Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. Along with other samples, UK Biobank data was combined to create the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N=184,765). In this study, we identified 8 genetic variants and 35 genes significantly associated with individual differences in using cannabis. Together, all measured genetic variants throughout the genome explained 11% of the variance in use/non-use between individuals.
Significant genetic correlations were found with a number of substance use and mental health related traits, including smoking, alcohol use, schizophrenia and risk-taking. This confirms previous expectations that these behaviors share some overlapping genetic influences. We also found evidence that schizophrenia leads to cannabis use rather than the opposite direction of effect, which had previously been proposed. Overall, this study provides new insights into the etiology of cannabis use and its relationship with mental health.
Causes of individual differences in cognitive and mental health
The main goal of our study is to quantify and understand the role of genetic variants, the environment (including lifestyle), and their interaction on outcomes related to cognitive health. In doing so we will combine expertise of statistical genetics, medical genetics, bioinformatics and functional genomics. We are specifically interested in the following health-relevant outcomes from the U.K. Biobank data: cognitive function (incl. normal function and dementia), mental health (incl. depression, neuroticism, personality, smoking, and alcohol drinking), and brain MRI. Our research will contribute to quantifying and understanding how several risk factors (e.g. lifestyle, environment, genes), both separately and in combination, influence cognitive health as well as the comorbidities between different cognitive health outcomes. Our study will consist of a combination of methods, including: - Genome-wide association studies (GWAS) that aim to identify individual genetic variants associated with a particular outcome. - Comorbidity analyses, using e.g. meta-analytic techniques, LD score regression or BOLD-GREML methods to quantify the extent of genetic overlap between particular outcomes - Gene-set analyses (e.g. using MAGMA and INRICH tools) and bioinformatic secondary analyses to understand genetic findings in terms of their biological function - Heterogeneity analyses to determine genetic subgroups of individuals - Annotation of genetic findings using external information from e.g. expression or quantitative proteomics data - Gene-by-environment correlation and interaction analyses to quantify the relevance of the interplay between genes and environment (including lifestyle) on outcomes related to cognitive health We aim to use all available observations in the UKB that are currently released and will be released in the future, and that have been successfully genotyped and have measures of relevant outcomes.
|Lead investigator:||Danielle Posthuma|
|Lead institution:||VU University Amsterdam|