Using Electrocardiogram Genetics to Inform Arrhythmia Risk

利用心电图遗传学来了解心律失常风险

• 批准号: 10366259
• 负责人: Patrick Thomas Ellinor
• 金额: $ 76.8万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-20 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10366259
• 关键词: Address; Adverse event; Affect; Arrhythmia; Cardiac; Cardiac Myocytes; Cardiovascular Diseases; Cellular Assay; ClinVar; Clinical; Clinical Data; Code; Collection; Data; Diagnostic; Disease; EKG P Wave; Electrocardiogram; Electronic Health Record; Electrophysiology (science); Fibroblasts; Genes; Genetic; Genetic Variation; Genomics; Goals; Healthcare; Heart Diseases; Heritability; Human Genetics; Individual; Lead; Link; Long QT Syndrome; Measurement; Measures; Medical Genetics; Methods; Mission; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Outcome; Pathogenicity; Persons; Physiological Processes; Prevention; Prognosis; Proxy; Public Health; Reproducibility; Resources; Risk; Sampling; Science; Testing; Trans-Omics for Precision Medicine; Translations; Validation; Variant; adjudication; biobank; causal variant; clinical phenotype; clinical practice; clinically relevant; cohort; endophenotype; exome; exome sequencing; genetic testing; genetic variant; genome sequencing; high throughput screening; improved; innovation; large scale data; mortality risk; novel; population based; programs; rare variant; repository; response; risk stratification; screening; stem cells; sudden cardiac death; trait; transmission process; variant of unknown significance; whole genome

Project summary Nearly one in three people will have an arrythmia during their lifetime and up to 10% may die of sudden cardiac death. Arrhythmias and sudden cardiac death are heritable and often caused by problems of cardiac conduction or repolarization. Nevertheless, the genetic causes are not well-defined. Moreover, interpretation of genetic variation is limited by the frequent discovery of variants of uncertain clinical significance. Our preliminary data suggest that using high-depth genomic sequencing data from large-scale biobanks with routinely measured electrocardiogram intervals – indicators of arrhythmia and sudden cardiac death risk – has the potential to address these challenges. Our overall goal is to minimize morbidity from cardiovascular disease. The specific objective of this proposal is to utilize high-depth whole genome and exome sequencing data to identify, functionally evaluate, and clinically characterize genetic variation that predisposes to arrhythmia risk and sudden cardiac death. To do so, we will leverage a unique and massive repository of individuals with genomic sequencing, electrocardiograms, and clinical data. The electrocardiogram is a widely utilized and inexpensive screening test. Standard electrocardiogram intervals are reliable and reproducible measurements that are associated with a variety of cardiac conditions, most notably arrhythmias and sudden cardiac death. Our overall hypothesis is that functional and clinically relevant rare genetic variation underlies population-based electrocardiographic interval variability. In Aim 1, we will identify rare coding variation associated with electrocardiographic intervals. We will use data from a unique resource of over 220,000 individuals with electrocardiograms and whole genome or exome sequence data in the National Heart Lung and Blood Institute’s Trans-Omics for Precision Medicine Program, UK Biobank, Geisinger MyCode/DiscovEHR cohort, and Mass General Brigham HealthCare Biobank. In Aim 2, we will validate and characterize the electrophysiological and structural impact of identified genes in stem cell derived cardiomyocytes. In Aim 3 we will assess whether variants with large electrocardiographic trait effect sizes are associated with arrhythmia risk using electronic health record data in nearly 400,000 sequenced individuals, and variant pathogenicity using ClinVar, a repository of clinical variant adjudications. Studying how rare genetic variants affect the electrocardiogram is an innovative approach for understanding arrhythmia and sudden cardiac death risk. We anticipate that this paradigm will be broadly applicable to other quantitative endophenotypes and heart diseases. We submit that our aims are consistent with the NHLBI’s mission of understanding the causes of disease and enabling translation of basic discoveries into clinical practice.

项目概要 近三分之一的人一生中会出现心律失常，高达 10% 的人可能死于心脏病 死亡。心律失常和心源性猝死具有遗传性，通常是由心脏问题引起的 传导或复极化。然而，遗传原因尚不明确。此外，解释 遗传变异因经常发现临床意义不确定的变异而受到限制。我们的 初步数据表明，使用来自大规模生物库的深度基因组测序数据 常规测量的心电图间隔（心律失常和心源性猝死风险的指标）已 应对这些挑战的潜力。 我们的总体目标是尽量减少心血管疾病的发病率。此次活动的具体目标 提议是利用高深度的全基因组和外显子组测序数据来识别、功能性 评估并临床表征易导致心律失常风险和突发事件的遗传变异 心源性死亡。为此，我们将利用一个独特且庞大的基因组个体存储库 测序、心电图和临床数据。 心电图是一种广泛使用且廉价的筛查测试。标准心电图 间隔是与各种心脏状况相关的可靠且可重复的测量， 最明显的是心律失常和心源性猝死。我们的总体假设是功能和临床 相关的罕见遗传变异是基于人群的心电图间隔变异的基础。 在目标 1 中，我们将识别与心电图间隔相关的罕见编码变异。我们将使用 来自超过 220,000 名个体的独特资源的数据，包括心电图和全基因组或外显子组 国家心肺和血液研究所精准医学跨组学计划中的序列数据， UK Biobank、Geisinger MyCode/DiscovEHR 队列和 Mass General Brigham HealthCare Biobank。瞄准 2，我们将验证和表征干细胞中已识别基因的电生理和结构影响 细胞衍生的心肌细胞。在目标 3 中，我们将评估是否具有较大心电图特征的变异 使用近 400,000 份电子健康记录数据，效应大小与心律失常风险相关 使用 ClinVar（临床变异裁决存储库）对个体进行测序，并确定变异致病性。 研究罕见的遗传变异如何影响心电图是一种创新方法 了解心律失常和心源性猝死的风险。我们预计这种范式将被广泛应用 适用于其他定量内表型和心脏病。我们认为我们的目标是一致的 NHLBI 的使命是了解疾病原因并转化基本发现 进入临床实践。