| Experimental Factor Ontology (EFO) Information | |
| Identifier | EFO_0005278 |
| Description | cardiovascular disease biomarkers, such as ST2 cardiac biomarker and C-reactive protein, are used as indicators for cardiovascular disease and as predictors for therapeutic responses | Trait category |
Cardiovascular measurement
|
| Child trait(s) |
14 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) |
|---|---|---|---|---|---|---|
| PGS000061 (GRS_LDL) |
PGP000045 | Johnson L et al. PLoS One (2015) |
Low-density lipoprotein (LDL) cholesterol | low density lipoprotein cholesterol measurement | 37 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000061/ScoringFiles/PGS000061.txt.gz |
| PGS000065 (GLGC2017_LDL) |
PGP000046 | Kuchenbaecker K et al. Nat Commun (2019) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 103 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000065/ScoringFiles/PGS000065.txt.gz |
| PGS000115 (LDL-C_20) |
PGP000053 | Trinder M et al. JAMA Cardiol (2020) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 223 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000115/ScoringFiles/PGS000115.txt.gz |
| PGS000192 (GS9) |
PGP000079 | Kathiresan S et al. N Engl J Med (2008) |
Cholesterol | low density lipoprotein cholesterol measurement, high density lipoprotein cholesterol measurement |
9 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000192/ScoringFiles/PGS000192.txt.gz |
| PGS000310 (GRS194_LDL) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 194 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000310/ScoringFiles/PGS000310.txt.gz |
| PGS000340 (LDL-Cpsp) |
PGP000107 | Trinder M et al. Circ Genom Precis Med (2020) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 28 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000340/ScoringFiles/PGS000340.txt.gz |
| PGS000661 (PRS-LDL) |
PGP000121 | Tam CHT et al. Genome Med (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 84 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000661/ScoringFiles/PGS000661.txt.gz | |
| PGS000671 (snpnet.Apolipoprotein_A) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Apolipoprotein A [g/L] | apolipoprotein A 1 measurement | 19,324 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000671/ScoringFiles/PGS000671.txt.gz |
| PGS000688 (snpnet.LDL_direct_adjstatins) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
LDL cholesterol [mmol/L] (statin adjusted) | low density lipoprotein cholesterol measurement | 16,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000688/ScoringFiles/PGS000688.txt.gz |
| PGS000735 (PRS_PR) |
PGP000144 | Tadros R et al. Eur Heart J (2019) |
PR interval | PR interval | 44 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000735/ScoringFiles/PGS000735.txt.gz |
| PGS000768 (PRS_QT) |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
QT-interval | QT interval | 68 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000768/ScoringFiles/PGS000768.txt.gz |
| PGS000814 (GRS12_LDLc) |
PGP000200 | Talmud PJ et al. Lancet (2013) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 16 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000814/ScoringFiles/PGS000814.txt.gz |
| PGS000824 (LDL-C_PGS) |
PGP000210 | Zubair N et al. Sci Rep (2019) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 809 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000824/ScoringFiles/PGS000824.txt.gz |
| PGS000846 (LDL) |
PGP000211 | Aly DM et al. Nat Genet (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 275 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000846/ScoringFiles/PGS000846.txt.gz |
| PGS000875 (PGS36_LDLc) |
PGP000221 | Leal LG et al. Mol Genet Genomic Med (2020) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 36 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000875/ScoringFiles/PGS000875.txt.gz | |
| PGS000886 (GLGC_2021_AFR_LDL_PRS_weights_PRS-CS) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,222,318 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000886/ScoringFiles/PGS000886.txt.gz |
| PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 295 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000887/ScoringFiles/PGS000887.txt.gz |
| PGS000888 (GLGC_2021_ALL_LDL_PRS_weights_PRS-CS) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,239,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000888/ScoringFiles/PGS000888.txt.gz |
| PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 9,009 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000889/ScoringFiles/PGS000889.txt.gz |
| PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,029,158 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000890/ScoringFiles/PGS000890.txt.gz |
| PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 66 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000891/ScoringFiles/PGS000891.txt.gz |
| PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,119,211 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000892/ScoringFiles/PGS000892.txt.gz |
| PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 5,427 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000893/ScoringFiles/PGS000893.txt.gz |
| PGS000894 (GLGC_2021_HIS_LDL_PRS_weights_PRS-CS) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,175,595 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000894/ScoringFiles/PGS000894.txt.gz |
| PGS000895 (GLGC_2021_HIS_LDL_PRS_weights_PT) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 76 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000895/ScoringFiles/PGS000895.txt.gz |
| PGS000896 (GLGC_2021_SAS_LDL_PRS_weights_PRS-CS) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,100,062 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000896/ScoringFiles/PGS000896.txt.gz |
| PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PGP000230 | Graham SE et al. Nature (2021) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 13 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000897/ScoringFiles/PGS000897.txt.gz |
| PGS000904 (PRS582_PR) |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
PR interval | PR interval | 582 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000904/ScoringFiles/PGS000904.txt.gz |
| PGS000905 (PRS743_PR) |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
PR interval | PR interval | 743 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000905/ScoringFiles/PGS000905.txt.gz |
| PGS001412 (GBE_INI22420) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
LV ejection fraction | left ventricular ejection fraction measurement | 266 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001412/ScoringFiles/PGS001412.txt.gz |
| PGS001413 (GBE_INI22423) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
LV stroke volume | left ventricular stroke volume measurement | 26 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001413/ScoringFiles/PGS001413.txt.gz |
| PGS001519 (GBE_INI4199) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Position of pulse wave notch | arterial stiffness measurement | 693 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001519/ScoringFiles/PGS001519.txt.gz |
| PGS001520 (GBE_INI4198) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Position of the pulse wave peak | arterial stiffness measurement | 1,358 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001520/ScoringFiles/PGS001520.txt.gz |
| PGS001521 (GBE_INI22330) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
PQ interval | PR interval | 391 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001521/ScoringFiles/PGS001521.txt.gz |
| PGS001526 (GBE_INI22331) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
QT interval | QT interval | 609 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001526/ScoringFiles/PGS001526.txt.gz |
| PGS001527 (GBE_INI22332) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
QTc interval (QT interval according to Bazett) | QT interval | 115 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001527/ScoringFiles/PGS001527.txt.gz |
| PGS001888 (portability-PLR_apoA) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Apolipoprotein A | apolipoprotein A 1 measurement | 74,596 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001888/ScoringFiles/PGS001888.txt.gz |
| PGS001902 (portability-PLR_ECG_P_duration) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
P duration | P wave duration | 640 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001902/ScoringFiles/PGS001902.txt.gz |
| PGS001903 (portability-PLR_ECG_PP_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
PP interval | PP interval | 4,368 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001903/ScoringFiles/PGS001903.txt.gz |
| PGS001904 (portability-PLR_ECG_PQ_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
PQ interval | PR interval | 826 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001904/ScoringFiles/PGS001904.txt.gz |
| PGS001905 (portability-PLR_ECG_QT_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
QT interval | QT interval | 2,300 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001905/ScoringFiles/PGS001905.txt.gz |
| PGS001906 (portability-PLR_ECG_QTC_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
QTc interval | QT interval | 641 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001906/ScoringFiles/PGS001906.txt.gz |
| PGS001907 (portability-PLR_ECG_RR_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
RR interval | RR interval | 1,964 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001907/ScoringFiles/PGS001907.txt.gz |
| PGS001933 (portability-PLR_LDL) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
LDL direct | low density lipoprotein cholesterol measurement | 25,604 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001933/ScoringFiles/PGS001933.txt.gz |
| PGS001981 (portability-PLR_log_ventricular_rate) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Ventricular rate | ventricular rate measurement | 6,324 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001981/ScoringFiles/PGS001981.txt.gz |
| PGS002101 (portability-ldpred2_apoA) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Apolipoprotein A | apolipoprotein A 1 measurement | 681,234 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002101/ScoringFiles/PGS002101.txt.gz |
| PGS002116 (portability-ldpred2_ECG_P_duration) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
P duration | P wave duration | 564,874 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002116/ScoringFiles/PGS002116.txt.gz |
| PGS002117 (portability-ldpred2_ECG_PP_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
PP interval | PP interval | 667,705 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002117/ScoringFiles/PGS002117.txt.gz |
| PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
PQ interval | PR interval | 413,539 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002118/ScoringFiles/PGS002118.txt.gz |
| PGS002119 (portability-ldpred2_ECG_QT_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
QT interval | QT interval | 571,268 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002119/ScoringFiles/PGS002119.txt.gz |
| PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
QTc interval | QT interval | 490,392 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002120/ScoringFiles/PGS002120.txt.gz |
| PGS002121 (portability-ldpred2_ECG_RR_interval) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
RR interval | RR interval | 616,710 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002121/ScoringFiles/PGS002121.txt.gz |
| PGS002150 (portability-ldpred2_LDL) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
LDL direct | low density lipoprotein cholesterol measurement | 360,007 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002150/ScoringFiles/PGS002150.txt.gz |
| PGS002199 (portability-ldpred2_log_ventricular_rate) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Ventricular rate | ventricular rate measurement | 705,680 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002199/ScoringFiles/PGS002199.txt.gz |
| PGS002267 (PRS89_AA) |
PGP000296 | Pirruccello JP et al. Nat Genet (2021) |
Ascending thoracic aortic diameter | cardiovascular disease biomarker measurement, aortic measurement |
89 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002267/ScoringFiles/PGS002267.txt.gz |
| PGS002274 (LDL-PRS) |
PGP000303 | Groenland EH et al. Atherosclerosis (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 279 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002274/ScoringFiles/PGS002274.txt.gz |
| PGS002276 (QTc_PRS-CS) |
PGP000304 | Nauffal V et al. Circulation (2022) |
QTc duration | QT interval | 1,110,494 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002276/ScoringFiles/PGS002276.txt.gz | |
| PGS002278 (GRS16_snLVEF) |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Supranormal left ventricular ejection fraction | left ventricular ejection fraction measurement | 16 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002278/ScoringFiles/PGS002278.txt.gz |
| PGS002279 (GRS22_rLVEF) |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reduced left ventricular ejection fraction | left ventricular ejection fraction measurement | 22 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002279/ScoringFiles/PGS002279.txt.gz |
| PGS002285 (GRS_286_LDL) |
PGP000313 | Kamiza AB et al. Nat Med (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 286 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002285/ScoringFiles/PGS002285.txt.gz | |
| PGS002337 (biochemistry_LDLdirect.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002337/ScoringFiles/PGS002337.txt.gz |
| PGS002369 (biochemistry_LDLdirect.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 920,930 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002369/ScoringFiles/PGS002369.txt.gz |
| PGS002409 (biochemistry_LDLdirect.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 7,626 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002409/ScoringFiles/PGS002409.txt.gz |
| PGS002458 (biochemistry_LDLdirect.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 20,708 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002458/ScoringFiles/PGS002458.txt.gz |
| PGS002507 (biochemistry_LDLdirect.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 105,053 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002507/ScoringFiles/PGS002507.txt.gz |
| PGS002556 (biochemistry_LDLdirect.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 3,244 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002556/ScoringFiles/PGS002556.txt.gz |
| PGS002605 (biochemistry_LDLdirect.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 2,337 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002605/ScoringFiles/PGS002605.txt.gz |
| PGS002654 (biochemistry_LDLdirect.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 274,585 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002654/ScoringFiles/PGS002654.txt.gz |
| PGS002703 (biochemistry_LDLdirect.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 970,081 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002703/ScoringFiles/PGS002703.txt.gz |
| PGS002730 (GRSlipid_35) |
PGP000337 | Mayerhofer E et al. Brain (2022) |
LDL lowering in response to statin | low density lipoprotein cholesterol measurement, response to statin |
35 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002730/ScoringFiles/PGS002730.txt.gz |
| PGS002782 (GLGC_2021_ALL_nonHDL_PRS_weights_PRS-CS) |
PGP000366 | Kanoni S et al. Genome Biol (2022) |
nonHDL Cholesterol | non-high density lipoprotein cholesterol measurement | 1,239,184 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002782/ScoringFiles/PGS002782.txt.gz |
| PGS003029 (ExPRSweb_LDL_30780-irnt_LASSOSUM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 549,112 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003029/ScoringFiles/PGS003029.txt.gz | |
| PGS003030 (ExPRSweb_LDL_30780-irnt_PT_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 2,066 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003030/ScoringFiles/PGS003030.txt.gz | |
| PGS003031 (ExPRSweb_LDL_30780-irnt_PLINK_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 2,609 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003031/ScoringFiles/PGS003031.txt.gz | |
| PGS003032 (ExPRSweb_LDL_30780-irnt_DBSLMM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 7,457,930 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003032/ScoringFiles/PGS003032.txt.gz | |
| PGS003033 (ExPRSweb_LDL_30780-irnt_PRSCS_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,113,830 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003033/ScoringFiles/PGS003033.txt.gz | |
| PGS003034 (ExPRSweb_LDL_30780-raw_LASSOSUM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 552,845 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003034/ScoringFiles/PGS003034.txt.gz | |
| PGS003035 (ExPRSweb_LDL_30780-raw_PT_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 2,288 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003035/ScoringFiles/PGS003035.txt.gz | |
| PGS003036 (ExPRSweb_LDL_30780-raw_PLINK_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 2,935 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003036/ScoringFiles/PGS003036.txt.gz | |
| PGS003037 (ExPRSweb_LDL_30780-raw_DBSLMM_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 8,918,470 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003037/ScoringFiles/PGS003037.txt.gz | |
| PGS003038 (ExPRSweb_LDL_30780-raw_PRSCS_MGI_20211120) |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,113,830 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003038/ScoringFiles/PGS003038.txt.gz | |
| PGS003339 (CVGRS_LDL) |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 65 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003339/ScoringFiles/PGS003339.txt.gz |
| PGS003348 (ALLGRS_LDL) |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 78 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003348/ScoringFiles/PGS003348.txt.gz |
| PGS003403 (PRS28_LDL) |
PGP000420 | Trinder M et al. J Am Coll Cardiol (2019) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 28 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003403/ScoringFiles/PGS003403.txt.gz |
| PGS003404 (wGRS) |
PGP000421 | Wang J et al. Arterioscler Thromb Vasc Biol (2016) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 10 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003404/ScoringFiles/PGS003404.txt.gz |
| PGS003405 (165SNP_PRS) |
PGP000422 | Vanhoye X et al. Transl Res (2022) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 169 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003405/ScoringFiles/PGS003405.txt.gz | |
| PGS003427 (lvmi) |
PGP000434 | Khurshid S et al. Nat Commun (2023) |
Left ventricular mass index (LVMI) | left ventricular mass index | 465 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003427/ScoringFiles/PGS003427.txt.gz | |
| PGS003474 (LDPred2_LDL) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
LDL levels | low density lipoprotein cholesterol measurement | 842,513 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003474/ScoringFiles/PGS003474.txt.gz |
| PGS003477 (LDPred2_PP) |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Pulse pressure | pulse pressure measurement | 847,747 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003477/ScoringFiles/PGS003477.txt.gz |
| PGS003499 (cont-decay-ECG_PQ_interval) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
PQ interval | PR interval | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003499/ScoringFiles/PGS003499.txt.gz |
| PGS003500 (cont-decay-ECG_QT_interval) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
QT interval | QT interval | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003500/ScoringFiles/PGS003500.txt.gz |
| PGS003501 (cont-decay-ECG_QTC_interval) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
QTc interval | QT interval | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003501/ScoringFiles/PGS003501.txt.gz |
| PGS003517 (cont-decay-LDL) |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
LDL direct | low density lipoprotein cholesterol measurement | 979,739 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003517/ScoringFiles/PGS003517.txt.gz |
| PGS003784 (LDL_EUR_CT) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 3,754 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003784/ScoringFiles/PGS003784.txt.gz | |
| PGS003785 (LDL_EUR_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,490,736 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003785/ScoringFiles/PGS003785.txt.gz | |
| PGS003786 (LDL_AFR_CT) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 188 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003786/ScoringFiles/PGS003786.txt.gz | |
| PGS003787 (LDL_AFR_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,679,610 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003787/ScoringFiles/PGS003787.txt.gz | |
| PGS003788 (LDL_AFR_weighted_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,679,610 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003788/ScoringFiles/PGS003788.txt.gz | |
| PGS003789 (LDL_AFR_PRSCSx) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,155,363 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003789/ScoringFiles/PGS003789.txt.gz | |
| PGS003790 (LDL_AFR_CTSLEB) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,120,053 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003790/ScoringFiles/PGS003790.txt.gz | |
| PGS003791 (LDL_EAS_CT) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 80 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003791/ScoringFiles/PGS003791.txt.gz | |
| PGS003792 (LDL_EAS_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,191,112 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003792/ScoringFiles/PGS003792.txt.gz | |
| PGS003793 (LDL_EAS_weighted_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,191,112 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003793/ScoringFiles/PGS003793.txt.gz | |
| PGS003794 (LDL_EAS_PRSCSx) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,155,363 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003794/ScoringFiles/PGS003794.txt.gz | |
| PGS003795 (LDL_EAS_CTSLEB) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 564,379 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003795/ScoringFiles/PGS003795.txt.gz | |
| PGS003796 (LDL_SAS_CT) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 25 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003796/ScoringFiles/PGS003796.txt.gz | |
| PGS003797 (LDL_SAS_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,507,815 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003797/ScoringFiles/PGS003797.txt.gz | |
| PGS003798 (LDL_SAS_weighted_LDpred2) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,507,815 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003798/ScoringFiles/PGS003798.txt.gz | |
| PGS003799 (LDL_SAS_PRSCSx) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,155,363 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003799/ScoringFiles/PGS003799.txt.gz | |
| PGS003800 (LDL_SAS_CTSLEB) |
PGP000489 | Zhang H et al. Nat Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 801,576 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003800/ScoringFiles/PGS003800.txt.gz | |
| PGS003855 (PRS44_LDL) |
PGP000493 | Li J et al. JAMA Netw Open (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 44 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003855/ScoringFiles/PGS003855.txt.gz |
| PGS003869 (LDL_PRScsx_ARB_AFRweights) |
PGP000501 | Shim I et al. Nature Communications (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,022,259 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003869/ScoringFiles/PGS003869.txt.gz |
| PGS003870 (LDL_PRScsx_ARB_AMRweights) |
PGP000501 | Shim I et al. Nature Communications (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,067,857 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003870/ScoringFiles/PGS003870.txt.gz |
| PGS003871 (LDL_PRScsx_ARB_ARBweights) |
PGP000501 | Shim I et al. Nature Communications (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 882,001 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003871/ScoringFiles/PGS003871.txt.gz |
| PGS003872 (LDL_PRScsx_ARB_EASweights) |
PGP000501 | Shim I et al. Nature Communications (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 958,649 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003872/ScoringFiles/PGS003872.txt.gz |
| PGS003873 (LDL_PRScsx_ARB_EURweights) |
PGP000501 | Shim I et al. Nature Communications (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,069,677 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003873/ScoringFiles/PGS003873.txt.gz |
| PGS003874 (LDL_PRScsx_ARB_SASweights) |
PGP000501 | Shim I et al. Nature Communications (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,054,648 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003874/ScoringFiles/PGS003874.txt.gz |
| PGS003974 (AFR_without-UKB_LDL) |
PGP000514 | Hassanin E et al. Front Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 886,257 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003974/ScoringFiles/PGS003974.txt.gz |
| PGS003975 (EAS_without-UKB_LDL) |
PGP000514 | Hassanin E et al. Front Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 862,971 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003975/ScoringFiles/PGS003975.txt.gz |
| PGS003976 (EUR_without-UKB_LDL) |
PGP000514 | Hassanin E et al. Front Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 821,134 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003976/ScoringFiles/PGS003976.txt.gz |
| PGS003977 (SAS_without-UKB_LDL) |
PGP000514 | Hassanin E et al. Front Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 826,248 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003977/ScoringFiles/PGS003977.txt.gz |
| PGS003978 (meta_without-UKB_LDL) |
PGP000514 | Hassanin E et al. Front Genet (2023) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 348,056 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS003978/ScoringFiles/PGS003978.txt.gz |
| PGS004605 (PP-meta-analysis) |
PGP000581 | Keaton JM et al. Nat Genet (2024) |
Pulse pressure | pulse pressure measurement | 7,356,519 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004605/ScoringFiles/PGS004605.txt.gz |
| PGS004637 (LDL_AFR_lassosum2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 173 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004637/ScoringFiles/PGS004637.txt.gz | |
| PGS004638 (LDL_AFR_ldpred2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,683,941 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004638/ScoringFiles/PGS004638.txt.gz | |
| PGS004639 (LDL_AFR_PROSPER) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,794,429 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004639/ScoringFiles/PGS004639.txt.gz | |
| PGS004640 (LDL_AFR_weighted_lassosum2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 107,449 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004640/ScoringFiles/PGS004640.txt.gz | |
| PGS004641 (LDL_AFR_weighted_ldpred2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,879,609 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004641/ScoringFiles/PGS004641.txt.gz | |
| PGS004642 (LDL_EAS_lassosum2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 8,643 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004642/ScoringFiles/PGS004642.txt.gz | |
