As discussed previously, because tests for G×E interaction are generally less powerful than those to detect main effects and current GWAS are typically only powered to detect main effects, it is important for investigators to choose an analysis method that has the most power to detect G×E interactions. Group 10 has investigated the use of many different methods, including traditional logistic regression [Arya et al., 2009; Zhuang and Morris, 2009], latent components analysis [Gu et al., 2009], machine learning algorithms [Maenner et al., 2009], an extended generalized estimating equations approach [Chiu et al., 2009], and hierarchical modeling [Shi et al. 2009]. As discussed previously, many of these analyses identified markers involved in G×E interactions that would have been missed if tested for main effects alone. Zhuang and Morris [2009] and Joubert et al. [2009] applied a two degree of freedom test that has been shown to be a more robust choice to detect markers involved in disease risk because it jointly tests for main effect and interaction [Kraft et al., 2007]. The case-only analysis has been shown to be a
