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

Wuttke, Matthias ; König, Eva ; Katsara, Maria-Alexandra ; Kirsten, Holger ; Farahani, Saeed Khomeijani ; Teumer, Alexander ; Li, Yong ; Lang, Martin ; Göcmen, Burulca ; Pattaro, Cristian ; Günzel, Dorothee ; Köttgen, Anna ; Fuchsberger, Christian

Nature communications, 2023-03, Vol.14 (1), p.1287-1287, Article 1287 [Periódico revisado por pares]

England: Nature Publishing Group

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Título:
Imputation-powered whole-exome analysis identifies genes associated with kidney function and disease in the UK Biobank
Autor: Wuttke, Matthias ; König, Eva ; Katsara, Maria-Alexandra ; Kirsten, Holger ; Farahani, Saeed Khomeijani ; Teumer, Alexander ; Li, Yong ; Lang, Martin ; Göcmen, Burulca ; Pattaro, Cristian ; Günzel, Dorothee ; Köttgen, Anna ; Fuchsberger, Christian
Assuntos: Alleles ; Biobanks ; Biological Specimen Banks ; Exome - genetics ; Genes ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Genotypes ; Humans ; Kidney ; Kidney diseases ; Kidneys ; Mutation ; Polymorphism, Single Nucleotide ; United Kingdom
É parte de: Nature communications, 2023-03, Vol.14 (1), p.1287-1287, Article 1287
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: 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.
Editor: England: Nature Publishing Group
Idioma: Inglês

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

Wuttke, Matthias ; König, Eva ; Katsara, Maria-Alexandra ; Kirsten, Holger ; Farahani, Saeed Khomeijani ; Teumer, Alexander ; Li, Yong ; Lang, Martin ; Göcmen, Burulca ; Pattaro, Cristian ; Günzel, Dorothee ; Köttgen, Anna ; Fuchsberger, Christian

England: Nature Publishing Group

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