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

被引：181

作者：

Stanzick, Kira J. ^{[1
]}

Li, Yong ^{[2
,3
]}

Schlosser, Pascal ^{[2
,3
]}

Gorski, Mathias ^{[1
]}

Wuttke, Matthias ^{[2
,3
]}

Thomas, Laurent F. ^{[4
,5
,6
]}

Rasheed, Humaira ^{[4
,7
]}

Rowan, Bryce X. ^{[8
,9
]}

Graham, Sarah E. ^{[10
]}

Vanderweff, Brett R. ^{[11
,12
]}

Patil, Snehal B. ^{[11
,12
,13
]}

Robinson-Cohen, Cassiane ^{[9
,14
,15
,16
]}

Gaziano, John M. ^{[17
,18
]}

O'Donnell, Christopher J. ^{[19
]}

Willer, Cristen J. ^{[10
,13
,20
]}

Hallan, Stein ^{[5
,21
]}

Asvold, Bjorn Olav ^{[3
,22
]}

Gessner, Andre ^{[23
]}

Hung, Adriana M. ^{[14
,15
,16
]}

Pattaro, Cristian ^{[24
]}

Koettgen, Anna ^{[2
,25
]}

Stark, Klaus J. ^{[1
]}

Heid, Iris M. ^{[1
,2
,3
,4
]}

Winkler, Thomas W. ^{[1
,2
,3
,4
]}

机构：

[1] Univ Regensburg, Dept Genet Epidemiol, Regensburg, Germany

[2] Univ Freiburg, Fac Med, Dept Biometry Epidemiol & Med Bioinformat, Inst Genet Epidemiol, Freiburg, Germany

[3] Univ Freiburg, Med Ctr, Freiburg, Germany

[4] Norwegian Univ Sci & Technol, Dept Publ Hlth & Nursing, Fac Med & Hlth, KG Jebsen Ctr Genet Epidemiol,NTNU, Trondheim, Norway

[5] Norwegian Univ Sci & Technol, Dept Clin & Mol Med, NTNU, Trondheim, Norway

[6] Norwegian Univ Sci & Technol, BioCore Bioinformat Core Facil, Trondheim, Norway

[7] Univ Bristol, Bristol Med Sch, Populat Hlth Sci, MRC Integrat Epidemiol Unit, Bristol, Avon, England

[8] Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA

[9] Vanderbilt Univ, Tennessee Valley Healthcare Syst 626, Dept Vet Affairs, 221 Kirkland Hall, Nashville, TN 37235 USA

[10] Univ Michigan, Dept Internal Med, Div Cardiol, Ann Arbor, MI 48109 USA

[11] Univ Michigan, Sch Publ Hlth, Dept Biostat, Ann Arbor, MI 48109 USA

[12] Univ Michigan, Sch Publ Hlth, Ctr Stat Genet, Ann Arbor, MI 48109 USA

[13] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA

[14] Vanderbilt Univ, Med Ctr, Vanderbilt Ctr Kidney Dis, Div Nephrol & Hypertens, Nashville, TN USA

[15] Integrated Program Acute Kidney Injury Res, Nashville, TN USA

[16] Vanderbilt Precis Nephrol Program Nashville, Nashville, TN USA

[17] VA Boston Healthcare Syst, VA Cooperat Studies Program, Massachusetts Area Vet Epidemiol Res & Informat C, Boston, MA USA

[18] Harvard Med Sch, Dept Internal Med, Boston, MA 02115 USA

[19] VA Boston Healthcare Syst, VA Cooperat Studies Program, Boston, MA USA

[20] Univ Michigan, Dept Human Genet, Ann Arbor, MI 48109 USA

[21] Trondheim Reg & Univ Hosp, St Olavs Hosp, Dept Nephrol, Trondheim, Norway

[22] Trondheim Reg & Univ Hosp, St Olavs Hosp, Clin Med, Dept Endocrinol, Trondheim, Norway

[23] Univ Hosp Regensburg, Inst Clin Microbiol & Hyg, Regensburg, Germany

[24] Univ Lubeck, Inst Biomed, Eurac Res, Bolzano, Italy

[25] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD USA

来源：

NATURE COMMUNICATIONS | 2021年 / 12卷 / 01期

基金：

英国医学研究理事会; 美国国家卫生研究院;

关键词：

GLOMERULAR-FILTRATION-RATE; SERUM CYSTATIN-C; FALSE DISCOVERY; ESTIMATING GFR; RARE VARIANTS; ASSOCIATION; CREATININE; STATISTICS; MASS;

D O I：

10.1038/s41467-021-24491-0

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. Identifying causal variants and genes in genome-wide association studies remains a challenge, an issue that is ameliorated with larger sample sizes. Here the authors meta-analyze kidney function genome-wide association studies to identify new loci and fine-map loci to home in on variants and genes involved in kidney function.

引用

页数：17

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