Genome-wide association study of coronary artery disease in individuals of African ancestry

项目概要/摘要 冠状动脉疾病（CAD）是美国成年人死亡的主要原因。其患病率 非洲血统的个体最高。据估计，遗传因素占26%至69%， CAD风险的个体间差异。CAD的大规模全基因组关联研究（GWAS）主要 在欧洲和东亚血统的人群中进行了研究，并确定了207个独立的基因座， 远了少数已确定的基因座已在非洲血统的人群中复制。大规模遗传学研究 在非洲血统人群中缺乏CAD。这项建议将有效地利用现有资源 使用基因组学和流行病学联盟，百万退伍军人计划和其他 建立了队列，为非洲血统人群的遗传研究创造了有史以来最大的样本量 CAD和相关心脏代谢特征的综合表型。我们建议处理以下问题 具体目标。目的1将使用混合作图、单变量GWAS、多变量GWAS和多变量GWAS来询问基因组。 GWAS和跨种族GWAS方法在非洲血统人群中识别与CAD相关的基因座 目标2将使用全表型关联研究，变异性状层次聚类和整合基因组 分析以表征CAD基因座，并深入了解表型，生理和机械影响， 是CAD病理生理学的基础。目的3将探讨CAD的公共卫生影响和临床相关性 通过构建多基因CAD风险评分并在孟德尔遗传学中识别致病性变异， 与非洲血统人群相关的CAD基因综合征。人口结构- 特异性多基因风险评分和鉴定对糖尿病有较大影响的罕见和低频致病性变异， CAD基因的孟德尔综合征将有助于非洲血统个体CAD风险的量化 并有可能缩小遗传信息在不同领域的临床应用的翻译差距， 人口。已确定的CAD风险基因座的综合性跨特质关联将促进以下发现： CAD的亚型。改善遗传CAD风险分类和细化CAD亚表型将有助于 在计算机辅助设计中实现精准医疗。从新基因座阐明的生物学新见解 在非洲血统人群中鉴定的基因也可以推广到其他人群进行诊断， 预防和治疗CAD。

PROJECT SUMMARY/ABSTRACT Coronary artery disease (CAD) is a leading cause of death among adults in the United States. Its prevalence is highest in individuals of African ancestry. It has been estimated that genetic factors account for 26% to 69% of interindividual variation in CAD risk. Large-scale genome-wide association studies (GWAS) of CAD have mainly been conducted in populations of European and East-Asian ancestries and identified 207 independent loci so far. Few of the identified loci have been replicated in populations of African ancestries. Large-scale genetic study of CAD in African-ancestry populations are lacking. This proposal will efficiently leverage the existing resources of the Population Architecture using Genomics and Epidemiology Consortium, Million Veteran Program and other established cohorts to create the largest-ever sample size for a genetic study of African-ancestry populations comprehensively phenotyped for CAD and related cardiometabolic traits. We propose to address the following Specific Aims. Aim 1 will interrogate the genome using admixture mapping, univariate GWAS, multi-variate GWAS and trans-ethnic GWAS approaches to identify loci associated with CAD in African-ancestry populations. Aim 2 will use phenome-wide association studies, variant-trait hierarchical clustering and integrative genomic analyses to characterize CAD loci and gain insights into phenotypic, physiologic, and mechanistic impacts that underlie the pathophysiology of CAD. Aim 3 will explore the public health impact and clinical relevance of CAD risk variants by constructing polygenic CAD risk scores and identifying pathogenic variants in Mendelian syndromes of CAD genes that are relevant to African-ancestry populations. The construction of population- specific polygenic risk scores and identification of rare and low-frequency pathogenic variants of large effect in Mendelian syndromes of CAD genes will facilitate quantification of CAD risk in individuals of African ancestry and potentially narrow the translational gap towards clinical use of genetic information across diverse populations. The comprehensive cross-trait associations of identified CAD risk loci will facilitate the discovery of subtypes of CAD. Both improved genetic CAD risk classifications and refined CAD sub-phenotyping would help with the implementation of precision medicine in CAD. The new biological insights elucidated from novel loci identified in African-ancestry populations may also be generalized to other populations for the diagnosis, prevention, and treatment of CAD.