Although variance heterogeneity tests are potentially powerful screening tools for G×E interactions, like most interaction tests, they may be bias prone. For example, apparent differences in phenotypic variances across genotypes may be caused by scaling, particularly when the phenotypic means also differ substantially , such that the per-genotype means and variances for index traits are correlated. However, where necessary we transformed variables, and the correlations between Pm and Pv were generally weak, excluding this as a likely source of bias. Using simulated data, we investigated whether the non-normal distribution of a trait can cause a spurious association between marginal and variance signals, which we show is highly improbable. Through further simulations, we assessed whether SNPs with large marginal effects inflate Pv, but observed no inflation, indicating that large genetic marginal effects do not artificially inflate variance heterogeneity to a meaningful extent, and SNPs with low Pm and low Pv-values are thus likely to be strong candidates for G×E interactions, at least in the case of BMI. It might also be that combining populations from ancestral (e.g., hunter-gatherers) and contemporary environments increases variance heterogeneity owing to diversity in population substructure rather than G×E interactions per se . However, this seems unlikely here, as the cohorts examined are from Westernized European-ancestry populations.