Functional Characterization of Coronary Artery Disease Loci

项目摘要 冠状动脉疾病（CAD）仍然是西方世界的主要死亡原因， 在早期发现和广泛使用降脂和抗高血压药物方面取得重大进展 毒品到目前为止，还没有开发出单一的药物来靶向原发性疾病过程， 血管壁迫切需要更全面地了解疾病的易感性， 开发其他疗法。动脉粥样硬化的常见形式涉及环境因素， 数百种遗传变异及其相互作用，每一种都产生相对较小的影响 影响疾病易感性。最新的人类GWAS确定了304个独立的 与CAD风险增加相关的变异。然而，大多数潜在的基因 以及这些基因座如何促进疾病过程的相关机制仍然存在， 未知该提案概述了一个全面的数据分析计划，以预测因果关系 基因和途径潜在的GWAS基因座，通过结合可获得的数据， 表达定量基因座研究，策划的遗传关联数据库，共表达和 贝叶斯基因表达网络和来自DNAse-Seq的调控元件预测 ENCODE和Roadmap表观基因组学研究的数据集。拟议的总体目标 研究的目的是整合系统生物学和计算管道， 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.