TATES allows researchers to test their genetic associations efficiently using standard GWAS software. In addition, TATES' reliance on p-value information assures that phenotypes of different measurement levels (e.g., dichotomous, ordinal, continuous) can easily be analyzed simultaneously, and that contrasting effects (i.e., GVs affect some phenotypes positively, some negatively) do not influence the power of the method. Note that the plausibility of contrasting genetic effects does not only depend on the magnitude of the phenotypic correlations and the effect size of the GV (i.e., the correlation matrix between the phenotypes and the GV should remain positive definite), but also on the underlying genotype-phenotype model. For instance, if the true genotype-phenotype model is a 1-factor model with the GV effect on the factor, the direction of the effect of the GV must be identical for all phenotypes (assuming that all phenotypes are coded such that higher scores imply higher trait levels). Yet, if the true genotype-phenotype model is a network model, contrasting GV effects are unproblematic from a statistical point of view. Whether contrasting effects are plausible from a biological perspective depends
