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.

概括扩张的心肌病（DCM）最多影响1：250个个体，负责约40％的心脏移植。指南建议在DCM检验中进行基因检测，以帮助建立诊断，指导医疗，告知风险分层，并确定处于危险的亲戚。但是，因果变体的识别不到一半患者，约30-40％的测试返回不确定意义的变体（VUS），并且数量适中的基因具有足够的基因型 - 表型数据为医疗管理提供信息。在此提案中，我们解决了DCM中的4个差距研究：1）大多数数据来自引用基因检测的欧洲血统的个人，在估计更广泛的临床和社区人群中的贡献，外观和表型。 2）大多数已建立的DCM基因的基因型 - 表型数据不足以告知基因特异性临床管理。 3）大多数候选基因的证据由于足够的研究缺乏研究而具有模棱两可大量评估致病性。 4）一些疾病基因座可能仍然未被发现，因为GWAS和连锁先前研究中使用的方法对于疾病（例如DCM）的动力不佳，具有变化的发病年龄，高遗传和等位基因异质性和不完整的外观。我们将解决这些基本使用创新的遗传方法和新型大型DCM研究平台的知识差距，其中包括来自5个大型生物库的统一表型，基因分型，测序和逐个状态（IBD）数据包括约100万参与者和> 10,000个DCM案例。具体而言，我们建议使用稀有变体和IBD- 基于：目标1）定义已建立疾病基因的贡献和表型表现在多种多样的非转让DCM种群中；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建立基因的VU；和目标3：发现通过IBD映射和跨生物库内和整个生物库的罕见变体关联的新型DCM基因按大规模进行。平衡这些目标的创新，我们提供了引人入胜的初步数据，证明了我们的可行性确定了一群远有相关个体的方法 RBM20。我们预计这些分析将大大扩展我们对遗传因素的理解 DCM的潜在风险及其临床表现。建立后，我们的平台将支持未来的临床以及遗传研究并促进在诊所实施有针对性干预措施的长期目标和人口水平减轻所有患者的DCM负担。