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Using Electrocardiogram Genetics to Inform Arrhythmia Risk

利用心电图遗传学来了解心律失常风险

基本信息

批准号：
10550134
负责人：
Patrick Thomas Ellinor
金额：
$ 72.02万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
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/10550134
关键词：
Address Adverse event Affect Arrhythmia Cardiac Cardiac Myocytes Cardiovascular Diseases Cellular Assay Classification 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 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 Proliferating 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 data submission 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

项目摘要

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.

项目摘要近三分之一的人一生中会有心律失常，高达10%的人可能死于突发性心脏病。死亡心律失常和心源性猝死是可遗传的，通常是由心脏问题引起的传导或复极化。然而，遗传原因并不明确。此外，对遗传变异受到临床意义不确定的变异体的频繁发现的限制。我们初步数据表明，使用来自大规模生物库的高深度基因组测序数据，常规测量的心电图间期--心律失常和心源性猝死风险的指标-- 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。这一具体目标建议是利用高深度的全基因组和外显子组测序数据，评估和临床表征易患心律失常风险和突发性心律失常的遗传变异，心源性死亡为了做到这一点，我们将利用一个独特的和巨大的个人与基因组库，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联的可靠且可再现的测量，最显著的是心律失常和心脏性猝死。我们的总体假设是，相关的罕见遗传变异是基于人群的心电图间期变异的基础。在目标1中，我们将识别与心电图间期相关的罕见编码变异。我们将使用来自超过220，000个心电图和全基因组或外显子组的独特资源的数据国家心肺和血液研究所的Trans-Omics for Precision Medicine项目中的序列数据，英国生物库、Geisinger MyCode/DiscovEHR队列和Mass General Brigham HealthCare生物库。在Aim中 2，我们将验证和表征茎中已鉴定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大心电图特征的变异体使用近400，000份电子健康记录数据，效应量与心律失常风险相关测序的个体，以及使用ClinVar的变异致病性，ClinVar是临床变异裁定的储存库。研究罕见的遗传变异如何影响心电图是一种创新的方法，了解心律失常和心源性猝死风险。我们预计，这一模式将广泛适用于适用于其他定量内表型和心脏疾病。我们认为我们的目标是一致的 NHLBI的使命是了解疾病的原因，并使基本发现能够转化临床实践中。