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Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci
Clint L Miller1, Milos Pjanic1, Ting Wang1
1Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California 94305, USA.
Insights
Researchers investigated genetic factors contributing to coronary artery disease (CAD). They identified specific gene variants and regulatory mechanisms linked to CAD, offering new insights into disease development.
Area of Science:
- Genomics
- Epigenomics
- Transcriptomics
- Cardiovascular Disease Research
Background:
- Coronary artery disease (CAD) is a major cause of death, influenced by genetics and environment.
- Genome-wide association studies identified over 150 CAD susceptibility loci, many in non-coding regions.
- Determining the function of non-coding variants linked to CAD is challenging.
Purpose of the Study:
- To identify causal regulatory variants and mechanisms underlying CAD associations.
- To integrate genomic, epigenomic, and transcriptomic data for CAD research.
- To prioritize and functionally characterize candidate variants in CAD.
Main Methods:
- Integrative profiling of human coronary artery smooth muscle cells and tissues.
- Genome-wide mapping of regulatory elements.
- Allele-specific binding and expression analyses at candidate loci.
- Validation in expression quantitative trait loci (eQTL) cohorts.
Main Results:
- Prioritized 64 candidate variants associated with CAD.
- Performed functional analyses at seven key loci (9p21.3, SMAD3, PDGFD, IL6R, BMP1, CCDC97/TGFB1, LMOD1).
- Demonstrated links between CAD associations and regulatory function.
Conclusions:
- Established a framework for identifying causal variants in complex diseases like CAD.
- Highlighted the role of non-coding regulatory variants in CAD pathogenesis.
- Provided a foundation for future functional studies and therapeutic target identification.
Abstract:
Coronary artery disease (CAD) is the leading cause of mortality and morbidity, driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies have identified >150 loci associated with CAD and myocardial infarction susceptibility in humans. A majority of these variants reside in non-coding regions and are co-inherited with hundreds of candidate regulatory variants, presenting a challenge to elucidate their functions. Herein, we use integrative genomic, epigenomic and transcriptomic profiling of perturbed human coronary artery smooth muscle cells and tissues to begin to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps, we prioritize 64 candidate variants and perform allele-specific binding and expression analyses at seven top candidate loci: 9p21.3, SMAD3, PDGFD, IL6R, BMP1, CCDC97/TGFB1 and LMOD1. We validate our findings in expression quantitative trait loci cohorts, which together reveal new links between CAD associations and regulatory function in the appropriate disease context.
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