GWAS is dependent on very large sample sizes, as the effects of individual genetic variants (SNPs) are quite small, even for brain endophenotypes (Hibar et al., 2015; Stein et al., 2012). Our analyses show that prioritizing SNPs on tissue‐specific expression and machine‐learning approaches are useful to reveal significant genetic associations and pathways for the expression of psychiatric liability in the brain. This bodes well for future GWAS of additional EEG parameters. For example, two recently published GWASs of bipolar EEG from families of African and European ancestry reported genome‐wide signal at 3q26 and 6q22, respectively (Meyers et al., 2017a; Meyers et al., 2017b). Bipolar EEG derivations show more localized activity than other EEG derivations and remove volume conduction effects, and have been particularly successful as a biomarker of alcohol dependence. Other EEG parameters of high interest are functional connectivity as biomarkers for various neurodevelopmental and psychiatric disorders. The current results indicate that finding specific molecular genetic variants related to EEG parameters is entirely feasible.
