Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies.
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Author
Gorski, MathiasRasheed, Humaira
Teumer, Alexander
Thomas, Laurent F
Graham, Sarah E
Sveinbjornsson, Gardar
Winkler, Thomas W
Günther, Felix
Stark, Klaus J
Chai, Jin-Fang
Tayo, Bamidele O
Wuttke, Matthias
Li, Yong
Tin, Adrienne
Ahluwalia, Tarunveer S
Ärnlöv, Johan
Åsvold, Bjørn Olav
Bakker, Stephan J L
Banas, Bernhard
Bansal, Nisha
Biggs, Mary L
Biino, Ginevra
Böhnke, Michael
Boerwinkle, Eric
Bottinger, Erwin P
Brenner, Hermann
Brumpton, Ben
Carroll, Robert J
Chaker, Layal
Chalmers, John
Chee, Miao-Li
Chee, Miao-Ling
Cheng, Ching-Yu
Chu, Audrey Y
Ciullo, Marina
Cocca, Massimiliano
Cook, James P
Coresh, Josef
Cusi, Daniele
de Borst, Martin H
Degenhardt, Frauke
Eckardt, Kai-Uwe
Endlich, Karlhans
Evans, Michele K
Feitosa, Mary F
Franke, Andre
Freitag-Wolf, Sandra
Fuchsberger, Christian
Gampawar, Piyush
Gansevoort, Ron T
Ghanbari, Mohsen
Ghasemi, Sahar
Giedraitis, Vilmantas
Gieger, Christian
Gudbjartsson, Daniel F
Hallan, Stein
Hamet, Pavel
Hishida, Asahi
Ho, Kevin
Hofer, Edith
Holleczek, Bernd
Holm, Hilma
Hoppmann, Anselm
Horn, Katrin
Hutri-Kähönen, Nina
Hveem, Kristian
Hwang, Shih-Jen
Ikram, M Arfan
Josyula, Navya Shilpa
Jung, Bettina
Kähönen, Mika
Karabegović, Irma
Khor, Chiea-Chuen
Koenig, Wolfgang
Kramer, Holly
Krämer, Bernhard K
Kühnel, Brigitte
Kuusisto, Johanna
Laakso, Markku
Lange, Leslie A
Lehtimäki, Terho
Li, Man
Lieb, Wolfgang
Lind, Lars
Lindgren, Cecilia M
Loos, Ruth J F
Lukas, Mary Ann
Lyytikäinen, Leo-Pekka
Mahajan, Anubha
Matias-Garcia, Pamela R
Meisinger, Christa
Meitinger, Thomas
Melander, Olle
Milaneschi, Yuri
Mishra, Pashupati P
Mononen, Nina
Morris, Andrew P
Mychaleckyj, Josyf C
Nadkarni, Girish N
Naito, Mariko
Nakatochi, Masahiro
Nalls, Mike A
Nauck, Matthias
Nikus, Kjell
Ning, Boting
Nolte, Ilja M
Nutile, Teresa
O'Donoghue, Michelle L
O'Connell, Jeffrey
Olafsson, Isleifur
Orho-Melander, Marju
Parsa, Afshin
Pendergrass, Sarah A
Penninx, Brenda W J H
Pirastu, Mario
Preuss, Michael H
Psaty, Bruce M
Raffield, Laura M
Raitakari, Olli T
Rheinberger, Myriam
Rice, Kenneth M
Rizzi, Federica
Rosenkranz, Alexander R
Rossing, Peter
Rotter, Jerome I
Ruggiero, Daniela
Ryan, Kathleen A
Sabanayagam, Charumathi
Salvi, Erika
Schmidt, Helena
Schmidt, Reinhold
Scholz, Markus
Schöttker, Ben
Schulz, Christina-Alexandra
Sedaghat, Sanaz
Shaffer, Christian M
Sieber, Karsten B
Sim, Xueling
Sims, Mario
Snieder, Harold
Stanzick, Kira J
Thorsteinsdottir, Unnur
Stocker, Hannah
Strauch, Konstantin
Stringham, Heather M
Sulem, Patrick
Szymczak, Silke
Taylor, Kent D
Thio, Chris H L
Tremblay, Johanne
Vaccargiu, Simona
van der Harst, Pim
van der Most, Peter J
Verweij, Niek
Völker, Uwe
Wakai, Kenji
Waldenberger, Melanie
Wallentin, Lars
Wallner, Stefan
Wang, Judy
Waterworth, Dawn M
White, Harvey D
Willer, Cristen J
Wong, Tien-Yin
Woodward, Mark
Yang, Qiong
Yerges-Armstrong, Laura M
Zimmermann, Martina
Zonderman, Alan B
Bergler, Tobias
Stefansson, Kari
Böger, Carsten A
Pattaro, Cristian
Köttgen, Anna
Kronenberg, Florian
Heid, Iris M
Date
2022-06-16Journal
Kidney InternationalPublisher
ElsevierType
Article
Metadata
Show full item recordAbstract
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.Data Availibility
To support future work, we provide genome-wide summary statistics on estimated glomerular filtration rate (eGFR) decline unadjusted for eGFR baseline (adjusted for age, sex, and diabetes mellitus [DM] status) overall and restricted to individuals with DM or chronic kidney disease (CKD) at baseline (all adjusted for age and sex; https://www.uni-regensburg.de/decline and http://ckdgen.imbi.uni-freiburg.de). The summary statistics on eGFR decline in individuals with CKD at baseline can be considered genetic effects on CKD progression. We also provide genome-wide summary statistics on eGFR decline adjusted for eGFR baseline (additionally to adjustment for age and sex), but these summary statistics should be used with great care and with an understanding that β estimates are subject to collider bias. For quantification of the genetic effect on eGFR decline, the results unadjusted for eGFR baseline should be utilized.; Summary statistics from published CKDGen Consortium papers can now be obtained through the CHARGE Consortium Summary Results from Genomic Studies on dbGaP (https://www.ncbi.nlm.nih.gov/gap), accession number phs000930.Data / Code Location
https://www.ncbi.nlm.nih.gov/gap/advanced_search/?TERM=phs000930Rights/Terms
Copyright © 2022. Published by Elsevier Inc.Identifier to cite or link to this item
http://hdl.handle.net/10713/19360ae974a485f413a2113503eed53cd6c53
10.1016/j.kint.2022.05.021
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