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Genetic Insights Into Smooth Muscle Cell Contributions to Coronary Artery Disease
Author(s) -
Doris Wong,
Adam W. Turner,
Clint L. Miller
Publication year - 2019
Publication title -
arteriosclerosis thrombosis and vascular biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.007
H-Index - 270
eISSN - 1524-4636
pISSN - 1079-5642
DOI - 10.1161/atvbaha.119.312141
Subject(s) - genome wide association study , disease , candidate gene , quantitative trait locus , coronary artery disease , biology , computational biology , locus (genetics) , genetic architecture , genetic association , genome , bioinformatics , gene , genetics , medicine , single nucleotide polymorphism , pathology , genotype
Coronary artery disease is a complex cardiovascular disease involving an interplay of genetic and environmental influences over a lifetime. Although considerable progress has been made in understanding lifestyle risk factors, genetic factors identified from genome-wide association studies may capture additional hidden risk undetected by traditional clinical tests. These genetic discoveries have highlighted many candidate genes and pathways dysregulated in the vessel wall, including those involving smooth muscle cell phenotypic modulation and injury responses. Here, we summarize experimental evidence for a few genome-wide significant loci supporting their roles in smooth muscle cell biology and disease. We also discuss molecular quantitative trait locus mapping as a powerful discovery and fine-mapping approach applied to smooth muscle cell and coronary artery disease-relevant tissues. We emphasize the critical need for alternative genetic strategies, including cis/trans-regulatory network analysis, genome editing, and perturbations, as well as single-cell sequencing in smooth muscle cell tissues and model organisms, under both normal and disease states. By integrating multiple experimental and analytical modalities, these multidimensional datasets should improve the interpretation of coronary artery disease genome-wide association studies and molecular quantitative trait locus signals and inform candidate targets for therapeutic intervention or risk prediction.

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