{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:21:04Z","timestamp":1787239264996,"version":"build-2736575974"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Changchun University level scientific research project\u2014Growth Climbing Plan","award":["2021JBD05L12"],"award-info":[{"award-number":["2021JBD05L12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>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\u2014DECMDR, SNPHarvester, AntEpiSeeker, HS-MMGKG, and SEE\u2014in 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.<\/jats:p>","DOI":"10.3390\/genes16091114","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:53:42Z","timestamp":1758300822000},"page":"1114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GPBSO: Gene Pool-Based Brain Storm Optimization for SNP Epistasis Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2145-6341","authenticated-orcid":false,"given":"Liyan","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5450-9291","authenticated-orcid":false,"given":"Yi","family":"Xin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shen","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linxuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linqing","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15173","DOI":"10.1073\/pnas.96.26.15173","article-title":"Genetic epidemiology of single-nucleotide polymorphisms","volume":"96","author":"Collins","year":"1999","journal-title":"Proc. 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