background Schizophrenia is a severe psychiatric disorder affecting about 1% of the population which is characterised by delusions and hallucinations, in addition to a range of negative symptoms. The heritability of the disease is estimated around 80%. However, little is known about the influence of genetics and transcriptomics on treatment response.
aim Therefore, in the present study, we aim to identify genes and gene networks that can be used for classification and treatment response, using gene expression.
methods We are using whole-genome gene expression profiling for the study of the molecular basis of schizophrenia using whole blood and brain tissue of schizophrenia patients. Patients include medicated as well as unmedicated individuals in addition to unaffected controls. In addition to case-control comparisons using a fdr correction, this data was analyzed using a weighted gene co-expression method. Clustering was performed to create networks of co-expressed genes. This in turn resulted in reconstruction of a limited number of groups of genes ('modules') with highly similar expression profiles. Within the co-expression modules, the most connected genes are driving that group of genes and are considered to be most important.
results Differences in network structure and connectivity between cases and controls were found and will point to genes of interest in the aetiology of schizophrenia. In addition, the effects of medication on gene-expression are assessed.
conclusion These effects can give insight into treatment-response and will be of importance in further studies in which medication- naive material is unavailable. Since for most subjects, genome-wide snp and dna methylation data is has been collected, we expect that analysis of multiple layers of genomic simultaneously will provide new insights into the aetiology of the disease.