Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A catalog of genetic loci associated with kidney function from analyses of a million individuals

Abstract

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Trans-ancestry GWAS meta-analysis identifies 308 loci associated with eGFR.
Fig. 2: Generalizability with respect to other populations and other kidney function markers.
Fig. 3: Human orthologs of genes with renal phenotypes in genetically manipulated mice are enriched for association signals with eGFR.
Fig. 4: Credible set size plotted against variant posterior probability for 3,655 SNPs in 253 99% credible sets according to variant annotation.
Fig. 5: Colocalization of eGFR association signals with gene expression in kidney tissues.
Fig. 6: Colocalization of independent eGFR association signals at the UMODPDILT locus with urinary uromodulin concentrations (UUCR) supports UMOD as the effector gene.

Similar content being viewed by others

Data availability

Genome-wide summary statistics for this study have been made publicly available at http://ckdgen.imbi.uni-freiburg.de.

References

  1. Eckardt, K. U. et al. Evolving importance of kidney disease: from subspecialty to global health burden. Lancet 382, 158–169 (2013).

    Article  PubMed  Google Scholar 

  2. Jha, V. et al. Chronic kidney disease: global dimension and perspectives. Lancet 382, 260–272 (2013).

    PubMed  Google Scholar 

  3. Ene-Iordache, B. et al. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Glob. Health 4, e307–e319 (2016).

    PubMed  Google Scholar 

  4. Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E. & Hsu, C. Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N. Engl. J. Med. 351, 1296–1305 (2004).

    CAS  PubMed  Google Scholar 

  5. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 1151-1210 (2017).

  6. Inrig, J. K. et al. The landscape of clinical trials in nephrology: a systematic review of ClinicalTrials.gov. Am. J. Kidney Dis. 63, 771–780 (2014).

    PubMed  Google Scholar 

  7. Levin, A. et al. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy. Lancet 390, 1888–1917 (2017).

    PubMed  Google Scholar 

  8. Wuttke, M. & Kottgen, A. Insights into kidney diseases from genome-wide association studies. Nat. Rev. Nephrol. 12, 549–562 (2016).

    CAS  PubMed  Google Scholar 

  9. Gorski, M. et al. 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function. Sci. Rep. 7, 45040 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. Pattaro, C. et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun. 7, 10023 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Chasman, D. I. et al. Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function. Hum. Mol. Genet. 21, 5329–5343 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Pattaro, C. et al. Genome-wide association and functional follow-up reveals new loci for kidney function. PLoS Genet. 8, e1002584 (2012).

    PubMed  PubMed Central  Google Scholar 

  13. Kottgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42, 376–384 (2010).

    PubMed  PubMed Central  Google Scholar 

  14. Chambers, J. C. et al. Genetic loci influencing kidney function and chronic kidney disease. Nat. Genet. 42, 373–375 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Kottgen, A. et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nat. Genet. 41, 712–717 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).

    CAS  PubMed  Google Scholar 

  17. Okada, Y. et al. Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat. Genet. 44, 904–909 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Hishida, A. et al. Genome-wide association study of renal function traits: results from the Japan Multi-institutional Collaborative Cohort study. Am. J. Nephrol. 47, 304–316 (2018).

    CAS  PubMed  Google Scholar 

  19. Lee, J. et al. Genome-wide association analysis identifies multiple loci associated with kidney disease-related traits in Korean populations. PLoS One 13, e0194044 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. Mahajan, A. et al. Trans-ethnic fine mapping highlights kidney-function genes linked to salt sensitivity. Am. J. Hum. Genet. 99, 636–646 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Devuyst, O. & Pattaro, C. The UMOD locus: insights into the pathogenesis and prognosis of kidney disease. J. Am. Soc. Nephrol. 29, 713–726 (2018).

    CAS  PubMed  Google Scholar 

  22. Yeo, N. C. et al. Shroom3 contributes to the maintenance of the glomerular filtration barrier integrity. Genome Res. 25, 57–65 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    PubMed  Google Scholar 

  24. Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    PubMed  Google Scholar 

  27. Li, M. et al. SOS2 and ACP1 loci identified through large-scale exome chip analysis regulate kidney development and function. J. Am. Soc. Nephrol. 28, 981–994 (2017).