| PGS004643 (LDL_EAS_ldpred2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,194,231 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004643/ScoringFiles/PGS004643.txt.gz | |
| PGS004644 (LDL_EAS_PROSPER) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,354,681 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004644/ScoringFiles/PGS004644.txt.gz | |
| PGS004645 (LDL_EAS_weighted_lassosum2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 107,449 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004645/ScoringFiles/PGS004645.txt.gz | |
| PGS004646 (LDL_EAS_weighted_ldpred2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,879,609 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004646/ScoringFiles/PGS004646.txt.gz | |
| PGS004647 (LDL_SAS_lassosum2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 307 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004647/ScoringFiles/PGS004647.txt.gz | |
| PGS004648 (LDL_SAS_ldpred2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,511,084 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004648/ScoringFiles/PGS004648.txt.gz | |
| PGS004649 (LDL_SAS_PROSPER) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,794,429 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004649/ScoringFiles/PGS004649.txt.gz | |
| PGS004650 (LDL_SAS_weighted_lassosum2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 107,449 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004650/ScoringFiles/PGS004650.txt.gz | |
| PGS004651 (LDL_SAS_weighted_ldpred2) |
PGP000595 | Zhang J et al. Nat Commun (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,879,609 | https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004651/ScoringFiles/PGS004651.txt.gz | |
| PGS004791 (ldl_PRSmix_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,098,797 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004791/ScoringFiles/PGS004791.txt.gz |
| PGS004792 (ldl_PRSmix_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 6,580,710 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004792/ScoringFiles/PGS004792.txt.gz |
| PGS004793 (ldl_PRSmixPlus_eur) |
PGP000604 | Truong B et al. Cell Genom (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 3,946,021 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004793/ScoringFiles/PGS004793.txt.gz |
| PGS004794 (ldl_PRSmixPlus_sas) |
PGP000604 | Truong B et al. Cell Genom (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 6,433,913 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004794/ScoringFiles/PGS004794.txt.gz |
| PGS004915 (LDL-C_PGS) |
PGP000647 | Trinder M et al. Arterioscler Thromb Vasc Biol (2019) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 223 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004915/ScoringFiles/PGS004915.txt.gz |
| PGS004936 (low_density_lipoprotein_cholesterol_measurement_combined) |
PGP000665 | Moreno-Grau S et al. Human Genomics (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 139,875 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004936/ScoringFiles/PGS004936.txt.gz |
| PGS004969 (LDLC_Mean_INT_ldpred_AFRss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004969/ScoringFiles/PGS004969.txt.gz |
| PGS004970 (LDLC_Mean_INT_ldpred_EURss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004970/ScoringFiles/PGS004970.txt.gz |
| PGS004971 (LDLC_Mean_INT_ldpred_HISss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004971/ScoringFiles/PGS004971.txt.gz |
| PGS004972 (LDLC_Mean_INT_ldpred_METAss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004972/ScoringFiles/PGS004972.txt.gz |
| PGS004973 (LDLC_Mean_INT_ldpred_METAss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004973/ScoringFiles/PGS004973.txt.gz |
| PGS004974 (LDLC_Mean_INT_ldpred_METAss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,286,612 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004974/ScoringFiles/PGS004974.txt.gz |
| PGS004975 (LDLC_Mean_INT_prscs_AFRss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,273,897 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004975/ScoringFiles/PGS004975.txt.gz |
| PGS004976 (LDLC_Mean_INT_prscs_EURss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,273,897 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004976/ScoringFiles/PGS004976.txt.gz |
| PGS004977 (LDLC_Mean_INT_prscs_HISss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,273,897 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004977/ScoringFiles/PGS004977.txt.gz |
| PGS004978 (LDLC_Mean_INT_prscs_METAss_afrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,273,897 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004978/ScoringFiles/PGS004978.txt.gz |
| PGS004979 (LDLC_Mean_INT_prscs_METAss_amrld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,273,897 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004979/ScoringFiles/PGS004979.txt.gz |
| PGS004980 (LDLC_Mean_INT_prscs_METAss_eurld) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,273,897 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004980/ScoringFiles/PGS004980.txt.gz |
| PGS004981 (LDLC_Mean_INT_prscsx_METAweight) |
PGP000679 | Gunn S et al. HGG Adv (2024) |
LDL cholesterol | low density lipoprotein cholesterol measurement | 1,277,825 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS004981/ScoringFiles/PGS004981.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 |
|---|---|---|---|---|---|---|---|---|---|
| PPM000155 | PGS000061 (GRS_LDL) |
PSS000098| European Ancestry| 2,063 individuals |
PGP000045 | Johnson L et al. PLoS One (2015) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | β: 15.0 | — | Beta (p-value): 0.0352 | age, age^2, sex, GRS_HDL, GRS_TC, GRS_TG | Association (p-value; unadjusted for covariates) < 0.001 |
| PPM000156 | PGS000061 (GRS_LDL) |
PSS000097| East Asian Ancestry| 666 individuals |
PGP000045 | Johnson L et al. PLoS One (2015) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | β: 5.58 | — | Beta (p-value): 0.697 | age, age^2, sex, GRS_HDL, GRS_TC, GRS_TG | Association (p-value; unadjusted for covariates) < 0.001 |
| PPM000157 | PGS000061 (GRS_LDL) |
PSS000096| African Ancestry| 1,355 individuals |
PGP000045 | Johnson L et al. PLoS One (2015) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | β: 30.04 | — | Beta (p-value): 0.00282 | age, age^2, sex, GRS_HDL, GRS_TC, GRS_TG | Association (p-value; unadjusted for covariates) < 0.001 |
| PPM000158 | PGS000061 (GRS_LDL) |
PSS000099| Hispanic or Latin American Ancestry| 1,256 individuals |
PGP000045 | Johnson L et al. PLoS One (2015) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | β: 42.86 | — | Beta (p-value): 2e-05 | age, age^2, sex, GRS_HDL, GRS_TC, GRS_TG | Association (p-value; unadjusted for covariates) < 0.001 |
| PPM000168 | PGS000065 (GLGC2017_LDL) |
PSS000104| European Ancestry| 9,962 individuals |
PGP000046 | Kuchenbaecker K et al. Nat Commun (2019) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | — | — | correlation (r): 0.274 [0.254, 0.294] | age, sex | Relatedness and population structure were accounted for using a linear mixed model with random polygenic effect implemented in GEMMA |
| PPM000171 | PGS000065 (GLGC2017_LDL) |
PSS000102| European Ancestry| 1,641 individuals |
PGP000046 | Kuchenbaecker K et al. Nat Commun (2019) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | — | — | correlation (r): 0.229 [0.172, 0.286] | age, sex | Relatedness and population structure were accounted for using a linear mixed model with random polygenic effect implemented in GEMMA |
| PPM000174 | PGS000065 (GLGC2017_LDL) |
PSS000103| European Ancestry| 1,945 individuals |
PGP000046 | Kuchenbaecker K et al. Nat Commun (2019) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | — | — | correlation (r): 0.29 [0.231, 0.349] | age, sex | Relatedness and population structure were accounted for using a linear mixed model with random polygenic effect implemented in GEMMA |
| PPM000177 | PGS000065 (GLGC2017_LDL) |
PSS000100| African Ancestry| 6,407 individuals |
PGP000046 | Kuchenbaecker K et al. Nat Commun (2019) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | — | — | correlation (r): 0.28 [0.257, 0.304] | age, sex | Relatedness and population structure were accounted for using a linear mixed model with random polygenic effect implemented in GEMMA |
| PPM000180 | PGS000065 (GLGC2017_LDL) |
PSS000101| East Asian Ancestry| 21,295 individuals |
PGP000046 | Kuchenbaecker K et al. Nat Commun (2019) |
Reported Trait: Serum low-density lipoprotein (LDL) levels | — | — | correlation (r): 0.198 [0.161, 0.235] | age, sex, region, 20 PCs of genetic ancestry | Relatedness and population structure were accounted for using a linear mixed model with random polygenic effect implemented in GEMMA |
| PPM000264 | PGS000115 (LDL-C_20) |
PSS000184| European Ancestry| 439,871 individuals |
PGP000053 | Trinder M et al. JAMA Cardiol (2020) |
Reported Trait: Serum low density lipoprotein cholesterol (LDL-C) levels | β: 28.01 (0.18) | — | R²: 0.09 | age, sex, 4 PCs of genetic ancestry, genotyping method (array and batch) | — |
| PPM000265 | PGS000115 (LDL-C_20) |
PSS000183| East Asian Ancestry| 10,640 individuals |
PGP000053 | Trinder M et al. JAMA Cardiol (2020) |
Reported Trait: Serum low density lipoprotein cholesterol (LDL-C) levels | β: 21.73 (1.25) | — | R²: 0.06 | age, sex, 4 PCs of genetic ancestry, genotyping method (array and batch) | — |
| PPM000266 | PGS000115 (LDL-C_20) |
PSS000181| African Ancestry| 4,680 individuals |
PGP000053 | Trinder M et al. JAMA Cardiol (2020) |
Reported Trait: Serum low density lipoprotein cholesterol (LDL-C) levels | β: 17.4 (1.91) | — | R²: 0.04 | age, sex, 4 PCs of genetic ancestry, genotyping method (array and batch) | — |
| PPM000267 | PGS000115 (LDL-C_20) |
PSS000185| Multi-ancestry (including European)| 455,191 individuals |
PGP000053 | Trinder M et al. JAMA Cardiol (2020) |
Reported Trait: Serum low density lipoprotein cholesterol (LDL-C) levels | β: 27.78 (0.18) | — | R²: 0.09 | age, sex, 4 PCs of genetic ancestry, genotyping method (array and batch) | — |
| PPM000268 | PGS000115 (LDL-C_20) |
PSS000182| Multi-ancestry (including European)| 47,845 individuals |
PGP000053 | Trinder M et al. JAMA Cardiol (2020) |
Reported Trait: Cardiovascular disease events | — | — | Hazard Ratio (HR; top vs. bottom decile of risk): 1.35 [1.3, 1.4] | age, sex, 4 PCs of genetic ancestry, genotyping method (array and batch) | — |
| PPM012859 | PGS000115 (LDL-C_20) |
PSS009580| European Ancestry| 33,787 individuals |
PGP000284 | Tapela NM et al. Eur J Prev Cardiol (2021) |Ext. |
Reported Trait: Uncontrolled hypercholesterolaemia | — | — | Odds Ratio (OR, top vs. bottom quintile): 2.78 [2.58, 3.0] | age, sex, socioeconomic characteristics (education, occupation, Townsend deprivation score, and country of residence), metabolic and lifestyle CVD risk factors (smoking status, body mass index, physical activity in METS, and weekly alcohol consumption), family history of CVD (diagnosis at any age), and the first four principal components of genetic ancestry, genotyping array and systolic blood pressure at baseline | 218 SNPs remained after QC |
| PPM012860 | PGS000115 (LDL-C_20) |
PSS009577| European Ancestry| 33,787 individuals |
PGP000284 | Tapela NM et al. Eur J Prev Cardiol (2021) |Ext. |
Reported Trait: Incident major adverse cardiovascular events in statin treatment | — | — | Hazard Ratio (HR, top vs. bottom quintile): 1.03 [0.92, 1.14] | age, sex, socioeconomic characteristics (education, occupation, Townsend deprivation score, and country of residence), metabolic and lifestyle CVD risk factors (smoking status, body mass index, physical activity in METS, and weekly alcohol consumption), family history of CVD (diagnosis at any age), and the first four principal components of genetic ancestry, genotyping array and systolic blood pressure at baseline | 218 SNPs remained after QC |
| PPM012861 | PGS000115 (LDL-C_20) |
PSS009578| European Ancestry| 33,787 individuals |
PGP000284 | Tapela NM et al. Eur J Prev Cardiol (2021) |Ext. |
Reported Trait: Incident myocardial infarction in statin treatment | — | — | Hazard Ratio (HR, top vs. bottom quintile): 1.08 [0.95, 1.23] | age, sex, socioeconomic characteristics (education, occupation, Townsend deprivation score, and country of residence), metabolic and lifestyle CVD risk factors (smoking status, body mass index, physical activity in METS, and weekly alcohol consumption), family history of CVD (diagnosis at any age), and the first four principal components of genetic ancestry, genotyping array and systolic blood pressure at baseline | 218 SNPs remained after QC |
| PPM012862 | PGS000115 (LDL-C_20) |
PSS009579| European Ancestry| 33,787 individuals |
PGP000284 | Tapela NM et al. Eur J Prev Cardiol (2021) |Ext. |
Reported Trait: Incident stroke in statin treatment | — | — | Hazard Ratio (HR, top vs. bottom quintile): 0.93 [0.77, 1.12] | age, sex, socioeconomic characteristics (education, occupation, Townsend deprivation score, and country of residence), metabolic and lifestyle CVD risk factors (smoking status, body mass index, physical activity in METS, and weekly alcohol consumption), family history of CVD (diagnosis at any age), and the first four principal components of genetic ancestry, genotyping array and systolic blood pressure at baseline | 218 SNPs remained after QC |
| PPM000563 | PGS000192 (GS9) |
PSS000292| European Ancestry| 4,232 individuals |
PGP000079 | Kathiresan S et al. N Engl J Med (2008) |
Reported Trait: Incident cardiovascular event | — | AUROC: 0.8 | Hazard Ratio (HR; per allele): 1.15 [1.07, 1.24] | age, sex, family history of MI, LDL cholesterol, HDL cholesterol, triglycerides, blood pressure, body mass index, diabetes status, smoking status, CRP, lipid lowering medication | — |
| PPM000562 | PGS000192 (GS9) |
PSS000292| European Ancestry| 4,232 individuals |
PGP000079 | Kathiresan S et al. N Engl J Med (2008) |
Reported Trait: High-density lipoprotein (HDL) levels | — | — | Association p-value: 2.00e-18 | — | — |
| PPM000561 | PGS000192 (GS9) |
PSS000292| European Ancestry| 4,232 individuals |
PGP000079 | Kathiresan S et al. N Engl J Med (2008) |
Reported Trait: Low-density lipoprotein (LDL) levels | — | — | Association p-value: 3.00e-24 | — | — |
| PPM000780 | PGS000310 (GRS194_LDL) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Low-density lipoprotein (mmol/l) | — | — | R²: 0.1849 | Sex, age, age^2 | — |
| PPM000924 | PGS000340 (LDL-Cpsp) |
PSS000466| European Ancestry| 389,127 individuals |
PGP000107 | Trinder M et al. Circ Genom Precis Med (2020) |
Reported Trait: Low-density lipoprotein cholesterol levels | β: 0.82 (0.006) | — | R²: 0.074 | Age, sex | — |
| PPM000923 | PGS000340 (LDL-Cpsp) |
PSS000465| Multi-ancestry (including European)| 1,120 individuals |
PGP000107 | Trinder M et al. Circ Genom Precis Med (2020) |
Reported Trait: Low-density lipoprotein cholesterol levels in familial hypercholesterolemia mutation carriers | — | — | Beta (per 20% increase in PGS): 0.13 [0.072, 0.19] | — | — |
| PPM000925 | PGS000340 (LDL-Cpsp) |
PSS000465| Multi-ancestry (including European)| 1,120 individuals |
PGP000107 | Trinder M et al. Circ Genom Precis Med (2020) |
Reported Trait: Atherosclerotic cardiovascular disease in familial hypercholesterolemia mutation carriers | — | — | Odds Ratio (OR; top 20% vs. rest): 1.48 [1.02, 2.14] | sex | — |
| PPM001361 | PGS000661 (PRS-LDL) |
PSS000588| East Asian Ancestry| 426 individuals |
PGP000121 | Tam CHT et al. Genome Med (2021) |
Reported Trait: LDL choldesterol at baseline (log transformed) | β: 0.072 (0.012) | — | Pearson Correlation Coefficient (r): 0.255 Incremental R² (PRS and covariates vs. covariates-alone): 0.0672 |
age, sex, BMI, PCs | — |
| PPM001362 | PGS000661 (PRS-LDL) |
PSS000594| East Asian Ancestry| 4,917 individuals |
PGP000121 | Tam CHT et al. Genome Med (2021) |
Reported Trait: LDL choldesterol at baseline (log transformed) | β: 0.059 (0.004) | — | Pearson Correlation Coefficient (r): 0.178 Incremental R² (PRS and covariates vs. covariates-alone): 0.0351 |
age, sex, BMI, PCs | — |
| PPM001363 | PGS000661 (PRS-LDL) |
PSS000590| East Asian Ancestry| 1,941 individuals |
PGP000121 | Tam CHT et al. Genome Med (2021) |
Reported Trait: LDL choldesterol at baseline (log transformed) | β: 0.054 (0.006) | — | Pearson Correlation Coefficient (r): 0.19 Incremental R² (PRS and covariates vs. covariates-alone): 0.036 |
age, sex, BMI, PCs | — |
| PPM001364 | PGS000661 (PRS-LDL) |
PSS000592| East Asian Ancestry| 865 individuals |
PGP000121 | Tam CHT et al. Genome Med (2021) |
Reported Trait: LDL choldesterol at baseline (log transformed) | β: 0.058 (0.01) | — | Pearson Correlation Coefficient (r): 0.195 Incremental R² (PRS and covariates vs. covariates-alone): 0.0374 |
age, sex, BMI, PCs | — |
| PPM001434 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS000638| East Asian Ancestry| 974 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Apolipoprotein A [g/L] | — | — | R²: 0.32046 Spearman's ρ: 0.329 |
Age, sex, PCs(1-40) | — |
| PPM001399 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS000637| African Ancestry| 5,550 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Apolipoprotein A [g/L] | — | — | R²: 0.1486 Spearman's ρ: 0.213 |
Age, sex, PCs(1-40) | — |
| PPM001469 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS000639| European Ancestry| 21,403 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Apolipoprotein A [g/L] | — | — | R²: 0.31504 Spearman's ρ: 0.385 |
Age, sex, PCs(1-40) | — |
| PPM001504 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS000640| South Asian Ancestry| 6,682 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Apolipoprotein A [g/L] | — | — | R²: 0.29281 Spearman's ρ: 0.358 |
Age, sex, PCs(1-40) | — |
| PPM001539 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS000641| European Ancestry| 57,932 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Apolipoprotein A [g/L] | — | — | R²: 0.30509 Spearman's ρ: 0.398 |
Age, sex, PCs(1-40) | — |
| PPM001580 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS000795| European Ancestry| 1,378 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: Apolipoprotein A [g/L] | — | — | Spearman's ρ: 0.241 | Age, sex | — |
| PPM007280 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS007086| African Ancestry| 5,632 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Apolipoprotein A | — | — | R²: 0.14722 [0.13131, 0.16312] Incremental R2 (full-covars): 0.04255 PGS R2 (no covariates): 0.03865 [0.02947, 0.04784] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007281 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS007087| East Asian Ancestry| 1,462 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Apolipoprotein A | — | — | R²: 0.28691 [0.25071, 0.3231] Incremental R2 (full-covars): 0.07041 PGS R2 (no covariates): 0.08155 [0.0567, 0.10641] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007282 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS007088| European Ancestry| 21,609 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Apolipoprotein A | — | — | R²: 0.28381 [0.27434, 0.29329] Incremental R2 (full-covars): 0.11215 PGS R2 (no covariates): 0.12323 [0.11558, 0.13087] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007283 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS007089| South Asian Ancestry| 6,776 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Apolipoprotein A | — | — | R²: 0.26533 [0.24858, 0.28209] Incremental R2 (full-covars): 0.1056 PGS R2 (no covariates): 0.11603 [0.1027, 0.12937] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007284 | PGS000671 (snpnet.Apolipoprotein_A) |
PSS007090| European Ancestry| 58,749 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: Apolipoprotein A | — | — | R²: 0.27548 [0.26974, 0.28122] Incremental R2 (full-covars): 0.12135 PGS R2 (no covariates): 0.1349 [0.1301, 0.13969] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001416 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000714| African Ancestry| 6,003 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | R²: 0.13357 Spearman's ρ: 0.289 |
Age, sex, PCs(1-40) | — |
| PPM001451 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000715| East Asian Ancestry| 1,082 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | R²: 0.13985 Spearman's ρ: 0.301 |
Age, sex, PCs(1-40) | — |
| PPM001486 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000716| European Ancestry| 23,535 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | R²: 0.26408 Spearman's ρ: 0.436 |
Age, sex, PCs(1-40) | — |
| PPM001521 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000717| South Asian Ancestry| 7,319 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | R²: 0.11217 Spearman's ρ: 0.289 |
Age, sex, PCs(1-40) | — |
| PPM001556 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000718| European Ancestry| 63,675 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | R²: 0.26409 Spearman's ρ: 0.446 |
Age, sex, PCs(1-40) | — |
| PPM001575 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000824| European Ancestry| 2,097 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | Spearman's ρ: 0.159 | Age, sex | — |
| PPM001576 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS000825| European Ancestry| 1,987 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: LDL cholesterol [mmol/L] (statin adjusted) | — | — | Spearman's ρ: 0.138 | Age, sex | — |
| PPM007365 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS007171| African Ancestry| 6,086 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: LDL cholesterol | — | — | R²: 0.05912 [0.048, 0.07024] Incremental R2 (full-covars): 0.05479 PGS R2 (no covariates): 0.06663 [0.05492, 0.07834] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007366 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS007172| East Asian Ancestry| 1,615 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: LDL cholesterol | — | — | R²: 0.06107 [0.03908, 0.08305] Incremental R2 (full-covars): 0.0498 PGS R2 (no covariates): 0.07486 [0.05087, 0.09885] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007367 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS007173| European Ancestry| 23,728 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: LDL cholesterol | — | — | R²: 0.10564 [0.09842, 0.11286] Incremental R2 (full-covars): 0.096 PGS R2 (no covariates): 0.11974 [0.11218, 0.12731] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007368 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS007174| South Asian Ancestry| 7,407 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: LDL cholesterol | — | — | R²: 0.05634 [0.04642, 0.06625] Incremental R2 (full-covars): 0.02423 PGS R2 (no covariates): 0.03502 [0.02702, 0.04301] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007369 | PGS000688 (snpnet.LDL_direct_adjstatins) |
PSS007175| European Ancestry| 64,356 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |Ext. |
Reported Trait: LDL cholesterol | — | — | R²: 0.09533 [0.09111, 0.09955] Incremental R2 (full-covars): 0.08543 PGS R2 (no covariates): 0.10869 [0.10426, 0.11313] |
age, sex, UKB array type, Genotype PCs | — |
| PPM001759 | PGS000735 (PRS_PR) |
PSS000905| European Ancestry| 1,185 individuals |
PGP000144 | Tadros R et al. Eur Heart J (2019) |
Reported Trait: Ajmaline-induced Type I Brugada syndrome electrocardiogram | OR: 1.017 [1.013, 1.022] | — | — | — | — |
| PPM001754 | PGS000735 (PRS_PR) |
PSS000904| European Ancestry| 1,257 individuals |
PGP000144 | Tadros R et al. Eur Heart J (2019) |
Reported Trait: PR slope | β: 0.22 (0.08) | — | — | — | — |
| PPM001750 | PGS000735 (PRS_PR) |
PSS000906| European Ancestry| 1,193 individuals |
PGP000144 | Tadros R et al. Eur Heart J (2019) |
Reported Trait: Baseline PR in non SCN5A mutation carriers | — | — | Correlation coefficent (r): 0.23 | — | — |
| PPM001752 | PGS000735 (PRS_PR) |
PSS000906| European Ancestry| 1,193 individuals |
PGP000144 | Tadros R et al. Eur Heart J (2019) |
Reported Trait: PR slope in non SCN5A mutation carriers | β: 0.16 (0.08) | — | Correlation coefficient (r): 0.09 | — | — |
| PPM001976 | PGS000768 (PRS_QT) |
PSS000987| European Ancestry| 9,457 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome | β: 0.322 (0.03) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 2.27 [1.9, 2.7] | PCs (1-10) | — |
| PPM001977 | PGS000768 (PRS_QT) |
PSS000987| European Ancestry| 9,457 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome in individuals with a single rare variant in a major LQTS gene | β: 0.277 (0.032) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 2.09 [1.74, 2.51] | PCs (1-10) | — |
| PPM001978 | PGS000768 (PRS_QT) |
PSS000987| European Ancestry| 9,457 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome in individuals without a single rare variant in a major LQTS gene | β: 0.733 (0.09) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 5.0 [2.73, 9.17] | PCs (1-10) | — |
| PPM001979 | PGS000768 (PRS_QT) |
PSS000988| East Asian Ancestry| 2,089 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome | β: 0.412 (0.055) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 2.9 [2.09, 4.04] | PCs (1-10) | Only 60 of the 68 SNP PRS were utilised. rs17457880, rs17460657, rs4656345, rs10040989, rs9920, rs1296720, rs17763769, rs1805128 were not included due to INFO < 0.3 and rs12300631 was used as a proxy for rs3026445. |
| PPM001980 | PGS000768 (PRS_QT) |
PSS000988| East Asian Ancestry| 2,089 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome in individuals with a single rare variant in a major LQTS gene | β: 0.384 (0.058) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 2.41 [1.71, 3.4] | PCs (1-10) | Only 60 of the 68 SNP PRS were utilised. rs17457880, rs17460657, rs4656345, rs10040989, rs9920, rs1296720, rs17763769, rs1805128 were not included due to INFO < 0.3 and rs12300631 was used as a proxy for rs3026445. |
| PPM001981 | PGS000768 (PRS_QT) |
PSS000988| East Asian Ancestry| 2,089 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome in individuals without a single rare variant in a major LQTS gene | β: 0.74 (0.129) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 12.6 [3.28, 41.67] | PCs (1-10) | Only 60 of the 68 SNP PRS were utilised. rs17457880, rs17460657, rs4656345, rs10040989, rs9920, rs1296720, rs17763769, rs1805128 were not included due to INFO < 0.3 and rs12300631 was used as a proxy for rs3026445. |
| PPM001982 | PGS000768 (PRS_QT) |
PSS000989| Multi-ancestry (including European)| 11,546 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome | β: 0.343 (0.0263) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 2.52 [2.16, 2.94] | PCs (1-10) | For Japanese individuals only 60 of the 68 SNP PRS were utilised. rs17457880, rs17460657, rs4656345, rs10040989, rs9920, rs1296720, rs17763769, rs1805128 were not included due to INFO < 0.3 and rs12300631 was used as a proxy for rs3026445. |
| PPM001983 | PGS000768 (PRS_QT) |
PSS000989| Multi-ancestry (including European)| 11,546 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome in individuals with a single rare variant in a major LQTS gene | β: 0.294 (0.028) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 2.23 [1.9, 2.62] | PCs (1-10) | For Japanese individuals only 60 of the 68 SNP PRS were utilised. rs17457880, rs17460657, rs4656345, rs10040989, rs9920, rs1296720, rs17763769, rs1805128 were not included due to INFO < 0.3 and rs12300631 was used as a proxy for rs3026445. |
