Abstract
Genome-wide association studies (GWAS) have identified loci for kidney disease, but the causal variants, genes, and pathways remain unknown. Here we identify two kidney disease genes Dipeptidase 1 (DPEP1) and Charged Multivesicular Body Protein 1 A (CHMP1A) via the triangulation of kidney function GWAS, human kidney expression, and methylation quantitative trait loci. Using single-cell chromatin accessibility and genome editing, we fine map the region that controls the expression of both genes. Mouse genetic models demonstrate the causal roles of both genes in kidney disease. Cellular studies indicate that both Dpep1 and Chmp1a are important regulators of a single pathway, ferroptosis and lead to kidney disease development via altering cellular iron trafficking.
Highlights
Genome-wide association studies (GWAS) have identified loci for kidney disease, but the causal variants, genes, and pathways remain unknown
With this simple model in mind, contemporary approaches to identify a potential target gene have relied on the expression of quantitative trait loci analysis, where genetic variants and gene expression changes are correlated in a tissue-specific manner[11]
Human kidney expression of quantitative trait loci analysis (eQTL) information demonstrated an association between variants at this region and the expression of Dipeptidase 1 (DPEP1) and Charged Multivesicular Body Protein 1 A (CHMP1A) (Fig. 1a, b)
Summary
Genome-wide association studies (GWAS) have identified loci for kidney disease, but the causal variants, genes, and pathways remain unknown. Genome-wide association analyses (GWAS) performed in large populations identified nearly 250 loci where genetic variants associated with kidney function[2,3,4]. Prior GWAS functional annotation studies have highlighted the enrichment of the causal variants in cell type-specific cis-regulatory regions[7,8]. One way in which a GWAS variant could influence the nearby gene is by altering the sequence where transcription factors (TFs) bind[10] With this simple model in mind, contemporary approaches to identify a potential target gene have relied on the expression of quantitative trait loci analysis (eQTL), where genetic variants and gene expression changes are correlated in a tissue-specific manner[11]. The extent to which multiple phenotype-causal genes are driven by a single or multiple causal variant at a single locus is not fully known
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