Functional Characterization of Coronary Artery Disease Loci

项目总结 冠状动脉疾病(CAD)仍然是西方世界的主要死亡原因，尽管 降脂降压药的早期发现和广泛应用取得重大进展 毒品。到目前为止，还没有一种药物被开发出来针对原发疾病的过程 血管壁。迫切需要对这种疾病的易感性有更全面的了解 开发其他疗法。动脉粥样硬化的常见形式涉及环境因素， 数以百计的遗传变异及其相互作用，每一种都产生了相对较小的 对疾病易感性的影响。最新的人类GWA鉴定出304个独立的 与增加冠心病风险相关的变异。然而，大多数潜在的基因 而这些基因座如何影响疾病过程的相关机制仍然存在 未知。这份提案概述了一个全面的数据分析计划，以预测原因 通过结合公众可访问的数据来确定GWAS基因座下的基因和途径 表达定量基因座研究，精选遗传关联数据库，共表达和 来自DNase-Seq的贝叶斯基因表达网络和调控元件预测 来自ENCODE和路线图表观基因组学研究的数据集。建议的总体目标是 研究是将系统生物学和计算管道相结合，从而实现机械化 对被CAD干扰的基因网络的预测。

PROJECT SUMMARY Coronary artery disease (CAD) remains the leading cause of death in the western world despite significant advances in early detection and extensive use of lipid-lowering and anti-hypertensive drugs. To date no single drug has been developed to target the primary disease process in the vessel wall. A more complete understanding of the disease susceptibility is urgently needed to develop additional therapies. Common forms of atherosclerosis involve environmental factors, hundreds of genetic variations, and their interactions, each of which exert a relatively small effect on disease susceptibility. The most recent human GWAS identified 304 independent variants that are associated with increased risk for CAD. However, most of the underlying genes and the related mechanisms of how these loci contribute to the disease process remain unknown. This proposal outlines a comprehensive data analysis plan to predict the causal genes and pathways underlying the GWAS loci by combining publically accessible data from expression quantitative loci studies, curated genetic association databases, co-expression and Bayesian gene expression networks, and regulatory element predictions from DNAse-Seq datasets from ENCODE and Roadmap Epigeneomics studies. The overall goal of the proposed studies is to integrate systems biology and computational pipelines leading to mechanistic predictions of the gene networks that are perturbed by CAD.