| Trait Information | |
| Identifier | PATO_0000070 |
| Description |
|
Trait category |
Other measurement
|
| Child trait(s) |
16 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) |
|---|---|---|---|---|---|---|
| PGS000113 (wbc) |
PGP000051 | Xu Y et al. Cell Genom (2022) |
White blood cell count | leukocyte quantity | 28,383 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000113/ScoringFiles/PGS000113.txt.gz | |
| PGS000191 (wbc) |
PGP000078 | Vuckovic D et al. Cell (2020) |
White blood cell count | leukocyte quantity | 636 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000191/ScoringFiles/PGS000191.txt.gz |
| PGS000305 (GRS31_FG) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Fasting glucose | blood glucose amount | 31 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000305/ScoringFiles/PGS000305.txt.gz |
| PGS000307 (GRS12_FI) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Fasting insulin | blood insulin amount | 12 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000307/ScoringFiles/PGS000307.txt.gz |
| PGS000315 (GRS7_lgE) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Immunoglobulin E (IgE) | serum IgE amount | 7 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000315/ScoringFiles/PGS000315.txt.gz |
| PGS000669 (snpnet.Albumin) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Albumin [g/L] | serum albumin amount | 11,912 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000669/ScoringFiles/PGS000669.txt.gz |
| PGS000678 (snpnet.Creatinine) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Creatinine [umol/L] | serum creatinine amount | 21,027 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000678/ScoringFiles/PGS000678.txt.gz |
| PGS000691 (snpnet.Non_albumin_protein) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Non-albumin protein [g/L] | level of serum globulin type protein | 18,670 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000691/ScoringFiles/PGS000691.txt.gz |
| PGS000693 (snpnet.Potassium_in_urine) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Potassium in urine [mmol/L] | urine potassium amount | 2,423 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000693/ScoringFiles/PGS000693.txt.gz |
| PGS000701 (snpnet.Urea) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Urea [mmol/L] | serum urea amount | 12,351 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000701/ScoringFiles/PGS000701.txt.gz |
| PGS000836 (F-INS) |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Fasting insulin | blood insulin amount | 223 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000836/ScoringFiles/PGS000836.txt.gz |
| PGS000838 (Fasting_Glucose) |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Fasting glucose | blood glucose amount | 224 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000838/ScoringFiles/PGS000838.txt.gz |
| PGS000844 (VAT_Mass) |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Predicted visceral adipose tissue | visceral adipose tissue quantity | 518 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000844/ScoringFiles/PGS000844.txt.gz |
| PGS001239 (GBE_INI30000) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
White blood cell count | leukocyte quantity | 13,785 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001239/ScoringFiles/PGS001239.txt.gz |
| PGS001350 (MAGICTA_EUR_PGS_FG) |
PGP000246 | Chen J et al. Nat Genet (2021) |
Fasting glucose | blood glucose amount | 1,023,373 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001350/ScoringFiles/PGS001350.txt.gz | |
| PGS001351 (MAGICTA_EUR_PGS_FI) |
PGP000246 | Chen J et al. Nat Genet (2021) |
Fasting insulin | blood insulin amount | 1,025,098 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001351/ScoringFiles/PGS001351.txt.gz | |
| PGS001408 (GBE_INI30280) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Immature reticulocyte fraction | reticulocyte amount | 10,871 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001408/ScoringFiles/PGS001408.txt.gz |
| PGS001772 (GBE_INI23005) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
ZEBRA antigen for Epstein-Barr Virus | blood immunoglobulin amount | 158 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001772/ScoringFiles/PGS001772.txt.gz |
| PGS001886 (portability-PLR_albumin) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Albumin | serum albumin amount | 60,423 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001886/ScoringFiles/PGS001886.txt.gz |
| PGS001930 (portability-PLR_immature_reticulocyte_frac) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Immature reticulocyte fraction | reticulocyte amount | 39,162 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001930/ScoringFiles/PGS001930.txt.gz |
| PGS001945 (portability-PLR_log_creatinine) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Creatinine | serum creatinine amount | 76,800 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001945/ScoringFiles/PGS001945.txt.gz |
| PGS001962 (portability-PLR_log_leukocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
White blood cell (leukocyte) count | leukocyte quantity | 80,228 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001962/ScoringFiles/PGS001962.txt.gz |
| PGS001974 (portability-PLR_log_potassium_urine) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Potassium in urine | urine potassium amount | 21,512 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001974/ScoringFiles/PGS001974.txt.gz |
| PGS002099 (portability-ldpred2_albumin) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Albumin | serum albumin amount | 816,264 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002099/ScoringFiles/PGS002099.txt.gz |
| PGS002147 (portability-ldpred2_immature_reticulocyte_frac) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Immature reticulocyte fraction | reticulocyte amount | 664,696 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002147/ScoringFiles/PGS002147.txt.gz |
| PGS002163 (portability-ldpred2_log_creatinine) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Creatinine | serum creatinine amount | 835,964 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002163/ScoringFiles/PGS002163.txt.gz |
| PGS002180 (portability-ldpred2_log_leukocyte) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
White blood cell (leukocyte) count | leukocyte quantity | 846,337 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002180/ScoringFiles/PGS002180.txt.gz |
| PGS002192 (portability-ldpred2_log_potassium_urine) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Potassium in urine | urine potassium amount | 881,567 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002192/ScoringFiles/PGS002192.txt.gz |
| PGS002295 (GRS413_IGF-1) |
PGP000325 | Tsai CW et al. American Journal of Cancer Research (2022) |
Insulin growth-like factor-1 level | insulin-like protein amount | 413 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002295/ScoringFiles/PGS002295.txt.gz |
| PGS002357 (blood_WHITE_COUNT.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002357/ScoringFiles/PGS002357.txt.gz |
| PGS002380 (blood_WHITE_COUNT.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 920,936 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002380/ScoringFiles/PGS002380.txt.gz |
| PGS002429 (blood_WHITE_COUNT.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 13,898 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002429/ScoringFiles/PGS002429.txt.gz |
| PGS002478 (blood_WHITE_COUNT.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 35,005 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002478/ScoringFiles/PGS002478.txt.gz |
| PGS002527 (blood_WHITE_COUNT.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 141,866 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002527/ScoringFiles/PGS002527.txt.gz |
| PGS002576 (blood_WHITE_COUNT.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 4,921 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002576/ScoringFiles/PGS002576.txt.gz |
| PGS002625 (blood_WHITE_COUNT.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 3,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002625/ScoringFiles/PGS002625.txt.gz |
| PGS002674 (blood_WHITE_COUNT.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 491,764 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002674/ScoringFiles/PGS002674.txt.gz |
| PGS002723 (blood_WHITE_COUNT.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
White blood cell count | leukocyte quantity | 983,751 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002723/ScoringFiles/PGS002723.txt.gz |
| PGS002811 (ASATadjbmi3_weights_select) |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Abdominal adipose tissue volumes adjusted for BMI and height | adipose amount | 1,125,301 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002811/ScoringFiles/PGS002811.txt.gz |
| PGS002812 (GFATadjbmi3_weights_select) |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Gluteofemoral adipose tissue volumes adjusted for BMI and height | adipose amount | 1,125,301 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002812/ScoringFiles/PGS002812.txt.gz |
| PGS002813 (VATadjbmi3_weights_select) |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Visceral adipose tissue volumes adjusted for BMI and height | visceral adipose tissue quantity | 1,125,301 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002813/ScoringFiles/PGS002813.txt.gz |
| PGS002924 (ExPRSweb_FPG_FGovertime-corrected_LASSOSUM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 281 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002924/ScoringFiles/PGS002924.txt.gz | |
| PGS002925 (ExPRSweb_FPG_FGovertime-corrected_PT_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 13 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002925/ScoringFiles/PGS002925.txt.gz | |
| PGS002926 (ExPRSweb_FPG_FGovertime-corrected_PLINK_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 13 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002926/ScoringFiles/PGS002926.txt.gz | |
| PGS002927 (ExPRSweb_FPG_FGovertime-corrected_DBSLMM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 5,106 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002927/ScoringFiles/PGS002927.txt.gz | |
| PGS002928 (ExPRSweb_FPG_FGovertime-corrected_PRSCS_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 392 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002928/ScoringFiles/PGS002928.txt.gz | |
| PGS002929 (ExPRSweb_FPG_MAGIC-FastingGlucose_LASSOSUM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 71,791 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002929/ScoringFiles/PGS002929.txt.gz | |
| PGS002930 (ExPRSweb_FPG_MAGIC-FastingGlucose_PT_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 264 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002930/ScoringFiles/PGS002930.txt.gz | |
| PGS002931 (ExPRSweb_FPG_MAGIC-FastingGlucose_PLINK_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 273 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002931/ScoringFiles/PGS002931.txt.gz | |
| PGS002932 (ExPRSweb_FPG_MAGIC-FastingGlucose_DBSLMM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 1,202,298 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002932/ScoringFiles/PGS002932.txt.gz | |
| PGS002933 (ExPRSweb_FPG_MAGIC-FastingGlucose_PRSCS_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Fasting plasma glucose | blood glucose amount | 1,007,415 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002933/ScoringFiles/PGS002933.txt.gz | |
| PGS002944 (ExPRSweb_Glucose_FGovertime-corrected_LASSOSUM_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 519 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002944/ScoringFiles/PGS002944.txt.gz | |
| PGS002945 (ExPRSweb_Glucose_FGovertime-corrected_PT_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 1,479 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002945/ScoringFiles/PGS002945.txt.gz | |
| PGS002946 (ExPRSweb_Glucose_FGovertime-corrected_PLINK_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 1,553 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002946/ScoringFiles/PGS002946.txt.gz | |
| PGS002947 (ExPRSweb_Glucose_FGovertime-corrected_DBSLMM_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 1,501 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002947/ScoringFiles/PGS002947.txt.gz | |
