Studies reporting candidate gene associations should apply appropriate correction for multiple testing and explicitly disclose the total number of tests performed. Data reduction may be necessary to avoid multiple testing, for example, a dataset including EEG spectral power measures obtained for multiple frequency bands and multiple electrodes can be subjected to principal components analysis in order to extract 3or 4 factor scores that explain the great bulk of variance in the original variables. Second, studies should use adequate sample sizes that are justified by power calculations and realistic estimation of the expected effect size. Given that effect sizes are likely to be small (Flint and Munafo, 2012; McCarthy et al., 2008), quite large samples may be needed by the standards of psychophysiology (not genetics!). Even when a single measure is tested for association with a single genetic variant, samples of the order of hundreds may be needed, especially, if there are covariates that have to be controlled for. A GWAS study of the human EEG (Hodgkinson et al., 2010) has reported effects sizes that are substantially larger relative to other
