Comparing Logic Regression Based Methods for Identifying SNP Interactions

In single-nucleotide polymorphism (SNP) association studies interactions are often of main interest. Logic regression is a regression methodology that can identify complex Boolean interactions of binary variables. It has been applied successfully to SNP data but only identifies a single best model, while usually there is a number of models that are almost as good. Extensions of logic regression that consider several plausible models are Monte Carlo logic regression (MCLR) and a full Bayesian version of logic regression (FBLR) proposed in this paper. FBLR allows the incorporation of biological knowledge such as known pathways. We compare the performance in identifying SNP interactions associated with the case-control status of the three logic regression based methods and stepwise logistic regression in a simulation study and in a study of breast cancer.