| PPM001984 | PGS000768 (PRS_QT) |
PSS000989| Multi-ancestry (including European)| 11,546 individuals |
PGP000175 | Lahrouchi N et al. Circulation (2020) |
Reported Trait: Long QT syndrome in individuals without a single rare variant in a major LQTS gene | β: 0.735 (0.0738) | — | Odds Ratio (OR, top 25% vs. bottom 25%): 6.13 [3.57, 10.52] | PCs (1-10) | For Japanese individuals only 60 of the 68 SNP PRS were utilised. rs17457880, rs17460657, rs4656345, rs10040989, rs9920, rs1296720, rs17763769, rs1805128 were not included due to INFO < 0.3 and rs12300631 was used as a proxy for rs3026445. |
| PPM012972 | PGS000814 (GRS12_LDLc) |
PSS009637| Ancestry Not Reported| 1,519 individuals |
PGP000311 | Olmastroni E et al. J Am Heart Assoc (2022) |Ext. |
Reported Trait: Polygenic hypercholesterolemia | — | AUROC: 0.59 [0.56, 0.62] | Sensitivity (%, cutoff of 0.905): 78.0 Specificity (%, cutoff of 0.905): 36.0 |
— | — |
| PPM002202 | PGS000814 (GRS12_LDLc) |
PSS001072| Ancestry Not Reported| 967 individuals |
PGP000205 | Rimbert A et al. Arterioscler Thromb Vasc Biol (2020) |Ext. |
Reported Trait: Liver steatosis | — | — | Odds Ratio (OR, polygenic vs monogenic hypobetalipoproteinemia cases): 0.13 [0.1, 1.16] | Age, sex | — |
| PPM002201 | PGS000814 (GRS12_LDLc) |
PSS001072| Ancestry Not Reported| 967 individuals |
PGP000205 | Rimbert A et al. Arterioscler Thromb Vasc Biol (2020) |Ext. |
Reported Trait: Hypobetalipoproteinemia | — | — | Percentage of cases with polygenic etiology (%): 34.0 | — | Polygenic etiology = PRS<10th percentile |
| PPM002170 | PGS000814 (GRS12_LDLc) |
PSS001058| European Ancestry| 3,020 individuals |
PGP000200 | Talmud PJ et al. Lancet (2013) |
Reported Trait: Low-density lipoprotein cholesterol level >4.9mmol/L | — | — | Risk Ratio (RR, top 10% vs bottom 10%): 4.17 [3.01, 5.78] | — | — |
| PPM002171 | PGS000814 (GRS12_LDLc) |
PSS001059| European Ancestry| 3,660 individuals |
PGP000200 | Talmud PJ et al. Lancet (2013) |
Reported Trait: Low-density lipoprotein cholesterol level >4.9mmol/L in individuals who have familial hypercholestrolaemia and no known mutation | — | AUROC: 0.65 [0.62, 0.68] | — | — | — |
| PPM002168 | PGS000814 (GRS12_LDLc) |
PSS001058| European Ancestry| 3,020 individuals |
PGP000200 | Talmud PJ et al. Lancet (2013) |
Reported Trait: Low-density lipoprotein (LDL) cholesterol | β: 0.33 [0.3, 0.37] | — | R²: 0.11 | — | — |
| PPM002169 | PGS000814 (GRS12_LDLc) |
PSS001058| European Ancestry| 3,020 individuals |
PGP000200 | Talmud PJ et al. Lancet (2013) |
Reported Trait: Low-density lipoprotein (LDL) cholesterol | β: 0.34 [0.31, 0.38] | — | — | Sex, age, lipid-lowering drug use, body-mass index, diabetes status, smoking status, blood pressure | — |
| PPM002503 | PGS000814 (GRS12_LDLc) |
PSS001124| European Ancestry| 4,787 individuals |
PGP000221 | Leal LG et al. Mol Genet Genomic Med (2020) |Ext. |
Reported Trait: Low-density lipoprotein cholesterol | — | AUROC: 0.65 | — | — | — |
| PPM013054 | PGS000814 (GRS12_LDLc) |
PSS009666| South Asian Ancestry| 7,016 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: LDL-C concentration | — | — | R²: 0.049 [0.035, 0.063] | Age, sex | — |
| PPM013055 | PGS000814 (GRS12_LDLc) |
PSS009668| European Ancestry| 353,166 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: LDL-C concentration >4.9 mmol/L | OR: 11.01 [10.08, 12.04] | — | — | — | — |
| PPM013056 | PGS000814 (GRS12_LDLc) |
PSS009667| African Ancestry| 7,082 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: LDL-C concentration >4.9 mmol/L | OR: 10.54 [5.29, 21.67] | — | — | — | — |
| PPM013057 | PGS000814 (GRS12_LDLc) |
PSS009666| South Asian Ancestry| 7,016 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: LDL-C concentration >4.9 mmol/L | OR: 6.64 [2.98, 15.22] | — | — | — | — |
| PPM013058 | PGS000814 (GRS12_LDLc) |
PSS009668| European Ancestry| 353,166 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: Coronary heart disease | OR: 1.76 [1.56, 1.99] | — | — | — | — |
| PPM013059 | PGS000814 (GRS12_LDLc) |
PSS009667| African Ancestry| 7,082 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: Coronary heart disease | OR: 2.26 [0.78, 7.22] | — | — | — | — |
| PPM013060 | PGS000814 (GRS12_LDLc) |
PSS009666| South Asian Ancestry| 7,016 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: Coronary heart disease | OR: 1.24 [0.62, 2.53] | — | — | — | — |
| PPM013061 | PGS000814 (GRS12_LDLc) |
PSS009668| European Ancestry| 353,166 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: Coronary heart disease | OR: 1.25 [1.13, 1.39] | — | — | — | — |
| PPM013062 | PGS000814 (GRS12_LDLc) |
PSS009667| African Ancestry| 7,082 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: Coronary heart disease | OR: 1.09 [0.48, 2.58] | — | — | — | — |
| PPM013063 | PGS000814 (GRS12_LDLc) |
PSS009666| South Asian Ancestry| 7,016 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: Coronary heart disease | OR: 1.98 [0.95, 4.25] | — | — | — | — |
| PPM013052 | PGS000814 (GRS12_LDLc) |
PSS009668| European Ancestry| 353,166 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: LDL-C concentration | — | — | R²: 0.108 [0.105, 0.111] | Age, sex | — |
| PPM013053 | PGS000814 (GRS12_LDLc) |
PSS009667| African Ancestry| 7,082 individuals |
PGP000330 | Gratton J et al. Front Genet (2022) |Ext. |
Reported Trait: LDL-C concentration | — | — | R²: 0.105 [0.086, 0.124] | Age, sex | — |
| PPM016205 | PGS000814 (GRS12_LDLc) |
PSS010056| Greater Middle Eastern Ancestry| 6,140 individuals |
PGP000406 | Gandhi GD et al. J Transl Med (2022) |Ext. |
Reported Trait: Probable vs. unlikely dyslipidemia | — | — | p: 0.0003 | — | — |
| PPM016207 | PGS000814 (GRS12_LDLc) |
PSS010058| Ancestry Not Reported| 237 individuals |
PGP000408 | Borg SÁ et al. Atheroscler Plus (2022) |Ext. |
Reported Trait: Coronary artery calcium score >0 in potential clinical FH cases | — | — | Odds ratio (OR, >80 percentile vs <= 80 percentile): 8.05 [1.65, 39.29] | Smoking, hypertension, waist circumference and lipoprotein(a) | — |
| PPM017045 | PGS000814 (GRS12_LDLc) |
PSS010104| European Ancestry| 89,528 individuals |
PGP000422 | Vanhoye X et al. Transl Res (2022) |Ext. |
Reported Trait: LDL-c blood concentration | β: 0.25 [0.25, 0.26] | AUROC: 0.6503 [0.644, 0.657] | R²: 0.1055 | Age, BMI, sex, age | — |
| PPM002230 | PGS000824 (LDL-C_PGS) |
PSS001083| Multi-ancestry (including European)| 2,531 individuals |
PGP000210 | Zubair N et al. Sci Rep (2019) |
Reported Trait: Low density lipoprotein cholesterol | — | — | R²: 0.111 | Age at baseline, sex, enrollment channel, PCs(1-7), observation season, observation vendor | — |
| PPM002313 | PGS000846 (LDL) |
PSS001086| European Ancestry| 3,194 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Autoimmune Diabetes | OR: 1.02 [0.92, 1.13] | — | — | PC1-10 | — |
| PPM002316 | PGS000846 (LDL) |
PSS001085| European Ancestry| 4,116 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Obesity-related Diabetes | OR: 1.0 [0.94, 1.07] | — | — | PC1-10 | — |
| PPM002317 | PGS000846 (LDL) |
PSS001084| European Ancestry| 5,597 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Moderate Age-Related Diabetes | OR: 0.95 [0.9, 1.0] | — | — | PC1-10 | — |
| PPM002315 | PGS000846 (LDL) |
PSS001088| European Ancestry| 3,869 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Resistant Diabetes | OR: 0.92 [0.86, 0.99] | — | — | PC1-10 | — |
| PPM002314 | PGS000846 (LDL) |
PSS001087| European Ancestry| 3,930 individuals |
PGP000211 | Aly DM et al. Nat Genet (2021) |
Reported Trait: Severe Insulin-Deficient Diabetes | OR: 0.95 [0.89, 1.02] | — | — | PC1-10 | — |
| PPM002500 | PGS000875 (PGS36_LDLc) |
PSS001124| European Ancestry| 4,787 individuals |
PGP000221 | Leal LG et al. Mol Genet Genomic Med (2020) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.08 | Age, gender, body mass index, ancestry differences captured by the first two components from multidimensional scaling | — |
| PPM002501 | PGS000875 (PGS36_LDLc) |
PSS001124| European Ancestry| 4,787 individuals |
PGP000221 | Leal LG et al. Mol Genet Genomic Med (2020) |
Reported Trait: Severe hypercholesterolemia | — | — | Risk Ratio (RR, top 30% vs bottom 30%): 4.8 [2.6, 8.9] | — | — |
| PPM002502 | PGS000875 (PGS36_LDLc) |
PSS001124| European Ancestry| 4,787 individuals |
PGP000221 | Leal LG et al. Mol Genet Genomic Med (2020) |
Reported Trait: Low-density lipoprotein cholesterol | — | AUROC: 0.67 | — | — | — |
| PPM002576 | PGS000886 (GLGC_2021_AFR_LDL_PRS_weights_PRS-CS) |
PSS001153| African Ancestry| 1,341 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.064 | sex, batch, PC1-4, and birth year | — |
| PPM002628 | PGS000886 (GLGC_2021_AFR_LDL_PRS_weights_PRS-CS) |
PSS001163| African Ancestry| 6,863 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.112 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002577 | PGS000886 (GLGC_2021_AFR_LDL_PRS_weights_PRS-CS) |
PSS001154| European Ancestry| 17,190 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.056 | sex, batch, PC1-4, and birth year | — |
| PPM002545 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001146| African Ancestry| 3,566 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.125 | age, sex, PC1-3 | — |
| PPM002557 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001149| African Ancestry| 4,972 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.128 | age, sex, PC1-6 | — |
| PPM002560 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001150| African Ancestry| 3,743 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.076 | age, sex, and PC1-4 | — |
| PPM002563 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001151| South Asian Ancestry| 15,242 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Max LDL cholesterol | — | — | R²: 0.069 | age, sex, and PC1-10 | — |
| PPM002568 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001152| East Asian Ancestry| 118,260 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.051 | age, sex, and recruitment area | — |
| PPM002574 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001153| African Ancestry| 1,341 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.115 | sex, batch, PC1-4, and birth year | — |
| PPM002575 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001154| European Ancestry| 17,190 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.074 | sex, batch, PC1-4, and birth year | — |
| PPM002584 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.148 | sex, PC1-4, birth year, and mean age | — |
| PPM002585 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.063 | sex, PC1-4, birth year, and mean age | — |
| PPM002587 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.071 | sex, PC1-4, birth year, and mean age | — |
| PPM002616 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001160| African Ancestry| 2,138 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.129 | birth year, sex, and PC1-4 | — |
| PPM002629 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001163| African Ancestry| 6,863 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.175 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002543 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001144| African Ancestry| 4,273 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.118 | age, sex, PC1-3 | — |
| PPM002544 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001145| African Ancestry| 707 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.101 | age, sex, PC1-3 | — |
| PPM002554 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001148| African Ancestry| 1,745 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.085 | age, sex, PC1-6 | — |
| PPM002586 | PGS000887 (GLGC_2021_AFR_LDL_PRS_weights_PT) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.103 | sex, PC1-4, birth year, and mean age | — |
| PPM002624 | PGS000888 (GLGC_2021_ALL_LDL_PRS_weights_PRS-CS) |
PSS001162| Multi-ancestry (including European)| 461,918 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.175 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002588 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.162 | sex, PC1-4, birth year, and mean age | — |
| PPM002589 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.1 | sex, PC1-4, birth year, and mean age | — |
| PPM002590 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.158 | sex, PC1-4, birth year, and mean age | — |
| PPM002546 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001144| African Ancestry| 4,273 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.114 | age, sex, PC1-3 | — |
| PPM002547 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001145| African Ancestry| 707 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.067 | age, sex, PC1-3 | — |
| PPM002548 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001146| African Ancestry| 3,566 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.123 | age, sex, PC1-3 | — |
| PPM002552 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001147| African Ancestry| 10,460 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.173 | age, sex, PC1-6 | — |
| PPM002555 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001148| African Ancestry| 1,745 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.069 | age, sex, PC1-6 | — |
| PPM002558 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001149| African Ancestry| 4,972 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.108 | age, sex, PC1-6 | — |
| PPM002561 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001150| African Ancestry| 3,743 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.075 | age, sex, and PC1-4 | — |
| PPM002569 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001152| East Asian Ancestry| 118,260 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.095 | age, sex, and recruitment area | — |
| PPM002578 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001153| African Ancestry| 1,341 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.122 | sex, batch, PC1-4, and birth year | — |
| PPM002579 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001154| European Ancestry| 17,190 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.13 | sex, batch, PC1-4, and birth year | — |
| PPM002591 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.134 | sex, PC1-4, birth year, and mean age | — |
| PPM002617 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001160| African Ancestry| 2,138 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.132 | birth year, sex, and PC1-4 | — |
| PPM002625 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001162| Multi-ancestry (including European)| 461,918 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.182 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002564 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001151| South Asian Ancestry| 15,242 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Max LDL cholesterol | — | — | R²: 0.105 | age, sex, and PC1-10 | — |
| PPM002619 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS001161| East Asian Ancestry| 28,217 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.098 | sex, age, recruitment method, and PC1-20 | — |
| PPM020883 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS011442| European Ancestry| 564 individuals |
PGP000599 | Guarischi-Sousa R et al. Circ Genom Precis Med (2023) |Ext. |
Reported Trait: Raised coronary lesion | OR: 1.42 [1.16, 1.73] | — | — | — | — |
| PPM020891 | PGS000889 (GLGC_2021_ALL_LDL_PRS_weights_PT) |
PSS011441| African Ancestry| 504 individuals |
PGP000599 | Guarischi-Sousa R et al. Circ Genom Precis Med (2023) |Ext. |
Reported Trait: Raised coronary lesion | OR: 1.18 [0.95, 1.47] | — | — | — | — |
| PPM002571 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001152| East Asian Ancestry| 118,260 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.094 | age, sex, and recruitment area | — |
| PPM002596 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.041 | sex, PC1-4, birth year, and mean age | — |
| PPM002598 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.083 | sex, PC1-4, birth year, and mean age | — |
| PPM002599 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.063 | sex, PC1-4, birth year, and mean age | — |
| PPM002620 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001161| East Asian Ancestry| 28,217 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.094 | sex, age, recruitment method, and PC1-20 | — |
| PPM002626 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001164| East Asian Ancestry| 1,441 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.113 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002597 | PGS000890 (GLGC_2021_EAS_LDL_PRS_weights_PRS-CS) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.09 | sex, PC1-4, birth year, and mean age | — |
| PPM002570 | PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PSS001152| East Asian Ancestry| 118,260 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.098 | age, sex, and recruitment area | — |
| PPM002592 | PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.056 | sex, PC1-4, birth year, and mean age | — |
| PPM002593 | PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.098 | sex, PC1-4, birth year, and mean age | — |
| PPM002594 | PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.083 | sex, PC1-4, birth year, and mean age | — |
| PPM002595 | PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.057 | sex, PC1-4, birth year, and mean age | — |
| PPM002627 | PGS000891 (GLGC_2021_EAS_LDL_PRS_weights_PT) |
PSS001164| East Asian Ancestry| 1,441 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.1 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002549 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001144| African Ancestry| 4,273 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.052 | age, sex, PC1-3 | — |
| PPM002550 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001145| African Ancestry| 707 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.063 | age, sex, PC1-3 | — |
| PPM002551 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001146| African Ancestry| 3,566 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.055 | age, sex, PC1-3 | — |
| PPM002553 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001147| African Ancestry| 10,460 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.122 | age, sex, PC1-6 | — |
| PPM002556 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001148| African Ancestry| 1,745 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.041 | age, sex, PC1-6 | — |
| PPM002559 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001149| African Ancestry| 4,972 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.04 | age, sex, PC1-6 | — |
| PPM002562 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001150| African Ancestry| 3,743 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.022 | age, sex, and PC1-4 | — |
| PPM002566 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001151| South Asian Ancestry| 15,242 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Max LDL cholesterol | — | — | R²: 0.088 | age, sex, and PC1-10 | — |
| PPM002573 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001152| East Asian Ancestry| 118,260 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.064 | age, sex, and recruitment area | — |
| PPM002582 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001153| African Ancestry| 1,341 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.062 | sex, batch, PC1-4, and birth year | — |
| PPM002583 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001154| European Ancestry| 17,190 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.129 | sex, batch, PC1-4, and birth year | — |
| PPM002604 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.04 | sex, PC1-4, birth year, and mean age | — |
| PPM002605 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.067 | sex, PC1-4, birth year, and mean age | — |
| PPM002606 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.153 | sex, PC1-4, birth year, and mean age | — |
| PPM002607 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.114 | sex, PC1-4, birth year, and mean age | — |
| PPM002618 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001160| African Ancestry| 2,138 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.069 | birth year, sex, and PC1-4 | — |
| PPM002621 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001161| East Asian Ancestry| 28,217 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.074 | sex, age, recruitment method, and PC1-20 | — |
| PPM002622 | PGS000892 (GLGC_2021_EUR_LDL_PRS_weights_PRS-CS) |
PSS001165| European Ancestry| 389,158 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.182 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002565 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001151| South Asian Ancestry| 15,242 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Max LDL cholesterol | — | — | R²: 0.096 | age, sex, and PC1-10 | — |
| PPM002580 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001153| African Ancestry| 1,341 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.058 | sex, batch, PC1-4, and birth year | — |
| PPM002581 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001154| European Ancestry| 17,190 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.123 | sex, batch, PC1-4, and birth year | — |
| PPM002600 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.042 | sex, PC1-4, birth year, and mean age | — |
| PPM002601 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.066 | sex, PC1-4, birth year, and mean age | — |
| PPM002602 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.152 | sex, PC1-4, birth year, and mean age | — |
| PPM002603 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.106 | sex, PC1-4, birth year, and mean age | — |
| PPM002623 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001165| European Ancestry| 389,158 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.176 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002572 | PGS000893 (GLGC_2021_EUR_LDL_PRS_weights_PT) |
PSS001152| East Asian Ancestry| 118,260 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.071 | age, sex, and recruitment area | — |
| PPM002632 | PGS000894 (GLGC_2021_HIS_LDL_PRS_weights_PRS-CS) |
PSS001155| Hispanic or Latin American Ancestry| 360 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.023 | sex, batch, PC1-4, and birth year | — |
| PPM002609 | PGS000895 (GLGC_2021_HIS_LDL_PRS_weights_PT) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.079 | sex, PC1-4, birth year, and mean age | — |
| PPM002610 | PGS000895 (GLGC_2021_HIS_LDL_PRS_weights_PT) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.119 | sex, PC1-4, birth year, and mean age | — |
| PPM002611 | PGS000895 (GLGC_2021_HIS_LDL_PRS_weights_PT) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.102 | sex, PC1-4, birth year, and mean age | — |
| PPM002633 | PGS000895 (GLGC_2021_HIS_LDL_PRS_weights_PT) |
PSS001155| Hispanic or Latin American Ancestry| 360 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.088 | sex, batch, PC1-4, and birth year | — |
| PPM002608 | PGS000895 (GLGC_2021_HIS_LDL_PRS_weights_PT) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.1 | sex, PC1-4, birth year, and mean age | — |
| PPM002630 | PGS000896 (GLGC_2021_SAS_LDL_PRS_weights_PRS-CS) |
PSS001166| South Asian Ancestry| 6,814 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.04 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002567 | PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PSS001151| South Asian Ancestry| 15,242 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Max LDL cholesterol | — | — | R²: 0.077 | age, sex, and PC1-10 | — |
| PPM002612 | PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PSS001156| African Ancestry| 18,251 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.076 | sex, PC1-4, birth year, and mean age | — |
| PPM002613 | PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PSS001157| Additional Asian Ancestries| 4,155 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.067 | sex, PC1-4, birth year, and mean age | — |
| PPM002614 | PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PSS001158| European Ancestry| 68,381 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.101 | sex, PC1-4, birth year, and mean age | — |
| PPM002615 | PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PSS001159| Hispanic or Latin American Ancestry| 7,669 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Mean LDL cholesterol | — | — | R²: 0.069 | sex, PC1-4, birth year, and mean age | — |
| PPM002631 | PGS000897 (GLGC_2021_SAS_LDL_PRS_weights_PT) |
PSS001166| South Asian Ancestry| 6,814 individuals |
PGP000230 | Graham SE et al. Nature (2021) |
Reported Trait: Baseline LDL cholesterol | — | — | R²: 0.058 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM002666 | PGS000904 (PRS582_PR) |
PSS001175| European Ancestry| 309,269 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Atrial fibrillation | OR: 0.95 β: -0.047 (0.009) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002667 | PGS000904 (PRS582_PR) |
PSS001178| European Ancestry| 290,252 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Distal conduction disease | OR: 1.11 β: 0.103 (0.019) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002668 | PGS000904 (PRS582_PR) |
PSS001176| European Ancestry| 309,041 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Atrioventricular preexcitation | OR: 0.85 β: -0.168 (0.057) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002669 | PGS000904 (PRS582_PR) |
PSS001179| European Ancestry| 309,241 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Implantable cardioverter defibrillator | OR: 1.09 β: 0.086 (0.04) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002670 | PGS000904 (PRS582_PR) |
PSS001180| European Ancestry| 309,246 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Mitral valve prolapse | OR: 1.1 β: 0.093 (0.044) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002671 | PGS000904 (PRS582_PR) |
PSS001181| European Ancestry| 305,471 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Non-ischemic cardiomyopathy | OR: 0.95 β: -0.051 (0.024) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002672 | PGS000904 (PRS582_PR) |
PSS001182| European Ancestry| 309,270 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Pacemaker | OR: 1.06 β: 0.062 (0.016) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002673 | PGS000904 (PRS582_PR) |
PSS001183| European Ancestry| 309,255 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Valve disease | OR: 1.03 β: 0.03 (0.013) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | Only 581 SNPs from the 582 SNP PRS were utilised. 1 SNP was not included due to low imputation quality. |
| PPM002674 | PGS000905 (PRS743_PR) |
PSS001175| European Ancestry| 309,269 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Atrial fibrillation | OR: 0.94 β: -0.058 (0.009) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | — |
| PPM002675 | PGS000905 (PRS743_PR) |
PSS001178| European Ancestry| 290,252 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Distal conduction disease | β: 0.105 (0.019) OR: 1.11 |
— | — | Baseline age, sex, genotyping array, trait-related principal components | — |
| PPM002676 | PGS000905 (PRS743_PR) |
