Waterworth Dawn M.; Rossing Peter; Carroll Robert J.; Sedaghat Sanaz; Sim Xueling; Tremblay Johanne; Sims Mario; Winkler Thomas W.; Lieb Wolfgang; Josyula Navya Shilpa; Lindgren Cecilia M.; Lukas Mary Ann; Chee Miao-Ling; Rosenkranz Alexander R.; Willer Cristen J.; Völker Uwe; White Harvey D.; Thio Chris H.L.; Böhnke Michael; Wallentin Lars; Nauck Matthias; Chai Jin-Fang; Köttgen Anna; Penninx Brenda W.J.H.; O’Donoghue Michelle L.; M.Raffield Laura; Parsa Afshin; Teumer Alexander; Ärnlöv Johan; Sabanayagam Charumathi; Kramer Holly; Nolte Ilja M.; Nutile Teresa; Kronenberg Florian; Hamet Pavel; Karabegović Irma; Wong Tien-Yin; Bottinger Erwin P.; Thomas Laurent F.; Vaccargiu Simona; Strauch Konstantin; Schmidt Reinhold; Ghanbari Mohsen; Bergler Tobias; Melander Olle; Brumpton Ben; Meitinger Thomas; Matias-Garcia Pamela R.; Waldenberger Melanie; Szymczak Silke; Psaty Bruce M.; Lange Leslie A.; Pirastu Mario; Endlich Karlhans; Freitag-Wolf Sandra; Bansal Nisha; Wuttke Matthias; Heid Iris M.; Lifelines Cohort Study; Cusi Daniele; Woodward Mark; Rotter Jerome I.; Rasheed Humaira; Stanzick Kira J.; Chalmers John; Preuss Michael H.; Tayo Bamidele O.; Hwang Shih-Jen; Degenhardt Frauke; Böger Carsten A.; Biggs Mary L.; Holm Hilma; Hishida Asahi; Feitosa Mary F.; Schulz Christina-Alexandra; Hoppmann Anselm; Bakker Stephan J.L.; Snieder Harold; Günther Felix; Biino Ginevra; Laakso Markku; Hutri-Kähönen Nina; Salvi Erika; Naito Mariko; Olafsson Isleifur; Lind Lars; Milaneschi Yuri; Loos Ruth J.F.; Mychaleckyj Josyf C.; Evans Michele K.; Ryan Kathleen A.; Sveinbjornsson Gardar; Thorsteinsdottir Unnur; Li Man; Li Yong; Kähönen Mika; Holleczek Bernd; Chaker Layal; Hofer Edith; Hveem Kristian; Gudbjartsson Daniel F.; Schöttker Ben; Lehtimäki Terho; Rice Kenneth M.; Gansevoort Ron T.; van der Harst Pim; Brenner Hermann; Lyytikäinen Leo-Pekka; Nikus Kjell; Yang Qiong; Shaffer Christian M.; Chee Miao-Li; Horn Katrin; Ciullo Marina; Giedraitis Vilmantas; Graham Sarah E.; Rheinberger Myriam; Wang Judy; van der Most Peter J.; Krämer Bernhard K.; Nakatochi Masahiro; Eckardt Kai-Uwe; Pattaro Cristian; Gorski Mathias; Cook James P.; Mononen Nina; Pendergrass Sarah A.; Scholz Markus; Ahluwalia Tarunveer S.; Yerges-Armstrong Laura M.; Wallner Stefan; Nalls Mike A.; Gieger Christian; Stark Klaus J.; Tin Adrienne; Cheng Ching-Yu; Stefansson Kari; Morris Andrew P.; O'Connell Jeffrey; Zonderman Alan B.; Verweij Niek; Hallan Stein; Coresh Josef; Koenig Wolfgang; Banas Bernhard; Ghasemi Sahar; Boerwinkle Eric; Åsvold Bjørn Olav; Nadkarni Girish N.; Stocker Hannah; Franke Andre; Jung Bettina; Zimmermann Martina; Mishra Pashupati P.; Ho Kevin; Ikram M. Arfan; Cocca Massimiliano; Kühnel Brigitte; Y.Chu Audrey; Ning Boting; Gampawar Piyush; de Borst Martin H.; Raitakari Olli T.; Sulem Patrick; Rizzi Federica; Meisinger Christa; Ruggiero Daniela; Khor Chiea-Chuen; Taylor Kent D.; Orho-Melander Marju; Mahajan Anubha; Schmidt Helena; Sieber Karsten B.; Fuchsberger Christian; Stringham Heather M.; Wakai Kenji; Kuusisto Johanna
<p>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.</p>...
