Partitioning the heritability of coronary artery disease highlights the importance of immune-mediated processes and epigenetic sites associated with transcriptional activity.

Abstract

With the availability of genome-wide genotype data from GWAS studies, it is now possible to compute the genetic relatedness among individuals and estimate its contribution (SNP-based heritability) to phenotypic variance using Mixed-Linear-Models (MLMs). The estimated heritability can be partitioned according to biological features to gain insight into the genetic architecture of a disease. Here, we aimed to examine the genetic structure of coronary artery disease (CAD). We investigated the genetic structure of CAD using 3,163,082 autosomal genome-wide SNPs (MAF ≥ 0.01) and MLMs in a sample of genetically 'unrelated' 4535 cases and 2977 controls. We find that genome-wide SNPs explain 22% of liability to CAD (55% of narrow-sense heritability) and sex-differences in CAD is not due to common SNPs on autosomal chromosomes. Heritability was proportionally distributed across the allele frequency spectrum and notably enriched among genic SNPs. We identified a number of modules that are significantly associated with CAD including: Dendritic cells stimulation; Basigin interactions; and a Cancer module. Of note, genes involved in inflammation account for one-fifth of SNP-based heritability. Heritability-enrichment analysis showed significant enrichment in epigenetic sites associated with transcriptionally activity; namely, enhancers, H3K9ac/H3K27ac/H3K4me1/H3K4me3 histone modifications, and Fetal DNase I hypersensitivity sites whereas heritability was highly depleted in transcriptionally repressed regions. More individual SNP associations will be detected for CAD as sample size increases. The identified modules provide further biological insight for CAD and highlight the importance of immune-mediated processes in CAD pathogenesis. Finally, we showed that genetic liability to CAD is mainly attributed to epigenetic sites associated with transcriptional activity which encourage the design of custom sequencing/genotyping panels based on transcriptionally active regions.