Genotype-Phenotype Associations in Pediatric Cardiomyopathy

描述（由申请人提供）：小儿心肌病是一种异质性遗传疾病，发病率和死亡率高，儿童经常出现导致死亡或移植的暴发性疾病。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，早期管理和预后。本研究的具体目的是：1）在一个仔细分型的队列中确定导致儿童心肌病的致病和疾病相关遗传变异; 2）确定基因型-表型相关性，以便进行风险分层并改善管理和治疗。外显子组测序将用作患有收缩期（扩张型心肌病）或舒张期（肥厚型或限制型心肌病）功能障碍的大型儿科心肌病受试者队列分层遗传分析的一部分。这项研究将通过确定已知引起心肌病的基因突变的患病率以及确定儿科人群中新的致病基因来显着增加我们对儿科心肌病的理解。基因关联测试将识别改变疾病的变异。用于解释外显子组水平遗传信息的新型生物信息学和系统生物学应用将为基因组数据转化为临床实用性贡献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型-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.