Translational Psychiatry - Genetic risk prediction and neurobiological understanding of alcoholism - Linkis.com

Translational Psychiatry - Genetic risk prediction and neurobiological understanding of alcoholism - Linkis.com

Original Article

Citation: Translational Psychiatry (2014) 4, e391; doi:10.1038/tp.2014.29
Published online 20 May 2014

Genetic risk prediction and neurobiological understanding of alcoholism
OPEN

D F Levey¹, H Le-Niculescu¹, J Frank², M Ayalew¹, N Jain¹, B Kirlin¹, R Learman¹, E Winiger¹, Z Rodd¹, A Shekhar¹, N Schork³, F Kiefe⁴, N Wodarz⁵, B Müller-Myhsok⁶, N Dahmen⁷, GESGA Consortium, M Nöthen⁸, R Sherva⁹, L Farrer⁹, A H Smith¹⁰, H R Kranzler¹¹, M Rietschel², J Gelernter¹⁰ and A B Niculescu^1,12

1. ¹Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
2. ²Central Institute of Mental Health, Mannheim, Germany
3. ³Department of Human Biology, The J. Craig Venter Institute, La Jolla, CA, USA
4. ⁴Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
5. ⁵Department of Psychiatry, University Medical Center Regensburg, University of Regensburg, Regensburg, Germany
6. ⁶Department of Statistical Genetics, Max-Planck-Institute of Psychiatry, Munich, Germany
7. ⁷Department of Psychiatry, University of Mainz, Mainz, Germany
8. ⁸Department of Genomics, Life & Brain Center, Institute of Human Genetics, University of Bonn, Bonn, Germany
9. ⁹Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
10. ¹⁰Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, VA CT Healthcare Center, New Haven, CT, USA
11. ¹¹Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, and Philadelphia VAMC, Philadelphia, PA, USA
12. ¹²Indianapolis VA Medical Center, Indianapolis, IN, USA

Correspondence: Professor AB Niculescu, Department of Psychiatry, Indiana University School of Medicine, Neuroscience Research Building, 320 W. 15th Street, Indianapolis, IN 46202, USA. E-mail: anicules@iupui.edu

Received 23 January 2014; Accepted 18 March 2014

Topof page

Abstract

We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significantP-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n=135 genes, 713 SNPs) were used to generate a Genetic Risk Prediction Score (GRPS), which showed a trend towards significance (P=0.053) in separating alcohol-dependent individuals from controls in an independent German test cohort. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress-reactive animal model cross-validation. We also tested this small panel of genes in two other independent test cohorts from the United States, one with alcohol dependence (P=0.00012) and one with alcohol abuse (a less severe form of alcoholism; P=0.0094). SNCA by itself was able to separate alcoholics from controls in the alcohol-dependent cohort (P=0.000013) and the alcohol abuse cohort (P=0.023). So did eight other genes from the panel of 11 genes taken individually, albeit to a lesser extent and/or less broadly across cohorts. SNCA, GRM3 and MBP survived strict Bonferroni correction for multiple comparisons. Taken together, these results suggest that our stress-reactive DBP animal model helped to validate and prioritize from the CFG-discovered genes some of the key behaviorally relevant genes for alcoholism. These genes fall into a series of biological pathways involved in signal transduction, transmission of nerve impulse (including myelination) and cocaine addiction. Overall, our work provides leads towards a better understanding of illness, diagnostics and therapeutics, including treatment with omega-3 fatty acids. We also examined the overlap between the top candidate genes for alcoholism from this work and the top candidate genes for bipolar disorder, schizophrenia, anxiety from previous CFG analyses conducted by us, as well as cross-tested genetic risk predictions. This revealed the significant genetic overlap with other major psychiatric disorder domains, providing a basis for comorbidity and dual diagnosis, and placing alcohol use in the broader context of modulating the mental landscape.