Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

Seong Kyu Han, Michelle T. McNulty, Christopher J. Benway, Pei Wen, Anya Greenberg, Ana C. Onuchic-Whitford, Dongkeun Jang, Jason Flannick, Noël P. Burtt, Parker C. Wilson, Benjamin D. Humphreys, Xiaoquan Wen, Zhe Han (), Dongwon Lee () and Matthew G. Sampson ()
Additional contact information
Seong Kyu Han: Division of Pediatric Nephrology, Boston Children’s Hospital
Michelle T. McNulty: Division of Pediatric Nephrology, Boston Children’s Hospital
Christopher J. Benway: Division of Pediatric Nephrology, Boston Children’s Hospital
Pei Wen: Center for Precision Disease Modeling, University of Maryland, School of Medicine
Anya Greenberg: Division of Pediatric Nephrology, Boston Children’s Hospital
Ana C. Onuchic-Whitford: Division of Pediatric Nephrology, Boston Children’s Hospital
Dongkeun Jang: Programs in Metabolism and Medical and Population Genetics, Broad Institute
Jason Flannick: Harvard Medical School
Noël P. Burtt: Programs in Metabolism and Medical and Population Genetics, Broad Institute
Parker C. Wilson: Washington University in St. Louis
Benjamin D. Humphreys: Washington University in St. Louis
Xiaoquan Wen: University of Michigan
Zhe Han: Center for Precision Disease Modeling, University of Maryland, School of Medicine
Dongwon Lee: Division of Pediatric Nephrology, Boston Children’s Hospital
Matthew G. Sampson: Division of Pediatric Nephrology, Boston Children’s Hospital

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37691-7

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