline height, baseline WC, year-1 weight and waist circumference, year-4 weight | — |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PSS009868 | — | — | 917 individuals | — | East Asian | — | UKB | — |
| PSS009869 | — | — | 43,384 individuals | — | European | Non-British European | UKB | — |
| PSS009870 | — | — | 8,026 individuals | — | South Asian | — | UKB | — |
| PSS011834 | — | — | 952 individuals | — | European | — | Look AHEAD | — |
| PSS011834 | — | — | 187 individuals | — | African unspecified | — | Look AHEAD | — |
| PSS011834 | — | — | 233 individuals | — | Hispanic or Latin American | — | Look AHEAD | — |
| PSS011834 | — | — | 43 individuals | — | Not reported | — | Look AHEAD | — |
| 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 |
| PSS000372 | 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). | — | 265 individuals, 69.2 % Male samples |
Mean = 19.2 years Sd = 0.66 years |
European | — | TRAILS, TRAILSCC | TRAILS Clinical Cohort |
| PSS000373 | 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). | — | 245 individuals, 69.2 % Male samples |
Mean = 22.04 years Sd = 0.69 years |
European | — | TRAILS, TRAILSCC | TRAILS Clinical Cohort |
| PSS011301 | — | — | [
|
— | South Asian (Indian) |
— | WellGen | — |
| 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 | — |
| PSS000377 | 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). | — | 1,174 individuals, 47.6 % Male samples |
Mean = 19.2 years | European | — | TRAILS | — |
| PSS000378 | 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). | — | 1,095 individuals, 47.6 % Male samples |
Mean = 22.4 years | European | — | TRAILS | — |
| PSS001084 | Moderate Age-Related Diabetes (MARD) vs. controls | — | [
|
— | European | Swedish | ANDIS | — |
| PSS001085 | Moderate Obesity-related Diabetes (MOD) vs. controls | — | [
|
— | European | Swedish | ANDIS | — |
| PSS001086 | Severe Autoimmune Diabetes (SAID) vs. controls | — | [
|
— | European | Swedish | ANDIS | — |
| PSS001087 | Severe Insulin-Deficient Diabetes (SIDD) vs. controls | — | [
|
— | European | Swedish | ANDIS | — |
| PSS001088 | Severe Insulin-Resistant Diabetes (SIRD) vs. controls | — | [
|
— | European | Swedish | ANDIS | — |
| PSS011302 | — | — | [
|
— | South Asian (Indian) |
— | WellGen | — |
| PSS010185 | — | — | 1,115 individuals, 41.1 % Male samples |
Mean = 46.18 years | Hispanic or Latin American | — | HCHS, SOL | — |
| PSS009867 | — | — | 6,423 individuals | — | African unspecified | — | UKB | — |