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    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, Mathias
    Rasheed, 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
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    Date
    2022-06-16
    Journal
    Kidney International
    Publisher
    Elsevier
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1016/j.kint.2022.05.021
    Abstract
    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=phs000930
    Rights/Terms
    Copyright © 2022. Published by Elsevier Inc.
    Keyword
    acute kidney injury
    chronic kidney disease
    diabetes
    gene expression
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/19360
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.kint.2022.05.021
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