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

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Citation: PLoS Genetics. 10(7):e1004502, 2014 Jul.PMID: 25033284Institution: MedStar Health Research InstituteForm of publication: Journal ArticleMedline article type(s): Journal Article | Meta-AnalysisSubject headings: *Coronary Artery Disease/ge [Genetics] | *Gene Regulatory Networks | *Genetic Predisposition to Disease | *Signal Transduction/ge [Genetics] | Animals | Coronary Artery Disease/pa [Pathology] | Gene Expression Regulation | Genome-Wide Association Study | Genomics | Humans | MiceLocal holdings: Available online through MWHC library: 2005 - presentISSN:

1553-7390

Name of journal: PLoS geneticsAbstract: 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.All authors: Assimes TL, Blakenberg S, Civelek M, Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Consortium, Epstein SE, Erdmann J, Ghosh S, Hazen SL, Huan T, Kathiresan S, Laaksonen R, Levian C, Lusis AJ, Makinen VP, Marz W, McPherson R, Meng Q, Nelson CP, Nikpay M, O'Donnell CJ, Quertermous T, Reilly MP, Samani NJ, Schunkert H, Segre AV, Shah SH, Stewart AF, Vivar J, Willenborg C, Yang X, Zhang B, Zhu JDigital Object Identifier:

http://dx.doi.org/10.1371/journal.pgen.1004502

Date added to catalog: 2016-01-13

Holdings ( 1 )
Title notes ( 3 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article		Available		25033284

Available online through MWHC library: 2005 - present

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.

English