Most notable is the relatively small sample size and related lack of statistical power to detect subtle genotypic effects. A recent article described the large projected sample sizes needed for a well powered genetic study of EEG, and highlighted the concerns that statistically underpowered genetic studies raise (Iacono et al., 2016). However, GWAS results seem reliable based on corroborating information (i.e., multiple genome-wide significant SNPs in high LD, biological plausibility). Nevertheless, genetic associations reported in this study must be replicated in a large, independent sample. Furthermore, given the nominal associations observed in eQTL analyses, these findings must also be replicated in larger samples. In addition, the current study includes participants with a wide age range (ages 7–74), which introduces potential for unmeasured confounding effects due to age-related changes in beta EEG; GWAS analyses were adjusted for age and age2 in an effort to minimize age related differences in beta EEG genetic association findings. However, future studies should examine the effects of genetic variants on trajectories of beta EEG during development in order to delineate age-specific effects, and the links between
