As prior knowledge about the exact location of the GV effect in a multivariate system is usually lacking, a computationally efficient multivariate procedure that performs well in many different circumstances is required to increase the success of future GWAS. Here, we introduce a new multivariate technique called TATES: Trait-based Association Test that uses Extended Simes procedure. TATES is based on the GATES procedure [15], which was developed to combine p-values of individual SNPs located within the same gene into one gene-based p-value PG (where the gene is considered a more attractive unit of analysis for association studies than the SNP because genes are the functional units in the genome). Similarly, for individual phenotypes characterizing a trait (e.g., items or symptoms), TATES combines the p-values obtained in standard univariate GWAS to arrive at a global trait-based p-value PT, while correcting for the observed correlational structure between the phenotypes. Here we show that TATES has correct false positive (type-I error) rate, and that TATES picks up both phenotype-specific genetic effects as well as genetic effects that are common to multiple correlated phenotypes.
