Abstract 468: Expression Qtl Analysis And Fine-mapping In Human Coronary Artery Prioritize Candidate Genes For Functional Characterization.

Abstract

Background: Atherosclerotic plaque progression to coronary artery disease is the leading cause of death worldwide. The basic pathophysiology of atherosclerosis is well described, but underlying genetic mechanisms of contributing cell types remain incompletely characterized. We utilized human coronary artery RNA sequencing data to identify differentially expressed genes and genetic variants that affect expression. Methods: We used bulk RNA sequencing data from coronary artery samples from 138 American adults (30% female) representing a broad phenotypic range of atherosclerosis. To account for ancestral diversity, we incorporated local ancestry into permutation-based eQTL analyses using a combination of published methods. We also used mixQTL to evaluate the contribution of allele-specific expression to identifying eQTLs. All analyses adjusted for age, sex, and ancestry. We utilized CAD GWAS summary statistics, coronary artery ENCODE annotations, and activity-by-contact (ABC) scores to fine-map eGenes. Results: In local-ancestry eQTL analyses, we report 467 eGenes (FDR<0.05), 75 of which had no significant coronary artery eQTLs in GTEx. Additionally, we identify over 4,000 eGenes using mixQTL. 25 GWAS signals colocalized using a European LD reference panel, 9 of which colocalized uniquely to 1 of 4 datasets analyzed. Fine-mapping demonstrated multiple independent associations at over 20% of loci, potentially representing ancestry-specific associations. Population-specific analyses are ongoing. Conclusion: Understanding changes in the gene expression program at all disease stages will help characterize coronary artery disease processes as well as identify potential therapeutic targets for functional examination. This work represents a step toward characterizing CAD-related gene expression programs and highlight the importance of inclusive, deliberate study design.