Gene expression technologies allow expression levels to be compared across treatments for thousands of genes simultaneously. Asymmetry in the empirical distribution of the test statistics from the analysis of a gene expression experiment is often observed. Statistical methods exist for identifying differentially expressed (DE) genes while controlling multiple testing error while taking into account the asymmetry of the distribution of the effect sizes. This paper compares three statistical methods (Modified Q-value, Modified SAM, and Asymmetric Local False Discovery Rate) used to identify differentially expressed (DE) genes that take into account such patterns while controlling false discovery rate (FDR). The results of the simulation studies performed suggest that the Modified Q-values outperforms the other methods most of the time and also better controls the FDR.