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。异常 心电图上测量的心率、心脏传导（PR和QRS）和复极（QT）易患 房颤（AF）和室颤（VF）的临床重要心律失常/突发性心脏病 死亡（SCD）。我们研究了这些ECG内表型的基因组基础，以解构 将心律失常转化为更接近的特征和离散成分，使我们能够更好地了解潜在的 机制，提供对心律失常产生的洞察，并帮助开发新的治疗方法。 心脏电活动和心律失常的分子结构尚未完全了解， 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 确定了许多与心脏电活动和心律失常相关的常见位点，但这些 常见的变异仅占电生理学和脑电生理学遗传性的一部分。 表型。基因组-表型关联的不可知论研究 广泛关联研究（GWAS）不包括功能基因组区域的知识， 重要的生物学关系。此外，我们目前缺乏对分子生物学的了解。 主要将基因间和内含子GWAS信号与表型联系起来的机制。因此我们 假设将遗传序列变异与组学数据相结合系统生物学方法 （表观基因组学，转录组学和蛋白质组学数据）将揭示新的关联，并阐明 与疟疾相关表型相关的生物学机制。我们进一步假设， 检查序列变异和多个心脏事件之间的同时关联 电生理表型将有助于揭示与心脏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， 表观基因组学、转录组学和蛋白质组学数据，提供了一个独特的机会， 对这些假说进行了全面的探讨。我们利用序列、组学和表型数据， 多队列研究，以有效和具有成本效益的检查和剖析组学相关性 心脏电生理学和心律失常风险的因素。我们的申请是一个雄心勃勃的， 非常可行的努力，整合临床，遗传学和系统生物学专业知识。我们的目标是 使用组学数据发现关联（目标1和2），并阐明特定基因和生物学特性 这些关联的潜在途径（目标3和4）。我们的最终目标是找出途径， 基因和遗传变异是临床相关的，因此可能是新的靶点。 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.