Genomics of pediatric cardiomyopathy

Abstract

Cardiomyopathy in children is a leading cause of heart failure and cardiac transplantation. Disease-associated genetic variants play a significant role in the development of the different subtypes of disease. Genetic testing is increasingly being recognized as the standard of care for diagnosing this heterogeneous group of disorders, guiding management, providing prognostic information, and facilitating family-based risk stratification. The increase in clinical and research genetic testing within the field has led to new insights into this group of disorders. Mutations in genes encoding sarcomere, cytoskeletal, Z-disk, and sarcolemma proteins appear to play a major role in causing the overlapping clinical phenotypes called cardioskeletal myopathies through "final common pathway" links. For myocarditis, the high frequency of infectious exposures and wide spectrum of presentation suggest that genetic factors mediate the development and course of the disease, including genetic risk alleles, an association with cardiomyopathy, and undiagnosed arrhythmogenic cardiomyopathy. Finally, while we have made strides in elucidating the genetic architecture of pediatric cardiomyopathy, understanding the clinical implications of variants of uncertain significance remains a major issue. The need for continued genetic innovation in this field remains great, particularly as a basis to drive forward targeted precision medicine and gene therapy efforts. IMPACT: Cardiomyopathy and skeletal myopathy can occur in the same patient secondary to gene mutations that encode for sarcomeric or cytoskeletal proteins, which are expressed in both muscle groups, highlighting that there are common final pathways of disease. The heterogeneous presentation of myocarditis is likely secondary to a complex interaction of multiple environmental and genetic factors, suggesting a utility to genetic testing in pediatric patients with myocarditis, particularly those in higher risk groups. Given the high prevalence of variants of uncertain significance in genetic testing, better bioinformatic tools and pipelines are needed to resolve their clinical meaning.