TOPMed WGS and Molecular Epidemiology Analyses for Cardiac Hypertrophy Phenotypes

项目总结/摘要 左心室肥厚是心血管疾病最重要的危险因素之一 (CVD)包括缺血性心脏病、慢性心力衰竭和心血管死亡。LVH的风险是 部分取决于遗传因素和全基因组关联研究（GWAS）和全基因组关联研究（GWAS）的结果。 外显子组测序（WES）已经确定了一些不同的变体和基因。 最近，NHLBI TOPMed计划已经产生了一个最大的全基因组测序， (WGS)数据集。我们的提案建立在来自大多数大型CV队列的超过26，000个WGS样本的基础上， 相关超声心动图结构表型。我们将利用这些广泛的关联结果， 数据集，因为它们将为LVH的遗传学和相关表型提供前所未有的见解。的 复杂疾病遗传学和遗传流行病学的下一个重要挑战将是阐明 和功能。功能研究基本上依赖于相关的人类疾病 模型，捕获遗传和基因组特征并模拟复杂疾病的多基因性质 表型。人类诱导多能干细胞（hiPSC）提供了一个“培养皿中的人类”研究平台 基因组功能对于这个应用，我们将集中分析新的WGS基因变异体对 通过使用hiPSC进行功能性分子流行病学和 基于网络优先级的方法。 我们扩展了我们以前的工作，并提出功能测试和注释的一个子集的重要WGS 在hiPSC中使用基因组和基因编辑的所选候选基因和变体的关联信号， 衍生的心肌细胞。将使用基于优先级的方法选择基因，以确定高影响 变体。我们还将进一步完善和开发基于网络的表达数据分析方法。 随后，我们将使用基于表达式的网络概念来描述细胞机制，并提供 特定WGS基因和变体的功能注释。结合起来，我们的分子流行病学 方法是一种新颖的WGS关联信号功能分析方法。

PROJECT SUMMARY/ABSTRACT Left ventricular hypertrophy (LVH) represents one of the most potent risk factors for cardiovascular disease (CVD), including ischemic heart disease, chronic heart failure, and cardiovascular death. The risk of LVH is determined in part by genetic factors and results from Genome-wide Association Studies (GWAS) and Whole Exome Sequencing (WES) already identified some distinct variants and genes. Recently, the NHLBI TOPMed program has been generating one of the largest Whole Genome Sequencing (WGS) data sets. Our proposal builds on over 26,000 WGS samples from most of the large CV cohorts with relevant echocardiographic structural phenotypes. We will utilize association results from these extensive datasets as they will provide unprecedented insights into the genetics of LVH and associated phenotypes. The next important challenge for complex disease genetics and genetic epidemiology will be to elucidate the role and function of identified WGS genes. Functional studies fundamentally rely on relevant human disease models, which capture genetic and genomic features and model the polygenic nature of complex disease phenotypes. Human induced pluripotent stem cells (hiPSCs) provide a ‘human in a dish’ platform to study genome function. For this application, we will focus on analyzing the effects of novel WGS gene variants for LVH and associated structural cardiac phenotypes by using hiPSCs for functional molecular epidemiology and network prioritization-based approaches. We expand on our previous work and propose to functionally test and annotate a subset of significant WGS association signals for selected candidate genes and variants using genome and gene editing in hiPSCs and derived cardiomyocytes. Genes will be selected using on a prioritization-based approach to identify high impact variants. We will also further refine and develop approaches for network-based expression data analyses. Subsequently, we will use expression-based network concepts to describe cellular mechanisms and provide functional annotations for specific WGS genes and variants. In combination, our molecular epidemiology approach is an innovative method to the functional analysis of WGS association signals.