Issues of statistical power are even more limiting when considering GxE effects separately by subgroups. For example, molecular genetic GxE studies typically included gender as a covariate to adjust for differences in prevalence of risk factors or outcomes, but did not have power to test whether genders varied in GxE. If males and females differ in their exposure to environments that promote AUD risk, or in their genotypic sensitivity to environmental factors, GxE will contribute to gender differences in AUD risk. Given the large literatures on sex differences in cultural influences (Nolen-Hoeksema & Hilt, 2006) and physiological consequences of drinking (Mancinelli, Vitali, & Ceccanti, 2009) there are good reasons to think genetic factors may work differently in women, underscoring the importance of having sufficient sample sizes to study GxE separately by gender as well as race (Prescott et al., 2005). On the other hand, it is important to also note that post hoc exploratory analyses, including dividing samples into subgroups (e.g., gender), greatly increase the likelihood of false positives and non-replications (Flint & Munafό, 2008).
