Multi-Population Ant Colony Optimization With Knowledge-Based Local Searches for Epistasis Detection

Analysis of epistatic interaction is an important means to study the pathogenesis of complex diseases in genome-wide association studies (GWAS). Epistatic interaction detection aims to identify the ideal combination among single nucleotide polymorphisms (SNPs) and determine whether this combination is significantly associated with complex diseases. However, they suffer from certain limitations, such as low detection power and long execution times. Therefore, this paper proposes a multi-population ant colony optimization algorithm with an adaptive heuristic strategy (MPACO-AHS). MPACO-AHS is a framework based on multi-population approaches, where multiple populations are employed to detect epistatic interactions, helping to avoid the data bias inherent in a single population. Moreover, to guide the search direction of each population, an adaptive heuristic strategy is introduced, allowing the algorithm to focus on areas more likely to contain epistatic interactions, thereby improving the accuracy of the results. Comprehensive experiments are conducted on simulated datasets. The results demonstrate that MPACO-AHS outperforms existing algorithms by overcoming the challenges in detecting epistatic interactions in GWAS.