IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVAL

心房颤动常见基因变异和 PR 间隔的识别

• 批准号: 10591513
• 负责人: Emelia J. Benjamin
• 金额: $ 72.07万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591513
• 关键词: Adherence; All of Us Research Program; Aorta; Atrial Fibrillation; Bioinformatics; Biological Assay; Biometry; Candidate Disease Gene; Cardiac Myocytes; Cardiovascular Diseases; Cardiovascular system; Cell physiology; Cellular Structures; Cessation of life; ChIP-seq; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Complement; Data; Data Set; Disease; Electrophysiology (science); Epidemiology; Epigenetic Process; Etiology; Evaluation; Fellowship; Foundations; Generations; Genes; Genetic; Genetic Transcription; Genetic Variation; Genetic study; Genomics; Health Care Costs; Heart; Heart Abnormalities; Heart Atrium; Heart failure; Histones; Human; Image; Incidence; Individual; International; Knock-out; Left; Maps; Measurement; Measures; Mediating; Mendelian randomization; Mentors; Morbidity - disease rate; Muscle Cells; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Post-Translational Protein Processing; Prevalence; Prevention; Productivity; Proteins; Public Health; Research; Research Personnel; Risk; Risk Factors; Stroke; Structure; Talents; Time; Training; Training Programs; Trans-Omics for Precision Medicine; Translating; United Kingdom; Variant; Viral; Work; biobank; cardiac magnetic resonance imaging; causal variant; computer program; connectin; cost; deep learning; deep learning model; disorder risk; diverse data; early onset; endophenotype; exome; exome sequencing; gene discovery; gene function; genetic variant; genome sequencing; genome wide association study; genomic locus; heart cell; heart dimension/size; heart function; heart rhythm; high risk; human data; innovation; knock-down; knockout gene; lifetime risk; loss of function; mortality; multidisciplinary; new therapeutic target; novel; overexpression; polygenic risk score; programs; risk stratification; risk variant; single cell sequencing; stem cells; stroke risk; success; tool; trait; transcriptome sequencing; virtual

Abstract Atrial fibrillation (AF) is of major public health importance because of increasing prevalence, and high lifetime risk, costs, morbidity, and mortality. Current AF therapies have partial efficacy, moderate adherence, high cost, and substantial morbidity. Hence, there is a profound need to develop a more comprehensive understanding of the etiology of AF to identify individuals at risk for AF and novel drug targets for AF therapies. In 2008, we organized the AFGen Consortium (AFGen) and since that time our highly collaborative, international consortium has led the field of AF genetics. We have described the vast majority of the more than 130 genetic loci that have been associated with AF. To complement our genome wide association data, we also have led efforts to perform whole exome (WES) and genome sequencing (WGS) in individuals with AF. We have identified loss of function variants in the sarcomeric protein, titin, that are significantly associated with early-onset AF. In our competitive renewal application, we now seek to extend our prior work in 4 directions. In Aim 1, we propose to conduct one of the largest disease-based analyses of WES and WGS data. In aggregate we will include over 91K AF cases and 769K controls from the NHLBI Trans-Omics for Precision Medicine program, the NHGRI Center for Common Disease Genomics program, the UK Biobank, 5 clinical trials from the TIMI Study, and the All of Us Program. Our primary analysis will be focused on the identification of AF associated genes. We will then take advantage of this multi-ancestry data to a) fine map loci to identify causal variants, b) develop polygenic risk scores that identify individuals at high risk across ancestries, and c) use Mendelian Randomization to assess both causal effects of risk factors on AF, and AF on heart failure and stroke. In Aim 2, we propose to use deep learning models to reconstruct left atrial (LA) size and function in the United Kingdom Biobank cardiac MRI imaging data in 100K individuals. In preliminary studies, we have derived measurements for 7 LA traits, identified more than 20 genetic loci associated with LA traits, and co-localized LA and AF risk genes. In Aim 3, we propose to validate the top 50 AF and LA associated genes by performing gene perturbation assays in stem cell derived atrial cardiomyocytes. We will perform gene knockouts or over expression followed by assays of myocyte structure and electrophysiology using high content imaging. Finally, in Aim 4, we will support the ongoing efforts of the AFGen Consortium and continuing training of early-stage investigators in a virtual fellowship. We believe that our multidisciplinary team brings together extraordinary expertise in AF genetics, epidemiology, bioinformatics, biostatistics, and basic cardiovascular research. Ultimately, we anticipate that our work will provide novel targets for the risk stratification, prevention, and treatment of AF.

抽象的 心房颤动 (AF) 由于患病率不断增加且寿命较长，因此对公共卫生具有重要意义 风险、成​​本、发病率和死亡率。目前的房颤治疗方法效果部分，依从性中等，费用较高， 和大量的发病率。因此，迫切需要对以下问题有一个更全面的认识： AF 的病因学，以确定有 AF 风险的个体和 AF 治疗的新药物靶点。 2008 年，我们组织了 AFGen 联盟 (AFGen)，从那时起我们就高度协作， 国际财团在房颤遗传学领域处于领先地位。我们已经描述了绝大多数 130 个与 AF 相关的基因位点。为了补充我们的全基因组关联数据，我们 还领导了对 AF 患者进行全外显子组 (WES) 和基因组测序 (WGS) 的工作。 我们已经确定了肌节蛋白肌联蛋白的功能丧失变异，这些变异与 早发性房颤。 在我们竞争性的续签申请中，我们现在寻求在 4 个方向上扩展我们之前的工作。在目标 1 中，我们 提议对 WES 和 WGS 数据进行最大的基于疾病的分析之一。总的来说，我们将 包括来自 NHLBI Trans-Omics for Precision Medicine 项目的超过 91,000 个 AF 病例和 769,000 个对照， NHGRI 常见疾病基因组学计划中心、英国生物库、TIMI 的 5 项临床试验 学习和我们所有人计划。我们的主要分析将集中于 AF 相关的识别 基因。然后，我们将利用这种多祖先数据 a) 精细图谱基因座来识别因果变异，b) 开发多基因风险评分来识别跨祖先的高风险个体，以及 c) 使用孟德尔 随机化评估危险因素对房颤的因果影响，以及房颤对心力衰竭和中风的因果影响。瞄准 2、我们建议在美国使用深度学习模型来重建左心房（LA）的大小和功能 Kingdom Biobank 10 万人的心脏 MRI 成像数据。在初步研究中，我们得出 对 7 个 LA 性状进行测量，确定了 20 多个与 LA 性状相关的遗传位点，并进行了共定位 LA 和 AF 风险基因。在目标 3 中，我们建议通过执行来验证前 50 个 AF 和 LA 相关基因 干细胞来源的心房心肌细胞的基因扰动测定。我们将进行基因敲除或以上 表达，然后使用高内涵成像分析心肌细胞结构和电生理学。最后， 在目标 4 中，我们将支持 AFGen 联盟的持续努力以及早期阶段的持续培训 虚拟联谊中的调查员。 我们相信，我们的多学科团队汇集了 AF 遗传学领域的非凡专业知识， 流行病学、生物信息学、生物统计学和基础心血管研究。最终，我们预计我们的 这项工作将为房颤的风险分层、预防和治疗提供新的目标。