One goal of PheWAS is to identify pleiotropy where a Single Nucleotide Polymorphism (SNP) controls multiple traits. Aspects that need the most consideration in detecting pleiotropy are the choice of method and the hypothesis tests. Because of its simplicity, the Generalized Linear Model with multiple testing correction is popular in the univariate approach. Generalized Estimation Equations is one favored method among the multivariate approaches since it takes correlations between traits into account. We apply both methods on a diverse range of simulated PheWAS data sets with varying minor allele frequencies at a SNP, different frequencies of traits associated and not associated with the SNP, varying phenotype prevalences, and varying correlation architectures between traits. We compare results from association tests using the traditional Bonferroni correction and the principal-component- based simpleM correction with those from the sequential test for pleiotropy developed by Schaid et al. in 2017. The project aims to compare power gained in each method while controlling the family-wide error rate to make conclusions on the best methods to use in different settings.