    CAS  PubMed  Google Scholar 

  28. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS  PubMed  Google Scholar 

  30. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Jing, J. et al. Combination of mouse models and genomewide association studies highlights novel genes associated with human kidney function. Kidney Int. 90, 764–773 (2016).

    CAS  PubMed  Google Scholar 

  32. Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).

    CAS  PubMed  Google Scholar 

  34. Tsuda, M. et al. Targeted disruption of the multidrug and toxin extrusion 1 (Mate1) gene in mice reduces renal secretion of metformin. Mol. Pharm. 75, 1280–1286 (2009).

    CAS  Google Scholar 

  35. Sieber, K. B. et al. Integrated functional genomic analysis enables annotation of kidney genome-wide association study loci. J. Am. Soc. Nephrol. 30, 421–441 (2019).

    PubMed Central  Google Scholar 

  36. Olden, M. et al. Common variants in UMOD associate with urinary uromodulin levels: a meta-analysis. J. Am. Soc. Nephrol. 25, 1869–1882 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Moreau, M. E. et al. The kallikrein–kinin system: current and future pharmacological targets. J. Pharm. Sci. 99, 6–38 (2005).

    CAS  Google Scholar 

  38. Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed  Google Scholar 

  39. Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Eckardt, K. U. et al. Autosomal dominant tubulointerstitial kidney disease: diagnosis, classification, and management—a KDIGO consensus report. Kidney Int. 88, 676–683 (2015).

    CAS  PubMed  Google Scholar 

  41. Gillies, C. E. et al. An eQTL landscape of kidney tissue in human nephrotic syndrome. Am. J. Hum. Genet. 103, 232–244 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Dudley, A. J., Bleasby, K. & Brown, C. D. The organic cation transporter OCT2 mediates the uptake of β-adrenoceptor antagonists across the apical membrane of renal LLC-PK1 cell monolayers. Br. J. Pharm. 131, 71–79 (2000).

    CAS  Google Scholar 

  43. Filipski, K. K., Mathijssen, R. H., Mikkelsen, T. S., Schinkel, A. H. & Sparreboom, A. Contribution of organic cation transporter 2 (OCT2) to cisplatin-induced nephrotoxicity. Clin. Pharm. Ther. 86, 396–402 (2009).

    CAS  Google Scholar 

  44. Motohashi, H. & Inui, K. Organic cation transporter OCTs (SLC22) and MATEs (SLC47) in the human kidney. AAPS J. 15, 581–588 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Humm, A., Huber, R. & Mann, K. The amino acid sequences of human and pig l-arginine:glycine amidinotransferase. FEBS Lett. 339, 101–107 (1994).

    CAS  PubMed  Google Scholar 

  47. Olives, B. et al. Molecular characterization of a new urea transporter in the human kidney. FEBS Lett. 386, 156–160 (1996).

    CAS  PubMed  Google Scholar 

  48. Phan, N. N. et al. Voltage-gated calcium channels: novel targets for cancer therapy. Oncol. Lett. 14, 2059–2074 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Thi Do, D., Phan, N. N., Wang, C. Y., Sun, Z. & Lin, Y. C. Novel regulations of MEF2-A, MEF2-D, and CACNA1S in the functional incompetence of adipose-derived mesenchymal stem cells by induced indoxyl sulfate in chronic kidney disease. Cytotechnology 68, 2589–2604 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Parsa, A. et al. Common variants in Mendelian kidney disease genes and their association with renal function. J. Am. Soc. Nephrol. 24, 2105–2117 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Xie, W. et al. Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetes. Diabetes 62, 2141–2150 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Raffler, J. et al. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 11, e1005487 (2015).

    PubMed  PubMed Central  Google Scholar 

  53. Janicki, N. et al. Increased occurence of valporoic acid-induced hyperammonemia in carriers of T1405N polymorphism in carbamoyl phosphate synthetase 1 gene. ISRN Neurol. 2013, 261497 (2013).

