Polygenic and Polyenvironment Interplay in Schizophrenia-Spectrum Disorder and Affective Psychosis; the EUGEI First Episode Study.

Background: Multiple genetic and environmental risk factors play a role in the development of both schizophrenia-spectrum disorders and affective psychoses. How they act in combination is yet to be clarified.

Methods: We analyzed 573 first episode psychosis cases and 1005 controls, of European ancestry.

The impact of schizophrenia genetic load and heavy cannabis use on the risk of psychotic disorder in the EU-GEI case-control and UK Biobank studies.

Background: The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.

Methods: Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank.

Cannabis use and cognitive biases in people with first-episode psychosis and their siblings.

Background: Cannabis use and familial vulnerability to psychosis have been associated with social cognition deficits. This study examined the potential relationship between cannabis use and cognitive biases underlying social cognition and functioning in patients with first episode psychosis (FEP), their siblings, and controls.

Methods: We analyzed a sample of 543 participants with FEP, 203 siblings, and 1168 controls from the EU-GEI study using a correlational design.

Prediction of pharmacological response in OCD using machine learning techniques and clinical and neuropsychological variables.

Introduction: Obsessive compulsive disorder is associated with affected executive functioning, including memory, cognitive flexibility, and organizational strategies. As it was reported in previous studies, patients with preserved executive functions respond better to pharmacological treatment, while others need to keep trying different pharmacological strategies.

Material And Methods: In this work we used machine learning techniques to predict pharmacological response (OCD patients' symptomatology reduction) based on executive functioning and clinical variables.