Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs
We're thrilled to announce our newest publication on "Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs" has been released in Nature Communications. Our study aimed to uncover how specific genes are regulated in the kidney by identifying genomic variants that influence their expression, referred to as expression quantitative trait loci (eQTLs). Using samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression.
Overall, this study highlights the value of tissue-specific eQTL maps and open chromatin data for analyses, which enhances our understanding of kidney function.
Abstract:
Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an “integrative prior” for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.
