Characterizing Pathways of Risk Associated with Identified Genes and Gene Networks

Danielle Dick, Kenneth Kendler, Brien Riley and Fazil Aliev

As individual genes and gene networks are associated with ethanol response in model organisms, it will be critical to understand their association with human alcohol related phenotypes. In this project we will conduct a series of analyses that test how genes/networks identified from the model organism components of the project, as integrated through the Analytics and Informatics core, are involved in pathways of risk for human alcohol-related outcomes. We will use data from two large, phenotypically rich, longitudinal samples: Spit for Science(S4S), an on-going project that is studying a sample of college students (N>6000) from a diverse university across their college years and beyond, and the Avon Longitudinal Study of Parents and Children (ALSPAC), an epidemiological sample identified through pregnant mothers in a defined, representative geographical area of England. ALSPAC families have been followed longitudinally since birth, with the children presently being assessed in young adulthood, and genotypic/phenotypic data on a subset of ~6000 individuals, parallel to S4S. The aims of the project are (1) to test the association of gene/networks from model organism systems with response to alcohol, alcohol consumption, and alcohol problems, as well as with more general externalizing behavior, in young adulthood, a period known to be of high risk for alcohol use disorders; (2) to understand how genetic risk unfolds across development by using the prospective longitudinal data collected from early in life; and (3) to understand what aspects of the environment mitigate or exacerbate genetic risk, focusing on three environments that have shown to have robust moderation effects in the twin data: parental monitoring, peer deviance, and life events. According, this project will provide a more informed understanding of how genetic factors translate into eventual alcohol use disorders by using two longitudinal samples with rich phenotypic and environmental assessments, in order to identify the spectrum of behavioral risk phenotypes associated with identified genes/networks, how risk unfolds across developmental stages, and how genetic risk interacts with environmental factors.