    PubMed  PubMed Central  Google Scholar 

  54. Seppala, A. et al. Genome-wide association study on dimethylarginines reveals novel AGXT2 variants associated with heart rate variability but not with overall mortality. Eur. Heart J. 35, 524–531 (2014).

    PubMed  Google Scholar 

  55. Sveinbjornsson, G. et al. Rare mutations associating with serum creatinine and chronic kidney disease. Hum. Mol. Genet. 23, 6935–6943 (2014).

    CAS  PubMed  Google Scholar 

  56. Pescio, L. G. et al. Changes in ceramide metabolism are essential in Madin–Darby canine kidney cell differentiation. J. Lipid Res. 58, 1428–1438 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Imgrund, S. et al. Adult ceramide synthase 2 (CERS2)-deficient mice exhibit myelin sheath defects, cerebellar degeneration, and hepatocarcinomas. J. Biol. Chem. 284, 33549–33560 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Shiffman, D. et al. A gene variant in CERS2 is associated with rate of increase in albuminuria in patients with diabetes from ONTARGET and TRANSCEND. PLoS One 9, e106631 (2014).

    PubMed  PubMed Central  Google Scholar 

  59. Yoshioka, K. et al. IgA nephropathy in patients with congenital C9 deficiency. Kidney Int. 42, 1253–1258 (1992).

    CAS  PubMed  Google Scholar 

  60. Fuchsberger, C., Taliun, D., Pramstaller, P. P. & Pattaro, C. GWAtoolbox: an R package for fast quality control and handling of genome-wide association studies meta-analysis data. Bioinformatics 28, 444–445 (2012).

    CAS  PubMed  Google Scholar 

  61. Coresh, J. et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. J. Am. Med. Assoc. 311, 2518–2531 (2014).

    Google Scholar 

  62. Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604–612 (2009).

    PubMed  PubMed Central  Google Scholar 

  63. Pattaro, C. et al. Estimating the glomerular filtration rate in the general population using different equations: effects on classification and association. Nephron Clin. Pract. 123, 102–111 (2013).

    PubMed  Google Scholar 

  64. Schwartz, G. J. et al. Improved equations estimating GFR in children with chronic kidney disease using an immunonephelometric determination of cystatin C. Kidney Int. 82, 445–453 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Higgins, J. P. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).

    PubMed  Google Scholar 

  68. Magi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet. 26, 3639–3650 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Hadfield, J. MCMC methods for multi-response generalized linear mixed models: the MCMC glmm R package. J. Stat. Softw. 33, 1–22 (2010).

    Google Scholar 

  70. Pattaro, C. et al. The Cooperative Health Research in South Tyrol (CHRIS) study: rationale, objectives, and preliminary results. J. Transl. Med. 13, 348 (2015).

    PubMed  PubMed Central  Google Scholar 

  71. Noce, D. et al. Sequential recruitment of study participants may inflate genetic heritability estimates. Hum. Genet. 136, 743–757 (2017).

    PubMed  Google Scholar 

  72. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    CAS  PubMed  Google Scholar 

  73. Kottgen, A. et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat. Genet. 45, 145–154 (2013).

    PubMed  Google Scholar 

  74. Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 8, e1002607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Fehrmann, R. S. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  PubMed  Google Scholar 

  77. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  78. Frey, B. J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

    CAS  PubMed  Google Scholar 

  79. Hoppmann, A. S., Schlosser, P., Backofen, R., Lausch, E. & Kottgen, A. GenToS: use of orthologous gene information to prioritize signals from human GWAS. PLoS One 11, e0162466 (2016).

    PubMed  PubMed Central  Google Scholar 

  80. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Wakefield, J. Bayes factors for genome-wide association studies: comparison with P-values. Genet. Epidemiol. 33, 79–86 (2009).

    PubMed  Google Scholar 

  83. Arnold, M., Raffler, J., Pfeufer, A., Suhre, K. & Kastenmuller, G. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics 31, 1334–1336 (2015).

