Genotype-Phenotype Associations in Pediatric Cardiomyopathy

描述(申请人提供)：儿童心肌病是一种发病率和死亡率高的异质性遗传病，儿童常伴有暴发性疾病，导致死亡或移植。该项目的长期目标是确定决定心肌病发展和进展的遗传因素，以改善预防、监测、早期治疗和预后。这项研究的具体目标是1)在仔细的表型队列中确定儿童心肌病的致病原因和与疾病相关的遗传变异，以及2)确定允许风险分层和改进管理和治疗的基因-表型相关性。外显子组测序将被用于对患有收缩功能障碍(扩张型心肌病)或舒张期功能障碍(肥厚型或限制性心肌病)的大量儿童心肌病患者进行分层遗传分析。这项研究将通过确定已知的导致心肌病的基因突变的流行率，以及在儿科人群中识别新的致病基因，显著增加我们对儿童心肌病的了解。基因关联测试将识别可以改变疾病的变异。新的生物信息学和系统生物学应用于外显子组水平的遗传信息的解释，将有助于将基因组数据转化为临床实用的基础知识和技术创新。这些目标将提供关键的遗传结构数据，识别具有较大影响的变异，并实现推进管理和治疗所需的基因-表型相关性。

DESCRIPTION (provided by applicant): Pediatric cardiomyopathy is a heterogeneous genetic disease with high morbidity and mortality in which children often present with fulminant disease leading to death or transplant. The long term goal of this project is to identify the genetic factors that determine the development and progression of cardiomyopathy in order to improve prevention, surveillance, early management, and prognosis. The specific aims of this study are 1) to identify the disease causing and disease associated genetic variants underlying pediatric cardiomyopathy in a carefully phenotyped cohort and 2) to identify genotype-phenotype correlations that allow for risk stratification and improve management and therapy. Exome sequencing will be used as part of a tiered genetic analysis in a large cohort of pediatric cardiomyopathy subjects with systolic (dilated cardiomyopathy) or diastolic (hypertrophic or restrictive cardiomyopathy) dysfunction. This study will significantly increase our understanding of pediatric cardiomyopathy by defining the prevalence of mutations in genes known to cause cardiomyopathy as well as identifying novel disease causing genes in the pediatric population. Genetic association tests will identify variants that modify disease. Novel bioinformatics and systems biology applications for interpretation of exome level genetic information will contribute fundamental knowledge and technical innovation to the translation of genomic data to clinical utility. These aims will provide critical genetic architecture data, identify variants with large effects, and enable genotype-phenotype correlations necessary for advancing management and therapy.