Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Korea.
Bioinformatics. 2012 Sep 15;28(18):i582-i588. doi: 10.1093/bioinformatics/bts415.
For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP-SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene-gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) method to the survival phenotype by using the log-rank test for defining a binary attribute. However, the Surv-MDR method has some drawbacks in the sense that it needs more intensive computations and does not allow for a covariate adjustment. In this article, we propose a new approach, called Cox-MDR, which is an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype by using a martingale residual as a score to classify multi-level genotypes as high- and low-risk groups. The advantages of Cox-MDR over Surv-MDR are to allow for the effects of discrete and quantitative covariates in the frame of Cox regression model and to require less computation than Surv-MDR.
Through simulation studies, we compared the power of Cox-MDR with those of Surv-MDR and Cox regression model for various heritability and minor allele frequency combinations without and with adjusting for covariate. We found that Cox-MDR and Cox regression model perform better than Surv-MDR for low minor allele frequency of 0.2, but Surv-MDR has high power for minor allele frequency of 0.4. However, when the effect of covariate is adjusted for, Cox-MDR and Cox regression model perform much better than Surv-MDR. We also compared the performance of Cox-MDR and Surv-MDR for a real data of leukemia patients to detect the gene-gene interactions with the survival time.
在过去的几十年中，许多基因组关联研究（GWAS）中的统计方法已经被开发出来，以识别病例对照研究中的 SNP-SNP 相互作用。然而，对于涉及生存时间的前瞻性队列研究，这方面的工作较少。最近，Gui 等人（2011 年）提出了一种新的方法，称为 Surv-MDR，用于检测与生存时间相关的基因-基因相互作用。Surv-MDR 是多维降维（MDR）方法的扩展，通过使用对数秩检验定义二进制属性，将其应用于生存表型。然而，Surv-MDR 方法存在一些缺点，即需要更密集的计算，并且不允许进行协变量调整。在本文中，我们提出了一种新的方法，称为 Cox-MDR，它是广义多维降维（GMDR）方法的扩展，通过使用马氏残差作为评分，将生存表型分类为高风险和低风险组。Cox-MDR 相对于 Surv-MDR 的优势在于，它可以在 Cox 回归模型的框架内允许离散和定量协变量的影响，并且需要的计算量比 Surv-MDR 少。
通过模拟研究，我们比较了 Cox-MDR、Surv-MDR 和 Cox 回归模型在不同遗传率和次要等位基因频率组合下的功效，包括是否调整协变量。我们发现，Cox-MDR 和 Cox 回归模型在次要等位基因频率为 0.2 时表现优于 Surv-MDR，但 surv-MDR 在次要等位基因频率为 0.4 时具有较高的功效。然而，当调整协变量的影响时，Cox-MDR 和 Cox 回归模型的表现要好得多。我们还比较了 Cox-MDR 和 Surv-MDR 在白血病患者生存时间的真实数据中检测基因-基因相互作用的性能。
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