Modeling Gene-Gene Interactions Using Graphical Chain Models

Abstract

Objective: To investigate whether graphical chain models are suitable to detect gene-gene interaction under different biological models. Methods: We conducted a simulation study comparing graphical chain models with logistic regression models regarding their ability to detect underlying biological interaction models. For both methods, we attempted to capture simulation data following 12 different biological models. We used 10 statistical models for both methods. Of the 12 different biological models, four contained no interaction effects, two were multiplicative, and six were epistasis models. For each situation, the choice for a statistical model was based on global model fit as judged by two different information criteria, the BIC and the AIC. Results: Both methods failed in most of the scenarios to capture the gene-gene interaction present in the simulation data. Only in very specific cases, when disease risk was high and both genes had a dominant effect, present gene-gene interaction was detected. Conclusions: Graphical chain models are, similar to logistic regression models, not able to capture gene-gene interactions for arbitrary biological models underlying the data.