Bibliographic Details
| Title: |
Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs |
| Authors: |
Seong Kyu Han, Michelle T. McNulty, Christopher J. Benway, Pei Wen, Anya Greenberg, Ana C. Onuchic-Whitford, Nephrotic Syndrome Study Network (NEPTUNE), Dongkeun Jang, Jason Flannick, Noël P. Burtt, Parker C. Wilson, Benjamin D. Humphreys, Xiaoquan Wen, Zhe Han, Dongwon Lee, Matthew G. Sampson |
| Source: |
Nature Communications, Vol 14, Iss 1, Pp 1-16 (2023) |
| Publisher Information: |
Nature Portfolio, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Science |
| Subject Terms: |
Science |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-023-37691-7 |
| Access URL: |
https://doaj.org/article/7d29a604d3e4432e9d83b826b3e053bc |
| Accession Number: |
edsdoj.7d29a604d3e4432e9d83b826b3e053bc |
| Database: |
Directory of Open Access Journals |
