Genome-wide association studies (GWAS) are a valuable tool for understanding the biology of complex traits, but the associations found rarely point directly to causal genes. We introduce a new method to identify the causal genes by integrating GWAS summary statistics with gene expression, biological pathway, and predicted protein-protein interaction data. We further propose an approach that effectively leverages both polygenic and locus-specific genetic signals by combining results across multiple gene prioritization methods, increasing confidence in prioritized genes. Using a large set of gold standard genes to evaluate our approach, we prioritize 10,642 unique gene-trait pairs with greater than 74% estimated precision across 113 complex traits and diseases. We also present preliminary results on quantifying the confidence and uncertainty of prioritized genes, and a technique for extracting distinct disease-relevant pathways from PoPS using non-negative matrix factorization.