The high-density genome-wide association studies (GWAS) has improved our understanding of the genetic basis of many complex traits. In this study, we consider the recent "omnigenic" or "core genes" model and incorporate functional annotations into polygenic prediction. Studies have shown that this framework can have the potential to advance our understanding of the genetic basis of complex traits. Here, we present OmniPRS, a summary statistics-based PGS method that utilizes mixed-model techniques to estimate the effect sizes and the Bayesian Model Averaging (BMA) method to integrate scores across functional categories. Our experiments with different simulation configurations and 12 real phenotypes from the UK Biobank dataset demonstrated that OmniPRS is consistently competitive with the state-of-the-art in prediction accuracy, regardless of the genetic architecture and SNP heritability of the phenotypes. Our study highlights the importance of incorporating functional annotations in predicting complex traits. We found that incorporating genetic variants with functional annotations resulted in higher accuracy of polygenic scores compared to using all genetic variants regardless of their functional annotations. This suggests that incorporating information about the biological functionality of genetic variants can improve the accuracy of predictions made by polygenic scores.