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Addiction-Related Genes in Gambling Disorders: New Insights from Parallel Human and Pre-Clinical Models

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Addiction-Related Genes in Gambling Disorders: New Insights from Parallel Human and Pre-Clinical Models Molecular Psychiatry (2015) 20, 1002–1010 © 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15 www.nature.com/mp ORIGINAL ARTICLE Addiction-related genes in gambling disorders: new insights from parallel human and pre-clinical models DSS Lobo1,2, L Aleksandrova2,3, J Knight1,2, DM Casey4, N el-Guebaly5, JN Nobrega2,6 and JL Kennedy1,2 Neurobiological research supports the characterization of disordered gambling (DG) as a behavioral addiction. Recently, an animal model of gambling behavior was developed (rat gambling task, rGT), expanding the available tools to investigate DG neurobiology. We investigated whether rGT performance and associated risk gene expression in the rat’s brain could provide cross-translational understanding of the neuromolecular mechanisms of addiction in DG. We genotyped tagSNPs (single-nucleotide polymorphisms) in 38 addiction-related genes in 400 DG and 345 non-DG subjects. Genes with Po0.1 in the human association analyses were selected to be investigated in the animal arm to determine whether their mRNA expression in rats was associated with the rat’s performance on the rGT. In humans, DG was significantly associated with tagSNPs in DRD3 (rs167771) and CAMK2D (rs3815072). Our results suggest that age and gender might moderate the association between CAMK2D and DG. Moderation effects could not be investigated due to sample power. In the animal arm, only the association between rGT performance and Drd3 expression remained significant after Bonferroni correction for 59 brain regions. As male rats were used, gender effects could not be investigated. Our results corroborate previous findings reporting the involvement of DRD3 receptor in addictions. To our knowledge, the use of human genetics, pre-clinical models and gene expression as a cross-translation paradigm has not previously been attempted in the field of addictions. The cross-validation of human findings in animal models is crucial for improving the translation of basic research into clinical treatments, which could accelerate neurobiological and pharmacological investigations in addictions. Molecular Psychiatry (2015) 20, 1002–1010; doi:10.1038/mp.2014.113; published online 30 September 2014 INTRODUCTION between DG and alcohol dependence are accounted for by 12 The worldwide expansion of legalized gambling has resulted in genetic factors in men. increased gambling-related harm, as observed by higher rates of Two large twin studies estimate that genetic factors account for – 13,14 bankruptcy, divorce and suicide secondary to excessive gambling 50 60% of the vulnerability for developing DG, showing that involvement.1 It is estimated that 0.6–1%1,2 of the population in clinical and subclinical forms of the disorder are part of a single the United States and Canada meet DSM-IV criteria for patholo- genetic continuum. However, there has been little advance in the gical gambling (PG). However, individuals who do not reach the understanding of the molecular genetic underpinnings of DG.15 Most molecular genetic studies in DG were performed DSM-IV diagnostic threshold also experience negative conse- in small samples and investigations have focused on the quences from their gambling behavior.3,4 Thus, in keeping with dopamine (DA) and serotonin system.15,16 Recently, the first the recent literature,5 we will use the term disordered gambling genome-wide association study in DG was published,17 and (DG) to characterize the full spectrum of gambling-related association trends were found with metaxin, ataxin and the very disorders, including PG and subclinical PG. The term PG will be low density lipoprotein receptor genes. In addition, a canonical fi fi reserved for the speci c DSM-IV de nition of the disorder. pathway analysis revealed that several addiction-related pathways Neurobiological research in DG has advanced in the last presented a higher frequency of single-nucleotide polymorphisms two decades, supporting its characterization as a behavioral 6 (SNPs) nominally associated with DG. Finally, the authors report addiction. Studies show that DG and substance addictions share, that SNPs associated with DG were more frequent among at least in part, dysfunctions in similar brain regions as well as ’ 7 candidate genes for DA agonist-induced PG in Parkinson s disease. performance on neuropsychological tasks. For instance, similar The combination of human genetic and animal research in deficits have been reported for both DG and substance addiction substance addictions has resulted in a better understanding of 8 9 in assessments of inhibitory response and reflection impulsivity. neural pathways