Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Mathias Gorski*, Humaira Rasheed, Lifelines Cohort Study, Alexander Teumer, Laurent F. Thomas, Sarah E. Graham, Gardar Sveinbjornsson, Thomas W. Winkler, Felix Günther, Klaus J. Stark, Jin Fang Chai, Bamidele O. Tayo, Matthias Wuttke, Yong Li, Adrienne Tin, Tarunveer S. Ahluwalia, Johan Ärnlöv, Bjørn Olav Åsvold, Stephan J.L. Bakker, Bernhard BanasNisha Bansal, Mary L. Biggs, Ginevra Biino, Michael Böhnke, Eric Boerwinkle, Erwin P. Bottinger, Hermann Brenner, Ben Brumpton, Robert J. Carroll, Layal Chaker, John Chalmers, Miao Li Chee, Miao Ling Chee, Ching Yu Cheng, Audrey Y. Chu, Marina Ciullo, Massimiliano Cocca, James P. Cook, Josef Coresh, Daniele Cusi, Martin H. de Borst, Frauke Degenhardt, Kai Uwe Eckardt, Karlhans Endlich, Mohsen Ghanbari, M. Arfan Ikram, Helena Schmidt, Markus Scholz, Sanaz Sedaghat, Niek Verweij, Tien Yin Wong, Irma Karabegovic, Florian Kronenberg, Iris M. Heid

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.

Original languageEnglish
Pages (from-to)624-639
Number of pages16
JournalKidney International
Issue number3
Early online date15 Jun 2022
Publication statusPublished - 1 Sep 2022

Bibliographical note

Funding Information:
The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) supported the meta-analysis—project identifier 387509280 —SFB1350 (subproject C6 to IMH). We conducted this research using the UK Biobank resource under the application number 20272. Extended acknowledgements and funding information are provided in the online Supplementary Material .

Funding Information:
JÄ reports personal fees from AstraZeneca, Boehringer Ingelheim, and Novartis, outside the submitted work. Sanofi Genzyme currently employs Kevin Ho. WK reports modest consultation fees for advisory board meetings from Amgen, DalCor, Kowa, Novartis, Pfizer, and Sanofi; and modest personal fees for lectures from Amgen, AstraZeneca, Novartis, Pfizer, and Sanofi, outside the scope of this work. CML received grants/research support from Bayer Ag/Novo Nordisk, and their husband works for Vertex. KBS, LMY-A, DMW, and MAL are full-time employees of GlaxoSmithKline. MLO received grant support from GlaxoSmithKline, MSD, Eisai, AstraZeneca, MedCo, and Janssen. BMP serves on the steering committee of the Yale Open Data Access Project, funded by Johnson & Johnson. PR received fees to his institution for research support from AstraZeneca and Novo Nordisk; for steering group participation from AstraZeneca, Gilead, Novo Nordisk, and Bayer; for lectures from Bayer, Eli Lilly, and Novo Nordisk; and for advisory boards from Sanofi and Boehringer Ingelheim, outside of this work. LW received institutional grants from GlaxoSmithKline, AstraZeneca, BMS, Boehringer-Ingelheim, Pfizer, MSD, and Roche Diagnostics. HDW received grants and nonfinancial support from GlaxoSmithKline, during the conduct of the study; received grants from Sanofi-Aventis, Eli Lilly, the National Institutes of Health, Omthera Pharmaceuticals, Pfizer New Zealand, Elsai Inc., and Dalcor Pharma UK; received honoraria and nonfinancial support from AstraZeneca; and is on advisory boards for Sirtex/Acetilion and received personal fees from CSL Behring and American Regent, outside the scope of this work. GS, DFG, HH, IO, KSte, PS, and UT are employees of deCODE/Amgen Inc. All the other authors declared no competing interests.

Publisher Copyright:
© 2022 International Society of Nephrology


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