PSS001176| European Ancestry| 309,041 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Atrioventricular preexcitation | OR: 0.83 β: -0.191 (0.057) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | — |
| PPM002677 | PGS000905 (PRS743_PR) |
PSS001177| European Ancestry| 309,246 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Coronary artery disease | OR: 0.99 β: -0.014 (0.007) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | — |
| PPM002678 | PGS000905 (PRS743_PR) |
PSS001182| European Ancestry| 309,270 individuals |
PGP000236 | Ntalla I et al. Nat Commun (2020) |
Reported Trait: Pacemaker | OR: 1.06 β: 0.056 (0.016) |
— | — | Baseline age, sex, genotyping array, trait-related principal components | — |
| PPM005316 | PGS001412 (GBE_INI22420) |
PSS004987| East Asian Ancestry| 110 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV ejection fraction | — | — | R²: 0.21638 [0.18183, 0.25092] Incremental R2 (full-covars): 0.00243 PGS R2 (no covariates): 0.0051 [-0.00163, 0.01183] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005317 | PGS001412 (GBE_INI22420) |
PSS004988| European Ancestry| 1,708 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV ejection fraction | — | — | R²: 0.07182 [0.06564, 0.07799] Incremental R2 (full-covars): 0.0014 PGS R2 (no covariates): 0.00189 [0.00081, 0.00297] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005315 | PGS001412 (GBE_INI22420) |
PSS004986| African Ancestry| 192 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV ejection fraction | — | — | R²: 0.07902 [0.06643, 0.0916] Incremental R2 (full-covars): 0.0065 PGS R2 (no covariates): 0.00921 [0.00459, 0.01383] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005318 | PGS001412 (GBE_INI22420) |
PSS004989| South Asian Ancestry| 319 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV ejection fraction | — | — | R²: 0.06719 [0.05648, 0.07789] Incremental R2 (full-covars): 0.00348 PGS R2 (no covariates): 0.00252 [0.00031, 0.00474] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005319 | PGS001412 (GBE_INI22420) |
PSS004990| European Ancestry| 5,528 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV ejection fraction | — | — | R²: 0.04862 [0.04545, 0.05179] Incremental R2 (full-covars): 0.00384 PGS R2 (no covariates): 0.00375 [0.00283, 0.00468] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005320 | PGS001413 (GBE_INI22423) |
PSS004991| African Ancestry| 192 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV stroke volume | — | — | R²: 0.2894 [0.27082, 0.30798] Incremental R2 (full-covars): 0.00042 PGS R2 (no covariates): 2e-05 [-0.00021, 0.00026] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005321 | PGS001413 (GBE_INI22423) |
PSS004992| East Asian Ancestry| 110 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV stroke volume | — | — | R²: 0.24626 [0.21082, 0.28171] Incremental R2 (full-covars): 0.00076 PGS R2 (no covariates): 0.00073 [-0.00183, 0.00328] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005322 | PGS001413 (GBE_INI22423) |
PSS004993| European Ancestry| 1,708 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV stroke volume | — | — | R²: 0.27363 [0.26419, 0.28306] Incremental R2 (full-covars): -0.00044 PGS R2 (no covariates): 8e-05 [-0.00014, 0.00031] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005323 | PGS001413 (GBE_INI22423) |
PSS004994| South Asian Ancestry| 319 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV stroke volume | — | — | R²: 0.21177 [0.19571, 0.22783] Incremental R2 (full-covars): -0.00201 PGS R2 (no covariates): 0.00017 [-0.00041, 0.00074] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005324 | PGS001413 (GBE_INI22423) |
PSS004995| European Ancestry| 5,528 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: LV stroke volume | — | — | R²: 0.20777 [0.20231, 0.21322] Incremental R2 (full-covars): 0.00246 PGS R2 (no covariates): 0.00431 [0.00333, 0.0053] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007125 | PGS001519 (GBE_INI4199) |
PSS007341| African Ancestry| 3,863 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of pulse wave notch | — | — | R²: 0.02994 [0.02178, 0.0381] Incremental R2 (full-covars): 0.00194 PGS R2 (no covariates): 0.00226 [-0.00004, 0.00457] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007126 | PGS001519 (GBE_INI4199) |
PSS007342| East Asian Ancestry| 807 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of pulse wave notch | — | — | R²: 0.02934 [0.01358, 0.04509] Incremental R2 (full-covars): 0.0026 PGS R2 (no covariates): 0.00283 [-0.0022, 0.00785] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007127 | PGS001519 (GBE_INI4199) |
PSS007343| European Ancestry| 11,021 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of pulse wave notch | — | — | R²: 0.02545 [0.02159, 0.02931] Incremental R2 (full-covars): 0.00502 PGS R2 (no covariates): 0.00515 [0.00337, 0.00692] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007128 | PGS001519 (GBE_INI4199) |
PSS007344| South Asian Ancestry| 5,226 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of pulse wave notch | — | — | R²: 0.04794 [0.03871, 0.05717] Incremental R2 (full-covars): 0.00501 PGS R2 (no covariates): 0.0043 [0.00141, 0.00719] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007129 | PGS001519 (GBE_INI4199) |
PSS007345| European Ancestry| 26,777 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of pulse wave notch | — | — | R²: 0.03558 [0.03284, 0.03833] Incremental R2 (full-covars): 0.00809 PGS R2 (no covariates): 0.00826 [0.0069, 0.00962] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007120 | PGS001520 (GBE_INI4198) |
PSS007336| African Ancestry| 3,863 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of the pulse wave peak | — | — | R²: 0.05763 [0.04663, 0.06862] Incremental R2 (full-covars): -0.00367 PGS R2 (no covariates): 3e-05 [-0.00023, 0.00029] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007121 | PGS001520 (GBE_INI4198) |
PSS007337| East Asian Ancestry| 807 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of the pulse wave peak | — | — | R²: 0.0542 [0.03334, 0.07507] Incremental R2 (full-covars): -0.00065 PGS R2 (no covariates): 0.00041 [-0.0015, 0.00231] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007122 | PGS001520 (GBE_INI4198) |
PSS007338| European Ancestry| 11,021 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of the pulse wave peak | — | — | R²: 0.09762 [0.09061, 0.10462] Incremental R2 (full-covars): 0.00071 PGS R2 (no covariates): 0.00137 [0.00045, 0.00229] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007123 | PGS001520 (GBE_INI4198) |
PSS007339| South Asian Ancestry| 5,226 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of the pulse wave peak | — | — | R²: 0.03074 [0.02321, 0.03826] Incremental R2 (full-covars): 0.0 PGS R2 (no covariates): 0.00076 [-0.00046, 0.00198] |
age, sex, UKB array type, Genotype PCs | — |
| PPM007124 | PGS001520 (GBE_INI4198) |
PSS007340| European Ancestry| 26,777 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: Position of the pulse wave peak | — | — | R²: 0.07148 [0.06773, 0.07522] Incremental R2 (full-covars): 0.00342 PGS R2 (no covariates): 0.00338 [0.00251, 0.00426] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005300 | PGS001521 (GBE_INI22330) |
PSS004966| African Ancestry| 120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: PQ interval | — | — | R²: 0.14397 [0.12818, 0.15975] Incremental R2 (full-covars): 0.00024 PGS R2 (no covariates): 0.01367 [0.00807, 0.01928] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005301 | PGS001521 (GBE_INI22330) |
PSS004967| East Asian Ancestry| 68 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: PQ interval | — | — | R²: 0.2664 [0.23052, 0.30228] Incremental R2 (full-covars): -0.00945 PGS R2 (no covariates): 0.00783 [-0.00049, 0.01615] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005302 | PGS001521 (GBE_INI22330) |
PSS004968| European Ancestry| 834 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: PQ interval | — | — | R²: 0.10209 [0.09497, 0.10922] Incremental R2 (full-covars): 0.04534 PGS R2 (no covariates): 0.05149 [0.04615, 0.05684] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005303 | PGS001521 (GBE_INI22330) |
PSS004969| South Asian Ancestry| 193 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: PQ interval | — | — | R²: 0.13589 [0.12179, 0.15] Incremental R2 (full-covars): -0.01928 PGS R2 (no covariates): 0.00031 [-0.00047, 0.00108] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005304 | PGS001521 (GBE_INI22330) |
PSS004970| European Ancestry| 3,353 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: PQ interval | — | — | R²: 0.07982 [0.0759, 0.08375] Incremental R2 (full-covars): 0.02363 PGS R2 (no covariates): 0.02488 [0.02256, 0.0272] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005305 | PGS001526 (GBE_INI22331) |
PSS004971| African Ancestry| 120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QT interval | — | — | R²: 0.07804 [0.06552, 0.09056] Incremental R2 (full-covars): -0.0033 PGS R2 (no covariates): 0.00046 [-0.00058, 0.0015] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005306 | PGS001526 (GBE_INI22331) |
PSS004972| East Asian Ancestry| 68 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QT interval | — | — | R²: 0.32528 [0.28882, 0.36175] Incremental R2 (full-covars): 0.0416 PGS R2 (no covariates): 0.07506 [0.05104, 0.09907] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005307 | PGS001526 (GBE_INI22331) |
PSS004973| European Ancestry| 872 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QT interval | — | — | R²: 0.05819 [0.05255, 0.06383] Incremental R2 (full-covars): 0.02543 PGS R2 (no covariates): 0.02774 [0.02372, 0.03176] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005308 | PGS001526 (GBE_INI22331) |
PSS004974| South Asian Ancestry| 201 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QT interval | — | — | R²: 0.06747 [0.05675, 0.0782] Incremental R2 (full-covars): -0.0012 PGS R2 (no covariates): 0.0053 [0.0021, 0.00851] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005309 | PGS001526 (GBE_INI22331) |
PSS004975| European Ancestry| 3,523 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QT interval | — | — | R²: 0.01777 [0.01579, 0.01974] Incremental R2 (full-covars): 0.01493 PGS R2 (no covariates): 0.01503 [0.01321, 0.01685] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005310 | PGS001527 (GBE_INI22332) |
PSS004976| African Ancestry| 120 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QTC interval | — | — | R²: 0.19276 [0.17553, 0.20998] Incremental R2 (full-covars): 0.00351 PGS R2 (no covariates): 0.00541 [0.00185, 0.00897] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005311 | PGS001527 (GBE_INI22332) |
PSS004977| East Asian Ancestry| 68 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QTC interval | — | — | R²: 0.21617 [0.18164, 0.25071] Incremental R2 (full-covars): 0.01252 PGS R2 (no covariates): 0.00397 [-0.00198, 0.00991] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005312 | PGS001527 (GBE_INI22332) |
PSS004978| European Ancestry| 872 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QTC interval | — | — | R²: 0.07278 [0.06657, 0.079] Incremental R2 (full-covars): 0.01137 PGS R2 (no covariates): 0.00935 [0.00697, 0.01173] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005313 | PGS001527 (GBE_INI22332) |
PSS004979| South Asian Ancestry| 201 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QTC interval | — | — | R²: 0.07001 [0.05911, 0.0809] Incremental R2 (full-covars): -0.00126 PGS R2 (no covariates): 0.00213 [0.00009, 0.00417] |
age, sex, UKB array type, Genotype PCs | — |
| PPM005314 | PGS001527 (GBE_INI22332) |
PSS004980| European Ancestry| 3,523 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: QTC interval | — | — | R²: 0.07628 [0.07243, 0.08013] Incremental R2 (full-covars): 0.01976 PGS R2 (no covariates): 0.02423 [0.02193, 0.02652] |
age, sex, UKB array type, Genotype PCs | — |
| PPM009988 | PGS001888 (portability-PLR_apoA) |
PSS009384| European Ancestry| 17,339 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.4103 [0.3978, 0.4226] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009990 | PGS001888 (portability-PLR_apoA) |
PSS008712| European Ancestry| 5,765 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.4138 [0.3921, 0.435] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009991 | PGS001888 (portability-PLR_apoA) |
PSS008486| Greater Middle Eastern Ancestry| 1,033 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.3636 [0.3089, 0.4158] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009992 | PGS001888 (portability-PLR_apoA) |
PSS008264| South Asian Ancestry| 5,470 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.3639 [0.3406, 0.3867] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009993 | PGS001888 (portability-PLR_apoA) |
PSS008042| East Asian Ancestry| 1,543 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.35 [0.3051, 0.3933] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009994 | PGS001888 (portability-PLR_apoA) |
PSS007828| African Ancestry| 2,151 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.2555 [0.2154, 0.2948] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009995 | PGS001888 (portability-PLR_apoA) |
PSS008932| African Ancestry| 3,389 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.2218 [0.1894, 0.2536] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM009989 | PGS001888 (portability-PLR_apoA) |
PSS009158| European Ancestry| 3,563 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.4163 [0.3887, 0.4432] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010099 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS009365| European Ancestry| 1,622 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.0975 [0.0487, 0.1458] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010100 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS009139| European Ancestry| 310 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.1126 [-0.0026, 0.2249] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010101 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS008693| European Ancestry| 474 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.0776 [-0.0146, 0.1684] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010102 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS008467| Greater Middle Eastern Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): -0.1487 [-0.4886, 0.2303] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010103 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS008247| South Asian Ancestry| 299 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.0714 [-0.0464, 0.1872] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010104 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS008025| East Asian Ancestry| 134 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.2031 [0.0199, 0.3731] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010105 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS007811| African Ancestry| 76 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.2803 [0.0188, 0.5059] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010106 | PGS001902 (portability-PLR_ECG_P_duration) |
PSS008915| African Ancestry| 138 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.1764 [-0.0045, 0.3461] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010108 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS009137| European Ancestry| 191 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.1015 [-0.0493, 0.2478] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010109 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS008691| European Ancestry| 225 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.1429 [0.006, 0.2745] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010110 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS008465| Greater Middle Eastern Ancestry| 25 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): -0.3367 [-0.9398, 0.7761] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010111 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS008245| South Asian Ancestry| 165 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.0817 [-0.0824, 0.2415] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010113 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS007809| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.0201 [-0.349, 0.3838] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010114 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS008913| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.2346 [-0.0787, 0.5057] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010107 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS009363| European Ancestry| 1,036 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.1051 [0.0439, 0.1655] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010112 | PGS001903 (portability-PLR_ECG_PP_interval) |
PSS008023| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.144 [-0.1314, 0.3987] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010115 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS009364| European Ancestry| 992 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.173 [0.1113, 0.2333] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010116 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS009138| European Ancestry| 181 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.1945 [0.041, 0.3389] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010117 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS008692| European Ancestry| 217 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.2467 [0.1108, 0.3736] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010118 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS008466| Greater Middle Eastern Ancestry| 25 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.2876 [-0.7969, 0.9331] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010119 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS008246| South Asian Ancestry| 159 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.09 [-0.0776, 0.2527] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010120 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS008024| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.0694 [-0.2047, 0.3335] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010121 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS007810| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.212 [-0.1676, 0.5368] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010122 | PGS001904 (portability-PLR_ECG_PQ_interval) |
PSS008914| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.2774 [-0.0331, 0.539] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010124 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS009141| European Ancestry| 193 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.1764 [0.0279, 0.3172] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010125 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS008695| European Ancestry| 226 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.2353 [0.1019, 0.3604] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010126 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS008469| Greater Middle Eastern Ancestry| 26 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): -0.1488 [-0.8569, 0.7538] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010127 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS008249| South Asian Ancestry| 165 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): -0.0176 [-0.18, 0.1459] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010128 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS008027| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.325 [0.06, 0.5472] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010129 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS007813| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): -0.034 [-0.3956, 0.3367] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010130 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS008917| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.1053 [-0.2091, 0.4] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010123 | PGS001905 (portability-PLR_ECG_QT_interval) |
PSS009367| European Ancestry| 1,042 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.1488 [0.0883, 0.2082] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010131 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS009366| European Ancestry| 1,040 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.2353 [0.1765, 0.2925] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010132 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS009140| European Ancestry| 191 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.2876 [0.1438, 0.4196] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010133 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS008694| European Ancestry| 225 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.1528 [0.0161, 0.2839] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010135 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS008248| South Asian Ancestry| 165 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): -0.0987 [-0.2576, 0.0654] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010136 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS008026| East Asian Ancestry| 72 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.2317 [-0.044, 0.4746] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010137 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS007812| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): -0.0515 [-0.4103, 0.321] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010138 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS008916| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.1979 [-0.1168, 0.4766] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010134 | PGS001906 (portability-PLR_ECG_QTC_interval) |
PSS008468| Greater Middle Eastern Ancestry| 26 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.5901 [-0.425, 0.9478] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010139 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS009368| European Ancestry| 1,042 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.065 [0.0037, 0.1258] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010140 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS009142| European Ancestry| 193 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.0882 [-0.0618, 0.2343] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010141 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS008696| European Ancestry| 226 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.1096 [-0.0276, 0.2426] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010142 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS008470| Greater Middle Eastern Ancestry| 26 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): -0.3743 [-0.9096, 0.628] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010143 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS008250| South Asian Ancestry| 166 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.0596 [-0.1039, 0.2199] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010144 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS008028| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.1051 [-0.1701, 0.365] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010146 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS008918| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.0322 [-0.2782, 0.3365] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010145 | PGS001907 (portability-PLR_ECG_RR_interval) |
PSS007814| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): -0.1946 [-0.5237, 0.1851] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010339 | PGS001933 (portability-PLR_LDL) |
PSS009376| European Ancestry| 18,968 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.3331 [0.3204, 0.3457] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010340 | PGS001933 (portability-PLR_LDL) |
PSS009150| European Ancestry| 3,946 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.3496 [0.3219, 0.3768] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010341 | PGS001933 (portability-PLR_LDL) |
PSS008704| European Ancestry| 6,312 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.3177 [0.2953, 0.3397] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010342 | PGS001933 (portability-PLR_LDL) |
PSS008478| Greater Middle Eastern Ancestry| 1,122 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.297 [0.2422, 0.3499] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010343 | PGS001933 (portability-PLR_LDL) |
PSS008256| South Asian Ancestry| 5,987 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2086 [0.1842, 0.2327] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010344 | PGS001933 (portability-PLR_LDL) |
PSS008034| East Asian Ancestry| 1,716 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2593 [0.2144, 0.3032] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010345 | PGS001933 (portability-PLR_LDL) |
PSS007820| African Ancestry| 2,338 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2864 [0.2486, 0.3234] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010346 | PGS001933 (portability-PLR_LDL) |
PSS008924| African Ancestry| 3,651 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2203 [0.1891, 0.251] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM022464 | PGS001933 (portability-PLR_LDL) |
PSS011950| Multi-ancestry (including European)| 151 individuals |
PGP000716 | Ying S et al. BMC Med Genomics (2023) |Ext. |
Reported Trait: LDLC in 22q11.2 microdeletion carriers | β: 0.2524 (0.0839) | — | — | sex, age, BMI, T2D, Psychotic illness, Originating ascertainment cohort, Sequencing platform, PC1-4 | — |
| PPM010723 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS009469| European Ancestry| 1,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.1331 [0.086, 0.1796] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010724 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS009243| European Ancestry| 329 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.0953 [-0.0164, 0.2047] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010726 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS008571| Greater Middle Eastern Ancestry| 50 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): -0.2266 [-0.5425, 0.1456] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010727 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS008349| South Asian Ancestry| 307 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.025 [-0.0911, 0.1404] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010728 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS008126| East Asian Ancestry| 137 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): -0.0998 [-0.2763, 0.0832] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010729 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS007913| African Ancestry| 78 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.0409 [-0.2198, 0.296] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010730 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS009017| African Ancestry| 140 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): -0.1197 [-0.2927, 0.0608] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM010725 | PGS001981 (portability-PLR_log_ventricular_rate) |
