Crow Search Algorithm Based on Information Interaction for Epistasis Detection

Abstract

Background: In the genome-wide association study, the interactions of single nucleotide polymorphisms (SNPs) play an important role in revealing the genetic mechanism of complex diseases, and such interaction is called epistasis or epistatic interactions. In recent years, swarm intelligence methods have been widely used to detect epistatic interactions because they can effectively deal with global optimization problems. Results: In this study, we propose a crow search algorithm based on information interaction (FICSA) to detect epistatic interactions. FICSA combines particle swarm optimization (PSO) and crow search algorithm (CSA) to balance the exploration and exploitation in the search process, which can effectively improve the ability of the algorithm to detect epistatic interactions. In addition, opposition-based learning strategy and adaptive parameters are used to further improve the performance of the algorithm. We compare FICSA with other five epistasis detection algorithms on simulated datasets and an age-related macular degeneration (AMD) dataset. The results on simulated datasets show that FICSA has better detection power, while the results on the real dataset demonstrate the effectiveness of the proposed algorithm. Conclusions: The results show that FICSA is better than other methods and can effectively detect epistatic interactions. In addition，FICSA was tested on AMD data, many of the epistatic interactions found have been proved to be related to AMD in the relevant literature. Therefore, FICSA has good performance in epistasis detection.