Data with excess zeros are common in multi-omic studies, such as in the RNA-sequencing data analysis. To investigate the association between a set of genetic variants and an outcome variable with excess zeros, two commonly used regression models are Hurdle models and zero-inflated models. In the talk, we propose a flexible framework to detect the mixed-effects in these two types of regression models. The proposed approach can test for both fixed effect and random effect for analyzing continuous or count outcome with excess zeros. Several efficient procedures have been proposed to combine the p-values of the test statistics for enhancing the statistical power. Simulation studies and a real omics data application demonstrate that the proposed method can significantly improve statistical power compared to competing methods, especially when the mixed effects are present in the analysis.