Using Genetics to Inform Mechanism of Cardiovascular Disease

项目摘要/摘要 R35提案的目标是发现新的基因发现和潜在的生物学机制 与毁灭性的心血管疾病有关。此建议建立在高吞吐量优势的基础上 遗传学和基因组学以及创新计算和统计方法的开发和应用 以及基因组学技术，以最大限度地发挥心血管疾病基因研究的好处。 我们将继续我们的发现努力，以发现与各种心血管疾病相关的基因变异 疾病包括房颤、主动脉瘤和夹层，以及心肌梗死和冠状动脉 动脉疾病。在我们之前工作的基础上，我们发现了一些与冠状动脉相关的新基因 疾病和血脂，我们还建议揭示在已知基因座潜在的关联机制 遗传学和表观基因组学。我们建议评估约1900万个变种和20K个变种的表型影响 从全基因组测序样本中鉴定INDELS和SVS，将它们输入到7万个新的GWAs中 具有多种心血管表型的样本。我们将利用表观基因组数据进行综合分析，以 突出显示具有相关功能的基因座簇。我们还建议对300个基因进行定向测序 在30,000例CAD病例和对照中寻找与CAD基因有关的CAD基因功能变异丢失。 我们将继续通过对所有与CAD相关的变体执行PheWAS来寻找机械性洞察力 识别、解开多个独立信号以及相关特征和临床终点 条件测试。 这些研究的完成将为疾病机制提供新的见解，这些机制有可能 催化心血管疾病预防、治疗和诊断方面的突破。

PROJECT SUMMARY/ABSTRACT The goal of this R35 proposal is to uncover novel genetic discoveries and biological mechanisms underlying association with devastating cardiovascular diseases. This proposal builds on strengths in high-throughput genetics and genomics and development and application of innovative computational and statistical methods and genomics technology to maximize the benefits of genetic studies of cardiovascular disease. We will continue our discovery efforts to uncover genetic variants associated with a variety of cardiovascular diseases including atrial fibrillation, aortic aneurysm and dissection, and myocardial infarction and coronary artery disease. Building on our previous work where we identified a number of new genes for coronary artery disease and lipids, we also propose to uncover the mechanisms underlying association at known loci using genetics and epigenomics. We propose to assess the phenotypic impact of the ~19 million variants and 20k indels and SVs identified from whole genome sequenced samples by imputing them into 70,000 new GWAS samples with many cardiovascular phenotypes. We will perform integrated analyses with epigenomics data to highlight clusters of loci with related function. We also propose to perform targeted sequencing of 300 genes in 30,000 CAD cases and controls to search for loss of function variants at CAD loci that implicate CAD genes. We will continue to search for mechanistic insight by performing a PheWAS for all CAD-associated variants identified, disentangling multiple independent signals and correlated traits and clinical endpoints using conditional testing. Completion of these studies will provide new insights into disease mechanisms that have the potential to catalyze breakthroughs in cardiovascular disease prevention, treatment, and diagnosis.