To investigate whether pan-schizophrenia genes could be leveraged for building cohort-specific signatures reflecting the functioning of the affected brain, we first collected 1,518 schizophrenia-related genes upon analysis of 12,316 independent peer-reviewed literature sources. More than half of these genes have been reported in at least 3 independent studies, and a majority (81.4%) were enriched within 156 functional pathways (p-values < 1e-15). Gene expression profiles of brain tissues were extracted from 14 publicly available independent datasets, and classified into "schizophrenia" and "normal" bins using dataset-specific subsets of core schizophrenia collection genes built with either a sparse representation-based variable selection (SRVS) approach or with analysis of variance (ANOVA)-based gene selection approach. Results showed that cohort-specific classifiers by both SRVS and ANOVA methods are capable of providing significantly higher accuracy in the diagnosis of schizophrenia than using the whole core genes (p < 3.38e-6), with relatively low sensitivity to the ethnic backgrounds or areas of brain biopsies. Our results suggest that the formation of consensus collection of pan-schizophrenia genes and its dissection into the functional components could be a feasible alternative to the expansion of sample size, which is needed for further in-depth studies of the pathophysiology of the human brain.