involved in the development and maintenance In particular, DG and substance-dependent subjects present of addiction-related processes.18,19 Recently, animal models of deficits in the Iowa Gambling Task,8,10 which assesses decision- ‘gambling-like’ behavior using a risky decision-making paradigm making mediated by the pre-frontal cortex.11 In regards to genetic have been developed.20,21 The utility of these animal models for vulnerability, it is estimated that as much as 74% of the overlap the understanding of DG and for pre-clinical investigation of 1Department of Psychiatry, University of Toronto, Centre for Addiction and Mental Health, Toronto, ON, Canada; 2Centre for Addiction and Mental Health, Toronto, ON, Canada; 3Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; 4Mental Health Commission of Canada, Calgary, AB, Canada; 5Division of Addiction, Department of Psychiatry, University of Calgary, Calgary, AB, Canada and 6Departments of Pharmacology and Toxicology, Psychiatry, and Psychology, University of Toronto, Toronto, ON, Canada. Correspondence: Dr DSS Lobo, 250 College Street, R121, Toronto, ON, Canada M5T 1R8. E-mail: [email protected] This study was conducted when Dr Casey was affiliated with the Department of Psychology, University of Calgary, Calgary, AB, Canada. Received 16 July 2013; revised 30 July 2014; accepted 4 August 2014; published online 30 September 2014 Addiction genes in gambling: human and rat models DSS Lobo et al 1003 pharmacological treatments depends on how findings in humans based on higher association reliability scores (⩾1) in the KARG database and animals can be replicated across species.22 and/or on preliminary evidence of association from our previous – Here, we have combined a molecular genetic association study studies,26 29 totaling 40 genes (384 SNP panel). Selection of tagSNPs was in humans with an in situ hybridization study in brains of rats performed using genotypic data from the International Haplotype Map submitted to an animal model of gambling behavior, the rat Project database public release #3 (www.hapmap.org) in the Utah 21 residents with ancestry from northern and western Europe sample (that Gambling Task (rGT). Our goal was to investigate whether this is, CEU population). TagSNPs with minor-allele frequencies ⩾ 0.10 and with three-component paradigm could provide preliminary support for a minimum distance of 60 bp between SNPs were selected using the human genetic findings in DG. aggressive pairwise-tagging algorithm in the software Haploview v4.2.30 Genomic DNA was extracted using standard methods from either blood lymphocytes or saliva (OrageneTM DNA Self-Collection Kit, Kanata, ON, MATERIALS AND METHODS Canada). Genotyping for tagSNPs was performed using Illumina Gold- ® Human arm enGate custom SNP genotyping protocols (Illumina, San Diego, CA, USA). Genetic analysis was performed using an Illumina Beadstation 500 G Subject recruitment. Subjects were recruited in the provinces of Ontario platform. Quality control metrics from GenCall v6.2.0.4 (Illumina) were used and Alberta, Canada between 2006 and 2010. In Alberta, subjects were to exclude SNPs with poor performance: genotype calls o95%, with invited to participate in the Leisure, Lifestyle and Lifecycle Project (LLLP)23 o10% minor-allele frequency, and not in Hardy–Weinberg equilibrium —a population-based study that investigates the natural progression − (Po10 3) in either cases or controls. A total of 320 tagSNPs distributed of gambling behavior. The LLLP sample recruitment strategy has been along 38 genes were included in the analysis (Table 1). previously described (see Supplementary Information).3 A total of 609 adults consented to donate blood or saliva samples for genetic analyses. Statistical analysis. Power calculations were performed through In Ontario, individuals from the Greater Toronto Area who perceived 31 – their gambling as excessive were invited to participate in a genetic study. QUANTO and revealed that a case control ratio of 1:1, with 400 cases would have ~ 80% power to detect associations with an odds ratio of 1.5 These individuals were recruited through advertisements in the commu- (two-tailed P-values), considering an minor-allele frequency of 0.10, and a nity and at problem gambling treatment centers. Thus, Ontario provided population prevalence of 0.05 (mean prevalence of DG in Canada).4,32,33 an enriched sample for DG. A total of 447 adult subjects were eligible to We compared DG and non-DG groups in regards to gender and age, participate in the study and donated blood for genetic analyses. using chi-squared and t-tests, respectively. Allelic-based genetic associa- Research protocols were approved by the participating institutions tions were investigated using chi-squared tests or logistic regressions as research ethics boards and were in compliance with the Declaration of appropriate,
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