• Epistasis;
  • GWAS;
  • Bayesian CART;
  • MCMC;
  • logistic regression;
  • Crohn's disease


Complex phenotypes are known to be associated with interactions among genetic factors. A growing body of evidence suggests that gene–gene interactions contribute to many common human diseases. Identifying potential interactions of multiple polymorphisms thus may be important to understand the biology and biochemical processes of the disease etiology. However, despite the great success of genome-wide association studies that mostly focus on single locus analysis, it is challenging to detect these interactions, especially when the marginal effects of the susceptible loci are weak and/or they involve several genetic factors. Here we describe a Bayesian classification tree model to detect such interactions in case-control association studies. We show that this method has the potential to uncover interactions involving polymorphisms showing weak to moderate marginal effects as well as multi-factorial interactions involving more than two loci.