PSS008797| European Ancestry| 490 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.1205 [0.0304, 0.2087] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011664 | PGS002101 (portability-ldpred2_apoA) |
PSS009384| European Ancestry| 17,339 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.4018 [0.3893, 0.4143] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011665 | PGS002101 (portability-ldpred2_apoA) |
PSS009158| European Ancestry| 3,563 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.4015 [0.3735, 0.4288] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011666 | PGS002101 (portability-ldpred2_apoA) |
PSS008712| European Ancestry| 5,765 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.4118 [0.3901, 0.433] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011667 | PGS002101 (portability-ldpred2_apoA) |
PSS008486| Greater Middle Eastern Ancestry| 1,033 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.3567 [0.3017, 0.4093] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011668 | PGS002101 (portability-ldpred2_apoA) |
PSS008264| South Asian Ancestry| 5,470 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.3678 [0.3447, 0.3906] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011670 | PGS002101 (portability-ldpred2_apoA) |
PSS007828| African Ancestry| 2,151 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.2377 [0.1973, 0.2774] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011671 | PGS002101 (portability-ldpred2_apoA) |
PSS008932| African Ancestry| 3,389 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.2133 [0.1808, 0.2453] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011669 | PGS002101 (portability-ldpred2_apoA) |
PSS008042| East Asian Ancestry| 1,543 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Apolipoprotein A | — | — | Partial Correlation (partial-r): 0.353 [0.3082, 0.3962] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011785 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS008693| European Ancestry| 474 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.0919 | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011783 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS009365| European Ancestry| 1,622 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.0694 [0.0205, 0.118] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011784 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS009139| European Ancestry| 310 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.1064 [-0.0089, 0.2189] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011786 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS008467| Greater Middle Eastern Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): -0.0742 [-0.429, 0.3005] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011787 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS008247| South Asian Ancestry| 299 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.0224 [-0.0953, 0.1394] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011788 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS008025| East Asian Ancestry| 134 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.1083 [-0.0771, 0.2865] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011789 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS007811| African Ancestry| 76 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.1808 [-0.0862, 0.4236] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011790 | PGS002116 (portability-ldpred2_ECG_P_duration) |
PSS008915| African Ancestry| 138 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: P duration | — | — | Partial Correlation (partial-r): 0.1921 [0.0118, 0.3604] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011796 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS008023| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.1932 [-0.0813, 0.4405] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011791 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS009363| European Ancestry| 1,036 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.0979 [0.0366, 0.1584] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011792 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS009137| European Ancestry| 191 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.2685 [0.1234, 0.4024] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011793 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS008691| European Ancestry| 225 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.1491 [0.0123, 0.2804] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011794 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS008465| Greater Middle Eastern Ancestry| 25 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.5089 [-0.6776, 0.9601] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011795 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS008245| South Asian Ancestry| 165 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.0824 [-0.0817, 0.2421] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011797 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS007809| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.0366 [-0.3344, 0.3978] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011798 | PGS002117 (portability-ldpred2_ECG_PP_interval) |
PSS008913| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PP interval | — | — | Partial Correlation (partial-r): 0.0184 [-0.2909, 0.3242] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011801 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS008692| European Ancestry| 217 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.2529 [0.1173, 0.3793] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011799 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS009364| European Ancestry| 992 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.1997 [0.1385, 0.2593] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011800 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS009138| European Ancestry| 181 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.2082 [0.0553, 0.3516] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011802 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS008466| Greater Middle Eastern Ancestry| 25 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.1708 [-0.8377, 0.9152] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011803 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS008246| South Asian Ancestry| 159 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.116 [-0.0515, 0.2771] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011804 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS008024| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.1237 [-0.1517, 0.3812] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011805 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS007810| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.1518 [-0.2274, 0.491] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011806 | PGS002118 (portability-ldpred2_ECG_PQ_interval) |
PSS008914| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: PQ interval | — | — | Partial Correlation (partial-r): 0.1868 [-0.1282, 0.4676] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011807 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS009367| European Ancestry| 1,042 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.1358 [0.0751, 0.1954] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011811 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS008249| South Asian Ancestry| 165 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): -0.0112 [-0.1738, 0.1521] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011813 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS007813| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.0468 [-0.3253, 0.4063] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011808 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS009141| European Ancestry| 193 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.2209 [0.0742, 0.3583] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011809 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS008695| European Ancestry| 226 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.197 [0.062, 0.3249] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011810 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS008469| Greater Middle Eastern Ancestry| 26 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): -0.0945 [-0.8415, 0.7766] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011812 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS008027| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.3302 [0.0658, 0.5513] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011814 | PGS002119 (portability-ldpred2_ECG_QT_interval) |
PSS008917| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QT interval | — | — | Partial Correlation (partial-r): 0.071 [-0.2419, 0.3705] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011815 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS009366| European Ancestry| 1,040 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.2417 [0.1831, 0.2987] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011816 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS009140| European Ancestry| 191 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.2789 [0.1344, 0.4117] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011817 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS008694| European Ancestry| 225 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.1159 [-0.0214, 0.249] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011818 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS008468| Greater Middle Eastern Ancestry| 26 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.3605 [-0.6376, 0.9068] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011819 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS008248| South Asian Ancestry| 165 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): -0.106 [-0.2645, 0.058] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011820 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS008026| East Asian Ancestry| 72 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.1801 [-0.0976, 0.4317] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011821 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS007812| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): -0.0667 [-0.4229, 0.3073] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011822 | PGS002120 (portability-ldpred2_ECG_QTC_interval) |
PSS008916| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: QTC interval | — | — | Partial Correlation (partial-r): 0.2359 [-0.0773, 0.5068] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011824 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS009142| European Ancestry| 193 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.2615 [0.1169, 0.3953] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011823 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS009368| European Ancestry| 1,042 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.1001 [0.039, 0.1604] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011825 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS008696| European Ancestry| 226 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.115 [-0.022, 0.2478] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011826 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS008470| Greater Middle Eastern Ancestry| 26 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.2994 [-0.6766, 0.8938] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011827 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS008250| South Asian Ancestry| 166 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.0599 [-0.1036, 0.2202] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011828 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS008028| East Asian Ancestry| 73 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.1681 [-0.107, 0.4194] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011829 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS007814| African Ancestry| 49 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): 0.0409 [-0.3305, 0.4014] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM011830 | PGS002121 (portability-ldpred2_ECG_RR_interval) |
PSS008918| African Ancestry| 61 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: RR interval | — | — | Partial Correlation (partial-r): -0.0491 [-0.3514, 0.2625] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012054 | PGS002150 (portability-ldpred2_LDL) |
PSS008924| African Ancestry| 3,651 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2251 [0.194, 0.2558] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012047 | PGS002150 (portability-ldpred2_LDL) |
PSS009376| European Ancestry| 18,968 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.3346 [0.3219, 0.3472] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012048 | PGS002150 (portability-ldpred2_LDL) |
PSS009150| European Ancestry| 3,946 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.3475 [0.3197, 0.3747] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012049 | PGS002150 (portability-ldpred2_LDL) |
PSS008704| European Ancestry| 6,312 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.3081 [0.2856, 0.3303] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012050 | PGS002150 (portability-ldpred2_LDL) |
PSS008478| Greater Middle Eastern Ancestry| 1,122 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2975 [0.2427, 0.3504] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012051 | PGS002150 (portability-ldpred2_LDL) |
PSS008256| South Asian Ancestry| 5,987 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2191 [0.1948, 0.2431] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012052 | PGS002150 (portability-ldpred2_LDL) |
PSS008034| East Asian Ancestry| 1,716 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.2555 [0.2104, 0.2994] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012053 | PGS002150 (portability-ldpred2_LDL) |
PSS007820| African Ancestry| 2,338 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: LDL direct | — | — | Partial Correlation (partial-r): 0.294 [0.2563, 0.3307] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM022508 | PGS002150 (portability-ldpred2_LDL) |
PSS011978| European Ancestry| 18,753 individuals |
PGP000721 | Sadler MC et al. Nat Commun (2025) |Ext. |
Reported Trait: LDL-C response to statin (absolute difference) | β: -0.092 (0.006) | — | — | sex, age and drug-specific covariates | beta unit = mmol/L |
| PPM022509 | PGS002150 (portability-ldpred2_LDL) |
PSS011978| European Ancestry| 18,753 individuals |
PGP000721 | Sadler MC et al. Nat Commun (2025) |Ext. |
Reported Trait: LDL-C response to statin (relative logarithmic difference) | β: 2.469 (0.202) | — | — | sex, age and drug-specific covariates | beta unit = % |
| PPM012439 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS009469| European Ancestry| 1,711 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.1563 [0.1094, 0.2024] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012440 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS009243| European Ancestry| 329 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.1975 [0.0879, 0.3024] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012441 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS008797| European Ancestry| 490 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.1785 [0.0894, 0.2646] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012442 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS008571| Greater Middle Eastern Ancestry| 50 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): -0.2917 [-0.59, 0.0766] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012443 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS008349| South Asian Ancestry| 307 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.066 [-0.0501, 0.1804] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012445 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS007913| African Ancestry| 78 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.0201 [-0.2394, 0.277] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012446 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS009017| African Ancestry| 140 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): -0.0905 [-0.2655, 0.0902] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012444 | PGS002199 (portability-ldpred2_log_ventricular_rate) |
PSS008126| East Asian Ancestry| 137 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Ventricular rate | — | — | Partial Correlation (partial-r): 0.0478 [-0.1349, 0.2274] | sex, age, birth date, deprivation index, 16 PCs | — |
| PPM012908 | PGS002267 (PRS89_AA) |
PSS009608| European Ancestry| 385,621 individuals |
PGP000296 | Pirruccello JP et al. Nat Genet (2021) |
Reported Trait: Incident thoracic aortic aneurysm or dissection | HR: 1.43 [1.32, 1.54] | — | — | Sex, prevalent diagnoses of type 2 diabetes or hypertension, tobacco smoking history (the number of pack years of tobacco smoking), body mass (the cubic natural spline of body mass index) and age (the cubic natural spline of age at enrollment). | — |
| PPM012944 | PGS002274 (LDL-PRS) |
PSS009627| European Ancestry| 4,416 individuals |
PGP000303 | Groenland EH et al. Atherosclerosis (2022) |
Reported Trait: Low-density lipoprotein cholesterol | β: 0.18 [0.15, 0.21] | — | — | Age, sex, the first 5 principal components, BMI, T2DM, smoking, alcohol use, systolic blood pressure, eGFR, triglycerides, lipid-lowering medication | — |
| PPM012946 | PGS002276 (QTc_PRS-CS) |
PSS009628| Multi-ancestry (including European)| 26,976 individuals |
PGP000304 | Nauffal V et al. Circulation (2022) |
Reported Trait: QTc | — | — | R²: 0.087 | age, sex, beta blocker use, calcium channel blocker use, heart failure, myocardial infarction, first 12 principal components of genetic ancestry (PC1-12) | PRS performance was overall similar across the individual genetic ancestries in TOPMed. (European R²: 0.074; African R²:0.077, Admixed American R²:0.148; Amish R²:0.197; Asian R²:0.245; Undetermined Genetic Ancestry R²:0.106) |
| PPM012952 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: All-cause mortality | — | — | HR (top 10% vs bottom 10%): 1.11 [1.04, 1.18] | Age, sex, body mass index (BMI; except for obesity),10 principal components of ancestry | — |
| PPM012954 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: All-cause mortality (with heart failure) | — | — | HR (top 10% vs bottom 10%): 1.26 [1.05, 1.5] | Age, sex, body mass index (BMI; except for obesity),10 principal components of ancestry | — |
| PPM012956 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: All-cause mortality (without heart failure) | — | — | HR (top 10% vs bottom 10%): 1.09 [1.02, 1.17] | Age, sex, body mass index (BMI; except for obesity),10 principal components of ancestry | — |
| PPM012957 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Heart failure diagnosis | OR: 0.97 [0.95, 0.99] | — | — | Age, sex, body mass index,10 principal components of ancestry | — |
| PPM012958 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Brain natriuretic peptide level | — | — | Median (top 10% vs bottom 10%): 146.0 [79, 228] pg/ml | — | — |
| PPM012959 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Prevalence of dyspnoea | — | — | OR (top 10% vs bottom 10%): 1.17 [1.01, 1.37] | — | — |
| PPM012960 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Exertional dyspnoea | — | — | OR (top 10% vs bottom 10%): 1.39 [1.12, 1.74] | — | — |
| PPM012961 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Peperipheral oedema | — | — | OR (top 10% vs bottom 10%): 1.07 [1.01, 1.13] | — | — |
| PPM012962 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Medication for heart failure (loop diuretics) | — | — | OR (top 10% vs bottom 10%): 1.03 [1.01, 1.05] | — | — |
| PPM012963 | PGS002278 (GRS16_snLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: Medication for heart failure (mineralocorticoid receptor antagonists) | — | — | OR (top 10% vs bottom 10%): 1.04 [1.01, 1.09] | — | — |
| PPM012953 | PGS002279 (GRS22_rLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: All-cause mortality | — | — | HR (top 10% vs bottom 10%): 1.1 [1.02, 1.12] | Age, sex, body mass index (BMI; except for obesity),10 principal components of ancestry | — |
| PPM012955 | PGS002279 (GRS22_rLVEF) |
PSS009631| Multi-ancestry (including European)| 486,754 individuals |
PGP000307 | Forrest IS et al. Eur J Heart Fail (2022) |
Reported Trait: All-cause mortality (with heart failure) | — | — | HR (top 10% vs bottom 10%): 1.31 [1.09, 1.58] | Age, sex, body mass index (BMI; except for obesity),10 principal components of ancestry | — |
| PPM012983 | PGS002285 (GRS_286_LDL) |
PSS009639| African Ancestry| 2,569 individuals |
PGP000313 | Kamiza AB et al. Nat Med (2022) |
Reported Trait: Low density lipoprotein cholesterol levels | — | — | R²: 0.0814 | age, sex, type 2 diabetes, PC1, PC2, PC3, PC4, PC5 | Nagelkerke’s R2 (estimate of variance explained by the PGS after covariate adjustment) |
| PPM013102 | PGS002337 (biochemistry_LDLdirect.BOLT-LMM) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0733 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013151 | PGS002337 (biochemistry_LDLdirect.BOLT-LMM) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0681 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013200 | PGS002337 (biochemistry_LDLdirect.BOLT-LMM) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.11 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013249 | PGS002337 (biochemistry_LDLdirect.BOLT-LMM) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0479 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013281 | PGS002369 (biochemistry_LDLdirect.BOLT-LMM-BBJ) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0314 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013304 | PGS002369 (biochemistry_LDLdirect.BOLT-LMM-BBJ) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.037 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013327 | PGS002369 (biochemistry_LDLdirect.BOLT-LMM-BBJ) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0112 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013350 | PGS002369 (biochemistry_LDLdirect.BOLT-LMM-BBJ) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0091 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013390 | PGS002409 (biochemistry_LDLdirect.P+T.0.0001) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0019 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013439 | PGS002409 (biochemistry_LDLdirect.P+T.0.0001) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0268 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013488 | PGS002409 (biochemistry_LDLdirect.P+T.0.0001) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0733 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013537 | PGS002409 (biochemistry_LDLdirect.P+T.0.0001) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0203 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013586 | PGS002458 (biochemistry_LDLdirect.P+T.0.001) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0011 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013635 | PGS002458 (biochemistry_LDLdirect.P+T.0.001) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0162 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013684 | PGS002458 (biochemistry_LDLdirect.P+T.0.001) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0672 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013733 | PGS002458 (biochemistry_LDLdirect.P+T.0.001) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0065 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013782 | PGS002507 (biochemistry_LDLdirect.P+T.0.01) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0002 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013831 | PGS002507 (biochemistry_LDLdirect.P+T.0.01) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0016 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013929 | PGS002507 (biochemistry_LDLdirect.P+T.0.01) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0002 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013880 | PGS002507 (biochemistry_LDLdirect.P+T.0.01) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0127 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM013978 | PGS002556 (biochemistry_LDLdirect.P+T.1e-06) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0407 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014027 | PGS002556 (biochemistry_LDLdirect.P+T.1e-06) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0185 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014076 | PGS002556 (biochemistry_LDLdirect.P+T.1e-06) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0705 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014125 | PGS002556 (biochemistry_LDLdirect.P+T.1e-06) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0205 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014174 | PGS002605 (biochemistry_LDLdirect.P+T.5e-08) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0428 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014223 | PGS002605 (biochemistry_LDLdirect.P+T.5e-08) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0167 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014272 | PGS002605 (biochemistry_LDLdirect.P+T.5e-08) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0679 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014321 | PGS002605 (biochemistry_LDLdirect.P+T.5e-08) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0195 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014370 | PGS002654 (biochemistry_LDLdirect.PolyFun-pred) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1091 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_LDLdirect.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014419 | PGS002654 (biochemistry_LDLdirect.PolyFun-pred) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0724 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_LDLdirect.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014468 | PGS002654 (biochemistry_LDLdirect.PolyFun-pred) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1195 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_LDLdirect.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014517 | PGS002654 (biochemistry_LDLdirect.PolyFun-pred) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0528 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | See biochemistry_LDLdirect.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
| PPM014566 | PGS002703 (biochemistry_LDLdirect.SBayesR) |
PSS009791| African Ancestry| 6,068 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0612 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014664 | PGS002703 (biochemistry_LDLdirect.SBayesR) |
PSS009793| European Ancestry| 41,139 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.1011 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014713 | PGS002703 (biochemistry_LDLdirect.SBayesR) |
PSS009794| South Asian Ancestry| 7,638 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0377 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014615 | PGS002703 (biochemistry_LDLdirect.SBayesR) |
