Numerous high-throughput omics studies have been conducted in schizophrenia, providing an accumulated catalog of susceptible variants and genes. The results from these studies, however, are highly heterogeneous. The variants and genes nominated by different omics studies often have limited overlap with each other. There is thus a pressing need for integrative analysis to unify the different types of data and provide a convergent view of schizophrenia candidate genes (SZgenes). In this study, we collected a comprehensive, multidimensional dataset, including 7819 brain-expressed genes. The data hosted genome-wide association evidence in genetics (eg, genotyping data, copy number variations, de novo mutations), epigenetics, transcriptomics, and literature mining. We developed a method named mega-analysis of odds ratio (MegaOR) to prioritize SZgenes. Application of MegaOR in the multidimensional data resulted in consensus sets of SZgenes (up to 530), each enriched with dense, multidimensional evidence. We proved that these SZgenes had highly tissue-specific expression in brain and nerve and had intensive interactions that were significantly stronger than chance expectation. Furthermore, we found these SZgenes were involved in human brain development by showing strong spatiotemporal expression patterns; these characteristics were replicated in independent brain expression datasets. Finally, we found the SZgenes were enriched in critical functional gene sets involved in neuronal activities, ligand gated ion signaling, and fragile X mental retardation protein targets. In summary, MegaOR analysis reported consensus sets of SZgenes with enriched association evidence to schizophrenia, providing insights into the pathophysiology underlying schizophrenia.