Evidence even for two-way interactions between gender and genotype has been slow to accumulate in the genetics literature, and replicability is often constrained by methodological issues. When gender-specific genetic effects are examined, the typical strategy is to split the sample and run analyses separately for men and women (Harrison and Tunbridge 2007; Ober et al. 2008). Among the significant limitations of this method is the high likelihood of inferential errors, particularly if the variance or sample size differs across groups (e.g. Ono et al. 2004; Stein et al. 2005). That is, when making group comparisons by estimating separate models, a significant effect of genotype in one gender and an insignificant effect in the other does not necessarily mean that the difference in effect size is statistically significant (Brambor et al. 2006). Moreover, splitting the analysis sample to roughly half its full size reduces statistical power to detect significance, particularly when effects are small (as most SNP effects are). For example, a regression with a main effect powered at 80 % (the accepted minimum level) has only 29 % power to
