In addition to model selection, it has long been known that the scale of the behavioral outcome can have an effect on the detection of GxE. Mather and Jinks (1982) demonstrated, using data from several published examples, instances of GxE that varied in their robustness to non-linear transformation of the behavioral outcome. GxE can be detected as a result of the change in variance as the mean increases (heteroscedasticity). Non-linear transformations of the behavioral outcome (square root, logarithmic, etc.) in these cases will reduce the variance and can eliminate the interaction effect. However, interactions that are not dependent on this increased variance should be robust to transformations of scale. Despite this known concern, many studies of GxE fail to address issues of scaling.