    PubMed  Google Scholar 

  84. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Google Scholar 

  87. Zeller, T. et al. Genetics and beyond—the transcriptome of human monocytes and disease susceptibility. PLoS One 5, e10693 (2010).

    PubMed  PubMed Central  Google Scholar 

  88. Fehrmann, R. S. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Joehanes, R. et al. Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol. 18, 16 (2017).

    PubMed  PubMed Central  Google Scholar 

  91. Kirsten, H. et al. Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci. Hum. Mol. Genet. 24, 4746–4763 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Beutner, F. et al. Rationale and design of the Leipzig (LIFE) Heart Study: phenotyping and cardiovascular characteristics of patients with coronary artery disease. PLoS One 6, e29070 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Loeffler, M. et al. The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 15, 691 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. Eckardt, K. U. et al. The German Chronic Kidney Disease (GCKD) study: design and methods. Nephrol. Dial. Transpl. 27, 1454–1460 (2012).

    CAS  Google Scholar 

Download references

Acknowledgements

We thank D. Di Domizio (Eurac Research) and J. Knaus (University of Freiburg) for IT assistance and T. Johnson (GlaxoSmithKline) for sharing his code and discussion on credible set fine-mapping and colocalization analysis. This research has been conducted using the UK Biobank resource under application number 20272. Study-specific acknowledgements and funding sources are listed in the Supplementary Information.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

