environmental indicators, consequently reducing statistical power. We recommend the use of quantile normalization as an alternative approach (Irizarry et al., 2003). Some investigators attempt to rely on methods that omit the main effects of the gene and environment and model only the interaction. Discussed elsewhere (Keller, 2014), this approach will yield inaccurate results. It is essential to include the main effect of any variable in a model that tests for its interaction with another variable to avoid an increase in Type I error. This increase in Type I error is magnified in models where gene-environment correlation is present (rGE). Recognizing the potential for spurious results, many statistical packages (including R), prohibit users from directly modelling interactions alone. A workaround used by some investigators is to pre-compute the interaction term (literally G × E) and test it as the lone independent variable in the model. This leads to highly spurious results when there are marginal effects of the gene or environment but no effect of GxE. Caution is advised when using this approach. It is well known that mean-centering of (quasi)continuous variables is requisite for removing any collinearity between an interaction (i.e., product) term and its component predictors, and avoids spurious