| PGS002948 (ExPRSweb_Glucose_FGovertime-corrected_PRSCS_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 392 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002948/ScoringFiles/PGS002948.txt.gz | |
| PGS002949 (ExPRSweb_Glucose_MAGIC-FastingGlucose_LASSOSUM_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 72,352 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002949/ScoringFiles/PGS002949.txt.gz | |
| PGS002950 (ExPRSweb_Glucose_MAGIC-FastingGlucose_PT_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 38 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002950/ScoringFiles/PGS002950.txt.gz | |
| PGS002951 (ExPRSweb_Glucose_MAGIC-FastingGlucose_PLINK_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 38 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002951/ScoringFiles/PGS002951.txt.gz | |
| PGS002952 (ExPRSweb_Glucose_MAGIC-FastingGlucose_DBSLMM_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 1,501 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002952/ScoringFiles/PGS002952.txt.gz | |
| PGS002953 (ExPRSweb_Glucose_MAGIC-FastingGlucose_PRSCS_UKB_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Glucose | blood glucose amount | 1,008,035 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002953/ScoringFiles/PGS002953.txt.gz | |
| PGS003336 (CVGRS_FPG) |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Fasting plasma glucose | blood glucose amount | 56 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003336/ScoringFiles/PGS003336.txt.gz |
| PGS003345 (ALLGRS_FPG) |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Fasting plasma glucose | blood glucose amount | 60 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003345/ScoringFiles/PGS003345.txt.gz |
| PGS003378 (PSA_PGS_128) |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Prostate-specific antigen (PSA) levels | prostate specific antigen amount | 128 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003378/ScoringFiles/PGS003378.txt.gz |
| PGS003379 (PSA_PGS_CSx) |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Prostate-specific antigen (PSA) levels | prostate specific antigen amount | 1,071,278 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003379/ScoringFiles/PGS003379.txt.gz |
| PGS003466 (LDPred2_FI) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Fasting insulin | blood insulin amount | 850,362 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003466/ScoringFiles/PGS003466.txt.gz |
| PGS003483 (LDPred2_WBC) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
White blood cell count | leukocyte quantity | 860,306 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003483/ScoringFiles/PGS003483.txt.gz |
| PGS003526 (cont-decay-log_creatinine) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Creatinine | serum creatinine amount | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003526/ScoringFiles/PGS003526.txt.gz |
| PGS003541 (cont-decay-log_leukocyte) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
White blood cell (leukocyte) count | leukocyte quantity | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003541/ScoringFiles/PGS003541.txt.gz |
| PGS003924 (INI30000) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
White blood cell (leukocyte) count | leukocyte quantity | 17,890 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003924/ScoringFiles/PGS003924.txt.gz |
| PGS003950 (INI30280) |
PGP000502 | Tanigawa Y et al. AJHG (2023) |
Immature reticulocyte fraction | reticulocyte amount | 16,307 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003950/ScoringFiles/PGS003950.txt.gz |
| PGS004328 (X30600.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Albumin | serum albumin amount | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004328/ScoringFiles/PGS004328.txt.gz |
| PGS004334 (X30700.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
Creatinine (umol/L) | serum creatinine amount | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004334/ScoringFiles/PGS004334.txt.gz |
| PGS004345 (X30000.score) |
PGP000561 | Jung H et al. Commun Biol (2024) |
White blood cell (leukocyte) count | leukocyte quantity | 1,059,939 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004345/ScoringFiles/PGS004345.txt.gz |
| PGS004707 (Albumin_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Albumin | serum albumin amount | 589,303 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004707/ScoringFiles/PGS004707.txt.gz |
| PGS004708 (Albumin_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Albumin | serum albumin amount | 2,071,924 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004708/ScoringFiles/PGS004708.txt.gz |
| PGS004709 (Albumin_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Albumin | serum albumin amount | 2,659,200 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004709/ScoringFiles/PGS004709.txt.gz |
| PGS004710 (Albumin_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Albumin | serum albumin amount | 2,071,924 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004710/ScoringFiles/PGS004710.txt.gz |
| PGS004747 (creatinine_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Serum creatinine | serum creatinine amount | 609,027 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004747/ScoringFiles/PGS004747.txt.gz |
| PGS004748 (creatinine_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Serum creatinine | serum creatinine amount | 2,653,053 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004748/ScoringFiles/PGS004748.txt.gz |
| PGS004749 (creatinine_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Serum creatinine | serum creatinine amount | 1,469,062 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004749/ScoringFiles/PGS004749.txt.gz |
| PGS004750 (creatinine_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
Serum creatinine | serum creatinine amount | 2,653,053 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004750/ScoringFiles/PGS004750.txt.gz |
| PGS004855 (WBC_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
White blood cell count | leukocyte quantity | 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 quantity | 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 quantity | 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 quantity | 6,594,323 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004858/ScoringFiles/PGS004858.txt.gz |
| PGS004906 (PRS60_TSH) |
PGP000639 | Mulder TA et al. Eur J Endocrinol (2023) |
Thyroid stimulating hormone concentration | thyroid stimulating hormone amount | 60 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004906/ScoringFiles/PGS004906.txt.gz |
| PGS005098 (psa2024_prs_318) |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Prostate specific antigen | prostate specific antigen amount | 318 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005098/ScoringFiles/PGS005098.txt.gz |
| PGS005099 (psa2024_prs_447) |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Prostate specific antigen | prostate specific antigen amount | 447 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005099/ScoringFiles/PGS005099.txt.gz |
| PGS005100 (psa2024_prs_disc) |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Prostate specific antigen | prostate specific antigen amount | 1,070,227 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005100/ScoringFiles/PGS005100.txt.gz |
| PGS005101 (psa2024_prs_joint) |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Prostate specific antigen | prostate specific antigen amount | 1,070,230 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005101/ScoringFiles/PGS005101.txt.gz |
| PGS005107 (PGS-PSA111) |
PGP000696 | Shi M et al. EBioMedicine (2023) |
Prostate-specific antigen (PSA) levels | prostate specific antigen amount | 111 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005107/ScoringFiles/PGS005107.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 |
|---|---|---|---|---|---|---|---|---|---|
| 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) | — |
| 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 | — | — |
| PPM000775 | PGS000305 (GRS31_FG) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Fasting glucose (mmol/l) | — | — | R²: 0.0367 | Sex, age, age^2 | — |
| PPM000777 | PGS000307 (GRS12_FI) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Fasting insulin (mU/I) | — | — | R²: 0.0145 | Sex, age | — |
| PPM000785 | PGS000315 (GRS7_lgE) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Immunoglobulin E (kU/I) | — | — | R²: 0.0206 | Sex, age | — |
| PPM001397 | PGS000669 (snpnet.Albumin) |
PSS000627| African Ancestry| 5,573 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Albumin [g/L] | — | — | R²: 0.10081 Spearman's ρ: 0.121 |
Age, sex, PCs(1-40) | — |
| PPM001432 | PGS000669 (snpnet.Albumin) |
PSS000628| East Asian Ancestry| 984 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Albumin [g/L] | — | — | R²: 0.08916 Spearman's ρ: 0.198 |
Age, sex, PCs(1-40) | — |
| PPM001467 | PGS000669 (snpnet.Albumin) |
PSS000629| European Ancestry| 21,516 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Albumin [g/L] | — | — | R²: 0.13257 Spearman's ρ: 0.25 |
Age, sex, PCs(1-40) | — |
| PPM001502 | PGS000669 (snpnet.Albumin) |
PSS000630| South Asian Ancestry| 6,687 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Albumin [g/L] | — | — | R²: 0.15158 Spearman's ρ: 0.216 |
Age, sex, PCs(1-40) | — |
| PPM001537 | PGS000669 (snpnet.Albumin) |
PSS000631| European Ancestry| 58,196 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Albumin [g/L] | — | — | R²: 0.13054 Spearman's ρ: 0.262 |
Age, sex, PCs(1-40) | — |
| PPM007271 | PGS000669 (snpnet.Albumin) |
PSS007067| East Asian Ancestry| 1,472 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Albumin | — | — | R²: 0.02049 [0.0072, 0.03378] Incremental R2 (full-covars): 0.00193 PGS R2 (no covariates): 0.05108 [0.03075, 0.0714] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007274 | PGS000669 (snpnet.Albumin) |
PSS007070| European Ancestry| 59,097 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Albumin | — | — | R²: 0.03793 [0.0351, 0.04076] Incremental R2 (full-covars): 0.00267 PGS R2 (no covariates): 0.06779 [0.06413, 0.07145] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007270 | PGS000669 (snpnet.Albumin) |
PSS007066| African Ancestry| 5,658 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Albumin | — | — | R²: 0.07255 [0.06041, 0.08469] Incremental R2 (full-covars): 0.00087 PGS R2 (no covariates): 0.01339 [0.00784, 0.01894] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007272 | PGS000669 (snpnet.Albumin) |
PSS007068| European Ancestry| 21,759 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Albumin | — | — | R²: 0.04362 [0.03866, 0.04859] Incremental R2 (full-covars): 0.00252 PGS R2 (no covariates): 0.0618 [0.05601, 0.06759] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007273 | PGS000669 (snpnet.Albumin) |
PSS007069| South Asian Ancestry| 6,786 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Albumin | — | — | R²: 0.0904 [0.07829, 0.10251] Incremental R2 (full-covars): 0.00198 PGS R2 (no covariates): 0.04739 [0.03821, 0.05657] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001406 | PGS000678 (snpnet.Creatinine) |
PSS000667| African Ancestry| 6,016 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Creatinine [umol/L] | — | — | R²: 0.37051 Spearman's ρ: 0.146 |
Age, sex, PCs(1-40) | — |
| PPM001441 | PGS000678 (snpnet.Creatinine) |
PSS000668| East Asian Ancestry| 1,081 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Creatinine [umol/L] | — | — | R²: 0.44314 Spearman's ρ: 0.229 |
Age, sex, PCs(1-40) | — |
| PPM001476 | PGS000678 (snpnet.Creatinine) |
PSS000674| European Ancestry| 23,576 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Creatinine [umol/L] | — | — | R²: 0.3939 Spearman's ρ: 0.325 |
Age, sex, PCs(1-40) | — |
| PPM001511 | PGS000678 (snpnet.Creatinine) |
PSS000675| South Asian Ancestry| 7,339 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Creatinine [umol/L] | — | — | R²: 0.40587 Spearman's ρ: 0.283 |
Age, sex, PCs(1-40) | — |
| PPM001546 | PGS000678 (snpnet.Creatinine) |
PSS000676| European Ancestry| 63,758 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Creatinine [umol/L] | — | — | R²: 0.41565 Spearman's ρ: 0.337 |
