Characterizing pleiotropy in cardiometabolic phenotypes among diverse populations

摘要 强调，遗传易感性是大多数心血管疾病(CVD)及其前驱疾病的基础 到目前为止，全基因组关联研究已经确定了1500个基因座。每个GWAS鉴定的基因座 潜在地提供了新的机械洞察力，但研究结果的翻译在很大程度上仍然不完整， 这是取得进展的关键障碍。多效性是一种影响多种表型的变异，是一种长期的- 被描述和普及，但在很大程度上没有特征的推进基因组医学的途径。具体地说，研究 多效性的研究有可能阐明分子的功能，识别机械的“公分母”， 告知诊断和治疗，并对功能性询问的变体进行优先排序。系统化和 对多效性的全面询问与心血管疾病的表型特别相关，因为几十年来人类和 动物研究支持共同的基因结构，共同影响下游临床疾病。然而， 很少有研究全面和系统地评估心血管内或跨心血管的多效性 研究多效性变异如何影响临床疾病的表型或扩展研究。此外，还有许多 心血管疾病及其前驱疾病对非裔美国人(AA)和西班牙裔/拉美裔美国人(HL)的影响不成比例。 然而，到目前为止，大多数(&gt；80%)的参与者都是欧洲(欧盟)血统。这 研究差距造成了对人类变异的偏见，未能利用人类独特的遗传结构 AAS和HLS用于精细绘图，并阻碍将基因发现转化为临床和公共卫生 适用于广大人群的应用程序。我们通过利用高质量、协调、 以及使用基因组学从人口结构中集中获得的表型和基因数据 流行病学(PAGE)联盟与卒中地理和种族差异的原因 (关于)研究(n=100,917；35%AA；32%Eu；24%HL)以及尖端统计方法 全面识别具有血压、胆固醇、 心脏传导、血糖、炎症和肥胖心血管领域以及发生的心肌梗死和 笔划(目标1)。在已知和具有潜在多效性强有力证据的新基因座上，我们将利用人口 通过多种族、多表型精细作图研究结构、单倍型结构和表型相关性 为进一步审讯排定变种的优先顺序(目标2)。最后，我们将利用纵向数据和路径 分解显示生物多效性证据的变体的模型(即，变体影响多个 来自显示中介多效性证据的变异体(例如变异体 影响一种表型，该表型影响第二种表型)(目标3)。我们假设 CVD表型和临床疾病可以更准确地描述为临床表现的变化， 有着共同的生物学机制。通过对多效性的研究，我们希望澄清这些机制，这些机制 有可能为表型分类、药物开发和再利用以及心血管疾病预防提供信息。

ABSTRACT Genetic susceptibility underlies a majority of cardiovascular diseases (CVD) and their antecedents, underscored by genome-wide association studies (GWAS) that identified >1,500 loci to-date. Each GWAS-identified locus potentially provides novel mechanistic insight, yet translation of study findings remains largely incomplete, representing a critical barrier to progress. Pleiotropy, a variant that affects multiple phenotypes, is a long- described and pervasive, but largely uncharacterized avenue to advance genomic medicine. Specifically, studies of pleiotropy have the potential to clarify molecular functions, identify mechanistic “common denominators", inform diagnosis and treatment, and prioritize variants for functional interrogation. Systematic and comprehensive interrogation of pleiotropy is particularly relevant for CVD phenotypes, as decades of human and animal studies support a shared genetic architecture that collectively affects downstream clinical disease. Yet, few studies have comprehensively and systematically evaluated pleiotropy within or across cardiovascular phenotypes or extended investigations to examine how pleiotropic variants affect clinical disease. Further, many CVDs and their antecedents disproportionately affect African Americans (AA) and Hispanic/Latinos (HL). However, the majority (>80%) of participants included in GWAS to-date are of European (EU) ancestry. This research disparity creates a biased view of human variation, fails to leverage the unique genetic architecture of AAs and HLs for fine-mapping, and hinders translation of genetic findings into clinical and public health applications relevant for broad populations. We respond to these gaps by leveraging high-quality, harmonized, and centrally available phenotype and genotype data from the Population Architecture Using Genomics in Epidemiology (PAGE) consortium and the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study (n=100,917; 35% AA; 32% EU; 24% HL) as well as cutting edge statistical methods to comprehensively identify loci with potential evidence of pleiotropy within and across blood pressure, cholesterol, cardiac conduction, glycemic, inflammatory, and obesity cardiovascular domains as well as incident MI and stroke (Aim 1). At known and novel loci with strong evidence of potential pleiotropy, we will leverage population structure, haplotypic architecture, and phenotype correlation through multi-ethnic, multi-phenotype fine-mapping to prioritize variants for further interrogation (Aim 2). Finally, we will leverage longitudinal data and pathway models to disaggregate variants displaying evidence of biological pleiotropy (i.e. variant affects multiple phenotypes due to shared biology) from variants displaying evidence of mediated pleiotropy (e.g. variant influences one phenotype and this phenotype influences a second phenotype) (Aim 3). We hypothesize that CVD phenotypes and clinical disease may be more accurately characterized as variations in clinical expression, with common biological mechanisms. By investigating pleiotropy, we hope to clarify these mechanisms, which has the potential to inform phenotype classification, drug development and repurposing, and CVD prevention.