Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease

Abstract

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

Figure 1. Schematic overview of the study design.

A) The SNP set enrichment analysis (SSEA) comprised four steps. First, gene sets from knowledge-driven pathways and data-driven co-expression modules were collected. Second, the gene sets were converted to expression SNP (eSNP) sets according to genetics of gene expression or eQTL studies. Third, P-values from CAD GWAS were extracted for each eSNP. Fourth, the GWAS P-values within eSNP sets were compared against random expectation to derive pathways and network modules enriched for CAD genetic signals. B) Overlapping CAD-associated gene sets were merged and trimmed into non-overlapping supersets. C) Integration of Bayesian gene-gene network models with CAD-associated supersets to determine key driver genes based on network topology.

Figure 2. Key driver genes of six CAD-associated supersets, and their adjacent regulatory partners.

Key driver genes were denoted as larger nodes in the network. Genes were colored based on their membership in the six CAD-associated supersets. A) ‘Lipid II’ superset in red. B) ‘Lipid I’ superset in yellow. C) ‘Unknow II’ superset in lime. D) ‘Immunity’ superset in green. E) ‘Antigen’ superset in blue. F) ‘Unknown I’ superset in magenta. Only edges that were present in at least two Bayesian networks constructed from independent studies were included.