Multivariate genotype-phenotype analyses are important for several reasons. First, most complex traits, such as cognitive ability, personality, problem behavior in humans [24]–[26], and anxiety in mice [27], are multi-dimensional, i.e., multiple common factors are required to describe the variance-covariance structure. Given this multidimensionality, multivariate genotype-phenotype analyses are indicated, as standard univariate analyses cannot accommodate genetic heterogeneity of subdimensions. Second, phenotypically distinguishable subdimensions need not correspond simply to genetic dimensions, and the information to parse a trait into genetically informative subdimensions is usually lacking. Consequently, researchers often focus on those GVs that are common to all subdimensions by studying a single, “general” composite measure. A simple, but deficient alternative is to conduct a series of independent univariate association studies without correcting for the dependency between the results caused by the correlations between the phenotypes. TATES offers a simply method to correct for this relatedness, while identifying GVs that are common to multiple phenotypes and GVs that are phenotype specific. As such TATES provides a more complete view of the genetic architecture of complex traits. Third, it is often unclear which phenotype(s)
