Optimizing Gene Selection and Network-Level Insights in Hypertrophic Cardiomyopathy: A Novel Genetic Algorithm Combined with WGCNA and Statistical Filtering

A cardiac condition known as hypertrophic cardiomyopathy (HCM) is characterized by an irregular thickening of the heart muscle. There is still much to learn about its molecular mechanics. In order to pinpoint important genes and regulatory abnormalities in HCM, this work offers a thorough computational analysis of gene expression data. Two strategies are employed here. Initially, hub genes were identified, co-expression networks were constructed, gene modules were detected, and they were linked to clinical characteristics using Weighted Gene Co-expression Network Analysis (WGCNA). Second, the same dataset was subjected to three different gene selection techniques: variance-based filtering, volcano plot analysis, and a Genetic Algorithm for Novel Gene Acquisition (GANGA). For the first time, GANGA successfully incorporates a previously defined objective function from simulated annealing into a genetic algorithm. Additionally, it uses two-point crossover, meticulous parameter optimization, and customizable elitism. Three genes were shown to be shared by all approaches, including WGCNA: RASID1, CEBPD, and S100A9. Through enrichment analysis, these were confirmed to be implicated in pathways linked to inflammation. Their incorporation into cytokine-driven networks was validated by investigation of protein-protein interactions. S100A9 emerged as a crucial regulator that activates RASD1 in illness, according to co-expression networks constructed for normal and HCM samples, which showed changed regulatory patterns. The methodological development of modifying and optimizing a simulated annealing-based objective function within a GA framework in GANGA for efficient gene selection, as well as the comprehensive multi-method pipeline for HCM analysis, are what make this study distinctive.