Abstract 13296: Risk Loci of Hypertrophic Cardiomyopathy Identified via the UK Biobank

Abstract

Introduction: Hypertrophic cardiomyopathy (HCM) is the most commonly inherited cardiac disease affecting 1:500 to 1:200 individuals worldwide. HCM has a heterogeneous genetic profile and phenotypic expression. More than 1400 known pathogenic variants have been identified in 11 sarcomere genes. In about 40% of HCM patients, the genetic cause may not be identified. The same mutation may lead to different phenotypes and severity in different individuals. Identification of novel HCM genes and modifiers will expand our understanding of the signaling pathways that are responsible for phenotypic expression of HCM. Methods: The UK Biobank comprises clinical and genetic data for greater than 500,000 individuals. We used OASIS, an information system for analyzing, searching, and visualizing associations between phenotype and genotype data to analyze this data. We compared control individuals to HCM individuals identified by ICD-10 code (I42.1 and I42.2) in a 20-to-1 fashion. Related individuals and those with confounding diagnoses were excluded. Results: The analysis was performed with Plink’s GLM option, and we identified 84 variants with a minor allele frequency of 0.5% or greater in 65 genes associated with HCM with a p < 1x10 -6 , including 4 with p < 5x10 -8 . The identified genes encode lncRNAs, miRNAs, and membrane proteins. Variants with high significance were identified in the genes encoding putative ciliary components DNAL4 (dynein axonemal light chain 4; p = 2.9x10 -8 ), MYO1D (unconventional myosin 1D; p = 3.1x10 -8 ), ITFAP (intraflagellar transport associated protein; p = 9.5x10 -8 ), CABCOCO1 (ciliary associated calcium biding coiled-coil 1; p = 3.7x 10 -7 ), EVL (Enah-Vasp-like; p = 4.4x 10 -7 ) and IFT122 (intraflagellar transport 122; p = 8.0 x10 -7 ). Conclusion: While none of these have previously associated with HCM, our findings suggest ciliary structure and function may play a role in disease manifestation. Our method is unique by pooling individuals in a large population set to identify potential causative or contributing mutations. Bioinformatic tools, such as OASIS, allow for the identification of previously unrecognized variants that may play a role in the development of HCM. This approach has identified numerous novel genes as possible risk loci.