Systems Biology Analysis of Cardiac Electrical Activity and Arrhythmias.

心律失常是美国发病率和死亡率的主要原因。中的异常现象 在心电图上测量心率、心脏传导(PR和QRS)和复极(QT)易患 房颤(AF)和室颤(VF)/突发心脏事件的临床重要心律失常 死亡(SCD)。我们研究了这些心电内表型的基因组基础，以便解构 心律失常转化为更接近的特征和离散的成分，使我们能够更好地了解潜在的 研究机制，提供对心律失常产生的洞察，并帮助针对性地开发新的治疗方法。 心脏电活动和心律失常的分子结构尚不完全清楚，但 可能涉及基因组、表观基因组和环境影响。在过去的10年里，我们已经 发现了许多与心脏电活动和心律失常相关的常见基因座，但这些 常见的变异只占电生理学和心律失常遗传率的一部分 表型。对基因组中使用的基因-表型关联的不可知性检查- 广谱关联研究(GWAS)不包含功能基因组区域或 重要的生物关系。此外，我们目前缺乏对分子的了解 主要是基因间和内含子Gwas信号与表型的联系机制。因此，我们 假设一种系统生物学方法将遗传序列变异与基因组数据相结合 (表观基因组、转录组和蛋白质组数据)将发现新的关联并阐明 与心律失常相关表型相关的生物机制。我们进一步假设 检测序列变异与多发性心脏疾病的同时关联性 电生理表型将有助于揭示与心脏疾病相关的其他新机制 电活动和心律失常。 TOPMed将丰富的表型数据与全基因组序列(WGS)相结合， 表观基因组、转录组和蛋白质组数据，提供了一个独特的机会 全面探索这些假说。我们利用序列、基因组和表型数据 多个队列研究以高效和经济高效地检查和剖析OMIC的相关性 心脏电生理因素和心律失常风险。我们的应用程序是一个雄心勃勃的 整合临床、遗传和系统生物学专业知识的非常可行的努力。我们的目标是 使用组学数据发现关联(目标1和2)并阐明特定的基因和生物 这些联系的基础路径(目标3和4)。我们的最终目标是确定路径， 与临床相关的基因和遗传变异，因此可能成为新的 治疗、诊断或风险预测。

Cardiac arrhythmias are a leading cause of morbidity and mortality in the United States. Abnormalities in heart rate, cardiac conduction (PR and QRS) and repolarization (QT) measured on the ECG predispose to the clinically important cardiac arrhythmias of atrial fibrillation (AF) and ventricular fibrillation (VF) / sudden cardiac death (SCD). We examine the genomic basis of these ECG endophenotypes in order to deconstruct arrhythmias into more proximate traits and discrete components, allowing us to better understand underlying mechanisms, provide insight into arrhythmia generation, and help target development of novel therapies. The molecular architecture of cardiac electrical activity and arrhythmias is not fully understood, but likely involves genomic, epigenomic, and environmental influences. Over the past 10 years, we have identified numerous common loci associated with cardiac electrical activity and arrhythmias, yet these common variants account for only a portion of the heritability of electrophysiologic and arrhythmic phenotypes. The agnostic examination of genotype-phenotype associations employed in genome- wide association studies (GWAS) does not incorporate knowledge of functional genomic regions or important biologic relationships. Additionally, we currently lack an understanding of the molecular mechanisms connecting mostly intergenic and intronic GWAS signals to phenotype. We therefore hypothesize that a systems biology approach integrating genetic sequence variation with omic data (epigenomic, transcriptomic, and proteomic data) will uncover novel associations and elucidate biologic mechanisms associated with arrhythmia-related phenotypes. We further hypothesize that examining the simultaneous association between sequence variation and multiple cardiac electrophysiologic phenotypes will help uncover additional novel mechanisms associated with cardiac electrical activity and arrhythmias. TOPMed's combination of rich phenotype data, with whole genome sequence (WGS), epigenomic, transcriptomic, and proteomic data, provides a unique opportunity to more comprehensively explore these hypotheses. We leverage sequence, omic, and phenotype data from multiple cohort studies to efficiently and cost-effectively examine and dissect association of omic factors with cardiac electrophysiology and arrhythmia risk. Our application is an ambitious yet eminently feasible effort that integrates clinical, genetic, and systems biology expertise. We aim to discover associations using omics data (Aims 1 and 2) and elucidate specific genes and biologic pathways underlying these associations (Aims 3 and 4). Our ultimate goal is to identify pathways, genes, and genetic variation that are clinically relevant, and therefore potentially the target of new therapies, diagnostics, or risk predictions.