Abstract
Cardiomyopathy presents a significant medical burden due to frequent hospitalizations and invasive interventions. While cardiomyopathy is considered a rare monogenic disorder caused by rare pathogenic variants in a few genes, emerging evidence suggests that common genetic modifiers influence disease penetrance and clinical variability. Quantifying the interplay between common genetic modifiers and rare pathogenic variants is challenging due to the rarity of subjects with cardiomyopathy and pathogenic variant carriers. In this study, we utilized large-scale genetic and phenotypic data from the UK Biobank to refine the genetic architecture of hypertrophic and dilated cardiomyopathies. Using ClinVar annotations and variant effect prediction tools, we first identified known and predicted pathogenic variants and evaluated their association with disease risk, age of diagnosis, and quantitative cardiac phenotypes that reflect disease progression. We next examined the impact of polygenic risk scores on disease in the combined sets of known and predicted pathogenic variant carriers. Indeed, the polygenic risk scores were significantly associated with increased disease risk, with rare pathogenic variant carriers in the top 20% of polygenic risk having 5.7 and 2.3 times higher risk than those in the bottom 20% for hypertrophic and dilated cardiomyopathies, respectively. We observed stronger associations in the carrier sets that included predicted pathogenic variant carriers, suggesting improved statistical power. In summary, our study adds to the evidence that common genetic modifiers influence the cardiomyopathy disease risk among rare pathogenic variant carriers and illustrates the benefits and limitations of incorporating variant effect predictions to examine the polygenic influence in rare disease variant carriers.