Principal Investigator: Professor Dr Jacqueline Vink
Department: Radboud University, Behavioural Science Institute, Montessorilaan 3, Nijmegen, 6525 HR, Netherlands
Lead Collaborators: 1) Dr Karin Verweij
Collaborating Institutions and Addresses: 1) VU University Amsterdam, Biological Psychology, Van der Boechorststraat 1, Amsterdam, 1081 BT, NetherlandsTags: 22529, alcohol, bivariate GWA, Genetic, polysubstance, smoking
1a: Epidemiological studies have highlighted how different substance use behaviours are strongly correlated. Alcohol users are more likely to smoke than nondrinkers and the prevalence of heavy alcohol use in smokers is higher than in nonsmokers. Individually, effects of these substances have profound health implications and dual users are at increased risk, especially for developing different types of cancer. Both twin and genetic-risk prediction studies have shown a common genetic basis for poly-substance use. Nevertheless, only limited efforts to identify these common genetic factors have been undertaken. We propose to systematically investigate these genetic commonalities using UK Biobank data.
1b: Investigating overlapping genetic vulnerabilities to alcohol use and smoking is of importance for public health. The type of research we propose is essential to obtain more insight into the nature of individual differences in substance use behaviours and, thus, understanding why some people are more vulnerable to suffer from poly-substance use than others. The use of genetic information to identify people at increased risk for addiction can eventually inform clinical practice and contribute to personalized medicine. Furthermore, by investigating genetic variants associated with both smoking and alcohol, we may identify biological mechanisms underlying addictive behaviours.
1c: We will use individuals’ genotypes information to run a bivariate genome-wide association analysis (GWA) in order to search for genetic variants underlying co-variance of both phenotypes: tobacco and alcohol use. This has been shown (Liu et al., 2009) to be an effective strategy to detect genes that concurrently influence two or more traits (pleiotropic genes).
1d: For sufficient power to run genetic association analyses and obtain accurate SNP effects, we require access to the full (available) cohort.