Testing differential networks with applications to the detection of gene-gene interactions

• 成果类型: Article
• 署名作者: Xia, Yin; Cai, Tianxi; Cai, T. Tony
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu074
• 发表日期: 2015
• 页码: 247266
• 关键词: Covariance matrices; polymorphism; association; strategies; apoptosis; EQUALITY; pathways; catenin; ORDER

摘要

Model organisms and human studies have yielded increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This poses a significant methodological challenge in the identification of gene-gene interactions. In this paper, by using a Gaussian graphical model framework, we translate the problem of identifying gene-gene interactions associated with a binary trait D into an inference problem on the difference of two high-dimensional precision matrices that summarize the conditional dependence network structures of the genes. We propose a procedure for testing the differential network globally, which is particularly powerful against sparse alternatives. In addition, a multiple testing procedure with false discovery rate control is developed to infer the specific structure of the differential network. Theoretical justification is provided to ensure the validity of the proposed tests, and optimality results are derived under sparsity assumptions. Through a simulation study we demonstrate that the proposed tests maintain the desired error rates under the null hypothesis and have good power under the alternative hypothesis. The methods are applied to a breast cancer gene expression study.