Discovery and Characterization of Rare Variant Effects in Dilated Cardiomyopathy via Large-Scale Biobank Analysis

通过大规模生物库分析发现和表征扩张型心肌病的罕见变异效应

基本信息

批准号：
10682290
负责人：
Jennifer Below
金额：
$ 76.92万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-15 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10682290
关键词：
Address Affect Age Age of Onset Alleles Arrhythmia Candidate Disease Gene Cardiac Cardiology Caring Clinic Clinical Clinical Management Clinical Research Communities DNA Data Data Set Diagnosis Dilated Cardiomyopathy Disease Distant England Equilibrium European ancestry Future Genes Genetic Genetic Research Genetic Variation Genomic Segment Genomics Genotype Goals Guidelines Heart Transplantation Heritability Heterogeneity Human Genetics Individual Informatics Intervention Knowledge Link Literature Maps Medical Medical Genetics Methods Participant Pathogenicity Patient Care Patients Penetrance Phenotype Population Recommendation Relative Risks Reporting Research Resources Risk Sample Size Site Testing Uncertainty Variant Work barrier to care biobank candidate identification causal variant clinical effect clinically significant cohort data harmonization defined contribution exome sequencing genetic testing genome wide association study identity by descent improved innovation novel personalized care personalized screening phenome phenotypic data proband rare variant risk stratification risk variant screening trait variant detection variant of unknown significance

项目摘要

SUMMARY Dilated cardiomyopathy (DCM) affects up to 1:250 individuals and is responsible for ~40% of cardiac transplants. Guidelines recommend genetic testing in DCM probands to help establish diagnosis, guide medical care, inform risk stratification, and identify at-risk relatives. However, causal variants are identified in fewer than half of patients, ~30-40% of tests return variants of uncertain significance (VUS), and a modest number of genes have adequate genotype-phenotype data to inform medical management. In this proposal we address 4 gaps in DCM research: 1) Most data are from individuals of European ancestry referred for genetic testing, creating bias in estimates of the contribution, penetrance, and phenotype in the broader clinical and community population. 2) Most established DCM genes have insufficient genotype-phenotype data to inform gene-specific clinical management. 3) The evidence for most candidate genes is equivocal due to lack of study in cohorts sufficiently large to evaluate pathogenicity. 4) Some disease loci likely remain undiscovered because GWAS and linkage approaches used in prior studies are not well-powered for diseases, such as DCM, with variable age of onset, both high genetic and allelic heterogeneity, and incomplete penetrance. We will address these fundamental knowledge gaps using innovative genetic methods and a novel, large-scale DCM research platform that includes harmonized phenotypic, genotyping, sequencing, and identity-by-descent (IBD) data from 5 large biobanks comprising ~1M participants and >10,000 DCM cases. Specifically, we propose to use rare variant and IBD- based methods to: Aim 1) Define the contribution and phenotypic manifestations of established disease genes in multiple diverse, non-referral DCM populations; Aim 2) Assess the pathogenicity of candidate DCM genes with equivocal evidence and establish a novel platform to evaluate VUS in established genes; and Aim 3: Discover novel DCM genes via IBD mapping and rare variant association within and across biobanks at scale. To balance the innovation of these aims, we present compelling preliminary data demonstrating the feasibility of our approaches which identified a cluster of distantly related individuals harboring a common pathogenic variant in RBM20. We anticipate these analyses will substantially expand our understanding of the genetic factors underlying DCM risk and their clinical manifestations. Once established, our platform will support future clinical and genetic research and advance the long-term goal of implementing targeted interventions at the clinic and population level to reduce the burden of DCM for all patients.

概括扩张型心肌病 (DCM) 影响多达 1:250 的个体，并导致约 40% 的心脏移植。指南建议对 DCM 先证者进行基因检测，以帮助确定诊断、指导医疗护理、提供信息风险分层，并识别高危亲属。然而，只有不到一半的人发现了因果变异患者中，约 30-40% 的测试返回意义不确定的变异 (VUS)，并且有一定数量的基因发生变异足够的基因型-表型数据为医疗管理提供信息。在此提案中，我们解决了 DCM 的 4 个差距研究：1）大多数数据来自接受基因检测的欧洲血统个体，从而造成了偏见对更广泛的临床和社区人群中的贡献、外显率和表型的估计。 2）大多数已建立的 DCM 基因没有足够的基因型-表型数据来指导基因特异性临床管理。 3) 由于缺乏足够的队列研究，大多数候选基因的证据是模棱两可的大以评估致病性。 4) 由于 GWAS 和连锁作用，一些疾病位点可能仍未被发现先前研究中使用的方法对于疾病（例如 DCM）的发病年龄各不相同，效果不佳，具有高度的遗传和等位基因异质性，以及不完全外显率。我们将解决这些基本问题使用创新的遗传方法和新颖的大规模 DCM 研究平台来弥补知识差距，其中包括来自 5 个大型生物库的统一表型、基因分型、测序和血统身份 (IBD) 数据包括约 100 万参与者和超过 10,000 个 DCM 病例。具体来说，我们建议使用罕见变异和 IBD- 基于方法来：目标 1) 定义已确定的疾病基因的贡献和表型表现在多个不同的、非转诊的 DCM 人群中；目标 2) 评估候选 DCM 基因的致病性模棱两可的证据并建立一个新的平台来评估已确定基因中的 VUS；目标 3：发现通过 IBD 作图和大规模生物库内和跨生物库的罕见变异关联来发现新的 DCM 基因。为了平衡为了实现这些目标的创新，我们提供了令人信服的初步数据，证明了我们的可行性方法鉴定了一群远亲携带共同致病变异的个体 RBM20。我们预计这些分析将大大扩展我们对遗传因素的理解潜在的 DCM 风险及其临床表现。一旦建立，我们的平台将支持未来的临床和基因研究，并推进在临床和临床实施有针对性的干预措施的长期目标人口水平，以减轻所有患者的 DCM 负担。