We are not measuring potentially important nonadditive effects, such as dominance and recessive single-locus effects. However, research on complex traits in humans has provided scant evidence that such effects are overwhelmingly important or detectable even with massive sample sizes. Had we adjusted our models to test for them, we would have added even more to the already high multiple testing burden. In response to the concern that we might be missing dominance effects, as a follow-up to our GCTA analyses, we reanalyzed the endophenotypes using a variant of the GCTA model recommended for family data (Yang, Lee, Goddard, & Visscher, 2013) in which we modeled, and thus statistically controlled, dominance effects (as well as shared environmental influences) from family relationships while estimating the magnitude of shared genetic influences. For the 17 endophenotypes, dominance accounted for 0 to 13% of the variance (median .05), producing 95% confidence intervals that did not overlap zero (and thus indicated significant dominance effects) for eight of the measures (antisaccade error [7%], skin conductance level [10%], and response frequency [7%], and several EEG measures: occipital-parietal alpha
