SU106 - DIFFERENTIAL DISTRIBUTION OF SCHIZOPHRENIA RISK GENES WITHIN GENE CO-EXPRESSION NETWORKS CONSTRUCTED FROM RNA-SEQ DATA (POSTMORTEM DLPFC) OF AFFECTED AND UNAFFECTED INDIVIDUALS.

Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. 1. Samples: DLPFC gene expression (RNA-Seq) abundance was quantified (RPKM) in postmortem human brains from the LIBD Postmortem Human Brain Repository (90 controls- CTRL, 76 schizophrenia- SCZ, Caucasians, age: 16–80, RIN≥7). Common Mind Consortium (CMC) RNA-Seq postmortem DLPFC (125 CTRL, 109 SCZ, Caucasians, age, RIN as above) was used for replication of LIBD co-expression networks. 2. Expression measures/ quality control: only genes with median RPKM≥0.1 (N=23132 genes; LIBD samples) were used. Expression data was normalized by log2 transformation and adjusted for RNA quality measures (RIN, PMI, total gene assignment, proportion of mitochondrial RNA) by empirical Bayesian models. 3. Data analysis: WGCNA was applied to the adjusted expression data to construct co-expression networks, separately for LIBD- CTRL and LIBD- SCZ. Modules of co-expressed genes were detected by dynamic tree cutting method and summarized as "module eigengenes" for further analyses. 4. Modules of LIBD (CTRL; SCZ) were tested for enrichment in PGC2 genes within the 108 loci associated with the schizophrenia risk in the latest GWAS. 5. Composite module preservation statistics- median rank and Zsummary was used for assessing the preservation of LIBD SCZ modules in LIBD CTRL network and for replication of LIBD co-expression modules in the CMC independent data sets. Gene enrichment analyses for testing modules' enrichment in Gene Ontology (GO) biological processes (BP) was performed with AmiGO/ PANTHER. 17 co-expression modules were identified in each LIBD group. Module preservation analysis showed a good preservation of co-expression modules between LIBD CTRL and SCZ networks, with all but one SCZ module showing evidence for strong preservation (Zsummary≥10). PGC2 genes were overrepresented in two CTRL modules (p=0.001973, p=0.028297) enriched for GO-BP such as translation, ATP synthesis, oxidative phosphorylation, homophilic cell adhesion via plasma membrane adhesion molecules, chemical synaptic transmission, axonogenesis, and in one SCZ module (p=0.000519) enriched for RNA processing, nervous system development and modulation of synaptic transmission. 58 PGC2 genes overlapped with the SCZ module (e.g., DRD2, FURIN, TSNARE1), whereas 48 + 17 PGC2 genes were overrepresented in the two CTRL modules (e.g., CACNA1C, CHRM4, CACNA1I, TCF20). Of note, only 9 overrepresented PGC2 genes were shared by CTRL and SCZ: AMBRA1, ANKRD63, HSPA9, LRP1, PRR12, PTPRF, RERE, TOM1L2, ZDHHC5. Replication of co-expression LIBD networks in CMC data set showed evidence of weak to strong preservation (Zsummary>2, respectively ≥10) for 16/ 17 modules for each LIBD group. Our results illustrate the complexity of SCZ risk distribution within the DLPFC co-expression networks in postmortem brains of affected and unaffected individuals. Importantly, not the same PGC2 genes are members of co-expression modules in CTRL and SCZ, nor do they aggregate in similar modules. These observations may be explained by differential transcriptional regulation due to pleiotropy, epistasis, epigenetic dysregulation, or even hidden RNA quality artifacts. Future studies are necessary to elucidate the participation of SCZ risk genes in brain co-expression networks. [ABSTRACT FROM AUTHOR]