Human studies to identify genes and characterize risk pathways involved in alcohol related outcomes.

Danielle M. Dick and Fazil Aliev

Project 4 of the VCU Alcohol Research Center will utilize human data to accomplish two complementary goals: (1) advancing discovery of genes involved in alcohol-related outcomes using new multivariate genomic techniques, and (2) characterizing the risk associated with identified variants in diverse longitudinal samples, in order to understand the spectrum of phenotypes associated with identified variants, across development, and in conjunction with the environment. Each of these areas represent critical steps in using genetic data to improve prevention, intervention, and treatment for alcohol use disorders (AUDs), and will lay the foundation as we move into an era of personalized medicine1. Human gene identification efforts for alcohol use disorders lag behind other areas of psychiatry, in part due to constrained sample sizes of available AUD cases. However, recent meta-analyses of consumption6, 7 and AUD 8 reveal significant genetic correlations with numerous other psychiatric and behavioral traits, as well as social and demographic outcomes, and other biomedical phenotypes. Project 4 will (Aim 1) apply new multivariate genetic methods to capitalize on genetic sharing between alcohol use phenotypes and other psychiatric and behavioral traits in order to boost power to detect common variants associated with alcohol use outcomes, and to characterize the latent pathways by which genetic variants operate. Bioinformatic characterization of these identified genetic variant results through the Bioinformatics and Analytics (BIA) core will help elucidate underlying biological risk pathways. We will then apply results from these multivariate analyses to three complementary longitudinal datasets, consisting of both population-based and high-risk samples, in order to (Aim 2a): map the behavioral phenotypes associated with the genetic risk scores identified in Aim 1 across adolescence and emerging adulthood; (Aim 2b) test for pathways of risk specific to sex and racial/ethnic background; and (Aim 2c) test for moderation of genetic risk by key environmental factors. The project will interface with the other ARC components in multiple ways: results from the gene discovery analyses (Aim 1) will be integrated with model organism results and expression data (Projects 1-3) in the BAI Core to create refined polygenic risk scores for further study in Aim 2. Project 5 will refine structural models using twin and epidemiological samples to further characterize the multivariate nature of genetic influences on alcohol-related outcomes, to iteratively inform and extend the multivariate analyses performed in Aim 1. Further, the genetic variants identified in Aim 1 can be advanced for further study in animal models via the Rodent Behavior Core.