Abstract 19717: Cardiac Structural and Sarcomere Genes Associated with Cardiomyopathy Exhibit Marked Intolerance of Genetic Variation

Abstract

Background - The clinical significance of variants in genes associated with inherited cardiomyopathies can be hard to determine as a result of uncertainty regarding population genetic variation and the recent finding of a surprising amount of tolerance in the genome even to loss of function variants. We hypothesized that genes associated with cardiomyopathy might be particularly resistant to the accumulation of genetic variation. Methods and Results - We analyzed the rate of single nucleotide genetic variation in the coding regions of all known genes sequenced from the exomes of over 5,000 individuals from the National Heart, Lung, and Blood Institute’s Exome Sequencing Project (ESP), as well as the rates of structural variation from the Database of Genomic Variants (DGV). Most variants were rare, with over half being found in only one individual. Cardiomyopathy associated genes exhibited very low rates of variation. Nonsense variants in particular were extremely rare in these genes, appearing at a rate 96.1% lower than in other Mendelian disease genes, and 98.7% lower than genes without Mendelian disease association. We tested the utility of in-silico algorithms to predict variant pathogenicity using a set of variants in MYBPC3, MYH7, and TNNT2 with strong evidence for disease causality compared with variants from the ESP data. Algorithms based on conservation at the nucleotide level (GERP, PhastCons) did not perform as well as amino acid level prediction algorithms (Polyphen-2, SIFT). Variants previously judged causal for disease were found in the ESP data at a prevalence higher than expected. Conclusion - Genes previously associated with cardiomyopathy carry very low rates of population variation. The existence in population data of variants previously held to be causal suggests that even for Mendelian disease genetics, a probabilistic weighting of multiple variants may be preferred over the ‘single gene’ causality model.