Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

Abstract

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

“…Zhang et al [46] combined CPSO with K2 algorithm and applied the method to Bayesian network structure learning. Yang et al [47] applied PSO with double-bottom chaotic maps (DBM-PSO) in order to assist statistical methods in the analysis of associated variations to disease susceptibility. Analysis results supported that the proposed DBM-PSO could identify good models and provided higher chi-square values than conventional PSO.…”

“…Due to the page limit, we cannot list all of them. [20], Jamalipour et al [21], Bagheri et al [22], Tang et al [23], Davoodi et al [24], Li and Xiao [25], Yumin and Li [26], Jia et al [27], and Gholizadeh and Moghadas [28] BBPSO Zhang et al [30], Zhang et al [31], Zhang et al [32], Zhang et al [33], Blackwell [34], Wang et al [35], Jiang and Wang [36], Liu et al [37], Campos et al [38], and Zhang et al [39] CPSO Chuang et al [40], Zhang and Wu [41], Dai et al [42], Li et al [43], Wu et al [44], Zhang et al [45], Zhang et al [46], Yang et al [47], Son [48], He et al [49], Zeng and Sun [50], and Pluhacek et al [ [95], and Kalayci and Gupta [96] Other modifications Chuang et al [97], Shi and Liu [98], Zhang et al [99], Liu et al [100], Shen et al [101], Lin et al [102], Wang and Watada [103], Li et al [104], Lu et al [105], Mattos et al [106], Wu et al …”

Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population

“…In particular, we investigated the role of combinational SNPs in three metabolism-related genes ( CYP26A1 , CYP26B1 , and CYP26C1 ) in oral malignant disorders. In association studies of disease predisposition, the interaction analyses of SNPs increased the performance [19, 28, 58, 69–71].…”

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

“…The PSO algorithm has a very high convergence rate for resolving certain complex optimization problems. Yang et al first presented a double-bottom chaotic map PSO algorithm (DBM-PSO) to detect high-order genetic interactions [48]. In DBM-PSO, double-bottom maps are adopted to balance the exploration power and exploitation power, with the aim of preventing the PSO from becoming trapped in a local optimum.…”