Inherited cardiomyopathy is a genetically diverse disease marked by considerable phenotypic heterogeneity. Mutations in more than 100 genes lead dilated, hypertrophic and other forms of cardiomyopathy. Within individual families, most single mutations display a range of clinical expression from severe early onset disease to minimal or no symptoms. While genetic mutations can provide a highly useful biomarker to indicate risk for developing disease, the highly level of variability associated with most mutations makes it difficult to accurately predict clinical courses. Similar to genetically complex diseases, genetic variations in noncoding regulatory regions are thought to play an important role in modifying phenotypes for these single gene disorders. The overall goal of this proposal is to study noncoding regulatory variation linked to known cardiomyopathy genes with the goal to better understand phenotypic heterogeneity in genetic cardiomyopathy. We hypothesize that variation in enhancer regions will modify cardiomyopathy gene expression and thus, also modify clinical phenotypes. The overall organization of this proposal is to identify candidate enhancer regions, validate enhancer regions, and study how variation affects their function.
In Aim 1, we will identify candidate enhancers for cardiomyopathy genes using bioinformatic approaches to intersect data from human hearts and cardiomyocytes derived from induced pluripotent stem cells. By focusing on enhancers active in the human adult heart and in the failed heart, and only on those that interact with a subset of cardiomyopathy genes, we will determine the reliability of prediction protocols for enhancer identification. Preliminary data supports that this approach identifies previously known enhancers and also classifies other regions as strong enhancer candidates. The goal of Aim 2 is to validate these enhancer predictions using reporter expression studies and deletion of candidate enhancers. Finally, Aim 3 will evaluate genetic variation found in whole genome sequencing data from a cohort of cardiomyopathy patients to characterize how sequence variation in enhancer regions affects expression and correlates with phenotypes. Preliminary data indicates that sequence variants present in the cardiomyopathy cohort fall within predicted transcription factor binding sites and thus, are potentially functional. The proposed approach can improve phenotypic prediction in genetic cardiomyopathy and revolutionize the clinical care of these patients. Moreover, these studies serve as a novel framework to characterize noncoding variation, which can be applied to many other genetically and phenotypically heterogeneous diseases that affect the heart.
Mutations in nearly 100 genes cause inherited forms of cardiomyopathy, and these mutations are associated with a great deal of variability in the clinical course they produce. We are investigating the noncoding regions of cardiomyopathy genes to identify the most critical regulatory regions and the genetic variation that falls within these regulatory regions. The goal of this project is to improve the predictive power of genetic analysis of cardiomyopathy by characterizing how noncoding variation influences patient phenotypes.
