Using these data sources, three broad strategies are possible (see Figure 1). First, pathway analysis using the genetic variants found to be associated with psychiatric disorders using gene-sets (pathways) annotated for their drug associations or corresponding to sets of ligands in publically available resources such as ChEMBL and KiDB to test whether these gene sets together harbour a significant association signal using the PGC pathway analysis pipeline43. Second, use relevant gene expression profiles identified from case-control transcriptome data and examine their similarity to induced gene expression changes in cell lines, as identified by the NIH LINCS project (URLs) or in studies of neuronal cells derived from iPSC, to identify potential pathways and molecules which impact the expression and/or function of identified targets44. This strategy of ‘connectivity mapping’ allows identification of compounds with a similar or opposite effect on gene expression as our findings and can point to possible new treatment targets. Finally, we can layer onto these approaches “traditional” pathway annotations and ontologies (particularly GO and REACTOME) and newer data sources that may be less biased and more complete45 to allow us to develop a mechanistic understanding.