PSS009792| East Asian Ancestry| 875 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: LDL Cholesterol | — | — | Incremental R2 (full model vs. covariates alone): 0.0478 | age, sex, age*sex, assessment center, genotyping array, 10 PCs | — |
| PPM014797 | PGS002730 (GRSlipid_35) |
PSS009892| European Ancestry| 75,973 individuals |
PGP000337 | Mayerhofer E et al. Brain (2022) |
Reported Trait: On-statin LDL decrease | β: -0.05 [-0.07, -0.02] | — | — | — | — |
| PPM014798 | PGS002730 (GRSlipid_35) |
PSS009892| European Ancestry| 75,973 individuals |
PGP000337 | Mayerhofer E et al. Brain (2022) |
Reported Trait: Incident intracerebral hemorrhage | HR: 1.16 [1.05, 1.28] | — | — | — | — |
| PPM014799 | PGS002730 (GRSlipid_35) |
PSS009892| European Ancestry| 75,973 individuals |
PGP000337 | Mayerhofer E et al. Brain (2022) |
Reported Trait: Incident myocardial infarction | HR: 0.98 [0.96, 0.99] | — | — | — | — |
| PPM014800 | PGS002730 (GRSlipid_35) |
PSS009892| European Ancestry| 75,973 individuals |
PGP000337 | Mayerhofer E et al. Brain (2022) |
Reported Trait: Incident Peripheral Artery Disease | HR: 0.93 [0.87, 0.99] | — | — | — | — |
| PPM016162 | PGS002782 (GLGC_2021_ALL_nonHDL_PRS_weights_PRS-CS) |
PSS010052| Multi-ancestry (including European)| 461,918 individuals |
PGP000366 | Kanoni S et al. Genome Biol (2022) |
Reported Trait: Baseline nonHDL cholesterol | — | — | R²: 0.14 | sex, batch, age at initial assessment, PCs1-4 | — |
| PPM015918 | PGS003029 (ExPRSweb_LDL_30780-irnt_LASSOSUM_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.06 (0.286) | — | R²: 0.0594 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015921 | PGS003030 (ExPRSweb_LDL_30780-irnt_PT_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.07 (0.283) | — | R²: 0.061 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015919 | PGS003031 (ExPRSweb_LDL_30780-irnt_PLINK_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.23 (0.283) | — | R²: 0.0635 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015917 | PGS003032 (ExPRSweb_LDL_30780-irnt_DBSLMM_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.6 (0.282) | — | R²: 0.0687 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015920 | PGS003033 (ExPRSweb_LDL_30780-irnt_PRSCS_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.36 (0.284) | — | R²: 0.0649 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015923 | PGS003034 (ExPRSweb_LDL_30780-raw_LASSOSUM_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.06 (0.286) | — | R²: 0.0593 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015926 | PGS003035 (ExPRSweb_LDL_30780-raw_PT_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.09 (0.283) | — | R²: 0.0612 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015924 | PGS003036 (ExPRSweb_LDL_30780-raw_PLINK_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.17 (0.283) | — | R²: 0.0625 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015922 | PGS003037 (ExPRSweb_LDL_30780-raw_DBSLMM_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.9 (0.281) | — | R²: 0.0753 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM015925 | PGS003038 (ExPRSweb_LDL_30780-raw_PRSCS_MGI_20211120) |
PSS010010| European Ancestry| 9,288 individuals |
PGP000393 | Ma Y et al. Am J Hum Genet (2022) |
Reported Trait: LDL | β: 7.42 (0.285) | — | R²: 0.0656 | SEX,AGE,Batch,PC1,PC2,PC3,PC4 | — |
| PPM016178 | PGS003339 (CVGRS_LDL) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: LDL cholesterol level | β: 0.31083 | — | — | — | — |
| PPM016195 | PGS003339 (CVGRS_LDL) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: Type 2 diabetes | OR: 0.99947 | — | — | — | — |
| PPM016187 | PGS003348 (ALLGRS_LDL) |
PSS010055| East Asian Ancestry| 22,608 individuals |
PGP000405 | Kim YJ et al. Nat Commun (2022) |
Reported Trait: LDL cholesterol level | β: 0.31961 | — | — | — | — |
| PPM017041 | PGS003403 (PRS28_LDL) |
PSS010102| European Ancestry| 626 individuals |
PGP000420 | Trinder M et al. J Am Coll Cardiol (2019) |
Reported Trait: LDL-C levels | — | — | pvalue (High >=80 percentile vs lower <80th percentile): 0.03 | — | — |
| PPM017046 | PGS003403 (PRS28_LDL) |
PSS010104| European Ancestry| 89,528 individuals |
PGP000422 | Vanhoye X et al. Transl Res (2022) |Ext. |
Reported Trait: LDL-c blood concentration | β: 0.18 [0.18, 0.18] | AUROC: 0.6233 [0.617, 0.63] | R²: 0.1055 | Age, BMI, sex, age | — |
| PPM017042 | PGS003403 (PRS28_LDL) |
PSS010102| European Ancestry| 626 individuals |
PGP000420 | Trinder M et al. J Am Coll Cardiol (2019) |
Reported Trait: Cardiovascular disease events in patients with monogenic familial hypercholesterolemia | — | — | Adjusted Hazard Ratio (aHR; top 20% vs. remaining): 3.06 [1.56, 5.99] | age, sex, LDL-C, diabetes mellitus, and hypertension | — |
| PPM017043 | PGS003404 (wGRS) |
PSS010103| Multi-ancestry (including European)| 313 individuals |
PGP000421 | Wang J et al. Arterioscler Thromb Vasc Biol (2016) |
Reported Trait: FH mutation-negative with severe hypercholesterolemia | OR: 3.02 [1.61, 5.68] | — | — | — | — |
| PPM017047 | PGS003404 (wGRS) |
PSS010104| European Ancestry| 89,528 individuals |
PGP000422 | Vanhoye X et al. Transl Res (2022) |Ext. |
Reported Trait: LDL-c blood concentration | β: 0.16 [0.15, 0.16] | AUROC: 0.6072 [0.6, 0.614] | R²: 0.09317 | Age, BMI, sex, age | — |
| PPM017044 | PGS003405 (165SNP_PRS) |
PSS010104| European Ancestry| 89,528 individuals |
PGP000422 | Vanhoye X et al. Transl Res (2022) |
Reported Trait: LDL-c blood concentration | β: 0.31 [0.3, 0.31] | AUROC: 0.6901 [0.684, 0.696] | R²: 0.1979 | Age, BMI, sex, age | — |
| PPM017101 | PGS003427 (lvmi) |
PSS010124| Multi-ancestry (including European)| 29,354 individuals |
PGP000434 | Khurshid S et al. Nat Commun (2023) |
Reported Trait: incident implantable cardioverter-defibrillator implant | HR: 1.05 | — | — | age, sex, PC 1-5 | — |
| PPM017100 | PGS003427 (lvmi) |
PSS010123| Multi-ancestry (including European)| 443,326 individuals |
PGP000434 | Khurshid S et al. Nat Commun (2023) |
Reported Trait: incident implantable cardioverter-defibrillator implant | HR: 1.07 | — | — | age, sex, PC 1-5 | — |
| PPM017284 | PGS003474 (LDPred2_LDL) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: 0.001 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017307 | PGS003474 (LDPred2_LDL) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: -0.023 (0.025) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017287 | PGS003477 (LDPred2_PP) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index | β: -0.022 (0.01) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017310 | PGS003477 (LDPred2_PP) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea | β: -0.032 (0.024) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017354 | PGS003477 (LDPred2_PP) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Respiratory event index in obsese | β: -0.013 (0.017) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017374 | PGS003477 (LDPred2_PP) |
PSS010185| Hispanic or Latin American Ancestry| 1,115 individuals |
PGP000456 | Zhang Y et al. EBioMedicine (2022) |
Reported Trait: Obstructive sleep apnea in obsese | β: -0.012 (0.034) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017375 | PGS003477 (LDPred2_PP) |
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: -0.057 (0.035) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017355 | PGS003477 (LDPred2_PP) |
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: -0.024 (0.012) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017399 | PGS003477 (LDPred2_PP) |
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: -0.001 (0.999) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017409 | PGS003477 (LDPred2_PP) |
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: 1.028 (0.05) | — | — | Age, sex, center, 5 genetic PCs, Hispanic/Latino background, BMI | — |
| PPM017427 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010860| European Ancestry| 995 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017511 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010776| European Ancestry| 180 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017595 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010608| European Ancestry| 213 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017679 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010524| Greater Middle Eastern Ancestry| 25 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.32 | sex, age, deprivation index, PC1-16 | — |
| PPM017763 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010188| European Ancestry| 103 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017847 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010440| South Asian Ancestry| 159 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017931 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010356| East Asian Ancestry| 73 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM018015 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010272| African Ancestry| 49 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM018099 | PGS003499 (cont-decay-ECG_PQ_interval) |
PSS010692| African Ancestry| 61 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: PQ interval | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017428 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010861| European Ancestry| 1,040 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017512 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010777| European Ancestry| 192 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017596 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010609| European Ancestry| 220 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.03 | sex, age, deprivation index, PC1-16 | — |
| PPM017680 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010525| Greater Middle Eastern Ancestry| 26 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.0 | sex, age, deprivation index, PC1-16 | — |
| PPM017764 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010189| European Ancestry| 107 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017848 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010441| South Asian Ancestry| 165 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.0 | sex, age, deprivation index, PC1-16 | — |
| PPM017932 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010357| East Asian Ancestry| 73 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM018016 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010273| African Ancestry| 49 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.0 | sex, age, deprivation index, PC1-16 | — |
| PPM018100 | PGS003500 (cont-decay-ECG_QT_interval) |
PSS010693| African Ancestry| 61 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QT interval | — | — | partial-R2: 0.0 | sex, age, deprivation index, PC1-16 | — |
| PPM017765 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010190| European Ancestry| 107 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017429 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010862| European Ancestry| 1,040 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017513 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010778| European Ancestry| 190 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM017597 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010610| European Ancestry| 219 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017681 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010526| Greater Middle Eastern Ancestry| 26 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM017849 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010442| South Asian Ancestry| 165 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.0 | sex, age, deprivation index, PC1-16 | — |
| PPM017933 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010358| East Asian Ancestry| 72 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.01 | sex, age, deprivation index, PC1-16 | — |
| PPM018017 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010274| African Ancestry| 49 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM018101 | PGS003501 (cont-decay-ECG_QTC_interval) |
PSS010694| African Ancestry| 61 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: QTC interval | — | — | partial-R2: 0.02 | sex, age, deprivation index, PC1-16 | — |
| PPM017445 | PGS003517 (cont-decay-LDL) |
PSS010879| European Ancestry| 19,077 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.11 | sex, age, deprivation index, PC1-16 | — |
| PPM017529 | PGS003517 (cont-decay-LDL) |
PSS010795| European Ancestry| 3,935 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.12 | sex, age, deprivation index, PC1-16 | — |
| PPM017697 | PGS003517 (cont-decay-LDL) |
PSS010543| Greater Middle Eastern Ancestry| 1,093 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM017781 | PGS003517 (cont-decay-LDL) |
PSS010207| European Ancestry| 2,235 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.07 | sex, age, deprivation index, PC1-16 | — |
| PPM017865 | PGS003517 (cont-decay-LDL) |
PSS010459| South Asian Ancestry| 5,934 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.04 | sex, age, deprivation index, PC1-16 | — |
| PPM017949 | PGS003517 (cont-decay-LDL) |
PSS010375| East Asian Ancestry| 1,705 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.06 | sex, age, deprivation index, PC1-16 | — |
| PPM018033 | PGS003517 (cont-decay-LDL) |
PSS010291| African Ancestry| 2,326 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.08 | sex, age, deprivation index, PC1-16 | — |
| PPM018117 | PGS003517 (cont-decay-LDL) |
PSS010711| African Ancestry| 3,622 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.05 | sex, age, deprivation index, PC1-16 | — |
| PPM017613 | PGS003517 (cont-decay-LDL) |
PSS010627| European Ancestry| 6,160 individuals |
PGP000457 | Ding Y et al. bioRxiv (2022) |Pre |
Reported Trait: LDL direct | — | — | partial-R2: 0.09 | sex, age, deprivation index, PC1-16 | — |
| PPM018591 | PGS003784 (LDL_EUR_CT) |
PSS011045| European Ancestry| 9,527 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.07541 | — | — |
| PPM018592 | PGS003785 (LDL_EUR_LDpred2) |
PSS011045| European Ancestry| 9,527 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.05473 | — | — |
| PPM018593 | PGS003786 (LDL_AFR_CT) |
PSS011043| African Ancestry| 4,292 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.08924 | — | — |
| PPM018594 | PGS003787 (LDL_AFR_LDpred2) |
PSS011043| African Ancestry| 4,292 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.03349 | — | — |
| PPM018595 | PGS003788 (LDL_AFR_weighted_LDpred2) |
PSS011043| African Ancestry| 4,292 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.04384 | — | — |
| PPM018596 | PGS003789 (LDL_AFR_PRSCSx) |
PSS011043| African Ancestry| 4,292 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.08679 | — | — |
| PPM018597 | PGS003790 (LDL_AFR_CTSLEB) |
PSS011043| African Ancestry| 4,292 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.11584 | — | — |
| PPM018598 | PGS003791 (LDL_EAS_CT) |
PSS011044| East Asian Ancestry| 970 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.02274 | — | — |
| PPM018599 | PGS003792 (LDL_EAS_LDpred2) |
PSS011044| East Asian Ancestry| 970 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.0184 | — | — |
| PPM018600 | PGS003793 (LDL_EAS_weighted_LDpred2) |
PSS011044| East Asian Ancestry| 970 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.03105 | — | — |
| PPM018601 | PGS003794 (LDL_EAS_PRSCSx) |
PSS011044| East Asian Ancestry| 970 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.0534 | — | — |
| PPM018602 | PGS003795 (LDL_EAS_CTSLEB) |
PSS011044| East Asian Ancestry| 970 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.0239 | — | — |
| PPM018603 | PGS003796 (LDL_SAS_CT) |
PSS011046| South Asian Ancestry| 5,137 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.02586 | — | — |
| PPM018604 | PGS003797 (LDL_SAS_LDpred2) |
PSS011046| South Asian Ancestry| 5,137 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.01341 | — | — |
| PPM018605 | PGS003798 (LDL_SAS_weighted_LDpred2) |
PSS011046| South Asian Ancestry| 5,137 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.02782 | — | — |
| PPM018606 | PGS003799 (LDL_SAS_PRSCSx) |
PSS011046| South Asian Ancestry| 5,137 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.04872 | — | — |
| PPM018607 | PGS003800 (LDL_SAS_CTSLEB) |
PSS011046| South Asian Ancestry| 5,137 individuals |
PGP000489 | Zhang H et al. Nat Genet (2023) |
Reported Trait: Low-density lipoprotein cholesterol | — | — | R²: 0.03323 | — | — |
| PPM018687 | PGS003855 (PRS44_LDL) |
PSS011066| East Asian Ancestry| 37,317 individuals |
PGP000493 | Li J et al. JAMA Netw Open (2023) |
Reported Trait: Estimated annual change of LDL cholesterol | — | — | p-value (inferior to): 0.001 | — | — |
| PPM018691 | PGS003855 (PRS44_LDL) |
PSS011068| East Asian Ancestry| 15,664 individuals |
PGP000493 | Li J et al. JAMA Netw Open (2023) |
Reported Trait: Estimated annual change of LDL cholesterol | — | — | p-value (inferior to): 0.001 | — | — |
| PPM018695 | PGS003855 (PRS44_LDL) |
PSS011067| East Asian Ancestry| 21,653 individuals |
PGP000493 | Li J et al. JAMA Netw Open (2023) |
Reported Trait: Estimated annual change of LDL cholesterol | — | — | p-value (inferior to): 0.001 | — | — |
| PPM018761 | PGS003869 (LDL_PRScsx_ARB_AFRweights) |
PSS011097| Greater Middle Eastern Ancestry| 2,669 individuals |
PGP000501 | Shim I et al. Nature Communications (2023) |
Reported Trait: LDL cholesterol | β: 9.4 (1.1) | — | R²: 0.0358 | age, sex, array version, and the first 10 principal components of ancestry | The reported performance was derived from a linearly combined score of 6 normalized ancestry-specific scores using the following coefficients: Score = (-0.1166049*zscoreAFR) + (0.812596*zscoreAMR) + (-2.1986549*zscoreARB) + (3.9764576*zscoreEAS) + (6.7068379*zscoreEUR) + (3.433336*zscoreSAS). |
| PPM018762 | PGS003870 (LDL_PRScsx_ARB_AMRweights) |
PSS011097| Greater Middle Eastern Ancestry| 2,669 individuals |
PGP000501 | Shim I et al. Nature Communications (2023) |
Reported Trait: LDL cholesterol | β: 9.4 (1.1) | — | R²: 0.0358 | age, sex, array version, and the first 10 principal components of ancestry | The reported performance was derived from a linearly combined score of 6 normalized ancestry-specific scores using the following coefficients: Score = (-0.1166049*zscoreAFR) + (0.812596*zscoreAMR) + (-2.1986549*zscoreARB) + (3.9764576*zscoreEAS) + (6.7068379*zscoreEUR) + (3.433336*zscoreSAS). |
| PPM018763 | PGS003871 (LDL_PRScsx_ARB_ARBweights) |
PSS011097| Greater Middle Eastern Ancestry| 2,669 individuals |
PGP000501 | Shim I et al. Nature Communications (2023) |
Reported Trait: LDL cholesterol | β: 9.4 (1.1) | — | R²: 0.0358 | age, sex, array version, and the first 10 principal components of ancestry | The reported performance was derived from a linearly combined score of 6 normalized ancestry-specific scores using the following coefficients: Score = (-0.1166049*zscoreAFR) + (0.812596*zscoreAMR) + (-2.1986549*zscoreARB) + (3.9764576*zscoreEAS) + (6.7068379*zscoreEUR) + (3.433336*zscoreSAS). |
| PPM018764 | PGS003872 (LDL_PRScsx_ARB_EASweights) |
PSS011097| Greater Middle Eastern Ancestry| 2,669 individuals |
PGP000501 | Shim I et al. Nature Communications (2023) |
Reported Trait: LDL cholesterol | β: 9.4 (1.1) | — | R²: 0.0358 | age, sex, array version, and the first 10 principal components of ancestry | The reported performance was derived from a linearly combined score of 6 normalized ancestry-specific scores using the following coefficients: Score = (-0.1166049*zscoreAFR) + (0.812596*zscoreAMR) + (-2.1986549*zscoreARB) + (3.9764576*zscoreEAS) + (6.7068379*zscoreEUR) + (3.433336*zscoreSAS). |
| PPM018765 | PGS003873 (LDL_PRScsx_ARB_EURweights) |
PSS011097| Greater Middle Eastern Ancestry| 2,669 individuals |
PGP000501 | Shim I et al. Nature Communications (2023) |
Reported Trait: LDL cholesterol | β: 9.4 (1.1) | — | R²: 0.0358 | age, sex, array version, and the first 10 principal components of ancestry | The reported performance was derived from a linearly combined score of 6 normalized ancestry-specific scores using the following coefficients: Score = (-0.1166049*zscoreAFR) + (0.812596*zscoreAMR) + (-2.1986549*zscoreARB) + (3.9764576*zscoreEAS) + (6.7068379*zscoreEUR) + (3.433336*zscoreSAS). |
| PPM018766 | PGS003874 (LDL_PRScsx_ARB_SASweights) |
PSS011097| Greater Middle Eastern Ancestry| 2,669 individuals |
PGP000501 | Shim I et al. Nature Communications (2023) |
Reported Trait: LDL cholesterol | β: 9.4 (1.1) | — | R²: 0.0358 | age, sex, array version, and the first 10 principal components of ancestry | The reported performance was derived from a linearly combined score of 6 normalized ancestry-specific scores using the following coefficients: Score = (-0.1166049*zscoreAFR) + (0.812596*zscoreAMR) + (-2.1986549*zscoreARB) + (3.9764576*zscoreEAS) + (6.7068379*zscoreEUR) + (3.433336*zscoreSAS). |
| PPM019145 | PGS003974 (AFR_without-UKB_LDL) |
PSS011206| African Ancestry| 3,802 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 9.2 % | — | — |
| PPM019140 | PGS003975 (EAS_without-UKB_LDL) |
PSS011203| East Asian Ancestry| 1,480 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 8.6 % | — | — |
| PPM019142 | PGS003975 (EAS_without-UKB_LDL) |
PSS011201| East Asian Ancestry| 68,978 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 9.3 % | — | — |
| PPM019138 | PGS003976 (EUR_without-UKB_LDL) |
PSS011207| European Ancestry| 423,596 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 10.6 % | — | — |
| PPM019143 | PGS003976 (EUR_without-UKB_LDL) |
PSS011201| East Asian Ancestry| 68,978 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 4.5 % | — | — |
| PPM019146 | PGS003977 (SAS_without-UKB_LDL) |
PSS011204| South Asian Ancestry| 6,303 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 5.6 % | — | — |
| PPM019139 | PGS003978 (meta_without-UKB_LDL) |
PSS011207| European Ancestry| 423,596 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 10.5 % | — | — |
| PPM019141 | PGS003978 (meta_without-UKB_LDL) |
PSS011203| East Asian Ancestry| 1,480 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 7.8 % | — | — |
| PPM019144 | PGS003978 (meta_without-UKB_LDL) |
PSS011201| East Asian Ancestry| 68,978 individuals |
PGP000514 | Hassanin E et al. Front Genet (2023) |
Reported Trait: LDL level | — | — | R²: 6.7 % | — | — |
| PPM020763 | PGS004605 (PP-meta-analysis) |
PSS011397| European Ancestry| 10,210 individuals |
PGP000581 | Keaton JM et al. Nat Genet (2024) |
Reported Trait: Pulse pressure | — | — | R²: 0.073 beta (high vs low tertile): 10.04 |
Age, Age2, Sex, BMI | — |
| PPM020766 | PGS004605 (PP-meta-analysis) |
PSS011396| African Ancestry| 21,843 individuals |
PGP000581 | Keaton JM et al. Nat Genet (2024) |
Reported Trait: Pulse pressure | — | — | beta (high vs low tertile): 5.45 | Age, Age2, Sex, BMI | — |
| PPM020822 | PGS004637 (LDL_AFR_lassosum2) |
PSS011420| African Ancestry| 4,292 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.141 | — | — |
| PPM020823 | PGS004638 (LDL_AFR_ldpred2) |
PSS011420| African Ancestry| 4,292 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.1212 | — | — |
| PPM020824 | PGS004639 (LDL_AFR_PROSPER) |
PSS011420| African Ancestry| 4,292 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.1538 | — | — |
| PPM020825 | PGS004640 (LDL_AFR_weighted_lassosum2) |
PSS011420| African Ancestry| 4,292 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.1472 | — | — |
| PPM020826 | PGS004641 (LDL_AFR_weighted_ldpred2) |
PSS011420| African Ancestry| 4,292 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.1314 | — | — |
| PPM020827 | PGS004642 (LDL_EAS_lassosum2) |
PSS011426| East Asian Ancestry| 970 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0329 | — | — |
| PPM020828 | PGS004643 (LDL_EAS_ldpred2) |
PSS011426| East Asian Ancestry| 970 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0348 | — | — |
| PPM020829 | PGS004644 (LDL_EAS_PROSPER) |
PSS011426| East Asian Ancestry| 970 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0668 | — | — |
| PPM020830 | PGS004645 (LDL_EAS_weighted_lassosum2) |
PSS011426| East Asian Ancestry| 970 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0805 | — | — |
| PPM020831 | PGS004646 (LDL_EAS_weighted_ldpred2) |
PSS011426| East Asian Ancestry| 970 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0721 | — | — |
| PPM020832 | PGS004647 (LDL_SAS_lassosum2) |
PSS011428| South Asian Ancestry| 5,137 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0332 | — | — |
| PPM020833 | PGS004648 (LDL_SAS_ldpred2) |
PSS011428| South Asian Ancestry| 5,137 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0343 | — | — |
| PPM020834 | PGS004649 (LDL_SAS_PROSPER) |
PSS011428| South Asian Ancestry| 5,137 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0561 | — | — |
| PPM020835 | PGS004650 (LDL_SAS_weighted_lassosum2) |
PSS011428| South Asian Ancestry| 5,137 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0482 | — | — |
| PPM020836 | PGS004651 (LDL_SAS_weighted_ldpred2) |
PSS011428| South Asian Ancestry| 5,137 individuals |
PGP000595 | Zhang J et al. Nat Commun (2024) |
Reported Trait: LDL cholesterol | — | — | adjusted R2: 0.0513 | — | — |
| PPM021016 | PGS004791 (ldl_PRSmix_eur) |
PSS011499| European Ancestry| 4,026 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Low-density lipoprotein | — | — | Incremental R2 (Full model versus model with only covariates): 0.076 [0.06, 0.092] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021017 | PGS004792 (ldl_PRSmix_sas) |
PSS011500| South Asian Ancestry| 5,735 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Low-density lipoprotein | — | — | Incremental R2 (Full model versus model with only covariates): 0.121 [0.105, 0.137] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021018 | PGS004793 (ldl_PRSmixPlus_eur) |
PSS011499| European Ancestry| 4,026 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Low-density lipoprotein | — | — | Incremental R2 (Full model versus model with only covariates): 0.077 [0.061, 0.093] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021019 | PGS004794 (ldl_PRSmixPlus_sas) |
PSS011500| South Asian Ancestry| 5,735 individuals |
PGP000604 | Truong B et al. Cell Genom (2024) |
Reported Trait: Low-density lipoprotein | — | — | Incremental R2 (Full model versus model with only covariates): 0.121 [0.105, 0.137] | age, sex, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10 | Incremental R2 (Full model versus model with only covariates) |
| PPM021380 | PGS004915 (LDL-C_PGS) |
PSS011715| European Ancestry| 389,564 individuals |
PGP000647 | Trinder M et al. Arterioscler Thromb Vasc Biol (2019) |
Reported Trait: Low-density lipoprotein cholesterol levels | — | — | R²: 0.05 | Age, sex, genotyping array/batch, 4 PCs | — |
| PPM021724 | PGS004936 (low_density_lipoprotein_cholesterol_measurement_combined) |
PSS011762| European Ancestry| 8,417 individuals |
PGP000665 | Moreno-Grau S et al. Human Genomics (2024) |
Reported Trait: Low density lipoprotein cholesterol level | OR: 1.35 [1.26, 1.46] | AUROC: 0.65 | — | — | — |
