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

• Published in: Statistics in medicine 2021-12, Vol.40 (29), p.6619-6633
• Main Authors: Qin, Xing, Ma, Shuangge, Wu, Mengyun
• Format: Article
• Language: English
• Subjects: Algorithms; assistance of network selection; Bayes Theorem; Biomarkers; Computer Simulation; Data Interpretation, Statistical; Gene Regulatory Networks; gene‐gene interaction; Humans; link network; Lung cancer; Melanoma - genetics; structured analysis
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.9202

Increasing evidence has shown that gene‐gene interactions have important effects in biological processes of human diseases. Due to the high dimensionality of genetic measurements, interaction analysis usually suffers from a lack of sufficient information and has unsatisfactory results. Biological network information has been massively accumulated, allowing researchers to identify biomarkers while taking a system perspective, conducting network selection (of functionally related biomarkers), and accommodating network structures. In main‐effect‐only analysis, network information has been incorporated. However, effort has been limited in interaction analysis. Recently, link networks that describe the relationships between genetic interactions have been demonstrated as effective for revealing multiscale hierarchical organizations in networks and providing interesting findings beyond node networks. In this study, we develop a novel structured Bayesian interaction analysis approach to effectively incorporate network information. This study is among the first to identify gene‐gene interactions with the assistance of network selection, while simultaneously accommodating the underlying network structures of both main effects and interactions. It innovatively respects multiple hierarchies among main effects, interactions, and networks. The Bayesian technique is adopted, which may be more informative for estimation and prediction over some other techniques. An efficient variational Bayesian expectation‐maximization 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.