Age, sex, PCs(1-40) | — |
| PPM001579 | PGS000678 (snpnet.Creatinine) |
PSS000800| European Ancestry| 2,129 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Creatinine [umol/L] | — | — | Spearman's ρ: 0.24 | Age, sex | — |
| PPM007315 | PGS000678 (snpnet.Creatinine) |
PSS007131| African Ancestry| 6,097 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Creatinine | — | — | R²: 0.1805 [0.16358, 0.19743] Incremental R2 (full-covars): 0.00023 PGS R2 (no covariates): 0.0048 [0.00145, 0.00815] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007316 | PGS000678 (snpnet.Creatinine) |
PSS007132| East Asian Ancestry| 1,615 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Creatinine | — | — | R²: 0.30903 [0.27263, 0.34543] Incremental R2 (full-covars): 0.0006 PGS R2 (no covariates): 0.01099 [0.00116, 0.02081] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007317 | PGS000678 (snpnet.Creatinine) |
PSS007133| European Ancestry| 23,762 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Creatinine | — | — | R²: 0.23065 [0.22147, 0.23982] Incremental R2 (full-covars): 0.00126 PGS R2 (no covariates): 0.04345 [0.0385, 0.0484] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007318 | PGS000678 (snpnet.Creatinine) |
PSS007134| South Asian Ancestry| 7,427 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Creatinine | — | — | R²: 0.2107 [0.19466, 0.22675] Incremental R2 (full-covars): 0.00063 PGS R2 (no covariates): 0.02244 [0.01595, 0.02892] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007319 | PGS000678 (snpnet.Creatinine) |
PSS007135| European Ancestry| 64,433 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Creatinine | — | — | R²: 0.23859 [0.23298, 0.24421] Incremental R2 (full-covars): 0.00127 PGS R2 (no covariates): 0.04554 [0.04246, 0.04861] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001419 | PGS000691 (snpnet.Non_albumin_protein) |
PSS000729| African Ancestry| 5,573 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Non-albumin protein [g/L] | — | — | R²: 0.07146 Spearman's ρ: 0.16 |
Age, sex, PCs(1-40) | — |
| PPM001454 | PGS000691 (snpnet.Non_albumin_protein) |
PSS000730| East Asian Ancestry| 984 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Non-albumin protein [g/L] | — | — | R²: 0.0886 Spearman's ρ: 0.216 |
Age, sex, PCs(1-40) | — |
| PPM001489 | PGS000691 (snpnet.Non_albumin_protein) |
PSS000731| European Ancestry| 21,516 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Non-albumin protein [g/L] | — | — | R²: 0.12839 Spearman's ρ: 0.31 |
Age, sex, PCs(1-40) | — |
| PPM001524 | PGS000691 (snpnet.Non_albumin_protein) |
PSS000732| South Asian Ancestry| 6,687 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Non-albumin protein [g/L] | — | — | R²: 0.15158 Spearman's ρ: 0.292 |
Age, sex, PCs(1-40) | — |
| PPM001559 | PGS000691 (snpnet.Non_albumin_protein) |
PSS000733| European Ancestry| 58,196 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Non-albumin protein [g/L] | — | — | R²: 0.12321 Spearman's ρ: 0.311 |
Age, sex, PCs(1-40) | — |
| PPM007380 | PGS000691 (snpnet.Non_albumin_protein) |
PSS006916| African Ancestry| 5,573 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Non-albumin protein | — | — | R²: 0.02946 [0.02136, 0.03756] Incremental R2 (full-covars): 0.02412 PGS R2 (no covariates): 0.02397 [0.01663, 0.03132] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007381 | PGS000691 (snpnet.Non_albumin_protein) |
PSS006917| East Asian Ancestry| 1,436 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Non-albumin protein | — | — | R²: 0.09152 [0.06547, 0.11756] Incremental R2 (full-covars): 0.05716 PGS R2 (no covariates): 0.06672 [0.04387, 0.08956] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007382 | PGS000691 (snpnet.Non_albumin_protein) |
PSS006918| European Ancestry| 21,514 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Non-albumin protein | — | — | R²: 0.09985 [0.09278, 0.10691] Incremental R2 (full-covars): 0.09891 PGS R2 (no covariates): 0.09939 [0.09234, 0.10644] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007383 | PGS000691 (snpnet.Non_albumin_protein) |
PSS006919| South Asian Ancestry| 6,643 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Non-albumin protein | — | — | R²: 0.09351 [0.08124, 0.10578] Incremental R2 (full-covars): 0.08375 PGS R2 (no covariates): 0.08889 [0.07686, 0.10091] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007384 | PGS000691 (snpnet.Non_albumin_protein) |
PSS006920| European Ancestry| 58,191 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Non-albumin protein | — | — | R²: 0.10006 [0.09577, 0.10436] Incremental R2 (full-covars): 0.10006 PGS R2 (no covariates): 0.10018 [0.09588, 0.10448] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001421 | PGS000693 (snpnet.Potassium_in_urine) |
PSS000739| African Ancestry| 5,786 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Potassium in urine [mmol/L] | — | — | R²: 0.03001 Spearman's ρ: 0.011 |
Age, sex, PCs(1-40) | — |
| PPM001456 | PGS000693 (snpnet.Potassium_in_urine) |
PSS000740| East Asian Ancestry| 1,045 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Potassium in urine [mmol/L] | — | — | R²: 0.01859 Spearman's ρ: 0.038 |
Age, sex, PCs(1-40) | — |
| PPM001491 | PGS000693 (snpnet.Potassium_in_urine) |
PSS000741| European Ancestry| 22,839 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Potassium in urine [mmol/L] | — | — | R²: 0.04847 Spearman's ρ: 0.036 |
Age, sex, PCs(1-40) | — |
| PPM001589 | PGS000693 (snpnet.Potassium_in_urine) |
PSS000835| European Ancestry| 2,117 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Potassium in urine [mmol/L] | — | — | Spearman's ρ: 0.065 | Age, sex | — |
| PPM001526 | PGS000693 (snpnet.Potassium_in_urine) |
PSS000742| South Asian Ancestry| 6,998 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Potassium in urine [mmol/L] | — | — | R²: 0.06035 Spearman's ρ: 0.027 |
Age, sex, PCs(1-40) | — |
| PPM001561 | PGS000693 (snpnet.Potassium_in_urine) |
PSS000743| European Ancestry| 62,013 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Potassium in urine [mmol/L] | — | — | R²: 0.04727 Spearman's ρ: 0.042 |
Age, sex, PCs(1-40) | — |
| PPM007390 | PGS000693 (snpnet.Potassium_in_urine) |
PSS007056| African Ancestry| 6,238 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Potassium in urine | — | — | R²: 0.01656 [0.01041, 0.02271] Incremental R2 (full-covars): 1e-05 PGS R2 (no covariates): 0.0 [-0.00009, 0.0001] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007391 | PGS000693 (snpnet.Potassium_in_urine) |
PSS007057| East Asian Ancestry| 1,642 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Potassium in urine | — | — | R²: 0.02067 [0.00733, 0.03401] Incremental R2 (full-covars): 6e-05 PGS R2 (no covariates): 0.00061 [-0.00173, 0.00295] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007392 | PGS000693 (snpnet.Potassium_in_urine) |
PSS007058| European Ancestry| 24,108 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Potassium in urine | — | — | R²: 0.02543 [0.02157, 0.02928] Incremental R2 (full-covars): 6e-05 PGS R2 (no covariates): 0.00097 [0.0002, 0.00175] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007393 | PGS000693 (snpnet.Potassium_in_urine) |
PSS007059| South Asian Ancestry| 7,464 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Potassium in urine | — | — | R²: 0.03366 [0.02581, 0.04151] Incremental R2 (full-covars): 6e-05 PGS R2 (no covariates): 0.00101 [-0.00039, 0.00242] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007394 | PGS000693 (snpnet.Potassium_in_urine) |
PSS007060| European Ancestry| 65,536 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Potassium in urine | — | — | R²: 0.02071 [0.01858, 0.02283] Incremental R2 (full-covars): 7e-05 PGS R2 (no covariates): 0.00149 [0.0009, 0.00207] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001429 | PGS000701 (snpnet.Urea) |
PSS000782| African Ancestry| 6,015 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Urea [mmol/L] | — | — | R²: 0.13035 Spearman's ρ: 0.111 |
Age, sex, PCs(1-40) | — |
| PPM001464 | PGS000701 (snpnet.Urea) |
PSS000783| East Asian Ancestry| 1,080 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Urea [mmol/L] | — | — | R²: 0.12883 Spearman's ρ: 0.147 |
Age, sex, PCs(1-40) | — |
| PPM001499 | PGS000701 (snpnet.Urea) |
PSS000784| European Ancestry| 23,562 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Urea [mmol/L] | — | — | R²: 0.15772 Spearman's ρ: 0.205 |
Age, sex, PCs(1-40) | — |
| PPM001534 | PGS000701 (snpnet.Urea) |
PSS000785| South Asian Ancestry| 7,336 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Urea [mmol/L] | — | — | R²: 0.15556 Spearman's ρ: 0.174 |
Age, sex, PCs(1-40) | — |
| PPM001569 | PGS000701 (snpnet.Urea) |
PSS000786| European Ancestry| 63,745 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Urea [mmol/L] | — | — | R²: 0.15214 Spearman's ρ: 0.223 |
Age, sex, PCs(1-40) | — |
| PPM007430 | PGS000701 (snpnet.Urea) |
PSS007116| African Ancestry| 6,097 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Urea | — | — | R²: 0.09637 [0.08274, 0.11001] Incremental R2 (full-covars): 0.00328 PGS R2 (no covariates): 0.00567 [0.00203, 0.00931] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007431 | PGS000701 (snpnet.Urea) |
PSS007117| East Asian Ancestry| 1,614 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Urea | — | — | R²: 0.12047 [0.09154, 0.1494] Incremental R2 (full-covars): 0.00765 PGS R2 (no covariates): 0.01582 [0.00409, 0.02754] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007432 | PGS000701 (snpnet.Urea) |
PSS007118| European Ancestry| 23,752 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Urea | — | — | R²: 0.10315 [0.096, 0.11031] Incremental R2 (full-covars): 0.01247 PGS R2 (no covariates): 0.03634 [0.03178, 0.0409] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007433 | PGS000701 (snpnet.Urea) |
PSS007119| South Asian Ancestry| 7,427 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Urea | — | — | R²: 0.11244 [0.09926, 0.12562] Incremental R2 (full-covars): 0.00839 PGS R2 (no covariates): 0.02026 [0.01408, 0.02643] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007434 | PGS000701 (snpnet.Urea) |
PSS007120| European Ancestry| 64,425 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Urea | — | — | R²: 0.08868 [0.08459, 0.09278] Incremental R2 (full-covars): 0.0137 PGS R2 (no covariates): 0.04033 [0.03742, 0.04324] |
age, sex, UKB array type, Genotype PCs | — |
| PPM002263 | PGS000836 (F-INS) |
PSS001086| European Ancestry| 3,194 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Autoimmune Diabetes | OR: 0.97 [0.88, 1.08] | — | — | PC1-10 | — |
| PPM002264 | PGS000836 (F-INS) |
PSS001087| European Ancestry| 3,930 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Deficient Diabetes | OR: 0.98 [0.91, 1.05] | — | — | PC1-10 | — |
| PPM002266 | PGS000836 (F-INS) |
PSS001085| European Ancestry| 4,116 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Obesity-related Diabetes | OR: 1.04 [0.97, 1.11] | — | — | PC1-10 | — |
| PPM002267 | PGS000836 (F-INS) |
PSS001084| European Ancestry| 5,597 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Age-Related Diabetes | OR: 0.96 [0.91, 1.01] | — | — | PC1-10 | — |
| PPM002265 | PGS000836 (F-INS) |
PSS001088| European Ancestry| 3,869 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Resistant Diabetes | OR: 1.22 [1.14, 1.31] | — | — | PC1-10 | — |
| PPM002273 | PGS000838 (Fasting_Glucose) |
PSS001086| European Ancestry| 3,194 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Autoimmune Diabetes | OR: 1.09 [0.98, 1.2] | — | — | PC1-10 | — |
| PPM002275 | PGS000838 (Fasting_Glucose) |
PSS001088| European Ancestry| 3,869 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Resistant Diabetes | OR: 1.05 [0.98, 1.13] | — | — | PC1-10 | — |
| PPM002276 | PGS000838 (Fasting_Glucose) |
PSS001085| European Ancestry| 4,116 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Obesity-related Diabetes | OR: 1.38 [1.29, 1.48] | — | — | PC1-10 | — |
| PPM002277 | PGS000838 (Fasting_Glucose) |
PSS001084| European Ancestry| 5,597 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Age-Related Diabetes | OR: 1.33 [1.26, 1.41] | — | — | PC1-10 | — |
| PPM002274 | PGS000838 (Fasting_Glucose) |
PSS001087| European Ancestry| 3,930 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Deficient Diabetes | OR: 1.33 [1.24, 1.42] | — | — | PC1-10 | — |
