PGS Catalog - platelet count [EFO:0004309] (Polygenic Trait)

Trait: platelet count

Experimental Factor Ontology (EFO) Information
Identifier	EFO_0004309
Description	The number of PLATELETS per unit volume in a sample of venous BLOOD.
Trait category	Hematological measurement
Synonym	blood platelet number
Mapped terms	4 mapped terms MeSH:D010976 MedDRA:10035525 NCIt:C51951 SNOMEDCT:61928009

Associated Polygenic Score(s)

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)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

Performance Metrics

Disclaimer: The performance metrics are displayed as reported by the source studies. It is important to note that metrics are not necessarily comparable with each other. For example, metrics depend on the sample characteristics (described by the PGS Catalog Sample Set [PSS] ID), phenotyping, and statistical modelling. Please refer to the source publication for additional guidance on performance.

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
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)	—
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	—
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	—
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	—
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	—
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	—
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	—
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	—
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	—
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	—
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	—
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	—
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
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	—
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	—
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)	—
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	—	—
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	—	—
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)

Evaluated Samples

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
PSS010911	—	—	19,418 individuals, 46.0 % Male samples	Mean = 56.9 years Sd = 7.9 years	European	white British	UKB	—
PSS010659	—	—	6,277 individuals, 45.0 % Male samples	Mean = 54.5 years Sd = 8.4 years	European	Italian	UKB	—
PSS011145	—	—	65,931 individuals	—	European (white British ancestry)	—	UKB	—
PSS010407	—	—	1,750 individuals, 33.0 % Male samples	Mean = 52.5 years Sd = 7.8 years	East Asian	Chinese	UKB	—
PSS008787	—	—	6,436 individuals	—	European	Italy (South Europe)	UKB	—
PSS011155	—	—	1,157 individuals	—	African unspecified	—	UKB	—
PSS011166	—	—	7,746 individuals	—	East Asian, Other admixed ancestry	East Asian, Other admixed ancestry	UKB	—
PSS007903	—	—	2,343 individuals	—	African American or Afro-Caribbean	Carribean	UKB	—
PSS011470	—	—	5,341 individuals	—	European	—	AllofUs	—
PSS011471	—	—	7,072 individuals	—	South Asian	—	G&H	—
PSS000290	—	—	2,314 individuals	—	European (French Canadian)	—	CARTaGENE	—
PSS000291	—	—	39,260 individuals	—	European	—	INTERVAL	—
PSS008561	—	—	1,153 individuals	—	Greater Middle Eastern (Middle Eastern, North African or Persian)	Iran (Middle East)	UKB	—
PSS010827	—	—	3,991 individuals, 38.0 % Male samples	Mean = 54.3 years Sd = 7.5 years	European	Polish	UKB	—
PSS009459	—	—	19,422 individuals	—	European	UK (+ Ireland)	UKB	—
PSS000911	—	—	13,989 individuals	—	Greater Middle Eastern (Middle Eastern, North African or Persian) (Qatari)	—	QBB	—
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	—
PSS010323	—	—	2,330 individuals, 37.0 % Male samples	Mean = 52.4 years Sd = 8.0 years	African American or Afro-Caribbean	Caribbean	UKB	—
PSS009815	—	—	6,143 individuals	—	African unspecified	—	UKB	—
PSS009816	—	—	895 individuals	—	East Asian	—	UKB	—
PSS009233	—	—	4,002 individuals	—	European	Poland (NE Europe)	UKB	—
PSS009817	—	—	42,007 individuals	—	European	Non-British European	UKB	—
PSS008339	—	—	6,076 individuals	—	South Asian	India (South Asia)	UKB	—
PSS000148	—	—	38,939 individuals, 49.0 % Male samples	Mean = 43.75 years Range = [18.0, 76.4] years	European	—	INTERVAL	—
PSS009818	—	—	7,730 individuals	—	South Asian	—	UKB	—
PSS010743	—	—	3,682 individuals, 47.0 % Male samples	Mean = 51.9 years Sd = 8.1 years	African unspecified	Nigerian	UKB	—
PSS011364	—	—	56,192 individuals	—	European	—	UKB	—
PSS010491	—	—	6,024 individuals, 55.0 % Male samples	Mean = 53.3 years Sd = 8.4 years	South Asian	Indian	UKB	—
PSS010239	—	—	2,263 individuals, 45.0 % Male samples	Mean = 58.0 years Sd = 7.1 years	European	Ashkenazi	UKB	—
PSS011109	—	—	2,813 individuals	—	European (non-white British ancestry)	—	UKB	—
PSS011113	—	—	1,433 individuals	—	South Asian	—	UKB	—
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	—
PSS000174	—	—	78,246 individuals, 46.0 % Male samples	Mean = 57.23 years Range = [39.98, 70.43] years	European	—	UKB	—
PSS006950	—	—	65,637 individuals	—	European	white British ancestry	UKB	Testing cohort (heldout set)
PSS009007	—	—	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.
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.
PSS008116	—	—	1,762 individuals	—	East Asian	China (East Asia)	UKB	—