Susceptibility to alcohol and substance dependence is known to be influenced by genetic factors. However, few specific genetic variations that alter susceptibility to addiction have been identified. This is partly because addiction is a polygenic trait, influenced by many genetic variations, each with a small marginal effect. Phenotypic characteristics are controlled by networks of interacting biochemical and physiological pathways influenced by the products of specific genes. While single genetic variants result in small changes in susceptibility to complex diseases, it is the combined effects of genes within a biochemical pathway that likely drive phenotype expression. The research proposed in this application aims to apply novel pathway-based methods to analyze existing data from a genome wide association study of alcoholism. Case-control alcohol dependence data from the Study of Addiction: Genetics and Environment (SAGE), collected by Dr. Laura Bierut and colleagues, will be analyzed. The methods that will be applied take into account known relationships between genes and their products and assess the effect of gene-sets that represent biological pathways, rather than assessing individual gene effects. Once significant pathways are identified, comprehensive analyses of genes within these pathways will be performed, to characterize the genetic effects, including interactions between genetic variations. These analyses will utilize random forest methods and LASSO logistic regression with gene-gene interaction effects. The long term goals of this research are to improve the detection of interacting genetic risk factors for substance addiction and subtypes of substance use disorders, by applying optimal statistical techniques. While studying all possible interactions between SNPs from a genome-wide scan may not be practical, a focused approach of investigating genetic interactions within a relevant pathway is expected to be more powerful and yield interpretable results. Analysis of existing data using new statistical methods that account for the relationships between genes that act as part of common neural or molecular pathways has great potential to identify genetic variations that contribute to individual differences in addiction susceptibility. Discovery of such genetic risk factors has important implications including increasing our understanding of the pathways of development of addiction and risk prediction. Perhaps more importantly, this knowledge is expected to help identify subtypes of addiction that require different interventions leading to personalized treatment with increased success rates.
Although progress has been made in terms of understanding the heritable aspects of alcoholism and other substance use disorders, few specific genetic risk factors have been identified. Recently, new methods for analyzing genetic data have been proposed that take into account known relationships between genes that contribute to common biological pathways. The research proposed in this application will apply such approaches to analyze data from a genetic study of alcoholism. Results of this study are expected to help identify sets of genes that contribute to individual differences in susceptibility to substance dependence, leading to improved diagnosis and management of addiction based on an individualized treatment approach.
| Biernacka, Joanna M; Geske, Jennifer; Jenkins, Gregory D et al. (2013) Genome-wide gene-set analysis for identification of pathways associated with alcohol dependence. Int J Neuropsychopharmacol 16:271-8 |
| Karpyak, Victor M; Geske, Jennifer R; Colby, Colin L et al. (2012) Genetic variability in the NMDA-dependent AMPA trafficking cascade is associated with alcohol dependence. Addict Biol 17:798-806 |
| Biernacka, Joanna M; Jenkins, Gregory D; Wang, Liewei et al. (2012) Use of the gamma method for self-contained gene-set analysis of SNP data. Eur J Hum Genet 20:565-71 |
| Chung, Sun Ju; Armasu, Sebastian M; Biernacka, Joanna M et al. (2011) Variants in estrogen-related genes and risk of Parkinson's disease. Mov Disord 26:1234-42 |
| Fridley, Brooke L; Biernacka, Joanna M (2011) Gene set analysis of SNP data: benefits, challenges, and future directions. Eur J Hum Genet 19:837-43 |
| Fridley, Brooke L; Jenkins, Gregory D; Biernacka, Joanna M (2010) Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. PLoS One 5: |