| PPM002303 | PGS000844 (VAT_Mass) |
PSS001086| European Ancestry| 3,194 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Autoimmune Diabetes | OR: 1.17 [1.06, 1.29] | — | — | PC1-10 | — |
| PPM002305 | PGS000844 (VAT_Mass) |
PSS001088| European Ancestry| 3,869 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Resistant Diabetes | OR: 1.26 [1.18, 1.36] | — | — | PC1-10 | — |
| PPM002306 | PGS000844 (VAT_Mass) |
PSS001085| European Ancestry| 4,116 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Obesity-related Diabetes | OR: 1.49 [1.39, 1.6] | — | — | PC1-10 | — |
| PPM002307 | PGS000844 (VAT_Mass) |
PSS001084| European Ancestry| 5,597 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Age-Related Diabetes | OR: 1.1 [1.04, 1.16] | — | — | PC1-10 | — |
| PPM002304 | PGS000844 (VAT_Mass) |
PSS001087| European Ancestry| 3,930 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Deficient Diabetes | OR: 1.27 [1.18, 1.36] | — | — | PC1-10 | — |
| PPM020182 | PGS000844 (VAT_Mass) |
PSS011301| South Asian Ancestry| 1,282 individuals |
PGP000525 | Yajnik CS et al. Lancet Reg Health Southeast Asia (2023) |Ext. |
Reported Trait: BMI | β: 0.14774 (0.027271) | — | — | — | — |
| 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 | — |
| PPM005137 | PGS001350 (MAGICTA_EUR_PGS_FG) |
PSS003587| European Ancestry| 45,038 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Fasting glucose | — | — | R²: 0.229 | — | — |
| PPM005140 | PGS001350 (MAGICTA_EUR_PGS_FG) |
PSS003585| African Ancestry| 16,579 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Fasting glucose | — | — | R²: 0.032 | — | — |
| PPM005143 | PGS001350 (MAGICTA_EUR_PGS_FG) |
PSS003586| East Asian Ancestry| 31,669 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Fasting glucose | — | — | R²: 0.027 | — | — |
| PPM005138 | PGS001351 (MAGICTA_EUR_PGS_FI) |
PSS003590| European Ancestry| 29,123 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Fasting insulin | — | — | R²: 0.095 | — | — |
| PPM005141 | PGS001351 (MAGICTA_EUR_PGS_FI) |
PSS003588| African Ancestry| 8,101 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Fasting insulin | — | — | R²: 0.028 | — | — |
| PPM005144 | PGS001351 (MAGICTA_EUR_PGS_FI) |
PSS003589| East Asian Ancestry| 26,691 individuals |
PGP000246 | Chen J et al. Nat Genet (2021) |
Reported Trait: Fasting insulin | — | — | R²: 0.014 | — | — |
| PPM020887 | PGS001351 (MAGICTA_EUR_PGS_FI) |
PSS011442| European Ancestry| 564 individuals |
PGP000599 | Guarischi-Sousa R et al. Circ Genom Precis Med (2023) |Ext. |
Reported Trait: Raised coronary lesion | OR: 1.1 [0.91, 1.34] | — | — | — | — |
| PPM020892 | PGS001351 (MAGICTA_EUR_PGS_FI) |
PSS011441| African Ancestry| 504 individuals |
PGP000599 | Guarischi-Sousa R et al. Circ Genom Precis Med (2023) |Ext. |
Reported Trait: Raised coronary lesion | OR: 1.15 [0.92, 1.42] | — | — | — | — |
| 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 | — |
| PPM005340 | PGS001772 (GBE_INI23005) |
PSS005016| African Ancestry| 119 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: ZEBRA antigen for Epstein-Barr Virus | — | — | R²: 0.13241 [0.11706, 0.14775] Incremental R2 (full-covars): 0.0307 PGS R2 (no covariates): 0.04734 [0.03727, 0.05742] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005341 | PGS001772 (GBE_INI23005) |
PSS005017| East Asian Ancestry| 41 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: ZEBRA antigen for Epstein-Barr Virus | — | — | R²: 0.42791 [0.39244, 0.46337] Incremental R2 (full-covars): 0.00179 PGS R2 (no covariates): 0.02952 [0.01372, 0.04533] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005342 | PGS001772 (GBE_INI23005) |
PSS005018| European Ancestry| 471 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: ZEBRA antigen for Epstein-Barr Virus | — | — | R²: 0.07688 [0.07052, 0.08324] Incremental R2 (full-covars): 0.02635 PGS R2 (no covariates): 0.02765 [0.02363, 0.03166] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005343 | PGS001772 (GBE_INI23005) |
PSS005019| South Asian Ancestry| 164 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: ZEBRA antigen for Epstein-Barr Virus | — | — | R²: 0.15445 [0.13974, 0.16917] Incremental R2 (full-covars): -0.01516 PGS R2 (no covariates): 0.00397 [0.00119, 0.00675] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005344 | PGS001772 (GBE_INI23005) |
PSS005020| European Ancestry| 1,275 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: ZEBRA antigen for Epstein-Barr Virus | — | — | R²: 0.05788 [0.05445, 0.0613] Incremental R2 (full-covars): 0.03995 PGS R2 (no covariates): 0.03909 [0.03622, 0.04196] |
age, sex, UKB array type, Genotype PCs | — |
| PPM009972 | PGS001886 (portability-PLR_albumin) |
PSS009382| European Ancestry| 17,457 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2949 [0.2813, 0.3084] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009973 | PGS001886 (portability-PLR_albumin) |
PSS009156| European Ancestry| 3,593 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2758 [0.2452, 0.3058] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009974 | PGS001886 (portability-PLR_albumin) |
PSS008710| European Ancestry| 5,803 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2722 [0.2482, 0.2959] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009975 | PGS001886 (portability-PLR_albumin) |
PSS008484| Greater Middle Eastern Ancestry| 1,036 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2704 [0.2124, 0.3264] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009976 | PGS001886 (portability-PLR_albumin) |
PSS008262| South Asian Ancestry| 5,478 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2714 [0.2467, 0.2958] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009977 | PGS001886 (portability-PLR_albumin) |
PSS008040| East Asian Ancestry| 1,560 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.226 [0.178, 0.2729] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009978 | PGS001886 (portability-PLR_albumin) |
PSS007826| African Ancestry| 2,159 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.1475 [0.1058, 0.1887] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009979 | PGS001886 (portability-PLR_albumin) |
PSS008930| African Ancestry| 3,401 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.1407 [0.1075, 0.1736] | 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 | — |
| PPM010441 | PGS001945 (portability-PLR_log_creatinine) |
PSS007883| African Ancestry| 2,345 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.1379 [0.0978, 0.1776] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010437 | PGS001945 (portability-PLR_log_creatinine) |
PSS008767| European Ancestry| 6,322 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3344 [0.3123, 0.3562] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010435 | PGS001945 (portability-PLR_log_creatinine) |
PSS009439| European Ancestry| 18,993 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3568 [0.3443, 0.3691] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010436 | PGS001945 (portability-PLR_log_creatinine) |
PSS009213| European Ancestry| 3,950 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3499 [0.3221, 0.377] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010438 | PGS001945 (portability-PLR_log_creatinine) |
PSS008541| Greater Middle Eastern Ancestry| 1,124 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3425 [0.2894, 0.3936] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010439 | PGS001945 (portability-PLR_log_creatinine) |
PSS008319| South Asian Ancestry| 6,003 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.2881 [0.2647, 0.3112] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010440 | PGS001945 (portability-PLR_log_creatinine) |
PSS008096| East Asian Ancestry| 1,716 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.2465 [0.2013, 0.2907] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010442 | PGS001945 (portability-PLR_log_creatinine) |
PSS008987| African Ancestry| 3,655 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.1382 [0.1062, 0.17] | 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 | — |
| PPM010667 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS009463| European Ancestry| 19,363 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0706 [0.0565, 0.0846] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010668 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS009237| European Ancestry| 3,970 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0851 [0.0541, 0.116] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010669 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS008791| European Ancestry| 6,432 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0709 [0.0465, 0.0952] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010671 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS008343| South Asian Ancestry| 6,020 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0626 [0.0373, 0.0877] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010672 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS008120| East Asian Ancestry| 1,743 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0368 [-0.0104, 0.0839] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010673 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS007907| African Ancestry| 2,382 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0381 [-0.0022, 0.0783] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010674 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS009011| African Ancestry| 3,772 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0225 [-0.0095, 0.0545] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010670 | PGS001974 (portability-PLR_log_potassium_urine) |
PSS008565| Greater Middle Eastern Ancestry| 1,146 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0884 [0.0302, 0.1461] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011648 | PGS002099 (portability-ldpred2_albumin) |
PSS009382| European Ancestry| 17,457 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.289 [0.2754, 0.3026] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011649 | PGS002099 (portability-ldpred2_albumin) |
PSS009156| European Ancestry| 3,593 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2771 [0.2465, 0.3071] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011650 | PGS002099 (portability-ldpred2_albumin) |
PSS008710| European Ancestry| 5,803 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2717 [0.2476, 0.2954] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011651 | PGS002099 (portability-ldpred2_albumin) |
PSS008484| Greater Middle Eastern Ancestry| 1,036 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2567 [0.1983, 0.3132] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011652 | PGS002099 (portability-ldpred2_albumin) |
PSS008262| South Asian Ancestry| 5,478 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.265 [0.2402, 0.2895] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011653 | PGS002099 (portability-ldpred2_albumin) |
PSS008040| East Asian Ancestry| 1,560 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.2305 [0.1827, 0.2773] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011654 | PGS002099 (portability-ldpred2_albumin) |
PSS007826| African Ancestry| 2,159 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.1481 [0.1063, 0.1893] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011655 | PGS002099 (portability-ldpred2_albumin) |
PSS008930| African Ancestry| 3,401 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Albumin | — | — | Partial Correlation (partial-r): 0.1569 [0.1238, 0.1896] | 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 | — |
