Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci

Chani J. Hodonsky, Adam W. Turner, Mohammad Daud Khan, Nelson B. Barrientos, Ruben Methorst, Lijiang Ma, Nicolas G. Lopez, Jose Verdezoto Mosquera, Gaëlle Auguste, Emily Farber, Wei Feng Ma, Doris Wong, Suna Onengut-Gumuscu, Maryam Kavousi, Patricia A. Peyser, Sander W. van der Laan, Nicholas J. Leeper, Jason C. Kovacic, Johan L.M. Björkegren, Clint L. Miller

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.

Original languageEnglish
Article number100465
JournalCell Genomics
Issue number1
StatePublished - 10 Jan 2024


  • RNA-seq
  • coronary artery
  • eQTL
  • fine-mapping
  • gene regulation
  • genetic diversity
  • genome-wide association studies


Dive into the research topics of 'Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci'. Together they form a unique fingerprint.

Cite this