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

Kira J Stanzick; Yong Li; Pascal Schlosser; Mathias Gorski; Matthias Wuttke; Laurent F Thomas; Humaira Rasheed; Bryce X Rowan; Sarah E Graham; Brett R Vanderweff; Snehal B Patil; VA Million Veteran Program; Cassiane Robinson-Cohen; John M Gaziano; Christopher J O'Donnell; Cristen J Willer; Stein Hallan; Bjørn Olav Åsvold; Andre Gessner; Adriana M Hung; Cristian Pattaro; Anna Köttgen; Klaus J Stark; Iris M Heid; Thomas W Winkler

doi:10.1038/s41467-021-24491-0

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

Nat Commun. 2021 Jul 16;12(1):4350. doi: 10.1038/s41467-021-24491-0.

Authors

Kira J Stanzick¹, Yong Li², Pascal Schlosser², Mathias Gorski¹, Matthias Wuttke², Laurent F Thomas^{3

4

5}, Humaira Rasheed^{3

6}, Bryce X Rowan^{7

8}, Sarah E Graham⁹, Brett R Vanderweff^{10

11}, Snehal B Patil^{10

11

12}; VA Million Veteran Program; Cassiane Robinson-Cohen^{8

13}, John M Gaziano^{14

15}, Christopher J O'Donnell¹⁶, Cristen J Willer^{9

12

17}, Stein Hallan^{4

18}, Bjørn Olav Åsvold^{3

19}, Andre Gessner²⁰, Adriana M Hung^{8

13}, Cristian Pattaro²¹, Anna Köttgen^{2

22}, Klaus J Stark¹, Iris M Heid¹, Thomas W Winkler²³

Affiliations

¹ Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
² Institute of Genetic Epidemiology, Department of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany.
³ K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁴ Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁵ BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway.
⁶ MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
⁷ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
⁸ Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA.
⁹ Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA.
¹⁰ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
¹¹ Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
¹² Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
¹³ Vanderbilt University Medical Center, Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for Acute Kidney Injury Research, and Vanderbilt Precision Nephrology Program Nashville, Nashville, TN, USA.
¹⁴ Massachusetts Area Veterans Epidemiology Research and Information Center (MAVERIC), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA.
¹⁵ Department of Internal Medicine, Harvard Medical School, Boston, MA, USA.
¹⁶ VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA.
¹⁷ Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
¹⁸ Department of Nephrology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
¹⁹ Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
²⁰ Institute of Clinical Microbiology and Hygiene, University Hospital Regensburg, Regensburg, Germany.
²¹ Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy.
²² Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
²³ Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany. thomas.winkler@klinik.uni-regensburg.de.

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.

Publication types

Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biomarkers
Creatinine / blood
Cystatins / pharmacology
Databases, Genetic
Europe
Gene Expression Regulation / genetics
Genetic Predisposition to Disease*
Genome-Wide Association Study
Glomerular Filtration Rate / genetics*
Humans
Kidney / metabolism*
Kidney / physiology
Organ Specificity
Quantitative Trait Loci
RNA-Seq
Renal Insufficiency, Chronic / genetics
Renal Insufficiency, Chronic / metabolism*
Risk Factors
Single-Cell Analysis

Substances

Biomarkers
Cystatins
Creatinine

Abstract

Publication types

MeSH terms

Substances

Grants and funding