| PPM012151 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS009439| European Ancestry| 18,993 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.353 [0.3405, 0.3654] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012152 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS009213| European Ancestry| 3,950 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3476 [0.3198, 0.3748] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012153 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS008767| European Ancestry| 6,322 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3305 [0.3083, 0.3523] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012154 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS008541| Greater Middle Eastern Ancestry| 1,124 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.3278 [0.2741, 0.3794] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012157 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS007883| African Ancestry| 2,345 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.1607 [0.1208, 0.2] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012158 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS008987| African Ancestry| 3,655 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.1261 [0.0939, 0.1579] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012155 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS008319| South Asian Ancestry| 6,003 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.2731 [0.2495, 0.2964] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012156 | PGS002163 (portability-ldpred2_log_creatinine) |
PSS008096| East Asian Ancestry| 1,716 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Creatinine | — | — | Partial Correlation (partial-r): 0.24 [0.1946, 0.2844] | 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 | — |
| PPM012383 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS009463| European Ancestry| 19,363 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0895 [0.0755, 0.1035] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012384 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS009237| European Ancestry| 3,970 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0989 [0.068, 0.1297] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012385 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS008791| European Ancestry| 6,432 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0851 [0.0607, 0.1093] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012387 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS008343| South Asian Ancestry| 6,020 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0781 [0.0529, 0.1032] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012388 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS008120| East Asian Ancestry| 1,743 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0486 [0.0014, 0.0956] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012389 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS007907| African Ancestry| 2,382 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0379 [-0.0024, 0.0782] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012386 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS008565| Greater Middle Eastern Ancestry| 1,146 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.1133 [0.0553, 0.1706] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012390 | PGS002192 (portability-ldpred2_log_potassium_urine) |
PSS009011| African Ancestry| 3,772 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Potassium in urine | — | — | Partial Correlation (partial-r): 0.0304 [-0.0016, 0.0623] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM013023 | PGS002295 (GRS413_IGF-1) |
PSS009653| European Ancestry| 1,010 individuals |
PGP000325 | Tsai CW et al. American Journal of Cancer Research (2022) |
Reported Trait: Recurrence in renal cell carcinoma | HR: 0.63 [0.42, 0.93] | — | Hazard Ratio (HR, above vs. below median): 0.63 [0.42, 0.93] Odds Ratio (OR, above vs. below 75th percentile GRS): 0.73 [0.55, 0.96] |
Age, gender, smoking status, BMI, histology, stage, grade, and treatment | — |
| PPM013024 | PGS002295 (GRS413_IGF-1) |
PSS009653| European Ancestry| 1,010 individuals |
PGP000325 | Tsai CW et al. American Journal of Cancer Research (2022) |
Reported Trait: Survival in renal cell carcinoma | HR: 0.65 [0.44, 0.95] | — | Hazard Ratio (HR, above vs. below median): 0.65 [0.44, 0.95] Odds Ratio (OR, above vs. below 75th percentile GRS): 0.66 [0.5, 0.87] |
Age, gender, smoking status, BMI, histology, stage, grade, and treatment | — |
| 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 | — |
| 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 | — |
| 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 | — |
| 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 | — |
| 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 | — |
| 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 | — |
| 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 | — |
| 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 |
| 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 | — |
| PPM015587 | PGS002811 (ASATadjbmi3_weights_select) |
PSS009993| Multi-ancestry (including European)| 7,795 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Abdominal adipose tissue volumes adjusted for BMI and height | β: 0.195 (0.016) | — | Odds ratio (OR, top 5% vs reminder): 2.13 [1.46, 3.03] | Age at time of imaging, sex, and the first 10 PCs of genetic ancestry | — |
| PPM015589 | PGS002811 (ASATadjbmi3_weights_select) |
PSS009992| European Ancestry| 7,888 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: HDL cholesterol | — | — | Beta (top 10 % vs reminder): 0.09 [0.02, 0.17] | Age at enrollment, sex, and the first 10 principal components of genetic ancestry | — |
| PPM015588 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009993| Multi-ancestry (including European)| 7,795 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Gluteofemoral adipose tissue volumes adjusted for BMI and height | β: 0.262 (0.016) | — | Odds ratio (OR, top 5% vs reminder): 3.81 [2.76, 5.17] | Age at time of imaging, sex, and the first 10 PCs of genetic ancestry | — |
| PPM015590 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009992| European Ancestry| 7,888 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: HDL cholesterol | — | — | Beta (top 10 % vs reminder): 0.14 [0.07, 0.22] | Age at enrollment, sex, and the first 10 principal components of genetic ancestry | — |
| PPM015591 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009992| European Ancestry| 7,888 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Triglycerides | — | — | Beta (top 10 % vs reminder): -0.16 [-0.23, -0.08] | Age at enrollment, sex, and the first 10 principal components of genetic ancestry | — |
| PPM015593 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009992| European Ancestry| 7,888 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Type 2 Diabetes | — | — | Odds ratio (OR, top 10% vs reminder): 0.57 [0.41, 0.78] | Age at enrollment, sex, and the first 10 principal components of genetic ancestry | — |
| PPM015594 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: HDL cholesterol | — | — | Beta (top 10 % vs reminder): 0.16 [0.15, 0.18] | — | — |
| PPM015595 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Triglycerides | — | — | Beta (top 10 % vs reminder): -0.16 [-0.18, -0.15] | — | — |
| PPM015596 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Alanine aminotransferase | — | — | Beta (top 10 % vs reminder): -0.09 [-0.1, -0.07] | — | — |
| PPM015597 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Type 2 Diabetes | — | — | Odds ratio (OR, top 10% vs reminder): 0.75 [0.7, 0.8] | — | — |
| PPM015598 | PGS002812 (GFATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Coronary artery disease | — | — | Odds ratio (OR, top 10% vs reminder): 0.89 [0.85, 0.93] | — | — |
| PPM015599 | PGS002813 (VATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Type 2 Diabetes | — | — | Odds ratio (OR, top 10% vs reminder): 1.18 | — | — |
| PPM015600 | PGS002813 (VATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Coronary artery disease | — | — | Odds ratio (OR, top 10% vs reminder): 1.12 | — | — |
| PPM015601 | PGS002813 (VATadjbmi3_weights_select) |
PSS009994| Multi-ancestry (including European)| 447,486 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Hypertension | — | — | Odds ratio (OR, top 10% vs reminder): 1.09 | — | — |
| PPM015586 | PGS002813 (VATadjbmi3_weights_select) |
PSS009993| Multi-ancestry (including European)| 7,795 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Visceral adipose tissue volumes adjusted for BMI and height | β: 0.221 (0.016) | — | Odds ratio (OR, top 5% vs reminder): 2.41 [1.7, 3.36] | Age at time of imaging, sex, and the first 10 PCs of genetic ancestry | — |
| PPM015592 | PGS002813 (VATadjbmi3_weights_select) |
PSS009992| European Ancestry| 7,888 individuals |
PGP000391 | Agrawal S et al. Nat Commun (2022) |
Reported Trait: Triglycerides | — | — | Beta (bottom 10 % vs reminder): -0.12 [-0.19, -0.05] | Age at enrollment, sex, and the first 10 principal components of genetic ancestry | — |
| PPM015803 | PGS002924 (ExPRSweb_FPG_FGovertime-corrected_LASSOSUM_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: -0.0308 (0.564) | — | R²: 0.0004 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015806 | PGS002925 (ExPRSweb_FPG_FGovertime-corrected_PT_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: -0.531 (0.592) | — | R²: 0.00991 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015804 | PGS002926 (ExPRSweb_FPG_FGovertime-corrected_PLINK_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: 0.294 (0.661) | — | R²: 0.00282 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015802 | PGS002927 (ExPRSweb_FPG_FGovertime-corrected_DBSLMM_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: -0.263 (0.601) | — | R²: 0.00714 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015805 | PGS002928 (ExPRSweb_FPG_FGovertime-corrected_PRSCS_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: -0.564 (0.613) | — | R²: 0.00161 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015808 | PGS002929 (ExPRSweb_FPG_MAGIC-FastingGlucose_LASSOSUM_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: 0.864 (0.634) | — | R²: 0.00726 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015811 | PGS002930 (ExPRSweb_FPG_MAGIC-FastingGlucose_PT_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: 1.6 (0.657) | — | R²: 0.0076 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015809 | PGS002931 (ExPRSweb_FPG_MAGIC-FastingGlucose_PLINK_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: 1.71 (0.658) | — | R²: 0.00975 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015807 | PGS002932 (ExPRSweb_FPG_MAGIC-FastingGlucose_DBSLMM_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: -0.414 (0.655) | — | R²: 6e-05 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015810 | PGS002933 (ExPRSweb_FPG_MAGIC-FastingGlucose_PRSCS_MGI_20211120) |
PSS010004| European Ancestry| 286 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Fasting Plasma Glucose | β: 1.15 (0.65) | — | R²: 0.011 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015823 | PGS002944 (ExPRSweb_Glucose_FGovertime-corrected_LASSOSUM_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: -0.00088 (0.00289) | — | R²: 5.33e-07 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015826 | PGS002945 (ExPRSweb_Glucose_FGovertime-corrected_PT_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.00046 (0.0029) | — | R²: 4.59e-07 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015824 | PGS002946 (ExPRSweb_Glucose_FGovertime-corrected_PLINK_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: -0.00035 (0.0029) | — | R²: 1.98e-08 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015822 | PGS002947 (ExPRSweb_Glucose_FGovertime-corrected_DBSLMM_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.00449 (0.00291) | — | R²: 2e-05 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015825 | PGS002948 (ExPRSweb_Glucose_FGovertime-corrected_PRSCS_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: -0.00129 (0.0029) | — | R²: 1.39e-06 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015828 | PGS002949 (ExPRSweb_Glucose_MAGIC-FastingGlucose_LASSOSUM_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.084 (0.00289) | — | R²: 0.00479 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015831 | PGS002950 (ExPRSweb_Glucose_MAGIC-FastingGlucose_PT_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.0808 (0.00289) | — | R²: 0.00442 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015829 | PGS002951 (ExPRSweb_Glucose_MAGIC-FastingGlucose_PLINK_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.0808 (0.00289) | — | R²: 0.00442 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015827 | PGS002952 (ExPRSweb_Glucose_MAGIC-FastingGlucose_DBSLMM_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.00449 (0.00291) | — | R²: 2e-05 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM015830 | PGS002953 (ExPRSweb_Glucose_MAGIC-FastingGlucose_PRSCS_UKB_20211120) |
