Possibly equally important is the functional annotation of GWAS results by aggregating results and/or prioritizing SNPs based on recent advances in bioinformatics and molecular knowledge of the genome. This was performed by several means. SNP‐based GWAS results were followed up with gene‐based analyses and expression‐based enrichment analyses, which further increases power to detect genes affecting functional brain activity (Neale & Sham, 2004; Ripke et al., 2014; Watanabe, Taskesen, Bochoven, & Posthuma, 2017). In addition, significant results from gene‐based tests were compared to known liability genes for behavioral phenotypes including neuropsychiatric disorders by searching GWAS results databases (http://www.ensembl.org, http://www.gwascentral.org). We opted for this method of operation—that is, a full genome scan for genetic variants, subsequent gene‐based analyses, matching against known GWAS results for psychiatric/behavioral phenotypes—rather than preselecting liability genes to analyze for three reasons. First, candidate gene associations have proven much less successful in the past than genome‐wide scans, even with severe adjustments for multiple testing in the latter. Second, genome‐wide scans allow for the calculation of summed statistics such as SNP coheritability between EEG and behavioral traits, without the need to preselect on genome‐wide significant effects. Third, a GWAS of EEG traits can be used for comparison by future studies.
