Identifying gene regulatory networks in schizophrenia

Area of Science:

• Neuroscience
• Genetics
• Psychiatry

Background:

• Imaging genetics integrates neuroimaging and genetic studies to understand disease.
• Brain imaging serves as an intermediate phenotype linking genes to brain function.
• Genome-wide association studies (GWAS) combined with quantitative trait (QT) analysis enhance risk gene identification.

Purpose of the Study:

• To identify novel risk genes for schizophrenia using an imaging genetics approach.
• To explore the functional roles of identified genes in brain development and molecular networks.
• To investigate gene regulatory networks and microRNA involvement in schizophrenia.

Main Methods:

• Quantitative trait (QT) analysis of dorsolateral prefrontal cortex (DLPFC) activation during a working memory task.
• Integration of GWAS data with brain imaging phenotypes.
• Gene set enrichment analysis (GSEA) of transcript levels in human and mouse postmortem brain tissue.
• Utilized Glypican 1 and FGF17 mouse models for network validation.

Main Results:

• Identified several unanticipated risk genes for schizophrenia (e.g., RSRC1, ARHGAP18, ROBO1-ROBO2).
• These genes are involved in crucial developmental processes like cell proliferation, migration, differentiation, and axonal connectivity.
• GSEA identified microRNAs (e.g., 448, 218, 137) associated with schizophrenia.
• Demonstrated epistatic interactions between FGF and glypican in brain development using mouse models.

Conclusions:

• The imaging genetics approach successfully identified novel schizophrenia risk genes.
• These genes participate in complex gene regulatory networks essential for brain development.
• The findings provide a foundation for further research into molecular mechanisms and potential therapeutic targets for schizophrenia.