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Investigation of selected signalling system genes in pathological gambling finalreport

Investigation of Selected Signalling System Genes in Pathological Gambling
Final Report
Principal Investigator and the Project Contact Person:
James L. Kennedy, M.D., Director, Neuroscience Research, CAMH
250 College St., R-31 – Toronto, ON – M5T 1R8 – Phone: 416- 979-4987 FAX: 416- 979-4666. [email protected] Principal Co-Investigator:
Daniela S. S. Lobo, M.D., Ph.D., Neurogenetics, CAMH
250 College St., R-32 – Toronto, ON – M5T 1R8 – Phone: 416- 535-8501 extension FAX: 416- 979-4666. [email protected] Table of Contents:
Abstract
1. Introduction
1.1 Literature Review 2. Research Design and Methodological Approach
2.2 Gene and Polymorphism (SNP) Selection 2.3 Statistical Analysis 2.4 Sequencing of DRD4 exon III region 3. Results
3.1 PG subjects x Control Subjects 3.2 PG subjects with alcohol and/or drug abuse x Control Subjects 3.3 PG subjects with alcohol and/or drug abuse – PG-ADA (n =99) x PG subjects without 3.4. DRD4 sequencing analysis 4. Limitations and Changes to Original Proposal
5. Discussion
6. References
List of Tables
List of genes and SNPs selected for genotyping within their respective chromosomes Haplotype comparisons between problem gamblers (PG) with healthy control subjects Haplotype comparisons between problem gamblers with alcohol and/or drug abuse (PG- ADA) with control subjects Haplotype comparisons between problem gamblers with alcohol and/or drug abuse (PG- ADA) with PG subjects without substance abuse List of Figures
Explanation of symbols in figures 1-5
Figure 1: Neuroactive ligand receptor interaction pathway
Figure 2: Long-term potentiation pathway
Figure 3: GnRH signalling pathway
Figure 4: MAPK signalling pathway
Figure 5: Gap junction pathway
Figure 6: Diagrammatic Representation of the DRD4 gene and DRD4 exon III VNTR
Figure 7: Dopamine D4 receptor graphical representation
Figure 8: Screenshot of the sequence alignment (DRD4 exon III)
Figure 9: Screenshot of the deletion on DRD4 exon III from one subject in our sample
We would like to thank Sajid Shaik for his contribution in the preparation of DNA samples and coordination of genotyping. We would like to thank Maria Tampakeras and Natalie Freeman for sequencing and analyzing our data on DRD4. As always, we are grateful for all the contribution provided by our research subjects. We would like to thank OPGRC for its continued support in funding our project. Abstract:
Background: Several studies have provided evidence for a biological basis for problem gambling
(PG): first-degree relatives (FDR) of PG subjects present a higher than expected chance to develop PG
and a large twin study has reported that genetic factors account for 52% of the variance of PG
diagnosis. The increased risk for developing substance addiction in FDR of PG subjects, the high
comorbidity between substance dependence and PG, and a common underlying genetic vulnerability
for PG and alcohol dependence provide converging evidence that a common neurobiological system
underlies both substance addiction and PG. Objectives: 1. to continue the investigation of the genetic
basis of problem gambling (PG), through the analysis of genes involved in brain's signalling system
that underlies addictive disorders; 2. to further investigate how the variations in the sequence of the 2
and 7-repeat alleles of the D4 receptor gene (DRD4) are associated with PG severity (from our
previous results on the association of DRD4 and PG). Methods: DNAs and clinical data from 514 PG
subjects and 214 controls assessed during our previously funded OPGRC projects. Hypotheses: 1.
genes underlying common genetic pathways in substance addiction will also be associated with
increased vulnerability for PG; 2. variations in the sequence of the 2 and 7-repeat alleles of the D4
receptor gene will better predict PG severity. Gene selection: 1. based on our previous association of
DRD4 2 and 7-repeat alleles with PG severity, we will proceed to sequence these gene variants; 2.
addiction-related genes were selected through the Knowledgebase for Addiction Related Genes; 3.
variants within each gene were selected using tag SNPs in order to provide maximum gene coverage.
