Understanding and predicting individual differences in cannabis-induced psychosis-like experiences

Background Observational and experimental evidence shows that cannabis induces acute psychosis-like experiences in some, but not all, individuals. However, little is known regarding individual vulnerabilities that underlie such cannabis-sensitivity. Equally, if prediction models are useful in identifying individuals at risk of cannabis-sensitivity is currently unknown. Approach To study cannabis-sensitivity, I will combine cutting-edge approaches from genetic epidemiology and machine learning. More specifically, I will (a) systematically summarise the available evidence on cannabis-sensitivity, (b) conduct the first genome-wide association study to assess biological pathways underlying cannabis-sensitivity, (c) apply modern genetically informed inference methods (e.g. Mendelian randomization) to explore the role of individual vulnerabilities (e.g. schizophrenia liability) in cannabis-sensitivity, and (c) develop and evaluate prediction models for cannabis-sensitivity. Impact Cannabis use is common and negatively affects some individuals. Knowledge on individual risk profiles is therefore paramount, in order to prevent adverse effects in users and to identify those at risk of cannabis-sensitivity.