| PPM022097 | PGS004969 (LDLC_Mean_INT_ldpred_AFRss_afrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.167 [0.14, 0.19] | — | R²: 0.028 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022103 | PGS004969 (LDLC_Mean_INT_ldpred_AFRss_afrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.269 [0.23, 0.31] | — | R²: 0.074 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022109 | PGS004969 (LDLC_Mean_INT_ldpred_AFRss_afrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.144 [0.08, 0.21] | — | R²: 0.021 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022099 | PGS004970 (LDLC_Mean_INT_ldpred_EURss_eurld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.219 [0.2, 0.24] | — | R²: 0.049 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022105 | PGS004970 (LDLC_Mean_INT_ldpred_EURss_eurld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.132 [0.09, 0.17] | — | R²: 0.018 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022111 | PGS004970 (LDLC_Mean_INT_ldpred_EURss_eurld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.226 [0.17, 0.29] | — | R²: 0.051 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022104 | PGS004971 (LDLC_Mean_INT_ldpred_HISss_amrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.165 [0.13, 0.2] | — | R²: 0.028 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022110 | PGS004971 (LDLC_Mean_INT_ldpred_HISss_amrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.167 [0.11, 0.23] | — | R²: 0.028 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022098 | PGS004971 (LDLC_Mean_INT_ldpred_HISss_amrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.178 [0.16, 0.2] | — | R²: 0.032 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022094 | PGS004972 (LDLC_Mean_INT_ldpred_METAss_afrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.256 [0.23, 0.28] | — | R²: 0.067 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022100 | PGS004972 (LDLC_Mean_INT_ldpred_METAss_afrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.258 [0.22, 0.3] | — | R²: 0.068 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022106 | PGS004972 (LDLC_Mean_INT_ldpred_METAss_afrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.217 [0.16, 0.28] | — | R²: 0.048 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022095 | PGS004973 (LDLC_Mean_INT_ldpred_METAss_amrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.274 [0.25, 0.3] | — | R²: 0.077 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022101 | PGS004973 (LDLC_Mean_INT_ldpred_METAss_amrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.292 [0.26, 0.33] | — | R²: 0.087 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022107 | PGS004973 (LDLC_Mean_INT_ldpred_METAss_amrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.253 [0.19, 0.31] | — | R²: 0.065 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022096 | PGS004974 (LDLC_Mean_INT_ldpred_METAss_eurld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.253 [0.23, 0.27] | — | R²: 0.065 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022102 | PGS004974 (LDLC_Mean_INT_ldpred_METAss_eurld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.246 [0.21, 0.28] | — | R²: 0.062 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022108 | PGS004974 (LDLC_Mean_INT_ldpred_METAss_eurld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.235 [0.17, 0.29] | — | R²: 0.055 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021996 | PGS004975 (LDLC_Mean_INT_prscs_AFRss_afrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.154 [0.13, 0.18] | — | R²: 0.024 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022003 | PGS004975 (LDLC_Mean_INT_prscs_AFRss_afrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.214 [0.18, 0.25] | — | R²: 0.046 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022010 | PGS004975 (LDLC_Mean_INT_prscs_AFRss_afrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.144 [0.08, 0.21] | — | R²: 0.021 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021998 | PGS004976 (LDLC_Mean_INT_prscs_EURss_eurld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.266 [0.24, 0.29] | — | R²: 0.072 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022005 | PGS004976 (LDLC_Mean_INT_prscs_EURss_eurld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.12 [0.08, 0.16] | — | R²: 0.015 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022012 | PGS004976 (LDLC_Mean_INT_prscs_EURss_eurld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.201 [0.14, 0.26] | — | R²: 0.041 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021997 | PGS004977 (LDLC_Mean_INT_prscs_HISss_amrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.182 [0.16, 0.2] | — | R²: 0.034 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022004 | PGS004977 (LDLC_Mean_INT_prscs_HISss_amrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.178 [0.14, 0.22] | — | R²: 0.032 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022011 | PGS004977 (LDLC_Mean_INT_prscs_HISss_amrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.18 [0.12, 0.24] | — | R²: 0.033 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021993 | PGS004978 (LDLC_Mean_INT_prscs_METAss_afrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.251 [0.23, 0.27] | — | R²: 0.064 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022000 | PGS004978 (LDLC_Mean_INT_prscs_METAss_afrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.226 [0.19, 0.26] | — | R²: 0.052 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022007 | PGS004978 (LDLC_Mean_INT_prscs_METAss_afrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.249 [0.19, 0.31] | — | R²: 0.062 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021994 | PGS004979 (LDLC_Mean_INT_prscs_METAss_amrld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.279 [0.26, 0.3] | — | R²: 0.079 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022001 | PGS004979 (LDLC_Mean_INT_prscs_METAss_amrld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.297 [0.26, 0.33] | — | R²: 0.09 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022008 | PGS004979 (LDLC_Mean_INT_prscs_METAss_amrld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.272 [0.21, 0.33] | — | R²: 0.075 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021995 | PGS004980 (LDLC_Mean_INT_prscs_METAss_eurld) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.268 [0.25, 0.29] | — | R²: 0.073 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022002 | PGS004980 (LDLC_Mean_INT_prscs_METAss_eurld) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.236 [0.2, 0.27] | — | R²: 0.056 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022009 | PGS004980 (LDLC_Mean_INT_prscs_METAss_eurld) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.244 [0.18, 0.3] | — | R²: 0.06 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021992 | PGS004981 (LDLC_Mean_INT_prscsx_METAweight) |
PSS011816| European Ancestry| 7,654 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.292 [0.27, 0.31] | — | R²: 0.087 | score previously adjusted for age, sex, 20 PCs | — |
| PPM022006 | PGS004981 (LDLC_Mean_INT_prscsx_METAweight) |
PSS011806| Hispanic or Latin American Ancestry| 1,000 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.271 [0.21, 0.33] | — | R²: 0.074 | score previously adjusted for age, sex, 20 PCs | — |
| PPM021999 | PGS004981 (LDLC_Mean_INT_prscsx_METAweight) |
PSS011796| African Ancestry| 2,490 individuals |
PGP000679 | Gunn S et al. HGG Adv (2024) |
Reported Trait: LDL cholesterol | β: 0.292 [0.26, 0.33] | — | R²: 0.087 | score previously adjusted for age, sex, 20 PCs | — |
|
PGS Sample Set ID (PSS) |
Phenotype Definitions and Methods | Participant Follow-up Time | Sample Numbers | Age of Study Participants | Sample Ancestry | Additional Ancestry Description | Cohort(s) | Additional Sample/Cohort Information |
|---|---|---|---|---|---|---|---|---|
| PSS011426 | — | — | 970 individuals, 32.0 % Male samples |
Mean = 52.47 years Sd = 7.81 years |
East Asian | — | UKB | — |
| PSS009158 | — | — | 3,563 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS011428 | — | — | 5,137 individuals, 53.0 % Male samples |
Mean = 53.37 years Sd = 8.43 years |
South Asian | — | UKB | — |
| PSS011441 | — | — | [ ,
82.0 % Male samples |
Mean = 27.5 years | African unspecified | — | PDAY | — |
| PSS000714 | — | — | 6,003 individuals | — | African unspecified | — | UKB | — |
| PSS000715 | — | — | 1,082 individuals | — | East Asian | — | UKB | — |
| PSS000716 | — | — | 23,535 individuals | — | European | Non-British White | UKB | — |
| PSS000717 | — | — | 7,319 individuals | — | South Asian | — | UKB | — |
| PSS000718 | — | — | 63,675 individuals | — | European (British) |
— | UKB | — |
| PSS001146 | — | — | 3,566 individuals | — | African unspecified | — | AADM | — |
| PSS001144 | — | — | 4,273 individuals | — | African unspecified | — | AADM | — |
| PSS001145 | — | — | 707 individuals | — | African unspecified | — | AADM | — |
| PSS001149 | — | — | 4,972 individuals | — | Sub-Saharan African | — | AWI-Gen | — |
| PSS001150 | — | — | 3,743 individuals | — | African unspecified | — | AWI-Gen | — |
| PSS001151 | — | — | 15,242 individuals | — | South Asian | — | G&H | — |
| PSS001152 | — | — | 118,260 individuals | — | East Asian | — | KoGES | — |
| PSS000292 | Composite end point of cardiovascular events was defined as myocardial infarction, ischemic stroke, and death from coronary heart disease. Death from coronary heart disease was defined on the basis of codes 412 and 414 (ICD-9) or I22–I23 and I25 (ICD-10) in the Swedish Cause of Death Register. Myocardial infarction was defined on the basis of codes 410 and I21 in the International Classification of Diseases, 9th Revision and 10th Revision (ICD-9 and ICD-10), respectively. Ischemic stroke was defined on the basis of codes 434 or 436 (ICD-9) and I63 or I64 (ICD-10). | Median = 10.6 years | [
|
— | European | — | MDC | — |
| PSS001153 | — | — | 1,341 individuals | — | African American or Afro-Caribbean | — | MGI | — |
| PSS001154 | — | — | 17,190 individuals | — | European | — | MGI | — |
| PSS001156 | — | — | 18,251 individuals | — | African American or Afro-Caribbean | — | MVP | Subset not used in discovery dataset |
| PSS001157 | — | — | 4,155 individuals | — | Asian unspecified | — | MVP | Subset not used in discovery dataset |
| PSS001158 | — | — | 68,381 individuals | — | European | — | MVP | Subset not used in discovery dataset |
| PSS001159 | — | — | 7,669 individuals | — | Hispanic or Latin American | — | MVP | Subset not used in discovery dataset |
| PSS001160 | — | — | 2,138 individuals | — | African American or Afro-Caribbean | — | PMB | — |
| PSS001161 | — | — | 28,217 individuals | — | East Asian | — | ToMMo | Only participants from Miyagi Prefecture were included |
| PSS001162 | — | — | 461,918 individuals | — | European, African unspecified, East Asian, South Asian | — | UKB | — |
| PSS001163 | — | — | 6,863 individuals | — | African unspecified | — | UKB | — |
| PSS001164 | — | — | 1,441 individuals | — | East Asian | — | UKB | — |
| PSS001165 | — | — | 389,158 individuals | — | European | — | UKB | — |
| PSS001166 | — | — | 6,814 individuals | — | South Asian | — | UKB | — |
| PSS008696 | — | — | 226 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008704 | — | — | 6,312 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS010010 | LOW DENSITY LIPOPROTEIN CHOL (LOINC: 13457-7); Quantitative | — | 9,288 individuals | — | European | — | MGI | — |
| PSS010711 | — | — | 3,622 individuals, 46.0 % Male samples |
Mean = 51.9 years Sd = 8.1 years |
African unspecified | Nigerian | UKB | — |
| PSS010207 | — | — | 2,235 individuals, 45.0 % Male samples |
Mean = 58.1 years Sd = 7.1 years |
European | Ashkenazi | UKB | — |
| PSS011715 | — | — | 389,564 individuals, 46.0 % Male samples |
Mean = 57.0 years | European (British) |
— | UKB | — |
| PSS008712 | — | — | 5,765 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS011066 | — | — | 37,317 individuals, 41.98 % Male samples |
Mean = 51.37 years Sd = 10.82 years |
East Asian | — | InterASIA | China MUCA, CIMIC |
| PSS011067 | — | — | 21,653 individuals, 41.98 % Male samples |
Mean = 51.22 years Sd = 10.78 years |
East Asian | — | InterASIA | China MUCA, CIMIC |
| PSS011068 | — | — | 15,664 individuals, 41.98 % Male samples |
Mean = 51.57 years Sd = 10.87 years |
East Asian | — | InterASIA | China MUCA, CIMIC |
| PSS009243 | — | — | 329 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009892 | — | — | 75,973 individuals, 57.2 % Male samples |
Mean = 60.4 years Sd = 6.6 years |
European | — | UKB | — |
| PSS009608 | — | Median = 11.2 yrs | [
|
— | European (British, Irish) |
— | UKB | — |
| PSS011762 | — | — | 8,417 individuals | — | European | — | BBofA | — |
| PSS001175 | Cases were individuals with atrial fibrillation. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001176 | Cases were individuals with atrioventricular preexcitation. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001177 | Cases were individuals with congential artery disease. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001178 | Cases were individuals with distal conduction disease. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001179 | Cases were individuals with implantable cardioverter defibrillators. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001180 | Cases were individuals with mitral valve prolapse. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001181 | Cases were individuals with non-ischemic cardiomyopathy. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001182 | Cases were individuals with a pacemaker. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS001183 | Cases were individuals with valve disease. Disease status was ascertained based on data from baseline interviews, hospital diagnosis codes (ICD-9 and ICD-10), cause of death codes (ICD-10), and operation codes. Details of individual selections and disease definitions are described in Supplementary Data 23. | — | [
|
— | European | — | UKB | — |
| PSS007336 | — | — | 3,863 individuals | — | African unspecified | — | UKB | — |
| PSS007337 | — | — | 807 individuals | — | East Asian | — | UKB | — |
| PSS007338 | — | — | 11,021 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007339 | — | — | 5,226 individuals | — | South Asian | — | UKB | — |
| PSS007341 | — | — | 3,863 individuals | — | African unspecified | — | UKB | — |
| PSS007342 | — | — | 807 individuals | — | East Asian | — | UKB | — |
| PSS007343 | — | — | 11,021 individuals | — | European | non-white British ancestry | UKB | — |
| PSS000795 | — | — | 1,378 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS000181 | LDL-C serum biochemistry was desribed previously (http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf). | — | 4,680 individuals, 45.8 % Male samples |
Mean = 56.6 years Sd = 8.1 years |
African unspecified | — | UKB | Genotyping Array Cohort |
| PSS000182 | Cardiovascular disease events were defined as coronary and carotid revascularization, myocardial infarction, ischemic stroke, and all-cause mortality. The CVD events occurring before and after enrollment were included. Events occurring prior to enrollment were identified by either self-reported medical history and/or previous hospital admission documented in an electronic health record. | — | [ ,
43.36 % Male samples |
Mean = 56.64 years Sd = 7.99 years |
European, East Asian, African unspecified | — | UKB | Genotyping Array & Exome Sequencing Cohort |
| PSS000183 | LDL-C serum biochemistry was desribed previously (http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf). | — | 10,640 individuals, 45.8 % Male samples |
Mean = 56.6 years Sd = 8.1 years |
East Asian | — | UKB | Genotyping Array Cohort |
| PSS000184 | LDL-C serum biochemistry was desribed previously (http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf). | — | 439,871 individuals, 45.8 % Male samples |
Mean = 56.6 years Sd = 8.1 years |
European | — | UKB | Genotyping Array Cohort |
| PSS000185 | LDL-C serum biochemistry was desribed previously (http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf). | — | 439,871 individuals, 45.8 % Male samples |
Mean = 56.6 years Sd = 8.1 years |
European | — | UKB | Genotyping Array Cohort |
| PSS000185 | LDL-C serum biochemistry was desribed previously (http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf). | — | 10,640 individuals, 45.8 % Male samples |
Mean = 56.6 years Sd = 8.1 years |
East Asian | — | UKB | Genotyping Array Cohort |
| PSS000185 | LDL-C serum biochemistry was desribed previously (http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf). | — | 4,680 individuals, 45.8 % Male samples |
Mean = 56.6 years Sd = 8.1 years |
African unspecified | — | UKB | Genotyping Array Cohort |
| PSS007344 | — | — | 5,226 individuals | — | South Asian | — | UKB | — |
| PSS007345 | — | — | 26,777 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008249 | — | — | 165 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008250 | — | — | 166 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS008256 | — | — | 5,987 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010776 | — | — | 180 individuals, 41.0 % Male samples |
Mean = 53.2 years Sd = 6.5 years |
European | Polish | UKB | — |
| PSS010272 | — | — | 49 individuals, 31.0 % Male samples |
Mean = 48.8 years Sd = 6.4 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010777 | — | — | 192 individuals, 42.0 % Male samples |
Mean = 53.2 years Sd = 6.4 years |
European | Polish | UKB | — |
| PSS008264 | — | — | 5,470 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010273 | — | — | 49 individuals, 31.0 % Male samples |
Mean = 48.8 years Sd = 6.4 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010778 | — | — | 190 individuals, 42.0 % Male samples |
Mean = 53.2 years Sd = 6.4 years |
European | Polish | UKB | — |
| PSS010185 | — | — | 1,115 individuals, 41.1 % Male samples |
Mean = 46.18 years | Hispanic or Latin American | — | HCHS, SOL | — |
| PSS010274 | — | — | 49 individuals, 31.0 % Male samples |
Mean = 48.8 years Sd = 6.4 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS010052 | — | — | 461,918 individuals | — | European, African unspecified, East Asian, South Asian | — | UKB | — |
| PSS010795 | — | — | 3,935 individuals, 38.0 % Male samples |
Mean = 54.4 years Sd = 7.5 years |
European | Polish | UKB | — |
| PSS009627 | — | — | 4,416 individuals, 75.0 % Male samples |
European | — | UCC-SMART | UCC-SMART | |
| PSS000824 | — | — | 2,097 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS000825 | — | — | 1,987 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS010056 | — | — | 6,140 individuals, 43.0 % Male samples |
Median = 39.0 years | Greater Middle Eastern (Middle Eastern, North African or Persian) (Middle Eastern Arabs) |
— | QBB | — |
| PSS009628 | Bazzet formula QT-corrected interval calculated from automated QT interval obtained from 12-lead electrocardiograms in TOPMed | — | 26,976 individuals, 65.4 % Male samples |
Mean = 59.8 years Sd = 12.5 years |
European, African unspecified, Asian unspecified, Other admixed ancestry, Not reported | Combined analysis of European (59.6%), African (18.1%), Asian (2.9%), Admixed American (2.2%), Amish (3.7%) and Undetermined (13.5%) genetic ancestries | 9 cohorts
|
— |
| PSS010058 | Cases are potential clinical FH cases | — | [
|
— | Not reported | — | NR | — |
| PSS010055 | — | — | 22,608 individuals | — | East Asian | — | KBA, KoGES | — |
| PSS010291 | — | — | 2,326 individuals, 37.0 % Male samples |
Mean = 52.5 years Sd = 8.1 years |
African American or Afro-Caribbean | Caribbean | UKB | — |
| PSS008797 | — | — | 490 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008691 | — | — | 225 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS009631 | ICD-10 diagnosis code for hypertensive heart disease with heart failure, hypertensive heart and chronic kidney disease with heart failure and stage 1 through stage 4 chronic kidney disease, hypertensive heart and chronic kidney disease with heart failure and with stage 5 chronic kidney disease, or heart failure (I11.0, I13.0, I13.2, I50) in BioMe biobank and ICD-10 diagnosis code for hypertensive heart disease with heart failure, hypertensive heart and chronic kidney disease with heart failure and stage 1 through stage 4 chronic kidney disease, hypertensive heart and chronic kidney disease with heart failure and with stage 5 chronic kidney disease, or heart failure (I11.0, I13.0, I13.2, I50) in UKB; ICD-10 diagnosis code for exertional dyspnea (R06.09) and/or physician-documented exertional dyspnea in the problem list; ICD-10 diagnosis code for peripheral edema (R60) and/or physician-documented peripheral edema in the problem list (e.g., “ankle swelling”, “lower extremity swelling”); (e.g., “shortness of breath”, “difficulty breathing”; and “on exertion”, “when exercising”); ICD-10 diagnosis code for dyspnea (R06.0) and/or physician-documented dyspnea in the problem list (e.g., “shortness of breath”, “trouble breathing”) | — | 486,754 individuals, 46.0 % Male samples |
Median = 58.0 years | African unspecified, Hispanic or Latin American, European, Asian unspecified, NR | African, Hispanic/Latino, European, Asian, Other ancestry | BioMe, UKB | — |
| PSS000465 | Individuals ≥18 years with clinically diagnosed heterozygous familial hypercholesterolemia (FH) from the BCFH cohort. Individuals who were positive for the common French Canadian variant in the LDLR gene including del.15 kb of the promoter and exon 1, del.5 kb of exons 2 and 3, p.W66G (exon 3), p.E207K (exon 4), p.Y468X (exon 10), or p.C646Y (exon 14) in this study. Fasting clinical lipid profiles were obtained following a 4-week washout of any cholesterol-lowering medications from the CNMA cohort. Individuals who were positive for a LDLR, APOB, or PCSK9 variant that was deemed to cause FH in the UKB cohort.Any atherosclerotic cardiovascular disease (ASCVD) event, which was defined as myocardial infarction, coronary artery disease or carotid revascularization, transient ischemic attack or stroke. For the UK Biobank, retrospecitvie ASCVD was self reported and prospective ASCVD were defined using hospital episode statistics and 10th revision of the International Statistical Classification of Diseases and Related Health Problems diagnosis codes and OPCS Classification of Interventions and Procedures version 4 procedure codes | — | 1,120 individuals, 40.4 % Male samples |
Mean = 41.36 years | European, NR | European (94%), Not reported (6%) | BCFH, CNMA, UKB | — |
| PSS000466 | Any atherosclerotic cardiovascular disease (ASCVD) event, which was defined as myocardial infarction, coronary artery disease or carotid revascularization, transient ischemic attack or stroke. For the UK Biobank, retrospecitvie ASCVD was self reported and prospective ASCVD were defined using hospital episode statistics and 10th revision of the International Statistical Classification of Diseases and Related Health Problems diagnosis codes and OPCS Classification of Interventions and Procedures version 4 procedure codes | — | 389,127 individuals | — | European | — | UKB | — |
| PSS008692 | — | — | 217 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008693 | — | — | 474 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS008694 | — | — | 225 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS011097 | — | — | 2,669 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) (Arab) |
— | NR | N total after excluding missing values = 2,553 |
| PSS009637 | — | — | [
|
— | Not reported | — | NR | LIPIGEN (Lipid TransPort Disorders italian Genetic Network) database |
| PSS008695 | — | — | 226 individuals | — | European | Italy (South Europe) | UKB | — |
| PSS011950 | Cohort of adults with a 22q11.2 microdeletion | — | 151 individuals, 50.0 % Male samples |
Median = 34.0 years | European, Not reported | — | NR | — |
| PSS009639 | Non-fasting serum lipid levels were measured using the Cobas Integra 400 Plus Chemistry analyser, an automated analyser that employs four different technologies: absorption photometry, fluorescence polarization immunoassay, immune-turbidimetry, and potentiometry for accurate analysis. LDL-C were measured using the homogeneous enzymatic colorimetric assays | — | 2,569 individuals, 42.9 % Male samples |
Mean = 33.1 years Ci = [18.0, 48.2] years |
Sub-Saharan African (South Africans) |
— | SAZ | — |
| PSS011499 | — | — | 4,026 individuals | — | European | — | AllofUs | — |
| PSS011500 | — | — | 5,735 individuals | — | South Asian | — | G&H | — |
| PSS011420 | — | — | 4,292 individuals, 42.0 % Male samples |
Mean = 51.82 years Sd = 8.06 years |
African American or Afro-Caribbean | — | UKB | — |
| PSS007809 | — | — | 49 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007810 | — | — | 49 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007811 | — | — | 76 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007812 | — | — | 49 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007813 | — | — | 49 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007814 | — | — | 49 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS007820 | — | — | 2,338 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010860 | — | — | 995 individuals, 51.0 % Male samples |
Mean = 55.4 years Sd = 7.5 years |
European | white British | UKB | — |
| PSS009363 | — | — | 1,036 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS007828 | — | — | 2,151 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS009365 | — | — | 1,622 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009364 | — | — | 992 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009367 | — | — | 1,042 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009366 | — | — | 1,040 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS009368 | — | — | 1,042 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010356 | — | — | 73 individuals, 34.0 % Male samples |
Mean = 50.3 years Sd = 7.1 years |
East Asian | Chinese | UKB | — |
| PSS010692 | — | — | 61 individuals, 64.0 % Male samples |
Mean = 49.5 years Sd = 6.5 years |
African unspecified | Nigerian | UKB | — |
| PSS010861 | — | — | 1,040 individuals, 51.0 % Male samples |
Mean = 55.5 years Sd = 7.5 years |
European | white British | UKB | — |
| PSS008349 | — | — | 307 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010357 | — | — | 73 individuals, 34.0 % Male samples |
Mean = 50.3 years Sd = 7.1 years |
East Asian | Chinese | UKB | — |
| PSS010693 | — | — | 61 individuals, 64.0 % Male samples |
Mean = 49.5 years Sd = 6.5 years |
African unspecified | Nigerian | UKB | — |
| PSS009376 | — | — | 18,968 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010862 | — | — | 1,040 individuals, 51.0 % Male samples |
Mean = 55.5 years Sd = 7.5 years |
European | white British | UKB | — |
| PSS010358 | — | — | 72 individuals, 35.0 % Male samples |
Mean = 50.2 years Sd = 7.1 years |
East Asian | Chinese | UKB | — |
| PSS010694 | — | — | 61 individuals, 64.0 % Male samples |
Mean = 49.5 years Sd = 6.5 years |
African unspecified | Nigerian | UKB | — |
| PSS011043 | — | — | 4,292 individuals, 42.0 % Male samples |
Mean = 51.82 years Sd = 8.06 years |
African American or Afro-Caribbean (African American) |
— | UKB | — |
| PSS010879 | — | — | 19,077 individuals, 46.0 % Male samples |
Mean = 56.9 years Sd = 7.9 years |
European | white British | UKB | — |
| PSS011044 | — | — | 970 individuals, 32.0 % Male samples |
Mean = 52.47 years Sd = 7.81 years |
East Asian | — | UKB | — |
| PSS009384 | — | — | 17,339 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS011045 | — | — | 9,527 individuals, 47.0 % Male samples |
Mean = 56.86 years Sd = 8.05 years |
European | — | UKB | — |
