Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals

Kira J. Stanzick(University of Regensburg), Yong Li(University of Freiburg), Pascal Schlosser(University of Freiburg), Mathias Gorski(University of Regensburg), Matthias Wuttke(University of Freiburg), Laurent F. Thomas(Norwegian University of Science and Technology), Humaira Rasheed(Norwegian University of Science and Technology), Bryce Rowan(Vanderbilt University), Sarah E. Graham(University of Michigan), Brett R. Vanderweff(University of Michigan), Snehal Patil(University of Michigan), VA Million Veteran Program(Vanderbilt University), Cassianne Robinson‐Cohen(Vanderbilt University), John Michael Gaziano(Harvard University), Christopher J. O’Donnell(University of Michigan), Cristen J. Willer(Norwegian University of Science and Technology), Stein Hallan(Norwegian University of Science and Technology), Bjørn Olav Åsvold(Norwegian University of Science and Technology), André Gessner(Vanderbilt University), Adriana M. Hung(Eurac Research), Cristian Pattaro(Eurac Research), Anna Köttgen(Johns Hopkins University), Klaus Stark(University of Regensburg), Iris M. Heid(University of Regensburg), Thomas W. Winkler(University Hospital Regensburg)
Nature Communications
July 16, 2021
10.1038/s41467-021-24491-0
Cited by 317Open Access
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Abstract

Genes underneath signals from genome-wide association studies (GWAS) for kidney function are promising targets for functional studies, but prioritizing variants and genes is challenging. By GWAS meta-analysis for creatinine-based estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics Consortium and UK Biobank (n = 1,201,909), we expand the number of eGFRcrea loci (424 loci, 201 novel; 9.8% eGFRcrea variance explained by 634 independent signal variants). Our increased sample size in fine-mapping (n = 1,004,040, European) more than doubles the number of signals with resolved fine-mapping (99% credible sets down to 1 variant for 44 signals, ≤5 variants for 138 signals). Cystatin-based eGFR and/or blood urea nitrogen association support 348 loci (n = 460,826 and 852,678, respectively). Our customizable tool for Gene PrioritiSation reveals 23 compelling genes including mechanistic insights and enables navigation through genes and variants likely relevant for kidney function in human to help select targets for experimental follow-up.

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