Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 80778, Taiwan.
Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.
Sci Rep. 2017 Oct 9;7(1):12869. doi: 10.1038/s41598-017-12773-x.
Epistasis within disease-related genes (gene-gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene-gene interactions; however, some gene-gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene-gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene-gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene-gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene-gene interactions in genome-wide association studies.
在疾病相关基因（基因-基因相互作用）内的上位性是通过基于多因素降维（MDR）的列联表度量来确定的，使用的是单核苷酸多态性（SNP）。大多数基于 MDR 的方法使用单个列联表度量来检测基因-基因相互作用；然而，一些基因-基因相互作用可能需要通过多个列联表度量来识别。在这项研究中，提出了一种多目标差分进化方法（称为 MODEMDR），用于基于 MDR 合并各种列联表度量，以检测显著的基因-基因相互作用。选择了两个列联表度量，即正确分类率和归一化互信息，用于设计 MODEMDR 中的适应度函数。多目标优化的特点使 MODEMDR 能够使用多种度量，在合理的时间内高效且同步地检测出显著的基因-基因相互作用。在各种参数设置（遗传性和次要等位基因频率）下，使用具有和不具有边缘效应的上位模型来评估现有方法，比较基因-基因相互作用的检测成功率。模拟数据集的结果表明，MODEMDR 优于现有方法。此外，还使用来自威康信托基金会病例对照联合会的大型数据集来评估 MODEMDR。MODEMDR 在识别全基因组关联研究中的显著基因-基因相互作用方面表现出效率。
