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Background and Rationale Hypertrophic cardiomyopathy (HCM) is a leading cause of heart failure and sudden cardiac death, characterised by increased left ventricular wall thickness not attributable to abnormal loading conditions. Although pathogenic variants in sarcomeric genes are well established in HCM, disease penetrance and clinical expressivity are highly variable, ranging from ~45% in familial settings to ~15% in population-based genetic screening, and more than half of clinically diagnosed cases lack identifiable sarcomeric mutations altogether. These observations challenge the traditional monogenic model of HCM and support an increasingly recognised polygenic architecture that includes candidate modifier loci. Genome-wide association studies (GWAS) have demonstrated common genetic variants contribute substantially to HCM susceptibility, with many loci shared with left ventricular hypertrophy (LVH) traits and predominantly localising to non-coding regions with potential roles in gene regulation. However, the functional consequences of these non-coding variants, particularly in the context of known pathogenic HCM mutations, remain poorly understood.Hypothesis We hypothesise that some non-coding GWAS loci can act as genetic modifiers by interacting with pathogenic HCM variants to alter cardiomyocyte gene regulation and cellular phenotypes, thereby contributing to the known clinical variability in HCM disease penetrance and expressivity.Aims This study aims to functionally characterise candidate non-coding modifier loci associated with HCM and left ventricular wall thickness and assess their molecular and cellular effects in human cardiomyocytes across diverse genetic backgrounds.Methods We will use a panel of induced pluripotent stem cell (iPSC) lines derived from HCM patients harbouring defined pathogenic variants, alongside matched control lines in age, sex, ethnicity and genetic background. Candidate modifier loci will be prioritised from HCM and LVH GWAS based on shared association signals and further refined using cardiomyocyte-specific epigenomic data, focusing on regulatory regions that respond to hypertrophic stimuli and can be linked to putative effector genes via chromatin accessibility and promoter–enhancer interaction mapping. Using a ribonucleoprotein-based CRISPR/Cas9 approach, we will generate targeted deletions of selected non-coding regions in both control and HCM iPSCs. Edited lines will be differentiated into left ventricular-like cardiomyocytes using established protocols that enhance maturation and purity.Transcriptomic and epigenomic consequences of modifier perturbations will be assessed using RNA sequencing and H3K27ac ChIP-seq to identify changes in gene expression and regulatory activity downstream of each locus. To connect molecular effects with HCM-relevant cellular outcomes, we will quantify cardiomyocyte hypertrophy using high-content microscopy under basal and hypertrophic conditions. For loci demonstrating significant effects on cell size, extended phenotyping will be performed to assess non-hypertrophic HCM traits, including metabolic dysfunction, sarcomeric organisation and electrophysiological abnormalities.Expected Results We expect that deletion of candidate modifier loci will lead to locus-specific changes in cardiomyocyte gene expression and enhancer activity, with potential differential effects between healthy and HCM genetic backgrounds. We anticipate that selected loci may modulate cardiomyocyte hypertrophy and influence additional cellular phenotypes implicated in HCM pathophysiology, thereby providing functional evidence for modifier effects on molecular disease presentation.Conclusion This study uses a systematic comparative framework for post-GWAS investigation of non-coding genetic variation in hypertrophic cardiomyopathy. By integrating genome editing, multi-omic profiling, and quantitative cellular phenotyping in patient-derived cardiomyocytes, our work has the potential to advance understanding of the molecular mechanisms underlying variable HCM expressivity and to inform the development of improved risk stratification and precision medicine strategies.