Cardiac Structural and Sarcomere Genes Associated With Cardiomyopathy Exhibit Marked Intolerance of Genetic Variation

Author:

Pan Stephen1,Caleshu Colleen A.1,Dunn Kyla E.1,Foti Marcia J.1,Moran Maura K.1,Soyinka Oretunlewa1,Ashley Euan A.1

Affiliation:

1. From Stanford Center for Inherited Cardiovascular Disease, Stanford Hospital & Clinics (S.P., C.A.C., K.E.D., M.J.F., M.K.M., O.S., E.A.A.) and Biomedical Informatics Training Program, Stanford University School of Medicine (S.P.), Stanford, CA.

Abstract

Background— The clinical significance of variants in genes associated with inherited cardiomyopathies can be difficult to determine because of uncertainty regarding population genetic variation and a surprising amount of tolerance of 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 rates of single nucleotide genetic variation in all known genes from the exomes of >5000 individuals from the National Heart, Lung, and Blood Institute’s Exome Sequencing Project, as well as the rates of structural variation from the Database of Genomic Variants. Most variants were rare, with over half unique to 1 individual. Cardiomyopathy-associated genes exhibited a rate of nonsense variants, about 96.1% lower than other Mendelian disease genes. We tested the ability of in silico algorithms to distinguish between a set of variants in MYBPC3 , MYH7 , and TNNT2 with strong evidence for pathogenicity and variants from the Exome Sequencing Project data. Algorithms based on conservation at the nucleotide level (genomic evolutionary rate profiling, PhastCons) did not perform as well as amino acid-level prediction algorithms (Polyphen-2, SIFT). Variants with strong evidence for disease causality were found in the Exome Sequencing Project data at prevalence higher than expected. Conclusions— Genes associated with cardiomyopathy carry very low rates of population variation. The existence in population data of variants with strong evidence for pathogenicity suggests that even for Mendelian disease genetics, a probabilistic weighting of multiple variants may be preferred over the single gene causality model.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Genetics (clinical),Cardiology and Cardiovascular Medicine,Genetics

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