Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. PrediXcan, a recent development in transcriptome-wide association studies, has shown promises for discovering novel variants by leveraging functional information from external multi-omics data. However, it suffers from potential power loss as a consequence of testing only the association of imputed gene expression with the phenotype. To tackle these challenges, we consider a unified mixed effects model that formulates the association of imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (Mixed effects Score Test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p-values for genome-wide analyses. Extensive simulations the application to real data demonstrate that the proposed approaches are more powerful than existing ones.