Multiomics Screening Identifies Molecular Biomarkers Causally Associated With the Risk of Coronary Artery Disease.

Background: In this study, we aimed to investigate functional mechanisms underlying coronary artery disease (CAD) loci and find molecular biomarkers for CAD.

Methods: We devised a multiomics data analysis approach based on Mendelian randomization and utilized it to search for molecular biomarkers causally associated with the risk of CAD within genomic regions known to be associated with CAD.

Results: Through our CAD-centered multiomics data analysis approach, we identified 33 molecular biomarkers (probes) that were causally associated with the risk of CAD. The majority of these (N=19) were methylation probes; moreover, methylation was often behind the causal effect of expression/protein probes. We identified a number of novel loci that have a causal impact on CAD including , , , and . Furthermore, by integrating the risk factors of CAD in our analysis, we were able to investigate the clinical pathways whereby several of our probes exert their effect. We found that the SELE protein level in the blood is under the trans-regulatory impact of methylation sites within the gene and that SELE exerts its effect on CAD through immune, glycemic, and lipid metabolism, making it a candidate of interest for therapeutic interventions. We found the methylation site, cg05126514 within the gene exert its effect on CAD through central nervous system-lifestyle risk factors. Finally, genes with a transcriptional regulatory role (, , and ) exert their effect on CAD through height.

Conclusions: We demonstrate that multiomics data analysis is a powerful approach to unravel the functional mechanisms underlying CAD loci and to identify novel molecular biomarkers. Our results indicate epigenetic modifications are important in the pathogenesis of CAD and identifying and targeting these sites is of potential therapeutic interest to address the detrimental effects of both environmental and genetic factors.