Manuscript writing group: M. Wuttke, Y.L., M. Li, K.B.S., M.F., M. Gorski, A. Tin, L. Wang, H. Kirsten, T.A., K. Ho, I.H., M. Scholz, A. Teumer, A. Köttgen, C.P. Design of the study: C.A.B., C.F., M. Gorski, A. Köttgen, A.P.M., C.P., A. Teumer, A. Tin, M. Wuttke. Management of an individual contributing study: T.S.A., E.d.A., S. Akilesh, S.J.B., G.B., M. Bochud, M. Boehnke, E.B., M.H.d.B., H.B., A.S.B., C.A.B., A.C., R.J.C., J.C.C., D.I.C., C.-Y.C., K.C., R.C., M. Ciullo, J.C., D.C., R.M.v.D., J. Danesh, O.D., C.M.v.D., K.-U.E., G.E., P.E., M.K.E., J.F.F., O.H.F., B.I.F., Y.F., R.T.G., H.G., P.G., J.M.G., V. Giedraitis, C.G., F.G., A.D.G., V. Gudnason, T.B.H., P.v.d.H., C.A.H., C.H., C.-K.H., A.A.H., K. Ho, A.M.H., M.A.I., O.S.I., E.I., V.W.J., J.B.J., B.J., C.M.K., C.-C.K., W. Kiess, M.E.K., W. Koenig, J.S.K., H. Kramer, F.K., B.K.K., M. Kubo, J.K., M. Kähönen, A. Körner, A. Köttgen, T.L., Y.L., S.-C.L., M. Loeffler, R.J.L., S.L., M.A.L., P.K.M., N.G.M., D.M., K. Matsuda, O.M., A. Metspalu, E.K.M., Y.M., K.L.M., G.W.M., A.P.M., R.d.M., W.M., G.N.N., J.O’C., M.L.O’D., A.J.O., M.O.-M., W.H.O., A.P., C.P., S.A.P., B.W.P., T. Perls, M. Perola, M. Pirastu, O.P., B.P., P.P.P., M.A.P., B.M.P., T.J.R., O.T.R., D.F.R., R. Rettig, M.R., P.M.R., D.J.R., P.R., I.R., C.S., V.S., K.-U.S., H. Schmidt, R.S., M. Scholz, B.S., X.S., H. Snieder, N. Soranzo, C.N.S., K. Stefansson, K. Strauch, M. Stumvoll, G.S., P.O.S., E.-S.T., B.O.T., Y.-C.T., J. Thiery, A. Tin, D.T., J. Tremblay, I.T., A. Tönjes, P.V., A.P.d.V., U.V., G.W., L. Wallentin, Y.X.W., D.M.W., W.B.W., H.W., J.B.W., S.H.W., J.G.W., C. Wong, T.-Y.W., M. Wuttke, L.X., Q.Y., M.Y., W.Z., A.B.Z. Statistical methods and analysis: T.S.A., M.A., P.A., M.L.B., G.B., M. Boissel, T.S.B., M. Brumat, C.A.B., M. Canouil, R.J.C., J.-F.C., D.I.C., Miao-Li Chee, X.C., Y.C., A.Y.C., M. Cocca, M.P.C., J.P.C., T.C., A. Dehghan, G.D., A. Demirkan, J. Divers, R.D., D.R.V.E., T.L.E., M.F.F., J.F.F., B.I.F., S.F.-W., C.F., S.G., A.G., M. Gorski, D.F.G., M. Gögele, T.H., P.H., P.v.d.H., I.M.H., J.N.H., E.H., A.H., K. Horn, S.-J.H., J.J., P.K.J., N.S.J., B.J., Y.K., M. Kanai, C.-C.K., H. Kirsten, M.E.K., A. Krajcoviechova, H. Kramer, M. Kuokkanen, A. Köttgen, B.K., L.A.L., C.D.L., M. Li, Y.L., Jianjun Liu, Jun Liu, L.-P.L., A. Mahajan, J. Marten, J. Martins, K. Miliku, P.P.M., N.M., A.P.M., P.J.v.d.M., W.M., M.A.N., M.N., B.N., D.N., I.M.N., R.N., T.N., Y.O., C.P., S.A.P., N.P., M.H.P., B.P.P., L.M.R., M.R., K.M.R., F. Rivadeneira, F. Rizzi, R. Rueedi, K.A.R., Y. Saba, E.S., M. Scholz, C.-A.S., S. Sedaghat, Y. Shi, K.B.S., X.S., A.V.S., C.N.S., H.M.S., G.S., S. Szymczak, S.M.T., B.O.T., A. Teumer, C.H.T., H.T., G.T., J. Tremblay, N.V., V.V., S. Vogelezang, C. Wang, L. Wang, J.F.W., M.K.W., M. Wuttke, Y.X., Q.Y., L.M.Y.-A., W.Z. Bioinformatics: T.S.A., S. Akilesh, P.A., D.B., S.B., A.S.B., C.A.B., E.C., R.J.C., X.C., A.Y.C., M. Cocca, M.P.C., T.C., E.W.D., F.D., A. Dehghan, J. Divers, R.D., G.E., A.F., H.G., S.G., A.G., S.D.G., M. Gorski, P.H., I.M.H., E.H., A.H., K. Horn, J.J., N.S.J., C.-C.K., H. Kirsten, M.E.K., A. Krajcoviechova, A. Köttgen, C.D.L., B.L., M. Li, Y.L., Jianjun Liu, L.-P.L., J. Marten, J. Martins, Y.M., P.P.M., K.L.M., D.O.M.-K., P.J.v.d.M., R.M., W.M., R.N., T.N., S.A.P., N.P., G.P., A.I.P., M.H.P., B.P.P., F. Rizzi, R. Rueedi, Y. Saba, E.S., M. Scholz, C.-A.S., S. Sedaghat, C.M.S., K.B.S., A.V.S., C.N.S., S. Szymczak, H.T., J. Tremblay, C. Wang, J.F.W., M. Wuttke, Y.X., L.M.Y.-A., Z.Y., W.Z. Interpretation of results: T.S.A., E.d.A., C.A.B., C.