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Increasing evidence has shown that gene-gene interactions have importanteffects on biological processes of human diseases. Due to the highdimensionality of genetic measurements, existing interaction analysis methodsusually suffer from a lack of sufficient information and are stillunsatisfactory. Biological networks have been massively accumulated, allowingresearchers to identify biomarkers from a system perspective by utilizingnetwork selection (consisting of functionally related biomarkers) as well asnetwork structures. In the main-effect analysis, network information has beenwidely incorporated, leading to biologically more meaningful and more accurateestimates. However, there is still a big gap in the context of interactionanalysis. In this study, we develop a novel structured Bayesian interactionanalysis approach, effectively incorporating the network information. Thisstudy is among the first to identify gene-gene interactions with the assistanceof network selection for phenotype prediction, while simultaneouslyaccommodating the underlying network structures. It innovatively respects themultiple hierarchies among main effects, interactions, and networks. Bayesianmethod is adopted, which has been shown to have multiple advantages over someother techniques. An efficient variational inference algorithm is developed toexplore the posterior distribution. Extensive simulation studies demonstratethe practical superiority of the proposed approach. The analysis of TCGA dataon melanoma and lung cancer leads to biologically sensible findings withsatisfactory prediction accuracy and selection stability.

Gene-gene interaction analysis incorporating network information via a structured Bayesian approach