There are also several machine learning approaches, including multifactor dimensionality reduction (MDR) [Ritchie, et al. 2001], random forest regression [Breiman 2001], and Bayesian network analysis [Baurley, et al. 2010; Chen and Thomas 2010; Wilson, et al. 2010] that can be considered for exploration of GxE interactions (for review see [Cordell 2009; Moore, et al. 2010]). Bayesian generalized linear models can be used to simultaneously test main effects for environmental exposures, multiple genetic variants along with GxG and GxE interactions [Yi, et al. 2011]. Additional methods incorporate different frameworks, such as the natural and orthogonal interaction framework [Ma, et al. 2012]. Many of these approaches consider more complex models of interaction as alternatives to additive or multiplicative scales. Furthermore, Bayesian approaches can potentially incorporate prior biological information or pathway data into interaction models [Hung, et al. 2004]. However, pathway definitions and functional annotation are not fully established [Kraft and Raychaudhuri 2009; Mechanic, et al. 2012; Wang, et al. 2010]. Specific considerations for these multilevel methods include incorporation of computational and bioinformatic advances, new approaches to interpretation of results and specific challenges for framing and performing replication.
