TRANSLATING TRANSCRIPTOME DATA MINING INTO NEUROBIOLOGICAL AND CLINICAL READOUTS

Pioneering views at the dawn of the GWAS era held that nodes of genetic convergence downstream of any individual molecule per se regulate genetic networks subserving the core neurophysiological elements affected in schizophrenia (SCZ). These nodes of genetic convergence might be, for example, transcription factors or miRNAs representing entry points into biologically valid pathways that affect cellular, systems-level and behavioral phenotypes. Since many of the SCZ risk variants are non-coding and may control gene expression, it is plausible that the regulatory elements of gene expression represent a mechanism of risk. Using Weighted Gene Co-expression Network Analysis (WGCNA), we have previously shown that genetic networks including SCZ risk genes indexed via co-expression quantitative traits loci (co-eQTLs) are associated with core neurophysiological systems-level processes affected in SCZ, like working memory brain activity patterns. However, genetic regulatory elements linking risk genes with altered regulation of gene expression at multiple risk loci are still missing.