PSS010028| European Ancestry| 173,111 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: Glucose | β: 0.0818 (0.00289) | — | R²: 0.00451 | Sex,birthYear,genotyping.array,PC1,PC2,PC3,PC4 | — |
| PPM016175 | PGS003336 (CVGRS_FPG) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Fasting plasma glucose | β: 0.20444 | — | — | — | — |
| PPM016192 | PGS003336 (CVGRS_FPG) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Type 2 diabetes | OR: 1.46435 | — | — | — | — |
| PPM016184 | PGS003345 (ALLGRS_FPG) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Fasting plasma glucose | β: 0.20689 | — | — | — | — |
| PPM016240 | PGS003378 (PSA_PGS_128) |
PSS010066| European Ancestry| 5,725 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.169 | — | Partial R2 (%, variation explained by the PGS only): 7.33 | Age at PSA measurement, genetic ancestry PC1-PC10 computed within ancestry group | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016241 | PGS003378 (PSA_PGS_128) |
PSS010073| Multi-ancestry (including European)| 25,917 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.207 | — | Partial R2 (%, variation explained by the PGS only): 8.0 | Age at PSA measurement, population-specific genetic ancestry PC1-PC10, genetic ancestry proportions (AFR + EAS) | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016242 | PGS003378 (PSA_PGS_128) |
PSS010072| European Ancestry| 22,173 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.213 | — | Partial R2 (%, variation explained by the PGS only): 8.78 | Age at PSA measurement, genetic ancestry PC1-PC10 computed within ancestry group | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016243 | PGS003378 (PSA_PGS_128) |
PSS010070| African Ancestry| 2,936 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.154 | — | Partial R2 (%, variation explained by the PGS only): 3.36 | Age at PSA measurement, population-specific genetic ancestry PC1-PC10, genetic ancestry proportions (AFR + EAS) | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016244 | PGS003378 (PSA_PGS_128) |
PSS010068| African Ancestry| 1,173 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.163 | — | Partial R2 (%, variation explained by the PGS only): 3.45 | Age at PSA measurement, genetic ancestry PC1-PC10 computed within ancestry group | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016245 | PGS003378 (PSA_PGS_128) |
PSS010069| African Ancestry| 1,763 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.146 | — | Partial R2 (%, variation explained by the PGS only): 3.32 | Age at PSA measurement, genetic ancestry PC1-PC10 computed within ancestry group | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016246 | PGS003378 (PSA_PGS_128) |
PSS010071| East Asian Ancestry| 257 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.136 | — | Partial R2 (%, variation explained by the PGS only): 2.45 | Age at PSA measurement, genetic ancestry PC1-PC10 computed within ancestry group | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016239 | PGS003378 (PSA_PGS_128) |
PSS010067| Multi-ancestry (including European)| 5,883 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.168 | — | Partial R2 (%, variation explained by the PGS only): 7.16 | Age at PSA measurement, population-specific genetic ancestry PC1-PC10, genetic ancestry proportions (AFR + EAS) | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1 |
| PPM016248 | PGS003379 (PSA_PGS_CSx) |
PSS010066| European Ancestry| 5,725 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.194 | — | Partial R2 (%, variation explained by the PGS only): 8.6 | Age at PSA measurement, within-population genetic ancestry PC1-PC10 | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,058,173 SNPs) |
| PPM016249 | PGS003379 (PSA_PGS_CSx) |
PSS010073| Multi-ancestry (including European)| 25,917 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.258 | — | Partial R2 (%, variation explained by the PGS only): 9.61 | Age at PSA measurement, within-population genetic ancestry PC1-PC10, genetic ancestry proportions (AFR + EAS) | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,071,278 SNPs) |
| PPM016250 | PGS003379 (PSA_PGS_CSx) |
PSS010072| European Ancestry| 22,173 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.283 | — | Partial R2 (%, variation explained by the PGS only): 10.94 | Age at PSA measurement, within-population genetic ancestry PC1-PC10 | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,071,278 SNPs) |
| PPM016251 | PGS003379 (PSA_PGS_CSx) |
PSS010070| African Ancestry| 2,936 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.134 | — | Partial R2 (%, variation explained by the PGS only): 3.11 | Age at PSA measurement, within-population genetic ancestry PC1-PC10, genetic ancestry proportions (AFR) | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,071,278 SNPs) |
| PPM016252 | PGS003379 (PSA_PGS_CSx) |
PSS010068| African Ancestry| 1,173 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.098 | — | Partial R2 (%, variation explained by the PGS only): 1.64 | Age at PSA measurement, within-population genetic ancestry PC1-PC10 | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,071,278 SNPs) |
| PPM016247 | PGS003379 (PSA_PGS_CSx) |
PSS010067| Multi-ancestry (including European)| 5,883 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.186 | — | Partial R2 (%, variation explained by the PGS only): 8.13 | Age at PSA measurement, within-population genetic ancestry PC1-PC10, genetic ancestry proportions (AFR + EAS) | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,058,173 SNPs) |
| PPM016253 | PGS003379 (PSA_PGS_CSx) |
PSS010069| African Ancestry| 1,763 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.157 | — | Partial R2 (%, variation explained by the PGS only): 4.22 | Age at PSA measurement, within-population genetic ancestry PC1-PC10 | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,071,278 SNPs) |
| PPM016254 | PGS003379 (PSA_PGS_CSx) |
PSS010071| East Asian Ancestry| 257 individuals |
PGP000412 | Kachuri L et al. Nat Med (2023) |
Reported Trait: Baseline/pre-randomization log(PSA) | β: 0.258 | — | Partial R2 (%, variation explained by the PGS only): 9.22 | Age at PSA measurement, within-population genetic ancestry PC1-PC10 | Beta per SD increase in PGS; PGS converted to standardized z-score with mean=0 and SD=1; SNPs and weights specific for use in this cohort (1,071,278 SNPs) |
| PPM017276 | PGS003466 (LDPred2_FI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: -0.004 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017299 | PGS003466 (LDPred2_FI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: -0.003 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017344 | PGS003466 (LDPred2_FI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index in obsese | β: 0.02 (0.017) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017345 | PGS003466 (LDPred2_FI) |
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: -0.013 (0.012) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017364 | PGS003466 (LDPred2_FI) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea in obsese | β: 0.039 (0.034) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017365 | PGS003466 (LDPred2_FI) |
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: -0.04 (0.035) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017394 | PGS003466 (LDPred2_FI) |
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: 0.025 (1.026) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017404 | PGS003466 (LDPred2_FI) |
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: 1.091 (0.049) | — | — | 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 | — |
| PPM017454 | PGS003526 (cont-decay-log_creatinine) |
PSS010889| European Ancestry| 19,112 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017538 | PGS003526 (cont-decay-log_creatinine) |
PSS010805| European Ancestry| 3,939 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017622 | PGS003526 (cont-decay-log_creatinine) |
PSS010637| European Ancestry| 6,170 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017706 | PGS003526 (cont-decay-log_creatinine) |
PSS010553| Greater Middle Eastern Ancestry| 1,095 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017790 | PGS003526 (cont-decay-log_creatinine) |
PSS010217| European Ancestry| 2,235 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017874 | PGS003526 (cont-decay-log_creatinine) |
PSS010469| South Asian Ancestry| 5,949 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017958 | PGS003526 (cont-decay-log_creatinine) |
PSS010385| East Asian Ancestry| 1,705 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM018042 | PGS003526 (cont-decay-log_creatinine) |
PSS010301| African Ancestry| 2,333 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM018126 | PGS003526 (cont-decay-log_creatinine) |
PSS010721| African Ancestry| 3,626 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: Creatinine | — | — | partial-R2: 0.02 | 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 | — |
| 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) | — |
| 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) | — |
| PPM020443 | PGS004328 (X30600.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Albumin | — | — | PGS R2 (no covariates): 0.18905 | — | — |
| PPM020449 | PGS004334 (X30700.score) |
PSS011364| European Ancestry| 56,192 individuals |
PGP000561 | Jung H et al. Commun Biol (2024) |
Reported Trait: Creatinine (umol/L) | — | — | PGS R2 (no covariates): 0.2141 | — | — |
| 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 | — | — |
| PPM020932 | PGS004707 (Albumin_PRSmix_eur) |
PSS011480| European Ancestry| 4,879 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Albumin | — | — | Incremental R2 (Full model versus model with only covariates): 0.029 [0.019, 0.038] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020933 | PGS004708 (Albumin_PRSmix_sas) |
PSS011460| South Asian Ancestry| 6,864 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Albumin | — | — | Incremental R2 (Full model versus model with only covariates): 0.029 [0.021, 0.037] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020934 | PGS004709 (Albumin_PRSmixPlus_eur) |
PSS011480| European Ancestry| 4,879 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Albumin | — | — | Incremental R2 (Full model versus model with only covariates): 0.042 [0.031, 0.053] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020935 | PGS004710 (Albumin_PRSmixPlus_sas) |
PSS011460| South Asian Ancestry| 6,864 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Albumin | — | — | Incremental R2 (Full model versus model with only covariates): 0.033 [0.025, 0.041] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020972 | PGS004747 (creatinine_PRSmix_eur) |
PSS011488| European Ancestry| 5,758 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Serum creatinine | — | — | Incremental R2 (Full model versus model with only covariates): 0.046 [0.035, 0.056] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020973 | PGS004748 (creatinine_PRSmix_sas) |
PSS011489| South Asian Ancestry| 6,954 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Serum creatinine | — | — | Incremental R2 (Full model versus model with only covariates): 0.029 [0.022, 0.037] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020974 | PGS004749 (creatinine_PRSmixPlus_eur) |
PSS011488| European Ancestry| 5,758 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Serum creatinine | — | — | Incremental R2 (Full model versus model with only covariates): 0.053 [0.042, 0.064] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM020975 | PGS004750 (creatinine_PRSmixPlus_sas) |
PSS011489| South Asian Ancestry| 6,954 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Serum creatinine | — | — | Incremental R2 (Full model versus model with only covariates): 0.035 [0.027, 0.044] | 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) |
| PPM021356 | PGS004906 (PRS60_TSH) |