Statistical analysis: Three groups were used for analysis: 1. PG subjects x Control subjects; 2.PG
subjects with alcohol and/or drug abuse x PG subjects without history of substance misuse and PG
subjects with substance misuse compared with healthy control subjects. Genetic analysis was
performed for haplotypes (blocks of variants) using modified qui-squared tests implemented in the
software Golden Helix SNP & variation suite version 7.Correction for multiple testing was performed
through False Discovery Rate tests (FDR). Results: Haplotypes in CAMK2D, HTR2A, PRKACB, and
PLD2 were significantly associated with PG. No significant associations were found in the comparison
between PG with alcohol and/or drug abuse and controls, and between PG without substance abuse and
PG with alcohol and/or drug abuse. Nominal associations were found with CAMK2D, dopamine D2,
serotonin 2A, and glutamate receptor genes. Sequencing of DRD4 exon III VNTR revealed a new
deletion on the 7-repeat allele sequence that may alter gene expression. Our associations suggest that,
similarly to substance addiction, PG is associated with genes involved in neuronal plasticity
(CAMK2D), signal transduction (PRKCAB), and serotonin signalling (HTR2A). PLD2A is regulated
by G-coupled receptors (dopamine, glutamate), suggesting that altered dopamine release reported in PG
may be linked to down-stream alterations in dopamine signalling pathways. Our findings corroborate
the view of PG as an addiction and suggest that further investigation of these signalling pathways
should provide a better understanding of the neurobiology of PG. Application of findings: Our results
have provided further insight in the neurobiological processes underlying PG. The reported
associations with genes that synthesize protein kinases (CAMK2A, PRKCAB) open the possibility of
testing protein-kinases inhibitors for the treatment of PG.
Key words:pathological gambling, problem gambling, genetics, addiction
• Goal #1: To continue the investigation of the genetic basis of problem gambling (PG), through the analysis of genes involved in brain's signalling systems that underlie addictive disorders, building on our readily available sample of DNAs from PG subjects and controls and on the newly available technology and evidence. • Goal #2: In our previous research we have found that the rare 7-repeat allele of the dopamine receptor type 4 gene polymorphism (DRD4 exon III VNTR) was significantly associated with PG and that the 2-repeat allele of DRD4 exon III VNTR showed a trend of association with PG. It is known that there is variation within the sequence that composes the 2- and 7-repeat alleles, but there has been little research on how these variations might impact associations of these alleles to complex traits (e.g., Novelty Seeking, PG). Our objective is to further investigate if the variations in the sequence of the 2 and 7-repeat alleles of the D4 receptor gene are associated with PG severity. 1.1 Literature Review:
Pathological gambling is defined as a persistent and recurrent maladaptive behaviour that disrupts personal, professional, and family relationships and is not better explained by a manic episode (DSM- IV, APA). Evidence from several studies suggests that PG presents a biological basis. Family studies have shown that first-degree relatives (FDR) of PG subjects present a higher than expected chance to develop PG and a large twin study has reported that the genetic factors account for approximately
50% of the variance of PG diagnosis in men (Eisen, et al., 1998; Eisen, et al., 2001) and women
