Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank

Matthias Wuttke (), Eva König, Maria-Alexandra Katsara, Holger Kirsten, Saeed Khomeijani Farahani, Alexander Teumer, Yong Li, Martin Lang, Burulca Göcmen, Cristian Pattaro, Dorothee Günzel, Anna Köttgen and Christian Fuchsberger ()
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Matthias Wuttke: University of Freiburg
Eva König: Institute for Biomedicine (affiliated to the University of Lübeck)
Maria-Alexandra Katsara: University of Freiburg
Holger Kirsten: University of Leipzig
Saeed Khomeijani Farahani: Charité - Universitätsmedizin Berlin
Alexander Teumer: University Medicine Greifswald
Yong Li: University of Freiburg
Martin Lang: Institute for Biomedicine (affiliated to the University of Lübeck)
Burulca Göcmen: University of Freiburg
Cristian Pattaro: Institute for Biomedicine (affiliated to the University of Lübeck)
Dorothee Günzel: Charité - Universitätsmedizin Berlin
Anna Köttgen: University of Freiburg
Christian Fuchsberger: Institute for Biomedicine (affiliated to the University of Lübeck)

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Genome-wide association studies have discovered hundreds of associations between common genotypes and kidney function but cannot comprehensively investigate rare coding variants. Here, we apply a genotype imputation approach to whole exome sequencing data from the UK Biobank to increase sample size from 166,891 to 408,511. We detect 158 rare variants and 105 genes significantly associated with one or more of five kidney function traits, including genes not previously linked to kidney disease in humans. The imputation-powered findings derive support from clinical record-based kidney disease information, such as for a previously unreported splice allele in PKD2, and from functional studies of a previously unreported frameshift allele in CLDN10. This cost-efficient approach boosts statistical power to detect and characterize both known and novel disease susceptibility variants and genes, can be generalized to larger future studies, and generates a comprehensive resource ( https://ckdgen-ukbb.gm.eurac.edu/ ) to direct experimental and clinical studies of kidney disease.

Date: 2023
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DOI: 10.1038/s41467-023-36864-8

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