Whole-genome sequencing analysis of coronary atherosclerosis and related traits

项目总结 全基因组关联研究已经确定了160多个遗传基因座上常见的单核苷酸变异 与冠状动脉疾病(CAD)和亚临床动脉粥样硬化(冠状动脉钙化， 颈动脉内膜中层厚度、颈动脉斑块)。这些发现为我们提供了对 促进亚临床动脉粥样硬化和冠心病的途径，以及对遗传结构的洞察 动脉粥样硬化。例如，常见的冠心病遗传变异所解释的遗传率似乎是 集中在监管区域。然而，无论是基因组范围的关联研究还是外显子组 到目前为止进行的测序研究已经能够检查编码和非编码变体 等位基因频率与亚临床动脉粥样硬化和冠心病的关系。此外，这些研究 主要集中在欧洲血统的参与者身上。确定多效性基因座或量化的方法 表型之间的遗传相关性存在，但尚未应用于亚临床动脉粥样硬化和 CAD。基于常见变异的遗传风险预测研究显示，在改善初级 预防，但将低频率和罕见变异添加到多基因风险评分中可以提高风险的程度 预测是未知的，也没有在非欧洲血统的人中开发和测试分数。 国家心脏等计划已经产生了丰富的全基因组测序(WGS)数据， 肺和血液研究所(NHLBI)精密医学反式组学(TOPMed)计划和国家 人类基因组研究所(NHGRI)常见病基因组学中心(CCDG)计划 来自不同祖先的种群。扩大我们对导致冠心病的遗传因素的认识 亚临床动脉粥样硬化表型，我们建议使用TOPMed和CCDG(高达101,295)的WGS数据 来自不同祖先的个人，其中58%是非欧洲血统)，基因组覆盖范围扩大 包括低频和稀有的遗传变异，以及更复杂的遗传变异，如结构变异。 WGS分析的结果将在几个大规模数据源中复制，包括exome 以TOPMed为参照板输入测序数据和基因分型数据。因此，我们将研究 到目前为止被忽略的遗传变异，包括结构变异。 我们将利用这些分析的结果来探索亚临床动脉粥样硬化的遗传结构。 研究亚临床动脉粥样硬化与冠心病及其相关基因的多效性和遗传相关性 心血管特征，以及评估低频和罕见变异对风险预测的贡献 CAD。最后，我们将创建并测试专门为非洲血统人口设计的多基因风险评分。 这项建议汇集了大规模的WGS数据集、临床和亚临床动脉粥样硬化表型， 并利用基因组技术和计算方法的进步。通过这样做，我们将推进 精准医学在CAD中的实现。

PROJECT SUMMARY Genome-wide association studies have identified common single nucleotide variants at over 160 genetic loci associated with coronary artery disease (CAD) and subclinical atherosclerosis (coronary artery calcification, carotid intima media thickness, and carotid plaque). These discoveries have led to important insights into the pathways that contribute to subclinical atherosclerosis and CAD, as well as insights into the genetic architecture of atherosclerosis. For example, the heritability explained by common genetic variants for CAD appears to be concentrated in regulatory regions. Nevertheless, neither the genome-wide association studies nor exome sequencing studies performed to date have been able to examine both coding and non-coding variants across the allele frequency spectrum in relation to subclinical atherosclerosis and CAD. Furthermore, these studies have largely focused on European ancestry participants. Approaches that identify pleiotropic loci or quantify genetic correlation among phenotypes exist, but have not yet been applied to subclinical atherosclerosis and CAD. Genetic risk prediction studies based on common variants show promise with regards to improving primary prevention, but the extent to which adding low-frequency and rare variants to polygenic risk scores improves risk prediction is not known, nor have scores been developed and tested in those of non-European ancestry. A wealth of whole-genome sequencing (WGS) data has been generated by initiatives such as the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program and the National Human Genome Research Institute (NHGRI) Centers for Common Disease Genomics (CCDG) program in populations from different ancestries. To expand our knowledge of genetic factors contributing to CAD and subclinical atherosclerosis phenotypes, we propose to use WGS data from TOPMed and CCDG (up to 101,295 individuals from diverse ancestries, of which 58% are non-European ancestry), with extended genomic coverage of low-frequency and rare genetic variants as well as more complex genetic variants such as structural variants. Findings from the WGS analysis will be replicated in several large-scale data sources, including exome sequencing data and genotype data imputed using TOPMed as the reference panel. Thus, we will examine genetic variation that has so far been missed, including structural variants. We will leverage the results of these analyses to explore the genetic architecture of subclinical atherosclerosis and CAD, investigate pleiotropy and genetic correlation between subclinical atherosclerosis and CAD and related cardiovascular traits, as well as assess the contribution of low-frequency and rare variants to risk prediction of CAD. Finally, we will create and test a polygenic risk score designed specifically for African ancestry population. This proposal brings together large-scale WGS datasets, clinical and subclinical atherosclerosis phenotypes, and exploits advances in genomic technologies and computational approaches. In doing so, we will advance the realization of precision medicine for CAD.