PSS011701| European Ancestry| 7,231 individuals |
PGP000639 | Mulder TA et al. Eur J Endocrinol (2023) |
Reported Trait: Thyroid stimulating hormone concentration | β: 0.16 (0.01) | — | — | — | — |
| PPM021357 | PGS004906 (PRS60_TSH) |
PSS011701| European Ancestry| 7,231 individuals |
PGP000639 | Mulder TA et al. Eur J Endocrinol (2023) |
Reported Trait: Free thyroxine (FT4) concentration | β: -0.04 (0.01) | — | — | — | — |
| PPM022212 | PGS005098 (psa2024_prs_318) |
PSS011848| African Ancestry| 1,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.065 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022216 | PGS005098 (psa2024_prs_318) |
PSS011849| Additional Asian Ancestries| 257 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.038 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022224 | PGS005098 (psa2024_prs_318) |
PSS011844| African Ancestry| 2,469 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.049 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022228 | PGS005098 (psa2024_prs_318) |
PSS011847| Additional Diverse Ancestries| 1,783 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.073 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022232 | PGS005098 (psa2024_prs_318) |
PSS011845| Hispanic or Latin American Ancestry| 1,336 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.08 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022208 | PGS005098 (psa2024_prs_318) |
PSS011850| European Ancestry| 22,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.095 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022220 | PGS005098 (psa2024_prs_318) |
PSS011846| European Ancestry| 11,922 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.096 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022209 | PGS005099 (psa2024_prs_447) |
PSS011850| European Ancestry| 22,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.109 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022213 | PGS005099 (psa2024_prs_447) |
PSS011848| African Ancestry| 1,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.07 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022217 | PGS005099 (psa2024_prs_447) |
PSS011849| Additional Asian Ancestries| 257 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.035 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022221 | PGS005099 (psa2024_prs_447) |
PSS011846| European Ancestry| 11,922 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.113 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022225 | PGS005099 (psa2024_prs_447) |
PSS011844| African Ancestry| 2,469 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.053 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022229 | PGS005099 (psa2024_prs_447) |
PSS011847| Additional Diverse Ancestries| 1,783 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.086 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022233 | PGS005099 (psa2024_prs_447) |
PSS011845| Hispanic or Latin American Ancestry| 1,336 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.096 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022210 | PGS005100 (psa2024_prs_disc) |
PSS011850| European Ancestry| 22,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.124 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022214 | PGS005100 (psa2024_prs_disc) |
PSS011848| African Ancestry| 1,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.073 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022218 | PGS005100 (psa2024_prs_disc) |
PSS011849| Additional Asian Ancestries| 257 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.086 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022222 | PGS005100 (psa2024_prs_disc) |
PSS011846| European Ancestry| 11,922 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.138 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022226 | PGS005100 (psa2024_prs_disc) |
PSS011844| African Ancestry| 2,469 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.055 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022230 | PGS005100 (psa2024_prs_disc) |
PSS011847| Additional Diverse Ancestries| 1,783 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.133 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022234 | PGS005100 (psa2024_prs_disc) |
PSS011845| Hispanic or Latin American Ancestry| 1,336 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.135 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022211 | PGS005101 (psa2024_prs_joint) |
PSS011850| European Ancestry| 22,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.139 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022215 | PGS005101 (psa2024_prs_joint) |
PSS011848| African Ancestry| 1,173 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.072 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022219 | PGS005101 (psa2024_prs_joint) |
PSS011849| Additional Asian Ancestries| 257 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.107 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022223 | PGS005101 (psa2024_prs_joint) |
PSS011846| European Ancestry| 11,922 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.147 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022227 | PGS005101 (psa2024_prs_joint) |
PSS011844| African Ancestry| 2,469 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.058 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022231 | PGS005101 (psa2024_prs_joint) |
PSS011847| Additional Diverse Ancestries| 1,783 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.14 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022235 | PGS005101 (psa2024_prs_joint) |
PSS011845| Hispanic or Latin American Ancestry| 1,336 individuals |
PGP000692 | Hoffmann TJ et al. Nat Genet (2025) |
Reported Trait: log(PSA) | — | — | Partial R2: 0.135 | age, ancestry pcs | Partial r2: variation explained by the PGS only, but derived from a fully adjusted model that includes listed covariates |
| PPM022292 | PGS005107 (PGS-PSA111) |
PSS011904| Multi-ancestry (including European)| 3,110 individuals |
PGP000696 | Shi M et al. EBioMedicine (2023) |
Reported Trait: Prostate specific antigen level > 4 ng/mL | HR: 1.22 [1.11, 1.35] | — | — | Age at first PSA measurement, 10 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 |
|---|---|---|---|---|---|---|---|---|
| PSS009156 | — | — | 3,593 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010654 | — | — | 6,278 individuals, 45.0 % Male samples |
Mean = 54.5 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS009871 | — | — | 6,149 individuals | — | African unspecified | — | UKB | — |
| PSS009872 | — | — | 893 individuals | — | East Asian | — | UKB | — |
| PSS009873 | — | — | 42,026 individuals | — | European | Non-British European | UKB | — |
| PSS009874 | — | — | 7,769 individuals | — | South Asian | — | UKB | — |
| PSS011701 | — | — | 2,169 individuals, 50.8 % Male samples |
Median = 6.0 years 95% Range = [5.7, 7.5] years |
European | — | Generation_R | — |
| PSS011701 | — | — | 3,382 individuals, 52.0 % Male samples |
Median = 7.5 years 95% Range = [7.3, 8.9] years |
European | — | ALSPAC | — |
| PSS011701 | — | — | 1,680 individuals, 49.3 % Male samples |
Median = 12.1 years 95% Range = [12.0, 12.5] years |
European | — | BLTS | — |
| PSS011441 | — | — | [ ,
82.0 % Male samples |
Mean = 27.5 years | African unspecified | — | PDAY | — |
| PSS011442 | — | — | [ ,
77.0 % Male samples |
Mean = 26.7 years | European | — | PDAY | — |
| PSS009191 | — | — | 3,924 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS000290 | — | — | 2,314 individuals | — | European (French Canadian) |
— | CARTaGENE | — |
| PSS000291 | — | — | 39,260 individuals | — | European | — | INTERVAL | — |
| PSS000729 | — | — | 5,573 individuals | — | African unspecified | — | UKB | — |
| PSS000730 | — | — | 984 individuals | — | East Asian | — | UKB | — |
| PSS000731 | — | — | 21,516 individuals | — | European | Non-British White | UKB | — |
| PSS000732 | — | — | 6,687 individuals | — | South Asian | — | UKB | — |
| PSS000733 | — | — | 58,196 individuals | — | European (British) |
— | UKB | — |
| PSS010004 | GLUCOSE, FASTING; Quantitative | — | 286 individuals | — | European | — | MGI | — |
| PSS011904 | — | Median = 6.2 years IQR = [3.7, 10.9] years |
3,110 individuals, 100.0 % Male samples |
Median = 56.6 years IQR = [51.4, 61.5] years |
European, African unspecified | — | BioVU | No prostate cancer and prostate specific antigen level < 4 mg/mL at baseline |
| PSS009213 | — | — | 3,950 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS000739 | — | — | 5,786 individuals | — | African unspecified | — | UKB | — |
| PSS000740 | — | — | 1,045 individuals | — | East Asian | — | UKB | — |
| PSS000741 | — | — | 22,839 individuals | — | European | Non-British White | UKB | — |
| PSS000742 | — | — | 6,998 individuals | — | South Asian | — | UKB | — |
| PSS000743 | — | — | 62,013 individuals | — | European (British) |
— | UKB | — |
| PSS008710 | — | — | 5,803 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS010721 | — | — | 3,626 individuals, 46.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS009225 | — | — | 4,002 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS010217 | — | — | 2,235 individuals, 45.0 % Male samples |
Mean = 58.1 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS010028 | Field ID: 30740; Quantitative | — | 173,111 individuals | — | European | — | UKB | — |
| PSS009237 | — | — | 3,970 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS000152 | — | — | 40,466 individuals, 49.0 % Male samples |
Mean = 43.84 years Range = [18.0, 76.4] years |
European | — | INTERVAL | — |
| PSS010738 | — | — | 3,682 individuals, 47.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010234 | — | — | 2,264 individuals, 45.0 % Male samples |
Mean = 58.0 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS008745 | — | — | 6,298 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS000782 | — | — | 6,015 individuals | — | African unspecified | — | UKB | — |
| PSS000783 | — | — | 1,080 individuals | — | East Asian | — | UKB | — |
| PSS000784 | — | — | 23,562 individuals | — | European | Non-British White | UKB | — |
| PSS000785 | — | — | 7,336 individuals | — | South Asian | — | UKB | — |
| PSS000786 | — | — | 63,745 individuals | — | European (British) |
— | UKB | — |
| PSS000178 | — | — | 81,606 individuals, 46.0 % Male samples |
Mean = 57.23 years Range = [38.87, 70.97] years |
European | — | UKB | — |
| PSS000800 | — | — | 2,129 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS008767 | — | — | 6,322 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008262 | — | — | 5,478 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008779 | — | — | 6,437 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS010055 | — | — | 22,608 individuals | — | East Asian | — | KBA, KoGES | — |
| PSS011460 | — | — | 6,864 individuals | — | South Asian | — | G&H | — |
| PSS008791 | — | — | 6,432 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS010805 | — | — | 3,939 individuals, 38.0 % Male samples |
Mean = 54.4 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS000835 | — | — | 2,117 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS010301 | — | — | 2,333 individuals, 37.0 % Male samples |
Mean = 52.5 years Sd = 8.1 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS011477 | — | — | 7,058 individuals | — | South Asian | — | G&H | — |
| PSS008297 | — | — | 5,937 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS011480 | — | — | 4,879 individuals | — | European | — | AllofUs | — |
| PSS010822 | — | — | 3,991 individuals, 38.0 % Male samples |
Mean = 54.3 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS011488 | — | — | 5,758 individuals | — | European | — | AllofUs | — |
| PSS011489 | — | — | 6,954 individuals | — | South Asian | — | G&H | — |
| 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 | — |
| PSS011301 | — | — | [
|
— | South Asian (Indian) |
— | WellGen | — |
| PSS006895 | — | — | 65,638 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS010318 | — | — | 2,330 individuals, 37.0 % Male samples |
Mean = 52.4 years Sd = 8.0 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010066 | Baseline/pre-randomization PSA in men without prostate cancer | — | 5,725 individuals, 100.0 % Male samples |
— | European (Predominantly European) |
