Lee Seungyeoun, Kim Yongkang, Kwon Min-Seok, Park Taesung
Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Republic of Korea.
Department of Statistics, Seoul National University, Seoul 151-747, Republic of Korea.
Biomed Res Int. 2015;2015:671859. doi: 10.1155/2015/671859. Epub 2015 Aug 3.
Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.
全基因组关联研究(GWAS)已经广泛分析了单核苷酸多态性(SNP)对多种常见和复杂疾病的影响,并发现了许多与疾病相关的基因变异。然而,仍有很大一部分基因变异无法得到解释。这种“遗传性缺失”问题可能是由于分析策略仅将分析局限于单个SNP。解决“遗传性缺失”问题的一种可能方法是考虑识别多SNP效应或基因-基因相互作用。多因素降维方法已被广泛用于基于构建性归纳来检测基因-基因相互作用,即将高维基因型组合分类为具有病例对照研究高风险和低风险两个属性的一维变量。已经提出了许多MDR的改进方法,并且也扩展到了生存表型。在本研究中,我们提出了几种针对生存表型的MDR扩展方法,并通过全面的模拟研究将所提出的扩展方法与早期的MDR进行比较。