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 由于患病率增加，心房颤动（AF）至关重要 风险，成本，发病率和死亡率。当前的AF疗法具有部分疗效，中等依从性，高成本， 和实质性发病率。因此，迫切需要对 AF的病因识别有AF和新型药物靶向AF疗法的人的病因。 在2008年，我们组织了Afgen联盟（AFGEN），从那时起，我们的高度合作， 国际财团领导了AF遗传学领域。我们已经描述了绝大多数不止 与AF相关的130个遗传基因座。为了补充我们的基因组广泛关联数据，我们 还领导了在患有AF的个体中执行整个外显子组（WES）和基因组测序（WGS）的努力。 我们已经确定了与肌膜蛋白Titin中功能变异的丧失，它们与 早期发作的AF。 在我们的竞争性更新应用中，我们现在寻求将以前的工作扩展到4个方向上。在AIM 1中，我们 建议对WES和WGS数据进行基于疾病的最大分析之一。在总体上，我们将 包括超过91K的AF病例和来自NHLBI Trans-Omics精密医学计划的769K控件， NHGRI普通疾病基因组学中心，英国生物库，TIMI的5项临床试验 研究，以及我们所有的计划。我们的主要分析将集中于识别与AF相关的识别 基因。然后，我们将利用此多项式数据到a）精细地图基因座来识别因果变体，b） 开发多基因风险评分，以识别各个祖先风险高的人，c）使用门德利安 随机分组以评估危险因素对AF的因果影响，以及AF对心力衰竭和中风的因果关系。目标 2，我们建议使用深度学习模型来重建曼联的心房（LA）大小和功能 王国生物银行心脏MRI成像数据中的100K个体。在初步研究中，我们得出了 7个LA性状的测量，确定了20多个与LA性状相关的遗传基因座，并共定位 LA和AF风险基因。在AIM 3中，我们建议通过执行前50个AF和LA相关的基因验证 基因扰动测定在衍生的心房心肌细胞的干细胞中。我们将执行基因淘汰或结束 表达，然后使用高含量成像对肌细胞结构和电生理学进行测定。最后， 在AIM 4中，我们将支持Afgen联盟的持续努力和继续对早期阶段的培训 虚拟奖学金的调查人员。 我们认为，我们的多学科团队将AF遗传学的非凡专业知识汇集在一起​​， 流行病学，生物信息学，生物统计学和基本心血管研究。最终，我们预计我们的 工作将为AF的风险分层，预防和治疗提供新的目标。