Imaging transcriptomics in obsessive-compulsive disorder

• Grant number: 22/10207-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: April 01, 2023
• End date: March 31, 2024
• Field of knowledge: Health Sciences - Medicine - Psychiatry
• Principal Investigator: Eurípedes Constantino Miguel Filho
• Grantee: Leonardo Cardoso Saraiva
• Supervisor: Christopher John Turner Pittenger
• Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Yale University, United States
• Associated to the scholarship: 20/12484-6 - Integrated investigation of gene expression and DNA metilation in obsessive-compulsive disorder, BP.DD

Abstract

Obsessive-compulsive disorder (OCD) is a debilitating psychiatry disorder with an elusive neurobiological basis, which hampers the development of effective treatments. Robust genetic and neuroimaging studies have implicated genetic factors and neural abnormalities, respectively, in OCD. However, few studies have performed investigations integrating genetics and neuroimaging in the disorder. Hence, the central aim of the BEPE proposal is to use imaging transcriptomics to investigate gene expression signatures of neural abnormalities in OCD. The currently available neuroimaging sample comprises three different cohorts, totaling x OCD cases and x controls. The neuroimaging sample of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, comprising 2,323 OCD cases and 2,325 controls, will soon be available. The morphometric similarity network will be used to estimate anatomical connectivity across cortical regions. The brain-wide gene expression data of the Allen Human Brain Atlas will be spatially associated with the OCD case-control differences in regional morphometric similarity by using partial least squares regression. Downstream analyses will further probe the biological correlates of the imaging transcriptomics findings. These will include enrichment analysis for OCD-risk genomic variants, biological processes, and brain cell types; and the use of finite mixture models to assess gene expression neurodevelopmental trajectories relevant for OCD. Correlation analysis will be used to associate the imaging transcriptomics findings to differential gene expression analysis findings in peripheral blood and in-vivo brain samples. A transcriptome-based polygenic risk for OCD will be constructed using the imaging transcriptomics findings, and its performance in predicting OCD diagnosis will be evaluated. (AU)