(Slutske, Zhu, Meier, & Martin, 2010). Since Marks (Marks, 1990) proposed that PG should be considered as an addictive disorder (i.e. as a behavioural addiction), studies have investigated the relationship between substance and behavioural addictions (Blanco, Moreyra, Nunes, Saiz-Ruiz, & Ibanez, 2001; Cardinal & Everitt, 2004). Currently, several lines of investigation provide converging evidence that a common neurobiological system underlies both substance addiction and PG: 1. Lifetime Comorbidity: Individuals diagnosed as pathological gamblers present a significantly higher risk to develop (at any point in life) substance dependence compared to the general population (Black & Moyer, 1998; Bland, Newman, Orn, & Stebelsky, 1993; Crockford & el-Guebaly, 1998; Cunningham-Williams, Cottler, Compton, & Spitznagel, 1998; Lesieur & Heineman, 1988; Lynch, Maciejewski, & Potenza, 2004; Maccallum & Blaszczynski, 2002; Petry, Stinson, & Grant, 2005; Ross, Glaser, & Germanson, 1988; Roy, et al., 1988; Vitaro, Brendgen, Ladouceur, & Tremblay, 2001). Likewise, a person who had a diagnosis of substance dependence (at any point in life) has a greater risk of being diagnosed with PG (Daghestani, Elenz, & Crayton, 1996; Feigelman, Wallisch, & Lesieur, 1998; Lesieur, Blume, & Zoppa, 1986; Petry, 2001; Ross, et al., 1988; Shaffer & Korn, 2002; Spunt, Lesieur, Hunt, & Cahill, 1995; Welte, Barnes, Wieczorek, Tidwell, & Parker, 2001). For instance, approximately 70% of PG subjects present nicotine dependence (Crockford & el-Guebaly, 1998) and 50 to 70% present alcohol abuse or dependence (McCormick, Russo, Ramirez, & Taber, 1984; Petry, et al., 2005). Pathological gambling rates has also been reported to be increased among subjects under methadone maintenance (7 – 52%) (Feigelman, Kleinman, Lesieur, Millman, & Lesser, 1995; Ledgerwood & Downey, 2002; Spunt, 2002; Spunt, et al., 1995; Weinstock, Blanco, & Petry, 2006), and among cocaine dependent subjects (8 – 12%) (Hall, et al., 2000; Kausch, 2003; Toneatto & Brennan, 2002). 2 Family History: FDR of PG subjects have significantly increased risk to develop substance dependence (Jacobs, 1989; Ramirez, McCormick, Russo, & Taber, 1984; Roy, et al., 1988) compared to the general population. FDR of substance dependent individuals are also at a higher than expected risk to develop PG (Herzog, Keller, Lavori, Kenny, & Sacks, 1992; Jacobs, 1989; Lesieur & Heineman, 1988; Ramirez, et al., 1984). 3 Genetics Research: In general, twin studies have provided evidence of a shared common vulnerability for any addiction, regardless of the substance used (Karkowski, Prescott, & Kendler, 2000; Kendler, Jacobson, Prescott, & Neale, 2003; Tsuang, et al., 1998). PG is reported to share 12 to 20% of its genetic risk with the risk for alcohol dependence (Slutske, et al., 2000), and research suggest that this shared vulnerability extends to the pathological use of natural rewards (i.e., food and sex) and to substance dependence (Kelley, 1999; Pelchat, 2002; Shaffer, et al., 2004). 4 Clinical Research: Clinical studies have shown that opioid antagonists, such as naltrexone and nalmefene, can be effective in the treatment of both PG (Grant, Kim, & Hartman, 2008; Grant, et al., 2006; Kim, Grant, Adson, & Shin, 2001) and alcohol dependence (Mason, Salvato, Williams, Ritvo, & Cutler, 1999; Volpicelli, Alterman, Hayashida, & O'Brien, 1992), suggesting that a common biological pathway is involved in the response to these medications in both conditions. The dopaminergic system has a well established role in the development of drug priming through the release of dopamine in the brain's reward system, more specifically in the nucleus accumbens (Kalivas 1 Please note that the dopaminergic system closely interacts with other neurotransmitter systems also involved in substance addiction (glutamate, serotonin), which will not be described here in respect to space limitations. & Volkow, 2005). The nucleus accumbens works in close relationship with other areas of the brain that compose the brain's reward system: ventral tegmental area, prefrontal cortex, locus coeruleous, amygdala, and hippocampus. Research studies have implicated also the brain's reward system in the pathophysiology of behavioural addictions (Chambers & Potenza, 2003; Chambers, Taylor, & Potenza, 2003; Potenza, 2001). Imaging studies on PG subjects have shown that: 1. when exposed to gambling- related cues, PG subjects (but not controls) present increased activation of the dorsolateral pre-frontal cortex (DLPFC) (Crockford, Goodyear, Edwards, Quickfall, & el-Guebaly, 2005) and a relatively decreased activity in brain regions implicated in impulse regulation (frontal and orbitofrontal cortex, caudate/basal ganglia, and thalamus) compared with controls (Potenza, et al., 2003); 2. PG is related to response perseveration and diminished reward and punishment sensitivity as indicated by hypoactivation of the ventrolateral prefrontal cortex (VLPFC) when money is gained and lost (de Ruiter, et al., 2009); as well as to heightened limbic and sensory activation when betting for money (Hollander, et al., 2005). In summary, research has shown that an overlap exists between brain regions associated with substance addiction and those associated with PG. Throughout the years, researchers have described different signalling pathways involved in substance addiction. For instance, the MAPK signalling pathway has been suggested to have a role in regulating synaptic plasticity related to long-lasting changes in memory associated with substance addiction (Wang, Fibuch, & Mao, 2007), and the long-term potentiation pathway has been linked to adaptations in glutamatergic transmission and synapse plasticity induced by substance addiction (Jones & Bonci, 2005). Recently, a group of researchers has integrated all the available research data (between 1976 and 2006) regarding genetic and biological pathways in addiction and developed a database of addiction related genes placed within five common signalling pathways for substance addiction (C. Y. Li, Mao, & Wei, 2008). Below are brief descriptions of each pathway. Figures 1-5 provide a diagrammatic representation of each pathway. It is important to note that these pathways are not isolated and members (genes, proteins) of one pathway may also play a role in another pathway. A. Neuroactive-ligand receptor interaction pathway:
The neuroactive-ligand pathway is one of the pathways involved in processing information from the environment through signalling molecules, such as neurotransmitters. G-coupled protein receptors, such as dopamine and metabotrobic glutamate family receptors, are part of this pathway. Both dopamine receptors and genes, and metabotropic glutamate receptors have been significantly associated with addictions. Several other peptides, proteins, and hormones are part of this pathway. B. Long-term potentiation:
Long-term potentiation is a pathway specific to the nervous system. Hippocampal long-term potentiation (LTP), a long-lasting increase in synaptic efficacy, is the molecular basis for learning
and memory. Stimulation of neuronal afferents in the hippocampus induces glutamate release and
activation of glutamate receptors in neuronal dendrites. A large increase in Ca2+ intake resulting from influx through NMDA receptors leads to constitutive activation of CaM kinase II (CaMKII). It is
hypothesized that postsynaptic Ca2+ increases generated through NMDA receptors activate several
signal transduction pathways including the MAPK and cAMP regulatory pathways.
