Citation: PLoS Genetics. 10(7):e1004502, 2014 Jul..Journal: PLoS genetics.ISSN: 1553-7390.Full author list: Makinen VP; Civelek M; Meng Q; Zhang B; Zhu J; Levian C; Huan T; Segre AV; Ghosh S; Vivar J; Nikpay M; Stewart AF; Nelson CP; Willenborg C; Erdmann J; Blakenberg S; O'Donnell CJ; Marz W; Laaksonen R; Epstein SE; Kathiresan S; Shah SH; Hazen SL; Reilly MP; Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Consortium; Lusis AJ; Samani NJ; Schunkert H; Quertermous T; McPherson R; Yang X; Assimes TL.UI/PMID: 25033284.Subject(s): Animals | *Coronary Artery Disease/ge [Genetics] | Coronary Artery Disease/pa [Pathology] | Gene Expression Regulation | *Gene Regulatory Networks | *Genetic Predisposition to Disease | Genome-Wide Association Study | Genomics | Humans | Mice | *Signal Transduction/ge [Genetics]Institution(s): MedStar Health Research InstituteActivity type: Journal Article.Medline article type(s): Journal Article | Meta-AnalysisOnline resources: Click here to access onlineDigital Object Identifier: http://dx.doi.org/10.1371/journal.pgen.1004502 (Click here)Abbreviated citation: PLoS Genet. 10(7):e1004502, 2014 Jul.Local Holdings: Available online through MWHC library: 2005 - present.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.