| PSS010188 | — | — | 103 individuals, 55.0 % Male samples |
Mean = 56.0 years Sd = 6.1 years |
European | Ashkenazi | UKB | — |
| PSS011978 | — | — | 18,753 individuals | — | European (British) |
— | UKB | — |
| PSS010375 | — | — | 1,705 individuals, 33.0 % Male samples |
Mean = 52.4 years Sd = 7.8 years |
East Asian | Chinese | UKB | — |
| PSS011046 | — | — | 5,137 individuals, 53.0 % Male samples |
Mean = 53.37 years Sd = 8.43 years |
South Asian | — | UKB | — |
| PSS009666 | — | — | 7,016 individuals, 54.3 % Male samples |
Median = 53.0 years IQR = [46.0, 60.0] years |
South Asian | — | UKB | — |
| PSS009667 | — | — | 7,082 individuals, 43.3 % Male samples |
Median = 59.0 years IQR = [45.0, 58.0] years |
African American or Afro-Caribbean | Black/Caribbean | UKB | — |
| PSS009668 | — | — | 353,166 individuals, 46.2 % Male samples |
Median = 58.0 years IQR = [51.0, 63.0] years |
European | White | UKB | — |
| PSS010189 | — | — | 107 individuals, 54.0 % Male samples |
Mean = 56.2 years Sd = 6.4 years |
European | Ashkenazi | UKB | — |
| PSS011442 | — | — | [ ,
77.0 % Male samples |
Mean = 26.7 years | European | — | PDAY | — |
| PSS010190 | — | — | 107 individuals, 54.0 % Male samples |
Mean = 56.2 years Sd = 6.4 years |
European | Ashkenazi | UKB | — |
| PSS010102 | — | — | 626 individuals, 49.4 % Male samples |
Mean = 46.2 years | European | — | BCFH | — |
| PSS010103 | — | — | 313 individuals, 41.5 % Male samples |
Mean = 51.0 years | South Asian, European, African unspecified, NR | South Asian, European, African, NR | NR | Ontario hypercholesterolemia cohort |
| PSS010104 | — | — | 89,528 individuals | — | European | — | UKB | — |
| PSS008913 | — | — | 61 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008914 | — | — | 61 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008915 | — | — | 138 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008916 | — | — | 61 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008917 | — | — | 61 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS000904 | Intravenous ajmaline was administered at consecutive boluses of 10 mg/min. A 10-s ECG was recorded ∼1 min after each bolus using a GE Healthcare electrocardiograph. The test was stopped when the target dose of 1 mg/kg rounded up to the next 10 mg was reached, if ventricular arrhythmia occurred, or at the manifestation of a Type I BrS pattern, defined as an ST elevation >2 mm with a coved morphology in any lead among V1–V2 in the 2nd to 4th intercostal spaces.12 | — | 1,257 individuals | — | European | — | Amsterdam | — |
| PSS000905 | Intravenous ajmaline was administered at consecutive boluses of 10 mg/min. A 10-s ECG was recorded ∼1 min after each bolus using a GE Healthcare electrocardiograph. The test was stopped when the target dose of 1 mg/kg rounded up to the next 10 mg was reached, if ventricular arrhythmia occurred, or at the manifestation of a Type I BrS pattern, defined as an ST elevation >2 mm with a coved morphology in any lead among V1–V2 in the 2nd to 4th intercostal spaces.15 | — | 1,185 individuals | — | European | — | Amsterdam | — |
| PSS000906 | Intravenous ajmaline was administered at consecutive boluses of 10 mg/min. A 10-s ECG was recorded ∼1 min after each bolus using a GE Healthcare electrocardiograph. The test was stopped when the target dose of 1 mg/kg rounded up to the next 10 mg was reached, if ventricular arrhythmia occurred, or at the manifestation of a Type I BrS pattern, defined as an ST elevation >2 mm with a coved morphology in any lead among V1–V2 in the 2nd to 4th intercostal spaces.13 | — | 1,193 individuals | — | European | — | Amsterdam | — |
| PSS008918 | — | — | 61 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008924 | — | — | 3,651 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS008932 | — | — | 3,389 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS007913 | — | — | 78 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
| PSS010123 | — | 443,326 individuals, 45.5 % Male samples |
Mean = 57.2 years | European, NR | 94% European | UKB | — | |
| PSS004966 | — | — | 120 individuals | — | African unspecified | — | UKB | — |
| PSS004967 | — | — | 68 individuals | — | East Asian | — | UKB | — |
| PSS004968 | — | — | 834 individuals | — | European | non-white British ancestry | UKB | — |
| PSS004969 | — | — | 193 individuals | — | South Asian | — | UKB | — |
| PSS004970 | — | — | 3,353 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS004971 | — | — | 120 individuals | — | African unspecified | — | UKB | — |
| PSS004972 | — | — | 68 individuals | — | East Asian | — | UKB | — |
| PSS004973 | — | — | 872 individuals | — | European | non-white British ancestry | UKB | — |
| PSS004974 | — | — | 201 individuals | — | South Asian | — | UKB | — |
| PSS004975 | — | — | 3,523 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS004976 | — | — | 120 individuals | — | African unspecified | — | UKB | — |
| PSS004977 | — | — | 68 individuals | — | East Asian | — | UKB | — |
| PSS004978 | — | — | 872 individuals | — | European | non-white British ancestry | UKB | — |
| PSS004979 | — | — | 201 individuals | — | South Asian | — | UKB | — |
| PSS004980 | — | — | 3,523 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS009469 | — | — | 1,711 individuals | — | European | UK (+ Ireland) | UKB | — |
| PSS010459 | — | — | 5,934 individuals, 54.0 % Male samples |
Mean = 53.3 years Sd = 8.4 years |
South Asian | Indian | UKB | — |
| PSS000588 | Derived from the Friedewald’s formula | — | 426 individuals, 46.0 % Male samples |
Mean = 43.3 years Sd = 11.4 years |
East Asian (Chinese) |
— | NR | Adults |
| PSS004987 | — | — | 110 individuals | — | East Asian | — | UKB | — |
| PSS000590 | Derived from the Friedewald’s formula | — | 1,941 individuals, 57.7 % Male samples |
Mean = 58.2 years Sd = 12.34 years |
East Asian (Chinese) |
— | HKDB | — |
| PSS004988 | — | — | 1,708 individuals | — | European | non-white British ancestry | UKB | — |
| PSS000592 | Derived from the Friedewald’s formula | — | 865 individuals, 57.6 % Male samples |
Mean = 57.0 years Sd = 12.08 years |
East Asian (Chinese) |
— | HKDB | — |
| PSS004986 | — | — | 192 individuals | — | African unspecified | — | UKB | — |
| PSS000594 | Derived from the Friedewald’s formula | — | 4,917 individuals, 44.9 % Male samples |
Mean = 56.3 years Sd = 13.5 years |
East Asian (Chinese) |
— | HKDR | — |
| PSS004989 | — | — | 319 individuals | — | South Asian | — | UKB | — |
| PSS004990 | — | — | 5,528 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS004991 | — | — | 192 individuals | — | African unspecified | — | UKB | — |
| PSS004992 | — | — | 110 individuals | — | East Asian | — | UKB | — |
| PSS004993 | — | — | 1,708 individuals | — | European | non-white British ancestry | UKB | — |
| PSS004994 | — | — | 319 individuals | — | South Asian | — | UKB | — |
| PSS004995 | — | — | 5,528 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS008465 | — | — | 25 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008466 | — | — | 25 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS000096 | Lipid levels are represented in mg/dL, individuals on any lipid-lowering medication (n = 1,018) were omitted from all analyses. | — | 1,355 individuals, 46.2 % Male samples |
Mean = 61.68 years | African American or Afro-Caribbean | — | MESA | MESA Classic Cohort |
| PSS000097 | Lipid levels are represented in mg/dL, individuals on any lipid-lowering medication (n = 1,018) were omitted from all analyses. | — | 666 individuals, 50.15 % Male samples |
Mean = 61.5 years | East Asian | — | MESA | MESA Classic Cohort |
| PSS000098 | Lipid levels are represented in mg/dL, individuals on any lipid-lowering medication (n = 1,018) were omitted from all analyses. | — | 2,063 individuals, 46.78 % Male samples |
Mean = 62.09 years | European | — | MESA | MESA Classic Cohort |
| PSS000099 | Lipid levels are represented in mg/dL, individuals on any lipid-lowering medication (n = 1,018) were omitted from all analyses. | — | 1,256 individuals, 48.89 % Male samples |
Mean = 60.65 years | Hispanic or Latin American | — | MESA | MESA Classic Cohort |
| PSS008467 | — | — | 49 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008469 | — | — | 26 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008468 | — | — | 26 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008470 | — | — | 26 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS008478 | — | — | 1,122 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS000100 | Serum levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides (TG) | — | 6,407 individuals, 44.0 % Male samples |
Mean = 34.0 years | Sub-Saharan African | — | APCDR | APCDR-Uganda study |
| PSS000101 | Serum levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides (TG) | — | 21,295 individuals, 38.0 % Male samples |
Mean = 60.0 years | East Asian (Chinese) |
— | CKB | - 20810 samples had HDL measurements - 17662 samples had LDL measurements - 20222 samples had triglyceride measurements |
| PSS000102 | Serum levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides (TG) | — | 1,641 individuals, 58.0 % Male samples |
Mean = 62.0 years | European (Greek) |
Population isolate from the Pomak villages in the North of Greece | HELIC | - 1186 samples had HDL measurements - 1186 samples had LDL measurements - 1176 samples had triglyceride measurements |
| PSS000103 | Serum levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides (TG) | — | 1,945 individuals, 66.0 % Male samples |
Mean = 45.0 years | European (Greek) |
Population isolate from the Mylopotamos villages in Crete | HELIC | - 1078 samples had HDL measurements - 1075 samples had LDL measurements - 1066 samples had triglyceride measurements |
| PSS000104 | Serum levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides (TG) | — | 9,962 individuals, 56.0 % Male samples |
Mean = 52.0 years | European | — | UKHLS | - 9706 samples had HDL measurements - 9767 samples had LDL measurements - 9635 samples had triglyceride measurements |
| PSS001058 | — | — | 3,020 individuals, 76.0 % Male samples |
Mean = 49.0 years Sd = 6.0 years |
European | — | Whitehall | — |
| PSS001059 | Cases are individuals with familial hypercholesterolaemia (FH). For the Simon Broome British Heart Foundation Study (SBFH), the diagnostic criteria for FH were defined by the Simon Broome Register criteria as an untreated total cholesterol above 7.5mmol/L or an LDL-C above 4.9mmol/L, and a family history of hypercholesterolaemia and/or early coronary heart disease for “possible FH”, and when together with the presence of tendon xanthomas either in the patient or in a first degree relative, as “definite FH”. Of the 640 FH individuals, 321 have FH with no known mutation, whilst 319 have FH with a known mutation. | — | [
|
— | European | — | Whitehall | Cases from the Oxford FH study (OXFH) and the Simon Broome British Heart Foundation Study (SBFH) |
| PSS008486 | — | — | 1,033 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS011796 | — | — | 2,490 individuals | — | African American or Afro-Caribbean | — | AllofUs | — |
| PSS011806 | — | — | 1,000 individuals | — | Hispanic or Latin American | — | AllofUs | — |
| PSS007086 | — | — | 5,632 individuals | — | African unspecified | — | UKB | — |
| PSS007087 | — | — | 1,462 individuals | — | East Asian | — | UKB | — |
| PSS007088 | — | — | 21,609 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007089 | — | — | 6,776 individuals | — | South Asian | — | UKB | — |
| PSS007090 | — | — | 58,749 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS009017 | — | — | 140 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
| PSS011816 | — | — | 7,654 individuals | — | European | — | AllofUs | — |
| PSS001072 | Cases were individuals with hypobetalipoproteinemia (HBL). Of the 111 individuals with HBL, 38 had polygenic HBL, 40 had monogenic HBL and 33 had HBL from an unknown cause. Polgenic HBL was defined by a polygenic risk score (PRS) < 10th percentile of controls (PRS < 0.5925). For the 40 monogenic HBL individuals, 38 carried heterozygous APOB loss of fucntion variants and 2 carried heterozygous PCSK9 loss of function variants. In a subset of HBL cases, 7 polygenic cases , 26 monogenic cases and 13 uknown cause cases had liver steatosis. Whilst 17, 6 and 9 individuals did not have liver steatosis, respectively. Liver steatosis was diagnosed by abdominal ultrasonography. Alanine aminotransferase (ALT), aspartate aminotransferase, and gamma-glutamyl transpeptidase were determined by IFCC-standardized enzymatic methods using dedicated commercial kits. Individuals with ALT >1 upper limit of normal (>97.5th percentile) were considered to likely have liver injury. | — | [
|
— | Not reported | — | NR | Cases were obtained from the HYPOCHOL and GENLIP studies. Controls were obtained from the PREGO and GAZEL cohorts and the Finstère area, which are part of the FranceGenRef Consortium. |
| PSS001147 | — | — | 10,460 individuals | — | African unspecified | — | AWI-Gen | — |
| PSS010524 | — | — | 25 individuals, 64.0 % Male samples |
Mean = 52.6 years Sd = 6.8 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS010525 | — | — | 26 individuals, 65.0 % Male samples |
Mean = 52.8 years Sd = 6.8 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS008126 | — | — | 137 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS010526 | — | — | 26 individuals, 65.0 % Male samples |
Mean = 52.8 years Sd = 6.8 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS001148 | — | — | 1,745 individuals | — | African unspecified | — | AWI-Gen | — |
| PSS011201 | — | — | 68,978 individuals, 31.2 % Male samples |
— | East Asian | — | TWB | — |
| PSS011203 | — | — | 1,480 individuals, 36.8 % Male samples |
— | East Asian | — | UKB | — |
| PSS011204 | — | — | 6,303 individuals, 54.1 % Male samples |
— | South Asian | — | UKB | — |
| PSS011206 | — | — | 3,802 individuals, 45.9 % Male samples |
— | African American or Afro-Caribbean | — | UKB | — |
| PSS011207 | — | — | 423,596 individuals, 45.9 % Male samples |
— | European | — | UKB | — |
| PSS010543 | — | — | 1,093 individuals, 60.0 % Male samples |
Mean = 51.9 years Sd = 7.9 years |
Greater Middle Eastern (Middle Eastern, North African or Persian) | Iranian | UKB | — |
| PSS008023 | — | — | 73 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008024 | — | — | 73 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008025 | — | — | 134 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008026 | — | — | 72 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008027 | — | — | 73 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008028 | — | — | 73 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS008034 | — | — | 1,716 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS001083 | Of the 2,531 participants, 1,809 had longitudinal observations for total cholesterol (mg/dL), high density lipoprotein cholesterol (mg/dL) and trigycerides (mg/dL), 1,801 had longitudinal observations for low density lipoprotein cholesterol (mg/dL), 1,325 had longitudinal observations for waist circumference (inches), 2,355 had longitudinal observations for body mass index (kg/m^2) and 1,572 had longitudinal observations for homocysteine (μmol/L). | — | 2,531 individuals, 40.0 % Male samples |
Mean = 48.0 years Sd = 12.0 years |
European, Asian unspecified, Hispanic or Latin American, African unspecified, NR | European = 1,999, Asian unspecified = 228, Hispanic or Latin American = 101, African unspecified = 51, Not reported = 152 | NR | Participants were obtained from the Scientific Wellness Program. |
| PSS008042 | — | — | 1,543 individuals | — | East Asian | China (East Asia) | UKB | — |
| PSS001155 | — | — | 360 individuals | — | Hispanic or Latin American | — | MGI | — |
| PSS011396 | the earliest median eligible non-Emergency Department outpatient measured SBP in the electronic health record, and the corresponding DBP. For individuals with an even number of SBP measures in their record, the lower value was used to compute the median. For individuals with fewer than three measurements available, the lowest available SBP and corresponding DBP were used. Measures were considered ineligible if they occurred at or after an ICD-9/10 billing code from the groups 585/N18 (chronic kidney disease), 405/I15 (secondary hypertension), or 428/I50 (heart failure). For participants who had started an antihypertensive medication before the date of their median SBP measurement, 15 mm Hg was added to SBP and 10 mm Hg to DBP. Eligible SBP measures were restricted to a range of 30 to 300 mmHg. Eligible DBP measures were restricted to values over 30 mmHg. Eligible DBP measures were restricted to values over 30 mmHg. Sample size for SBP, DBP, and PP GWAS analysis included 21,843 individuals. Pulse pressure was defined as SBP minus DBP. Hypertension status was defined by phecodes 401* and/or antihypertensive medication use. | — | 21,843 individuals, 42.69 % Male samples |
Mean = 48.3 years Sd = 14.75 years |
African American or Afro-Caribbean (American) |
— | AllofUs | — |
| PSS011397 | In Lifelines, BP was measured every minute during a period of ten minutes using an automated DINAMAP Monitor (GE Healthcare) and the average of the final three readings was recorded for SBP and DBP. Participants with a measured BP ≥140/90 mm Hg irrespective of treatment and those taking antihypertensive medication (ATC codes C02, C03, C07, C08, C09) irrespective of BP were defined as having hypertension. In continuous trait analyses, 15 mm Hg was added to SBP and 10 mm Hg was added to DBP for 1,236 individuals who were taking antihypertensive medication. PP was calculated using these medication-adjusted BP values. | — | 10,210 individuals, 41.6 % Male samples |
Mean = 44.66 years Sd = 13.05 years |
European (Dutch) |
— | LifeLines | — |
| PSS009791 | — | — | 6,068 individuals | — | African unspecified | — | 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 | — |
| PSS007340 | — | — | 26,777 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS009792 | — | — | 875 individuals | — | East Asian | — | UKB | — |
| PSS001084 | Moderate Age-Related Diabetes (MARD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
| PSS001085 | Moderate Obesity-related Diabetes (MOD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
| PSS001086 | Severe Autoimmune Diabetes (SAID) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
| PSS001087 | Severe Insulin-Deficient Diabetes (SIDD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
| PSS001088 | Severe Insulin-Resistant Diabetes (SIRD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
| PSS008571 | — | — | 50 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
| PSS009793 | — | — | 41,139 individuals | — | European | Non-British European | UKB | — |
| PSS009794 | — | — | 7,638 individuals | — | South Asian | — | UKB | — |
| PSS007171 | — | — | 6,086 individuals | — | African unspecified | — | UKB | — |
| PSS007172 | — | — | 1,615 individuals | — | East Asian | — | UKB | — |
| PSS007173 | — | — | 23,728 individuals | — | European | non-white British ancestry | UKB | — |
| PSS007174 | — | — | 7,407 individuals | — | South Asian | — | UKB | — |
| PSS007175 | — | — | 64,356 individuals | — | European | white British ancestry | UKB | Testing cohort (heldout set) |
| PSS010124 | — | 29,354 individuals, 45.5 % Male samples |
Mean = 55.4 years | European, NR | 85% European | PHB | — | |
| PSS010608 | — | — | 213 individuals, 48.0 % Male samples |
Mean = 53.8 years Sd = 7.4 years |
European | Italian | UKB | — |
| PSS010609 | — | — | 220 individuals, 48.0 % Male samples |
Mean = 53.8 years Sd = 7.4 years |
European | Italian | UKB | — |
| PSS010610 | — | — | 219 individuals, 48.0 % Male samples |
Mean = 53.8 years Sd = 7.4 years |
European | Italian | UKB | — |
| PSS000637 | — | — | 5,550 individuals | — | African unspecified | — | UKB | — |
| PSS000638 | — | — | 974 individuals | — | East Asian | — | UKB | — |
| PSS000639 | — | — | 21,403 individuals | — | European | Non-British White | UKB | — |
| PSS000640 | — | — | 6,682 individuals | — | South Asian | — | UKB | — |
| PSS000641 | — | — | 57,932 individuals | — | European (British) |
— | UKB | — |
| PSS008245 | — | — | 165 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010440 | — | — | 159 individuals, 65.0 % Male samples |
Mean = 51.1 years Sd = 8.2 years |
South Asian | Indian | UKB | — |
| PSS008246 | — | — | 159 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010441 | — | — | 165 individuals, 66.0 % Male samples |
Mean = 51.2 years Sd = 8.1 years |
South Asian | Indian | UKB | — |
| PSS008247 | — | — | 299 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS010627 | — | — | 6,160 individuals, 45.0 % Male samples |
Mean = 54.4 years Sd = 8.4 years |
European | Italian | UKB | — |
| PSS010442 | — | — | 165 individuals, 66.0 % Male samples |
Mean = 51.1 years Sd = 8.1 years |
South Asian | Indian | UKB | — |
| PSS008248 | — | — | 165 individuals | — | South Asian | India (South Asia) | UKB | — |
| PSS000987 | Cases are individuals with a clinical diagnosis of long QT syndrome. Of the 1238 cases, 1115 were genotype positive meaning they carried a single rare variant in 1 of the 3 established major LQTS genes (KCNQ1 [LQT1], KCNH2 [LQT2] and SCN5A [LQT3]). 123 cases were genotype negative meaning no rare variant was identified in genes unequivocally associated with nonsyndromic LQTS (KCNQ1, KCNH2, SCN5A, CALM1-3, and TRDN). | — | [
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— | European | — | NR | — |
| PSS000988 | Cases are individuals with a clinical diagnosis of long QT syndrome. Of the 418 cases, 356 were genotype positive meaning they carried a single rare variant in 1 of the 3 established major LQTS genes (KCNQ1 [LQT1], KCNH2 [LQT2] and SCN5A [LQT3]). 62 cases were genotype negative meaning no rare variant was identified in genes unequivocally associated with nonsyndromic LQTS (KCNQ1, KCNH2, SCN5A, CALM1-3, and TRDN). | — | [
|
— | East Asian (Japanese) |
— | NR | — |
| PSS000989 | Cases are individuals with a clinical diagnosis of long QT syndrome. Of the 1238 cases, 1115 were genotype positive meaning they carried a single rare variant in 1 of the 3 established major LQTS genes (KCNQ1 [LQT1], KCNH2 [LQT2] and SCN5A [LQT3]). 123 cases were genotype negative meaning no rare variant was identified in genes unequivocally associated with nonsyndromic LQTS (KCNQ1, KCNH2, SCN5A, CALM1-3, and TRDN). | — | [
|
— | European | — | NR | — |
| PSS000989 | Cases are individuals with a clinical diagnosis of long QT syndrome. Of the 418 cases, 356 were genotype positive meaning they carried a single rare variant in 1 of the 3 established major LQTS genes (KCNQ1 [LQT1], KCNH2 [LQT2] and SCN5A [LQT3]). 62 cases were genotype negative meaning no rare variant was identified in genes unequivocally associated with nonsyndromic LQTS (KCNQ1, KCNH2, SCN5A, CALM1-3, and TRDN). | — | [
|
— | East Asian (Japanese) |
— | NR | — |
| PSS009137 | — | — | 191 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009139 | — | — | 310 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009577 | Self-reported statin-use. Uncontrolled hypercholesterolaemia was defined as having baseline LDL-cholesterol >_3 mmol/L, among individuals in the treated hypercholesterolaemia sub-cohort. We used prospective follow-up data to assess the composite outcome of incident major adverse cardiovascular events (MACE), which we defined as the first non-fatal stroke (ischaemic or haemorrhagic), non-fatal myocardial infarction, or fatal cardiovascular events, or disease-modifying cardiovascular procedures. We identified MACE components using International Classification of Diseases (ICD-9 and ICD-10) and the Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes from Hospital Episodes Statistics (HES) data, and death registries data. | Median = 11.4 years | [ ,
58.5 % Male samples |
Mean = 61.7 years | European (white British) |
— | UKB | — |
| PSS009578 | Self-reported statin-use. Uncontrolled hypercholesterolaemia was defined as having baseline LDL-cholesterol >_3 mmol/L, among individuals in the treated hypercholesterolaemia sub-cohort. We used prospective follow-up data to assess the composite outcome of incident major adverse cardiovascular events (MACE), which we defined as the first non-fatal stroke (ischaemic or haemorrhagic), non-fatal myocardial infarction, or fatal cardiovascular events, or disease-modifying cardiovascular procedures. We identified MACE components using International Classification of Diseases (ICD-9 and ICD-10) and the Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes from Hospital Episodes Statistics (HES) data, and death registries data. | Median = 11.4 years | [ ,
58.5 % Male samples |
Mean = 61.7 years | European (white British) |
— | UKB | — |
| PSS009579 | Self-reported statin-use. Uncontrolled hypercholesterolaemia was defined as having baseline LDL-cholesterol >_3 mmol/L, among individuals in the treated hypercholesterolaemia sub-cohort. We used prospective follow-up data to assess the composite outcome of incident major adverse cardiovascular events (MACE), which we defined as the first non-fatal stroke (ischaemic or haemorrhagic), non-fatal myocardial infarction, or fatal cardiovascular events, or disease-modifying cardiovascular procedures. We identified MACE components using International Classification of Diseases (ICD-9 and ICD-10) and the Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes from Hospital Episodes Statistics (HES) data, and death registries data. | Median = 11.4 years | [ ,
58.5 % Male samples |
Mean = 61.7 years | European (white British) |
— | UKB | — |
| PSS009580 | Self-reported statin-use. Uncontrolled hypercholesterolaemia was defined as having baseline LDL-cholesterol >_3 mmol/L, among individuals in the treated hypercholesterolaemia sub-cohort. We used prospective follow-up data to assess the composite outcome of incident major adverse cardiovascular events (MACE), which we defined as the first non-fatal stroke (ischaemic or haemorrhagic), non-fatal myocardial infarction, or fatal cardiovascular events, or disease-modifying cardiovascular procedures. We identified MACE components using International Classification of Diseases (ICD-9 and ICD-10) and the Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes from Hospital Episodes Statistics (HES) data, and death registries data. | Median = 11.4 years | [ ,
58.5 % Male samples |
Mean = 61.7 years | European (white British) |
— | UKB | — |
| PSS009138 | — | — | 181 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009141 | — | — | 193 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009140 | — | — | 191 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS009142 | — | — | 193 individuals | — | European | Poland (NE Europe) | UKB | — |
| PSS001124 | Individuals with severe hypercholesterolemia (HC) had a LDL-C level > 4.9 mmol/L. 124 individuals had severe HC, based on this criteria. Individuals with intermediate HC had a LDL-C level 3.0 ≤ LDL‐C ≤ 4.9 mmol/L. 1927 individuals had intermediate HC, based on this criteria. Individuals classified as having normal LDL-C levels had LDL-C levels < 3.0 mmol/L. 2733 individuals had normal LDL-C levels, based on this criteria. | — | 4,787 individuals, 48.0 % Male samples |
Mean = 31.0 years Sd = 0.2 years |
European | — | NFBC | — |
| PSS009150 | — | — | 3,946 individuals | — | European | Poland (NE Europe) | UKB | — |