Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan.; Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung City 80708, Taiwan..
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan.
J Theor Biol. 2019 Jan 14;461:68-75. doi: 10.1016/j.jtbi.2018.10.012. Epub 2018 Oct 5.
Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.
研究多基因相互作用主要调查了相关基因的遗传变异与患者组织病理学肿瘤特征之间的关联。然而，目前，识别和表征复杂疾病的易感基因仍然是遗传学家面临的巨大挑战。在这项研究中，提出了一种基于粒子群优化（PSO）的多因子降维（MDR）方法，称为 PBMDR。MDR 用于基于 PSO 算法检测多基因相互作用。从 8 个乳腺癌相关基因的 26 个 SNP 的基因型频率中模拟了一个测试数据集。在模拟的疾病模型中，我们证明了 PBMDR 在探索和检测特定 SNP 基因型组合的能力方面优于现有的全局优化算法。此外，将 PBMDR 算法与包括 PSO 和混沌 PSO 在内的其他算法进行了比较，结果表明，PBMDR 算法的准确率和卡方值均高于其他算法。
