Publication Information (EuropePMC) | |
Title | Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline. |
PubMed ID | 33137338(Europe PMC) |
doi | 10.1016/j.kint.2020.09.030 |
Publication Date | Oct. 30, 2020 |
Journal | Kidney Int |
Author(s) | Gorski M, Jung B, Li Y, Matias-Garcia PR, Wuttke M, Coassin S, Thio CHL, Kleber ME, Winkler TW, Wanner V, Chai JF, Chu AY, Cocca M, Feitosa MF, Ghasemi S, Hoppmann A, Horn K, Li M, Nutile T, Scholz M, Sieber KB, Teumer A, Tin A, Wang J, Tayo BO, Ahluwalia TS, Almgren P, Bakker SJL, Banas B, Bansal N, Biggs ML, Boerwinkle E, Bottinger EP, Brenner H, Carroll RJ, Chalmers J, Chee ML, Chee ML, Cheng CY, Coresh J, de Borst MH, Degenhardt F, Eckardt KU, Endlich K, Franke A, Freitag-Wolf S, Gampawar P, Gansevoort RT, Ghanbari M, Gieger C, Hamet P, Ho K, Hofer E, Holleczek B, Xian Foo VH, Hutri-Kähönen N, Hwang SJ, Ikram MA, Josyula NS, Kähönen M, Khor CC, Koenig W, Kramer H, Krämer BK, Kühnel B, Lange LA, Lehtimäki T, Lieb W, Lifelines cohort study, Regeneron Genetics Center, Loos RJF, Lukas MA, Lyytikäinen LP, Meisinger C, Meitinger T, Melander O, Milaneschi Y, Mishra PP, Mononen N, Mychaleckyj JC, Nadkarni GN, Nauck M, Nikus K, Ning B, Nolte IM, O'Donoghue ML, Orho-Melander M, Pendergrass SA, Penninx BWJH, Preuss MH, Psaty BM, Raffield LM, Raitakari OT, Rettig R, Rheinberger M, Rice KM, Rosenkranz AR, Rossing P, Rotter JI, Sabanayagam C, Schmidt H, Schmidt R, Schöttker B, Schulz CA, Sedaghat S, Shaffer CM, Strauch K, Szymczak S, Taylor KD, Tremblay J, Chaker L, van der Harst P, van der Most PJ, Verweij N, Völker U, Waldenberger M, Wallentin L, Waterworth DM, White HD, Wilson JG, Wong TY, Woodward M, Yang Q, Yasuda M, Yerges-Armstrong LM, Zhang Y, Snieder H, Wanner C, Böger CA, Köttgen A, Kronenberg F, Pattaro C, Heid IM. |
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) |
---|---|---|---|---|---|---|
PGS000664 (GRS7_GFR) |
PGP000124 | Gorski M et al. Kidney Int (2020) |
Rapid decline of glomerular filtration rate (GFR) | GFR change measurement | 7 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000664/ScoringFiles/PGS000664.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 |
---|---|---|---|---|---|---|---|---|---|
PPM001371 | PGS000664 (GRS7_GFR) |
PSS000600| European Ancestry| 11,440 individuals |
PGP000124 | Gorski M et al. Kidney Int (2020) |
Reported Trait: Rapid decline of glomerular filtration rate estimated from creatinine (CKDi25) | — | — | Odds Ratio (OR, high vs. low risk): 1.29 [1.06, 1.57] | Age, sex and baseline eGFRcrea | — |
PPM001372 | PGS000664 (GRS7_GFR) |
PSS000599| European Ancestry| 3,447 individuals |
PGP000124 | Gorski M et al. Kidney Int (2020) |
Reported Trait: Acute kidney injury | — | — | Odds Ratio (OR, high vs. low risk): 1.2 [1.08, 1.33] | Matching variables (age-group and sex), quantitative age | — |
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 |
---|---|---|---|---|---|---|---|---|
PSS000600 | CKDi25 cases defined as >25% eGFRcrea decline during follow-up together with a movement from eGFRcrea≥60 mL/min/1.73m^2 at baseline to eGFR<60 mL/min/1.73m^2 at follow up compared to CKDi25 controls defined as eGFRcrea≥60 mL/min/1.73m^2. High risk groups had 8-14 adverse alleles. Low risk groups had 0-5 adverse alleles. | — | [
|
— | European | — | DIACORE, KORA, UKB | 87.61% overlap between the CKDi25 GWAS cohort and this dataset. |
PSS000599 | Cases: ICD 10 code N17. Controls: no ICD10 code N17, frequency-matched by age-group and sex | — | [
|
— | European | — | UKB | Possible overlap between GWAS cohorts and this dataset. |