17 March 23

Identification of genes associated with kidney function and disease

Global characterization of multiple kidney phenotypes through imputation-powered whole-exome analysis.

  • English

Genome-wide association studies have discovered hundreds of associations between common genotypes and kidney function but cannot comprehensively investigate rare coding variants. We applied a genotype imputation approach to whole exome sequencing data from the UK Biobank to increase sample size from 166,891 to 408,511.

We detected 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.

Associated genes across phenotypes. Circular heatmap for genes significantly associated with at least one phenotype of interest. Genes are depicted in the radials with one band per phenotype and divided by chromosome. Coloring according to effect size and direction. Significant gene-phenotype pairs (p < 6.8 × 10−7) are marked with a small black box. Effect size color is only shown for nominally significant (p < 0.05) gene-phenotype associations. Binary trait effect sizes are scaled by 10% (range: −2 to 2). Two-sided p-values were obtained from linear regression models of mask variant risk allele dosage on phenotypes.

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 to direct experimental and clinical studies of kidney disease.

Read the full article here:https://www.nature.com/articles/s41467-023-36864-8#Sec15

Our researchers and authors:

Christian Fuchsberger

Research Group Leader

Designed and supervised the study; contributed to the writing and proof reading of the paper. Corresponding author.

Cristian Pattaro

Research Group Leader

Contributed to the writing and proof reading of the paper.

Eva König

Researcher

Performed and validated the imputation; contributed to the writing and proof reading of the paper.

Martin Lang

Senior Researcher

Contributed to the writing and proof reading of the paper.

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