Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm

• Published in: PloS one 2024-10, Vol.19 (10), p.e0311223
• Main Authors: Sun, Liyan, Bian, Jingwen, Xin, Yi, Jiang, Linqing, Zheng, Linxuan
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptability; Algorithms; Biology and Life Sciences; Computer and Information Sciences; Computer Simulation; Datasets; Deep learning; Disease; Disease control; Efficiency; Epistasis; Epistasis, Genetic; Flaw detection; Genes; Genetic algorithms; Genetic epistasis; Genetic screening; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Humans; Medical research; Medicine and Health Sciences; Medicine, Experimental; Methods; Models, Genetic; Multitasking; Optimization algorithms; Pathogenesis; Performance evaluation; Physical Sciences; Polymorphism, Single Nucleotide; Research and Analysis Methods; Search algorithms; Single-nucleotide polymorphism; Source code; Task complexity; Testing
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0311223

Genome-wide association studies typically considers epistatic interactions as a crucial factor in exploring complex diseases. However, the current methods primarily concentrate on the detection of two-order epistatic interactions, with flaws in accuracy. In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions. To evaluate its performance, we conducted a comprehensive comparison between Epi-SSA and seven other methods using five simulation datasets: DME 100, DNME 100, DME 1000, DNME 1000 and DNME3 100. The DME 100 dataset encompasses eight second-order epistasis disease models with marginal effects, each comprising 100 simulated data instances, featuring 100 SNPs per instance, alongside 800 case and 800 control samples. The DNME 100 encompasses eight second-order epistasis disease models without marginal effects and retains other properties consistent with DME 100. Experiments on the DME 100 and DNME 100 datasets were designed to evaluate the algorithms' capacity to detect epistasis across varying disease models. The DME 1000 and DNME 1000 datasets extend the complexity with 1000 SNPs per simulated data instance, while retaining other properties consistent with DME 100 and DNME 100. These experiments aimed to gauge the algorithms' adaptability in detecting epistasis as the number of SNPs in the data increases. The DNME3 100 dataset introduces a higher level of complexity with six third-order epistasis disease models, otherwise paralleling the structure of DNME 100, serving to test the algorithms' proficiency in identifying higher-order epistasis. The highest average F-measures achieved by the seven other existing methods on the five datasets are 0.86, 0.86, 0.41, 0.56, and 0.79 respectively, while the average F-measures of Epi-SSA on the five datasets are 0.92, 0.97, 0.79, 0.86, and 0.97 respectively. The experimental results demonstrate that the Epi-SSA algorithm outperforms other methods in a variety of epistasis detection tasks. As the number of SNPs in the data set increases and the order of epistasis rises, the advantages of the Epi-SSA algorithm become increasingly pronounced. In addition, we applied Epi-SSA to the analysis of the WTCCC dataset, uncovering numerous