Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
09 03 2023
09 03 2023
Historique:
received:
14
08
2022
accepted:
20
02
2023
entrez:
8
3
2023
pubmed:
9
3
2023
medline:
11
3
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36890159
doi: 10.1038/s41467-023-36864-8
pii: 10.1038/s41467-023-36864-8
pmc: PMC9995463
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1287Informations de copyright
© 2023. The Author(s).
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