European (EUR) ancestry cluster based on SNPWEIGHTS score >=0.80; median EUR = 0.998; mean EUR = 0.991 | PCPT | Minimum enrollment age was 55 years with serum PSA <= 3 ng/mL |
| PSS010067 | Baseline/pre-randomization PSA in men without prostate cancer | — | 5,883 individuals, 100.0 % Male samples |
— | European, Not reported | Multi-ancestry pooled sample | PCPT | Minimum enrollment age was 55 years with serum PSA <= 3 ng/mL |
| PSS010068 | Baseline/pre-randomization PSA in men without prostate cancer | — | 1,173 individuals, 100.0 % Male samples |
— | African American or Afro-Caribbean (Predominantly West African) |
West African (AFR) ancestry cluster based on SNPWEIGHTS score >=0.80; median AFR = 0.855; mean AFR = 0.861 | SELECT | Minimum enrollment age was 50 years for African Americans and 55 years for all other men; serum PSA <= 4 ng/mL |
| PSS008319 | — | — | 6,003 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010070 | Baseline/pre-randomization PSA in men without prostate cancer | — | 2,936 individuals, 100.0 % Male samples |
— | African American or Afro-Caribbean (West African admixed) |
West African (AFR) ancestry score >0.20 based on SNPWEIGHTS; median AFR = 0.765; mean AFR = 0.705 | SELECT | Minimum enrollment age was 50 years for African Americans and 55 years for all other men; serum PSA <= 4 ng/mL |
| PSS010069 | Baseline/pre-randomization PSA in men without prostate cancer | — | 1,763 individuals, 100.0 % Male samples |
— | African American or Afro-Caribbean (West African admixed) |
0.20 < West African (AFR) ancestry score <0.80 based on SNPWEIGHTS; median AFR = 0.673; mean AFR = 0.602 | SELECT | Minimum enrollment age was 50 years for African Americans and 55 years for all other men; serum PSA <= 4 ng/mL |
| PSS010072 | Baseline/pre-randomization PSA in men without prostate cancer | — | 22,173 individuals, 100.0 % Male samples |
— | European (Predominantly European) |
European (EUR) ancestry cluster based on SNPWEIGHTS score >=0.80; median EUR = 0.995; mean EUR = 0.986 | SELECT | Minimum enrollment age was 50 years for African Americans and 55 years for all other men; serum PSA <= 4 ng/mL |
| PSS010073 | Baseline/pre-randomization PSA in men without prostate cancer | — | 25,917 individuals, 100.0 % Male samples |
— | European, African American or Afro-Caribbean | Multi-ancestry pooled sample | SELECT | Minimum enrollment age was 50 years for African Americans and 55 years for all other men; serum PSA <= 4 ng/mL |
| PSS010071 | Baseline/pre-randomization PSA in men without prostate cancer | — | 257 individuals, 100.0 % Male samples |
— | East Asian (Predominantly East Asian) |
East Asian (EAS) ancestry cluster based on SNPWEIGHTS score >=0.80; median EAS = 0.978; mean EAS = 0.972 | SELECT | Minimum enrollment age was 50 years for African Americans and 55 years for all other men; serum PSA <= 4 ng/mL |
| PSS011512 | — | — | 5,118 individuals | — | European | — | AllofUs | — |
| PSS008331 | — | — | 6,078 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS006916 | — | — | 5,573 individuals | — | African unspecified | — | UKB | — |
| PSS006917 | — | — | 1,436 individuals | — | East Asian | — | UKB | — |
| PSS006918 | — | — | 21,514 individuals | — | European | non-white British ancestry | UKB | — |
| PSS006919 | — | — | 6,643 individuals | — | South Asian | — | UKB | — |
| PSS006920 | — | — | 58,191 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS007826 | — | — | 2,159 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS009653 | — | — | 1,010 individuals | — | European | — | NR | — |
| PSS008343 | — | — | 6,020 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS011100 | — | — | 2,768 individuals | — | European (non-white British ancestry) |
— | UKB | — |
| PSS011109 | — | — | 2,813 individuals | — | European (non-white British ancestry) |
— | UKB | — |
| PSS009382 | — | — | 17,457 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS011111 | — | — | 1,393 individuals | — | South Asian | — | UKB | — |
| PSS011113 | — | — | 1,433 individuals | — | South Asian | — | UKB | — |
| PSS010889 | — | — | 19,112 individuals, 46.0 % 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 | — |
| PSS010385 | — | — | 1,705 individuals, 33.0 % Male samples |
Mean = 52.4 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS010906 | — | — | 19,415 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS011136 | — | — | 65,932 individuals | — | European (white British ancestry) |
— | UKB | — |
| PSS011143 | — | — | 64,816 individuals | — | European (white British ancestry) |
— | 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 | — |
| PSS007883 | — | — | 2,345 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS011152 | — | — | 1,124 individuals | — | African unspecified | — | UKB | — |
| PSS011155 | — | — | 1,157 individuals | — | African unspecified | — | UKB | — |
| PSS007895 | — | — | 2,343 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS011166 | — | — | 7,746 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS009439 | — | — | 18,993 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS008930 | — | — | 3,401 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS007907 | — | — | 2,382 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS011170 | — | — | 7,574 individuals | — | East Asian, Other admixed ancestry | East Asian, Other admixed ancestry | UKB | — |
| PSS009451 | — | — | 19,419 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS000911 | — | — | 13,989 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) (Qatari) |
— | QBB | — |
| PSS009463 | — | — | 19,363 individuals | — | European | UK (+ Ireland) | UKB | — |
| 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) |
| PSS008965 | — | — | 3,602 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS010469 | — | — | 5,949 individuals, 54.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS011846 | Prostate specific antigen | — | 11,922 individuals, 100.0 % Male samples |
— | European | — | AllofUs | — |
| PSS007056 | — | — | 6,238 individuals | — | African unspecified | — | UKB | — |
| PSS007057 | — | — | 1,642 individuals | — | East Asian | — | UKB | — |
| PSS007058 | — | — | 24,108 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007059 | — | — | 7,464 individuals | — | South Asian | — | UKB | — |
| PSS007060 | — | — | 65,536 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008987 | — | — | 3,655 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS010486 | — | — | 6,026 individuals, 55.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS011364 | — | — | 56,192 individuals | — | European | — | UKB | — |
| PSS005016 | — | — | 119 individuals | — | African unspecified | — | UKB | — |
| PSS005017 | — | — | 41 individuals | — | East Asian | — | UKB | — |
| PSS007066 | — | — | 5,658 individuals | — | African unspecified | — | UKB | — |
| PSS007067 | — | — | 1,472 individuals | — | East Asian | — | UKB | — |
| PSS007068 | — | — | 21,759 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007069 | — | — | 6,786 individuals | — | South Asian | — | UKB | — |
| PSS007070 | — | — | 59,097 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS005018 | — | — | 471 individuals | — | European | non-white British ancestry | UKB | — |
| PSS005019 | — | — | 164 individuals | — | South Asian | — | UKB | — |
| PSS005020 | — | — | 1,275 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008484 | — | — | 1,036 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008999 | — | — | 3,711 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009011 | — | — | 3,772 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS009992 | — | — | 7,888 individuals | — | European (White) |
— | ARIC | — |
| PSS009993 | — | — | 7,795 individuals | — | European, African unspecified, East Asian, South Asian, Not reported | White British, Black, East Asian, South Asian, Other | UKB | 20% of original GWAS cohort |
| PSS009994 | — | — | 447,486 individuals | — | European, African unspecified, East Asian, South Asian, Not reported | White British, Black, East Asian, South Asian, Other | UKB | Unimaged cohort of UKB |
| PSS003585 | — | — | 16,579 individuals | — | African American or Afro-Caribbean | — | 10 cohorts
|
— |
| PSS003586 | — | — | 31,669 individuals | — | East Asian (Japanese, Chinese, Malay, Filipino, Han Chinese) |
— | 13 cohorts
|
— |
| PSS003587 | — | — | 45,038 individuals | — | European | — | ARIC, LifeLines, METSIM, TwinGene | Additional cases and controls were obtained from Fenland-OMICS. |
| PSS003588 | — | — | 8,101 individuals | — | African American or Afro-Caribbean | — | 9 cohorts
|
— |
| PSS003589 | — | — | 26,691 individuals | — | East Asian (Japanese, Filipino, Chinese, Han Chinese) |
— | 10 cohorts
|
— |
| PSS003590 | — | — | 29,123 individuals | — | European | — | ARIC, METSIM, NTR, PROCARDIS | — |
| PSS008519 | — | — | 1,127 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS007116 | — | — | 6,097 individuals | — | African unspecified | — | UKB | — |
| PSS007117 | — | — | 1,614 individuals | — | East Asian | — | UKB | — |
| PSS007118 | — | — | 23,752 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007119 | — | — | 7,427 individuals | — | South Asian | — | UKB | — |
| PSS007120 | — | — | 64,425 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008541 | — | — | 1,124 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS010553 | — | — | 1,095 individuals, 60.0 % Male samples |
Mean = 51.9 years Sd = 8.0 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS007131 | — | — | 6,097 individuals | — | African unspecified | — | UKB | — |
| PSS007132 | — | — | 1,615 individuals | — | East Asian | — | UKB | — |
| PSS007133 | — | — | 23,762 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007134 | — | — | 7,427 individuals | — | South Asian | — | UKB | — |
| PSS007135 | — | — | 64,433 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008040 | — | — | 1,560 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008553 | — | — | 1,153 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | 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 | — |
| PSS008565 | — | — | 1,146 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 | — |
| PSS010185 | — | — | 1,115 individuals, 41.1 % Male samples |
Mean = 46.18 years | Hispanic or Latin American | — | HCHS, SOL | — |
| 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 | — |
| PSS008074 | — | — | 1,722 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS011844 | Prostate specific antigen | — | 2,469 individuals, 100.0 % Male samples |
— | African unspecified | — | AllofUs | — |
| PSS011845 | Prostate specific antigen | — | 1,336 individuals, 100.0 % Male samples |
— | Hispanic or Latin American | — | AllofUs | — |
| PSS011847 | Prostate specific antigen | — | 1,783 individuals, 100.0 % Male samples |
— | Other | — | AllofUs | — |
| PSS011848 | Prostate specific antigen | — | 1,173 individuals, 100.0 % Male samples |
— | African unspecified | — | SELECT | — |
| PSS011849 | Prostate specific antigen | — | 257 individuals, 100.0 % Male samples |
— | Asian unspecified | — | SELECT | — |
| PSS000627 | — | — | 5,573 individuals | — | African unspecified | — | UKB | — |
| PSS000628 | — | — | 984 individuals | — | East Asian | — | UKB | — |
| PSS000629 | — | — | 21,516 individuals | — | European | Non-British White | UKB | — |
| PSS000630 | — | — | 6,687 individuals | — | South Asian | — | UKB | — |
| PSS000631 | — | — | 58,196 individuals | — | European (British) |
— | UKB | — |
| PSS011850 | Prostate specific antigen | — | 22,173 individuals, 100.0 % Male samples |
— | European | — | SELECT | — |
| PSS008096 | — | — | 1,716 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008108 | — | — | 1,762 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS010637 | — | — | 6,170 individuals, 45.0 % Male samples |
Mean = 54.4 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS000667 | — | — | 6,016 individuals | — | African unspecified | — | UKB | — |
| PSS000668 | — | — | 1,081 individuals | — | East Asian | — | UKB | — |
| PSS008120 | — | — | 1,743 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS000674 | — | — | 23,576 individuals | — | European | Non-British White | UKB | — |
| PSS000675 | — | — | 7,339 individuals | — | South Asian | — | UKB | — |
| PSS000676 | — | — | 63,758 individuals | — | European (British) |
— | UKB | — |