IN-SILICO ANALYSIS OF GENETIC VARIANTS IN HYPERTROPHIC CARDIOMYOPATHY (HCM) USING ACMG/AMP GUIDELINES

Background: Hypertrophic cardiomyopathy (HCM) is a genetically heterogeneous myocardial disorder, most commonly caused by pathogenic variants in genes encoding sarcomeric proteins.The increasing application of next-generation sequencing (NGS) has enhanced the detection of single-nucleotide variants (SNVs) and single-nucleotide polymorphisms (SNPs); however, accurate clinical interpretation remains challenging due to extensive allelic heterogeneity, incomplete penetrance, and the presence of splice-altering or deep intronic variants that are not readily identified through conventional exonic analyses.Objectives: This study aims to (i) establish an integrated bioinformatics workflow for SNV and SNP detection, annotation, and interpretation in HCM; (ii) apply the ACMG/AMP variant classification framework with cardiomyopathy-specific refinements proposed by the ClinGen Cardiomyopathy Variant Curation Expert Panel; and (iii) demonstrate how systematic evidence integration can reduce the burden of variants of uncertain significance (VUS).Methods: Computational analyses were performed using pre-processed whole-exome variant datasets in Variant Call Format (VCF).Variants underwent quality-based filtering, gene-based prioritisation, and population frequency assessment using gnomAD.ANNOVAR, ClinVar, and Ensembl Variant Effect Predictor (VEP) were used for functional and clinical annotation.In-silico pathogenicity prediction tools, including REVEL, CADD, PolyPhen-2, SIFT, and SpliceAI, were applied as supporting evidence.Final variant classification followed ACMG/AMP guidelines with ClinGen cardiomyopathy-specific adaptations.