TOPMed WGS and Molecular Epidemiology Analyses for Cardiac Hypertrophy Phenotypes

项目摘要/摘要 左心室肥厚(LVH)是心血管疾病最重要的危险因素之一 (心血管疾病)，包括缺血性心脏病、慢性心力衰竭和心血管死亡。左心室肥厚的风险是 部分由遗传因素和全基因组关联研究(GWAS)和整体研究结果决定 外显子组测序(WES)已经发现了一些不同的变异和基因。 最近，NHLBI TOPMed计划已经产生了最大的全基因组测序之一 (WGS)数据集。我们的建议建立在来自大多数大型简历队列的26,000多个WGS样本的基础上 相关的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，因为它们将提供对LVH和相关表型的遗传学的前所未有的见解。这个 复杂疾病遗传学和遗传流行病学面临的下一个重要挑战将是阐明 以及已鉴定的WGS基因的功能。功能研究基本上依赖于相关的人类疾病 模型，捕捉遗传和基因组特征并模拟复杂疾病的多基因性质 表型。人诱导多能干细胞(HiPSCs)为研究提供了一个“盘中人”的平台 基因组的功能。对于这一应用，我们将重点分析新的WGS基因变体对 利用HiPSCs进行功能分子流行病学及相关心脏结构表型研究 基于网络优先级的方法。 我们在以前工作的基础上进行了扩展，并建议对重要的WG子集进行功能测试和注释 在HiPSC和HIPSC中使用基因组和基因编辑为选定的候选基因和变体提供关联信号 衍生的心肌细胞。将使用基于优先级的方法来选择基因，以确定高影响 变种。我们还将进一步完善和发展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蜂窝机制并提供 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 该方法是对WGS关联信号进行功能分析的一种创新方法。

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.