When high dimensional microarray data is given, it is of interest to select significant genes by controlling a given level of Type-I error. One popular way to control the level is the false discovery rate (FDR). This talk will consider gene selection based on the local false discovery rate. In most of the previous studies, the null distribution of gene expression is commonly assumed to be a normal distribution. However, if the null distribution is different from normal, we may have misleading results such as failure of controlling a given level of FDR. We propose a novel procedure which enriches a class of null distribution based on a mixture of normals. We will present simulation studies to show that our proposed procedure is less sensitive to variation of null distribution than local false discovery rate with a single normal for the null. We will provide motivating real examples of gene expression data and fMRI data.