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.

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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.

Data availability

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

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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.

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