Increasing evidence has shown that gene-gene interactions have important effects on biological processes of human diseases. Due to the high dimensionality of genetic measurements, existing interaction analysis methods usually suffer from a lack of sufficient information and are still unsatisfactory. Biological networks have been massively accumulated, allowing researchers to identify biomarkers from a system perspective by utilizing network selection (consisting of functionally related biomarkers) as well as network structures. In the main-effect analysis, network information has been widely incorporated, leading to biologically more meaningful and more accurate estimates. However, there is still a big gap in the context of interaction analysis. In this study, we develop a novel structured Bayesian interaction analysis approach, effectively incorporating the network information. This study is among the first to identify gene-gene interactions with the assistance of network selection for phenotype prediction, while simultaneously accommodating the underlying network structures. It innovatively respects the multiple hierarchies among main effects, interactions, and networks. Bayesian method is adopted, which has been shown to have multiple advantages over some other techniques. An efficient variational inference algorithm is developed to explore the posterior distribution. Extensive simulation studies demonstrate the practical superiority of the proposed approach. The analysis of TCGA data on melanoma and lung cancer leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.
翻译:越来越多的证据表明,基因基因基因相互作用对人类疾病的生物过程有着重要影响。由于遗传测量的高度维度,现有的相互作用分析方法通常缺乏足够的信息,而且仍然不能令人满意。生物网络已经大量积累,使研究人员能够利用网络选择(由功能上的生物标志组成)和网络结构,从系统角度确定生物标志。在主要效果分析中,网络信息被广泛纳入,导致生物上更有意义和更准确的估计。然而,在互动分析方面仍然存在着巨大的差距。在本研究中,我们开发了一种新型结构化贝叶西亚互动分析方法,有效地纳入网络信息。这一研究是第一个在利用网络选择对苯型预测的帮助下确定基因基因-基因相互作用,同时兼顾基本网络结构。它创新地尊重主要影响、互动和网络之间的多重等级。采用巴伊西亚方法,这证明它比其他一些技术具有多重优势。我们开发了一种高效的变异算法,以探索海床前的准确性分析方法,有效地纳入了网络信息。这一研究是首先在网络选择中确定基因基因-基因类型的相互作用,同时同时兼顾基本的网络结构结构结构结构结构结构结构结构结构结构结构结构结构结构,并展示了对癌症的精确性分析。 基础模拟研究研究,以研究提出了对癌症的准确性癌症的精确性研究。高能的分析结果的理论分析,以研究。