Identification of Candidate Cardiomyopathy Modifier Genes through Genome Sequencing and RNA Profiling

Malene E Lindholm ^1* Sarah Abramowitz ¹ Daryl M Waggott ¹ Megan E Grove ¹ Frederick E Dewey ¹ Cuiping Pan ¹ Aleksandra Pavlovic ¹ Ching Shang ¹ Yong Huang ¹ Leore Bensabath ¹ Rachel L Goldfeder ¹ Sergio Pablo Cordero ¹ Ayca Erbilgin ¹ James Priest ¹ Hassan Chaib ¹ Megan Roy-Puckelwartz ² Sharlene M Day ³ Elizabeth M McNally ² Thomas Cappola ³ Gerald Dorn ⁴ Euan A Ashley ¹ Matthew T Wheeler ¹

• ¹ Stanford University, Stanford, United States
• ² Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
• ³ University of Pennsylvania, Philadelphia, Pennsylvania, United States
• ⁴ School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States

Background. Phenotypic heterogeneity is apparent among individuals with putative monogenic disease, such as familial hypertrophic cardiomyopathy. Genome sequencing (GS) allows interrogation of the full spectrum of inborn genetic variation in an individual and RNA profiling provides a snapshot of the cardiac-specific pathogenic effects on gene expression. Objectives. Identify candidate genetic modifiers of hypertrophic cardiomyopathy phenotype. Methods. We performed GS of 48 individuals with variants in MYH7, the gene encoding beta myosin heavy chain, and a personal or family history of cardiomyopathy. The genome sequences were annotated with a custom pipeline optimized for cardiovascular gene variant detection.We utilized multiple lines of evidence to prioritize genes together with rare variant gene-based association testing to identify candidate genetic modifiers. Results. GS identified the MYH7 variant in all 48 cases. Several variants were reclassified based on best available data. We identified known disease-associated genes (MYBPC3, FHOD3), a priori candidate modifiers (ATP1A2, RYR2), and novel candidate modifiers of cardiomyopathy including PACSIN3 and SORBS2. We identified regulatory variants and intergenic regions associated with the phenotypes. Using RNA profiling, we show that several genes identified through gene-based association testing are differentially regulated in human hypertrophic cardiomyopathy, and in models of disease. Conclusion. Evaluation of the whole genome, even in the case of alleged monogenic disease, leads to important new insights. The identified variants, regions, and genes are candidates to modify disease presentation in cardiomyopathy.