Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci

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作者
Clint L. Miller
Milos Pjanic
Ting Wang
Trieu Nguyen
Ariella Cohain
Jonathan D. Lee
Ljubica Perisic
Ulf Hedin
Ramendra K. Kundu
Deshna Majmudar
Juyong B. Kim
Oliver Wang
Christer Betsholtz
Arno Ruusalepp
Oscar Franzén
Themistocles L. Assimes
Stephen B. Montgomery
Eric E. Schadt
Johan L.M. Björkegren
Thomas Quertermous
机构
[1] Stanford University School of Medicine,Department of Medicine, Division of Cardiovascular Medicine
[2] Icahn Institute for Genomics and Multiscale Biology,Department of Genetics and Genomic Sciences
[3] Icahn School of Medicine at Mount Sinai,Department of Genetics
[4] Stanford University School of Medicine,Department of Molecular Medicine and Surgery
[5] Karolinska Institutet,Department of Immunology
[6] Genetics and Pathology,Department of Medical Biochemistry and Biophysics
[7] Rudbeck Laboratory,Department of Cardiac Surgery
[8] Uppsala University,Department of Pathology
[9] Vascular Biology Unit,Department of Physiology
[10] Karolinska Institutet,undefined
[11] Tartu University Hospital,undefined
[12] Clinical Gene Networks AB,undefined
[13] Stanford University School of Medicine,undefined
[14] Institute of Biomedicine and Translation Medicine,undefined
[15] University of Tartu,undefined
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摘要
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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