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Coronary Artery Disease Genetics Enlightened by Genome-Wide Association Studies.

ABSTRACT: Many cardiovascular diseases are facilitated by strong inheritance. For example, large-scale genetic studies identified hundreds of genomic loci that affect the risk of coronary artery disease. At each of these loci, common variants are associated with disease risk with robust statistical evidence but individually small effect sizes. Only a minority of candidate genes found at these loci are involved in the pathophysiology of traditional risk factors, but experimental research is making progress in identifying novel, and, in part, unexpected mechanisms. Targets identified by genome-wide association studies have already led to the development of novel treatments, specifically in lipid metabolism. This review summarizes recent genetic and experimental findings in this field. In addition, the development and possible clinical usefulness of polygenic risk scores in risk prediction and individualization of treatment, particularly in lipid metabolism, are discussed.

SUBMITTER: Kessler T

PROVIDER: S-EPMC8326228 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Coronary Artery Disease Genetics Enlightened by Genome-Wide Association Studies.

Kessler Thorsten T Schunkert Heribert H

JACC. Basic to translational science 20210726 7

Many cardiovascular diseases are facilitated by strong inheritance. For example, large-scale genetic studies identified hundreds of genomic loci that affect the risk of coronary artery disease. At each of these loci, common variants are associated with disease risk with robust statistical evidence but individually small effect sizes. Only a minority of candidate genes found at these loci are involved in the pathophysiology of traditional risk factors, but experimental research is making progress ...[more]

PMID: 34368511

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Project description:Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Project description:Genome-wide association studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks.Using pathways (gene sets) from Reactome, we carried out a 2-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CAD genome-wide association study data sets (9889 cases/11?089 controls), nominally significant gene sets were tested for replication in a meta-analysis of 9 additional studies (15?502 cases/55?730 controls) from the Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication P<0.05). These pathways resided in 9 of 21 core biological processes represented in Reactome, and included pathways relevant to extracellular matrix (ECM) integrity, innate immunity, axon guidance, and signaling by PDRF (platelet-derived growth factor), NOTCH, and the transforming growth factor-?/SMAD receptor complex. Many of these pathways had strengths of association comparable to those observed in lipid transport pathways. Network analysis of unique genes within the replicated pathways further revealed several interconnected functional and topologically interacting modules representing novel associations (eg, semaphoring-regulated axonal guidance pathway) besides confirming known processes (lipid metabolism). The connectivity in the observed networks was statistically significant compared with random networks (P<0.001). Network centrality analysis (degree and betweenness) further identified genes (eg, NCAM1, FYN, FURIN, etc) likely to play critical roles in the maintenance and functioning of several of the replicated pathways.These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD.

Dataset Information

Coronary Artery Disease Genetics Enlightened by Genome-Wide Association Studies.

Publications

Coronary Artery Disease Genetics Enlightened by Genome-Wide Association Studies.

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