C. Gonadotropin Releasing Hormone (GnRH) signalling pathway:
The GnRH pathway is an endocrine pathway involved in hormone secretion. Gonadotropin-releasing hormone (GnRH) secretion from the hypothalamus acts upon its receptor in the anterior pituitary to regulate the production and release of the gonadotropins, LH and FSH. The GnRHreceptor is coupled to proteins that activate phospholipase C which transmits its signal to diacylglycerol (DAG) and inositol 1,4,5-trisphosphate (IP3). DAG activates the intracellular protein kinase C (PKC) pathway and IP3 stimulates release of intracellular calcium. Signalling downstream of protein kinase C (PKC)
leads to transactivation of the epidermal growth factor (EGF) receptor and activation of mitogen-
activated protein kinases (MAPKs). Active MAPKs translocate to the nucleus, resulting in activation
of transcription factors and rapid induction of genes. D. Mitogen-activated protein kinase (MAPK) signalling pathway:
The MAPK pathway is also one of the pathways involved in processing information from the environment through signal transduction, by regulating neuronal plasticity associated with memory function and addictive properties of substances. The MAPK cascade is a highly conserved module that is involved in various cellular functions in various animal species, including cell proliferation, differentiation and migration. Mammals express at least four distinctly regulated groups of MAPKs, extracellular signal-related kinases (ERK)-1/2, Jun amino-terminal kinases (JNK1/2/3), p38 proteins (p38alpha/beta/gamma/delta) and ERK5 that are activated by specific MAPK-kinases (MAPKK): MEK1/2 for ERK1/2, MKK3/6 for the p38, MKK4/7 (JNKK1/2) for the JNKs, and MEK5 for ERK5. Each MAPK kinase, however, can be activated by more than one MAPK kinase-kinase (a kinase that is activated by another kinase), increasing the complexity and diversity of MAPK signalling. Presumably each MAPKKK confers responsiveness to distinct stimuli. E. Gap junction pathway:
The Gap junction pathway is involved in cell (including neuronal) communication. Gap junctions contain intercellular channels that allow direct communication between the cytosolic compartments of adjacent cells. Each gap junction channel is formed by docking of two 'hemichannels', each containing six connexins, contributed by each neighbouring cell. These channels permit the direct transfer of small molecules including ions, amino acids, nucleotides, second messengers and other metabolites between adjacent cells. Gap junctional communication is essential for many physiological events, including embryonic development, electrical coupling, metabolic transport, apoptosis, and tissue homeostasis. Communication through Gap Junction is sensitive to a variety of stimuli, including changes in the level of intracellular Ca2+, pH, transjunctional applied voltage and phosphorylation/ dephosphorylation
As it can be inferred from the descriptions above, these pathways have points at which they merge or interact with one another, at times sharing gene products (proteins, enzymes, hormones) and at times activating another pathway. Through the construction of the KARG database, it was observed that positive feedback loops interlinked the pathways with each other through CAMKII. Two of these positive feedback loops involved signal transduction and would be considered ‘‘fast'' loops, whereas the other two loops involved transcription and translation and would be considered ‘‘slow'' loops (C. Y. Li, et al., 2008). Previous research had found that coupled fast and slow positive feedback loops could create a switch that was inducible and resistant to noise and played key roles in discontinuous and irreversible biological process, features characteristic of addiction (Abrieu, Doree, & Fisher, 2001; Brandman, Ferrell, Li, & Meyer, 2005). Activation of CAMKII has also been reported to play fundamental roles in the development and maintenance of addiction states (Noda & Nabeshima, 2004; Tang, Shukla, Wang, & Wang, 2006). Disruption of CaMKII translation in neurons (dendrites) impaired the stabilization of synaptic plasticity and memory consolidation (Miller, et al., 2002; Valjent, Corbille, Bertran-Gonzalez, Herve, & Girault, 2006). Taking this evidence together, the KERG database authors have suggested that the fast and slow positive feedback loops interlinked through CAMKII may be essential for the development and consolidation of addiction and may provide a systems-level explanation for some of the characteristics of addictive disorders (C. Y. Li, et al., 2008). In summary, based on research evidence suggesting common biological pathways for substance addictions and PG and on the recently compiled database of addiction related genetic pathways (KARG), we have hypothesized that genes underlying signalling pathways in substance addiction