-Y.C., K.D., J. Divers, R.D., K.E., M.F.F., J.F.F., B.I.F., S.G., C.G., A.G., M. Gorski, P.H., P.v.d.H., H.T., I.M.H., K. Ho, K. Horn, W.H., S.-J.H., B.J., H. Kirsten, W. Koenig, A. Krajcoviechova, A. Köttgen, M. Laakso, C.D.L., M. Li, Y.L., P.K.M., J. Marten, K. Miliku, K.L.M., A.P.M., N.D.P., C.P., S.A.P., B.P.P., D.F.R., M.R., P.M.R., M. Scholz, S. Sedaghat, K.B.S., C.N.S., P.O.S., B.O.T., A. Teumer, A. Tin, J. Tremblay, I.T., A.G.U., N.V., V.V., S. Vogelezang, L. Wallentin, H.W., M. Wuttke, Y.X., M.Y., L.M.Y.-A. Genotyping: N.A., D.B., R.B., A.S.B., C.A.B., A.C., H.C., D.I.C., C.-Y.C., E.W.D., A. Demirkan, R.D., C.M.v.D., G.E., M.K.E., M.F.F., A.F., Y.F., C.F., R.T.G., H.G., S.D.G., P.H., P.v.d.H., H.T., C.H., C.-K.H., W.H., E.I., C.-C.K., M.E.K., W. Koenig, J.S.K., P.K., A.T.K., A. Krajcoviechova, F.K., M. Kubo, M. Kähönen, A. Körner, L.A.L., T.L., L.-P.L., P.K.M., T.M., O.M., Y.M., K.L.M., N.M., G.W.M., D.O.M.-K., A.P.M., J.C.M., W.M., M.A.N., M.O.-M., S.P., N.D.P., B.W.P., M. Perola, D.J.P., M.H.P., O.T.R., D.F.R., F. Rivadeneira, F. Rizzi, J.I.R., D.R., V.S., E.S., B.H.S., C.N.S., S.M.T., K.T., A. Teumer, D.T., J. Tremblay, A.G.U., S. Vaccargiu, U.V., M. Waldenberger, C. Wang, L. Wang, Y.X.W., J.G.W., M.K.W., A.B.Z., J.Ä. Critical review of manuscript: T.S.A., S. Akilesh, P.A., E.d.A., S.J.B., N.B., M.L.B., G.B., M.H.d.B., E.P.B., T.S.B., H.B., A.S.B., C.A.B., H.C., D.I.C., X.C., Y.C., A.Y.C., M. Ciullo, J.C., R.M.v.D., G.D., O.D., J. Divers, R.D., K.-U.E., D.R.V.E., T.L.E., P.E., K.E., M.K.E., M.F.F., J.F.F., O.H.F., A.F., B.I.F., Y.F., C.F., H.G., S.G., C.G., A.G., S.D.G., M. Gorski, D.F.G., P.H., T.B.H., P.v.d.H., C.A.H., C.H., I.M.H., J.N.H., C.-K.H., K. Ho, A.H., W.H., N.H.-K., S.-J.H., O.S.I., E.I., V.W.J., J.J., J.B.J., P.K.J., B.J., M. Kastarinen, S.M.K., M.E.K., W. Koenig, A.T.K., H. Kramer, F.K., B.K.K., M. Kuokkanen, M. Kähönen, A. Körner, A. Köttgen, B.K., M. Laakso, L.A.L., C.D.L., J.J.-M.L., T.L., M. Li, Y.L., W.L., L.L., C.M.L., M. Loeffler, R.J.L., L.-P.L., P.K.M., A. Mahajan, J. Marten, N.G.M., D.M., C.M., T.M., O.M., E.K.M., K. Miliku, K.L.M., G.W.M., D.O.M.-K., R.d.M., W.M., G.N.N., M.A.N., M.N., K.N., B.N., I.M.N., R.N., T.N., M.L.O’D., A.J.O., M.O.-M., N.D.P., R.P., A.P., C.P., S.A.P., B.W.P., M. Perola, O.P., M.H.P., B.P.P., B.M.P., T.J.R., L.M.R., O.T.R., R. Rettig, M.R., K.M.R., P.M.R., F. Rivadeneira, D.J.R., P.R., I.R., D.R., C.S., V.S., K.-U.S., M. Scholz, C.-A.S., N. Schupf, B.S., S. Sedaghat, K.B.S., X.S., A.V.S., H. Snieder, C.N.S., K. Strauch, G.S., P.O.S., S.M.T., N.Y.Q.T., B.O.T., A. Teumer, H.T., A. Tin, J. Tremblay, I.T., A. Tönjes, A.G.U., N.V., V.V., S. Vogelezang, A.P.d.V., U.V., M. Waldenberger, L. Wallentin, D.M.W., H.W., J.B.W., S.H.W., J.G.W., M. Wuttke, Q.Y., Z.Y., A.B.Z. Subject recruitment: S. Afaq, E.P.B., H.B., C.A.B., A.C., H.C., J.C.C., Miao-Ling Chee, K.C., R.C., M. Ciullo, D.C., K.D., M.K.E., V.H.X.F., B.I.F., R.T.G., V. Gudnason, C.A.H., W.H., N.H.-K., O.S.I., M.I., V.W.J., J.B.J., B.J., C.M.K., M. Kastarinen, J.S.K., A. Krajcoviechova, F.K., M. Kubo, M. Kähönen, A. Köttgen, M. Laakso, J.J.-M.L., T.L., W.L., L.L., N.G.M.,. K. Matsuda, C.M., A. Metspalu, R.d.M., W.M., K.N., M.L.O’D., I.O., A.J.O., S.P., C.P., S.A.P., B.W.P., M. Perola, O.P., B.P., D.J.P., T. Poulain, M.A.P., T.J.R., O.T.R., M.R., P.M.R., P.R., I.R., D.R., V.S., R.S., B.H.S., P.O.S., N.Y.Q.T., A. Teren, Y.-C.T., J. Tremblay, I.T., A. Tönjes, S. Vaccargiu, S. Vogelezang, P.V., A.P.d.V., G.W., L. Wallentin, H.W., J.B.W., S.H.W., J.G.W., A.B.Z., J.Ä.

