GPBSO: Gene Pool-Based Brain Storm Optimization for SNP Epistasis Detection

Background/Objectives: Detecting high-order epistatic interactions in genome-wide association studies (GWAS) is essential for understanding complex diseases, yet most existing approaches are limited to pairwise interactions. We propose GPBSO (Gene Pool-Based Brain Storm Optimization for Epistasis Detection), a novel stochastic framework that integrates Brain Storm Optimization with a dynamic gene pool to efficiently explore high-order SNP combinations. Methods: Epistasis is evaluated using the k2 Bayesian network scoring criterion and the G-test, with iterative updates to the gene matrix enhancing search diversity. Results: Comparative experiments on simulated datasets generated from five epistatic models demonstrated that GPBSO consistently outperformed a set of well-established methods—DECMDR, SNPHarvester, AntEpiSeeker, HS-MMGKG, and SEE—in terms of F-measure and statistical power, particularly for third-order interactions. Conclusions: GPBSO provides an effective and scalable approach for detecting high-order epistatic interactions, offering methodological advancements for genetic epidemiology and complex disease analysis.