Identification of Novel Genetic Variants and Comorbidities Associated With ICD-10-Based Diagnosis of Hypertrophic Cardiomyopathy Using the UK Biobank Cohort

Abstract

Objectives: To identify previously unrecognized genetic variants and clinical variables associated with the ICD-10 (International Classification of Diseases 10)-based diagnosis of hypertrophic cardiomyopathy in the UK Biobank cohort. Background: Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiovascular disorder with more than 2000 known mutations in one of eight genes encoding sarcomeric proteins. However, there is considerable variation in disease manifestation, suggesting the role of additional unrecognized contributors, genetic and otherwise. There is substantial interest in the use of real-world data, such as electronic health records to better understand disease mechanisms and discover new treatment strategies, but whether ICD-10-based diagnosis can be used to study HCM genetics is unknown. Methods: In a genome-wide association study (GWAS) using the UK Biobank, we analyzed the genomes of 363 individuals diagnosed with HCM based on ICD-10 coding compared to 7,260 age, ancestry, and sex-matched controls in a 1:20 case: control design. Genetic variants were analyzed by Plink’s firth logistic regression and assessed for association with HCM. We also examined 61 biomarkers and other diagnoses in the 363 HCM cases and matched controls. Results: The prevalence of ICD-10-based diagnosis of HCM in the UK Biobank cohort was 1 in 1,342, suggesting disease assignment based on the two ICD-10 codes underestimates HCM prevalence. In addition, common cardiovascular comorbidities were more prevalent in ICD-10-based HCM cases in comparison to controls. We identified two novel, non-sarcomeric genetic variants in KMT2C rs78630626, and PARD3B rs188937806 that were associated with ICD-10 codes for HCM with genome-wide significance (p < 5 x 10−8). These are associated with an increased odds ratio (OR) of ~3.8 for being diagnosed with HCM. Minor allele frequency (MAF) of each variant was >1%. Discussion: Disease assignment based strictly on ICD-10 codes may underestimate HCM prevalence. Individuals with HCM were more frequently diagnosed with several comorbid conditions, such as hypertension, atherosclerotic heart disease, diabetes, and kidney failure, suggesting they may contribute to disease manifestation. This UK Biobank database-based GWAS identified common variants in KMT2C and PARD3B that are associated with HCM diagnosis, which may represent novel modifier genes. Our study demonstrates the feasibility and limitations of conducting phenotypic and genotypic characterization of HCM based on ICD-10 diagnosis in a large population-based cohort.