GWAS represents a potentially powerful genome-wide approach. Because it is atheoretical, it is immune to any claims of cherry picking that can be made about candidate gene studies. The genotyping arrays contain many more variants than can be examined in linkage analysis, facilitating the identification of specific causal variants, and judicious selection of markers that “tag” others makes it possible to obtain much more comprehensive coverage of the genome. Moreover, EEG rhythms appear to reflect common characteristics of the mammalian brain, which suggests that a “common disease [phenotype]–common variant” model of inheritance is particularly appropriate. Genotyping arrays used in GWAS are designed to assess such common genetic variants. However, only one GWAS of resting EEG parameters has been conducted to date, which did not demonstrate significant associations with variants in any of the genes reviewed above that have been reported to be associated with EEG parameters. It demonstrated instead an association between theta power and several SNPs in the SGIP1 gene, involved in neurotransmission through synaptic vesicle formation (Hodgkinson et al., 2010). This finding was replicated in the same study
