Yang Cheng-Hong, Lin Yu-Da, Chuang Li-Yeh, Chang Hsueh-Wei
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan.
Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan.
Biomed Res Int. 2014;2014:172049. doi: 10.1155/2014/172049. Epub 2014 May 7.
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.
基因-基因相互作用研究聚焦于疾病易感性相关基因的单核苷酸多态性（SNP）之间关联的调查。统计方法被广泛用于寻找用于疾病分析的基因-基因相互作用的良好模型，并且先前确定的模型已成功解释了SNP与疾病之间的效应。然而，SNP基因型的大量潜在组合限制了用于分析高阶相互作用的统计方法的使用，找到可用的高阶基因-基因相互作用模型仍然是一项挑战。在本研究中，一种改进的具有双底混沌映射的粒子群优化算法（DBM-PSO）被应用于辅助统计方法分析与疾病易感性相关的变异。使用已发表的八个乳腺癌相关基因中26个SNP的基因型频率模拟了一个大数据集。结果表明，所提出的DBM-PSO成功确定了与乳腺癌风险关联的二至六阶基因-基因相互作用模型（优势比>1.0；P值<0.05）。分析结果支持所提出的DBM-PSO能够识别良好模型，并比传统粒子群优化算法提供更高的卡方值。本研究表明，DBM-PSO是一种用于确定乳腺癌基因-基因相互作用模型的稳健且精确的算法。
