Rohan H Palmer, PhD - US grants

DESCRIPTION (provided by applicant): This Mentored Research Scientist Career Development Award will afford Dr. Rohan Palmer focused training for his programmatic line of research in the genetics of alcohol dependence and related phenotypes (AD). Given that gene identification studies (in particular genomewide association studies) on alcoholism are challenged by the fact that alcoholism is a multifactorial disorder influenced by multiple interacting genes, each with small effect, the

PROJECT SUMMARY/ABSTRACT The primary goals of this project are to identify and characterize gene sets that reflect individual differences in the propensity to develop alcohol or other forms of drug addiction and to develop a novel tool to enhance the prediction of drug addiction risk using genome-wide data. Alcohol, tobacco, and illicit drug use and dependence are complex biological and psychosocial problems that can be conceptualized as alternate forms of an underlying behavioral predisposition to the development of generalized drug dependence (DD). Despite the fact that twin and family studies suggest that genetic variation contributes to the preoccupation with alcohol and other multiple substances of abuse, the identification of specific causal loci has been limited. In fact, studies have confirmed that unlike monogenic disorders, alcohol and other drugs of abuse are influenced by numerous genetic variants. While both human and animal studies of addiction have indicated that multiple forms of drug addiction are genetically influenced, the full complement of biological mechanisms and genetic factors are still unknown. The current application proposes a novel framework and tool that integrates Bayesian statistics and functional genomics to enhance the identification of a set(s) of causal genetic factors for alcohol and other drug dependence. In the first component of the framework, we will use Bayesian mixture modeling to identify a set(s) of markers that comprise the additive genetic effect on generalized drug dependence (DD). In the second component, we will identify cross-species-functionally-annotated gene sets for alcohol, tobacco, and other illicit drug use/dependence, determine their relative contribution to variation in DD, and test for enrichment of the Bayesian-derived gene set(s) in (a) gene sets ascertained using cross- species-functional genomic studies of alcohol and other drug addiction, and (b) a range of biological gene sets based on known molecular pathways. In the third component, we will identify combinatorial effects within and between gene sets. In the final component, we develop prediction models, as well as test a novel kinship- based approach to predict the risk for DD given a specified gene set. Each of these components provides novel information that will help to formulate new research hypotheses and prediction models for addictive behaviors and other behaviors/traits.

PROJECT ABSTRACT The discovery of genetic risk factors for alcohol and tobacco use and dependence has largely focused on the contribution of individual genetic markers, such as single nucleotide polymorphisms (SNPs). While this approach has been useful in mapping the genetic liability of rare diseases with a Mendelian pattern of inheritance, the approach has had limited success with complex diseases characterized by a more polygenetic architecture. The identification of a set of genetic factors that account for individual differences in the liability to use and misuse alcohol and tobacco/nicotine will be a major step in understanding mechanisms of undercontrolled use. Moreover, the development of novel resources to predict or infer risk for problematic use of alcohol and tobacco based on genetic factors that largely explain individual differences in alcohol and tobacco use/problems, as well as other behavioral or neurocognitive phenotypes, will be a major step in translating research discoveries into prevention strategies. The current proposal addresses the problem of limited predictive power in alcohol and tobacco/nicotine genetic studies by proposing novel research that uses an integrative approach and existing data and bioinformatics resources to: (1) localize genetic variants that comprise the additive genetic effects on alcohol and tobacco use and dependence, and (2) developing a novel and freely-available resource that enhances the way existing genetically informed samples are used for alcohol and tobacco risk prediction. The current application assembles a multidisciplinary team of molecular genetics and computational and statistical geneticists to execute these project aims.