F104. GENE CO-EXPRESSION NETWORKS REVEAL PATHWAYS OF CONVERGENCE OF SCHIZOPHRENIA RISK GENES AND OF RESPONSE TO TREATMENT

BACKGROUND: Schizophrenia (SCZ) is associated with genetic factors, and specific risk loci have been identified. Still, the biology and clinical translation of genetic risk remain largely unknown. Gene co-expression networks are relevant to functional and clinical translation of SCZ risk. We hypothesized that SCZ risk genes may converge into co-expression pathways which, in turn, may be associated with clinical phenotypes in SCZ patients. METHODS: We used Weighted Gene Co-expression Network Analysis to identify co-expression modules in two prefrontal cortex post-mortem RNA sequencing datasets, including 379 healthy controls (HC) and 309 SCZ. We used four replication datasets (HC=339). To identify modules enriched for SCZ risk genes, we computed hypergeometric tests and corrected for multiple comparisons. To translate post mortem information into clinical phenotypes, we identified polymorphisms predicting co-expression and combined them to obtain an index approximating module co-expression (Polygenic Co-expression Index: PCI). We used two independent replication datasets (HC=131). Finally, we tested the association between PCI and treatment response in two independent SCZ cohorts (SCZ=167). RESULTS: We identified and replicated (all p-values