Corresponding authors

Correspondence to Anna Köttgen or Cristian Pattaro.

Ethics declarations

Competing interests

W. Koenig reports modest consultation fees for advisory board meetings from Amgen, DalCor, Kowa, Novartis, Pfizer and Sanofi and modest personal fees for lectures from Amgen, AstraZeneca, Novartis, Pfizer and Sanofi, all outside the scope of the submitted work. W.M. is employed with Synlab Services and holds shares of Synlab Holding Deutschland. D.O.M.-K. is a part-time research consultant at Metabolon. M.A.N. is supported by a consulting contract between Data Tecnica International and the National Institute on Aging (NIA), National Institutes of Health (NIH) and consults for Illumina, the Michael J. Fox Foundation and University of California Healthcare. O.H.F. works in ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec); Metagenics; and AXA. K.B.S., L.Y.-A., D.M.W. and M.A.L. are full-time employees of GlaxoSmithKline. M.L.O’D. received grant support from GlaxoSmithKline, MSD, Eisai, AstraZeneca, MedCo and Janssen. H.W. received grants and non-financial support from GlaxoSmithKline, during the conduct of the study; grants from Sanofi-Aventis, Eli Lilly, the National Institute of Health, Omthera Pharmaceuticals, Pfizer New Zealand, Elsai Inc. and Dalcor Pharma UK; honoraria and non-financial support from AstraZeneca; and is on advisory boards for Sirtex and Acetilion and received personal fees from CSL Behring and American Regent outside the scope of the submitted work. L. Wallentin received institutional grants from GlaxoSmithKline, AstraZeneca, BMS, Boehringer-Ingelheim, Pfizer, MSD and Roche Diagnostics. D.F.R. and A.I.P. are employees of MSD. M. Scholz received consultancy of and grant support from Merck Serono not related to this project. B.M.P. serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. J. Danesh is a member of the Novartis Cardiovascular and Metabolic Advisory Board and received grant support from Novartis. A.S.B. received grants from MSD, Pfizer, Novartis, Biogen and Bioverativ and personal fees from Novartis. V.S. has participated in a conference trip sponsored by Novo Nordisk and received a honorarium from the same source for participating in an advisory board meeting. A. Köttgen received grant support from Gruenenthal. All other authors declare no conflicts of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1–3 and Supplementary Figures 1 and 3–9

Reporting Summary

Supplementary Fig. 2

Supplementary Tables 1–16

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wuttke, M., Li, Y., Li, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51, 957–972 (2019). https://doi.org/10.1038/s41588-019-0407-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-019-0407-x

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing