To test whether this assumption was met, we conducted simulations in R version 3.1.1 (R Development Core Team 2014) using normally distributed phenotypes. In this paper we provide examples with two and three phenotypes, but the same arguments are true with more than three phenotypes as well. The correlated normal phenotypes were created as linear combinations of independent standard normal distributions. We used two seeds for genotype and phenotype creation and changed coefficients of normal distributions to get different correlations between created phenotypes. The R script for this example, which illustrates inflation in TATES p-values, can be found in Appendix 1. By changing script parameters it is possible to run up to six phenotype examples. Appendix 1 also contains example of three phenotype simulations. For more than six phenotypes, the script can be slightly modified to add more coefficients. For example, to run 8 phenotypes we need to choose n_pheno=8 and add lines coeff[7,]=…, coeff[8,]=… after coeff[6,]=c(0.7,0.9,0.4,0.4,0.1,0.9) line. Similarly, to add more normal variables we need to change n_norm and also number of columns of coeff[,] matrix like coeff[6,]=c(0.7,0.9,0.4,0.4,0.1,0.9,0.4,0.5,0.7). The