will also be associated with increased vulnerability for PG. More specifically, we expect that genes
located on chromosomes 4, 5, 9, 10, 11 and 17 will be associated with PG, since these chromosomes
have the strongest evidence for harbouring susceptibility genes for addictions (M. D. Li & Burmeister, Aside from investigating the aforementioned hypothesis, we have also proposed to investigate whether the variations in the sequence of the 2 and 7-repeat alleles of the D4 receptor gene (DRD4 exon III VNTR – see Figure 6) are associated with PG severity. This investigation is based on our previous results showing that DRD4 (exonIII) 7-repeat allele is associated with severity of PG, and that this association is not moderated by sex or age. We also found a trend of association of the 2-repeat allele with PG. The discovery of a functional polymorphism in the dopamine D4 receptor gene (DRD4, OMIM *126452) (Van Tol, et al., 1992) and its higher expression in the prefrontal cortex and temporo- limbic regions (Mulcrone & Kerwin, 1997) made DRD4 one of the most investigated genes in behavior and psychiatry. Several studies reported associations of this polymorphism with impulsive personality traits (Becker, Laucht, El-Faddagh, & Schmidt, 2005; LaHoste, et al., 1996; Laucht, Becker, Blomeyer, & Schmidt, 2007), addictions (Hill, Zezza, Wipprecht, Xu, & Neiswanger, 1999), and impulse control disorders (Comings, et al., 1999; Perez de Castro, Ibanez, Torres, Saiz-Ruiz, & Fernandez-Piqueras, 1997) with conflicting results. Thus far, the most consistent findings were reported for attention-deficit hyperactivity disorder (LaHoste, et al., 1996), as confirmed by meta-analyses (Bobb, Castellanos, Addington, & Rapoport, 2005; D. Li, Sham, Owen, & He, 2006; Thapar, Langley, Owen, & O'Donovan M, 2007). In the previous decade, there was preliminary evidence of an inverse relationship between length of repeats, clozapine biding affinity, and gene expression levels (Asghari, et al., 1995; Jovanovic, Guan, & Van Tol, 1999; Schoots & Van Tol, 2003), and various studies compared the presence of the 7-repeat allele against short allelic variants of this polymorphism. There is evidence that transcripts from 7 and 2-repeat alleles have a lower potency when coupling with adenylyl-cyclase compared with transcripts from 4 and 10-repeat alleles respectively (Asghari, et al., 1995; Jovanovic, et al., 1999). In vitro experiments suggest that the 2 and the 7 forms of the D4 receptor (see Figure 7) present higher rates of degradation due to their structure rigidity and length, respectively (Van Craenenbroeck, et al., 2005). These findings challenge the initial view that the length of the repeats is inversely associated with gene function. Another factor that increases the complexity in the study of DRD4 exon III VNTR is the fact that there is variation in the sequences within the alleles (Ding, et al., 2002), i.e. not all individuals with the 7-repeat allele will have the exact same sequence within the allele. Thus, since our initial results indicate an association between PG severity and the 7-repeat allele, and a marginal association between PG severity and the 2-repeat allele, we hypothesize that
variations in the sequence of the 2- and 7-repeat alleles of DRD4 exon III VNTR polymorphism
will better predict PG severity.
2.Research Design and Methodological Approach:
2.1 Sample:
Subjects were assessed for PG diagnosis through the South Oaks Gambling Screen (SOGS) (Lesieur & Blume, 1987) or the Problem Gambling Severity Index (PGSI) (Ferris & Wynne, 2001), resulting in a sample of 541 PG subjects (highest lifetime PGSI or SOGS ≥3; with either positive or negative screening for lifetime substance misuse) and 214 control subjects (highest lifetime PGSI or SOGS =0, negative screening for lifetime substance misuse). Clinical data on this sample was obtained in our previous OPGRC funded projects. All subjects were assessed through the Structured Clinical Interview Diagnosis in Psychiatry based on DSM-IV criteria (SCID-NP). The SCID is a well tested, reliable and widely used instrument in psychiatric research. It provides an assessment of lifetime diagnosis of major psychiatric disorders (mood disorders, psychotic disorders, anxiety disorders and substance addiction). The SCID is used worldwide (Amaral & Malbergiera, 2004; Chung, Tso, Cheung, & Wong, 2008; Healey, Kneebone, Carroll, & Anderson, 2008; Hodgins, Dufour, & Armstrong, 2000; Nilsson & Svedin, 2006; Rueda-Jaimes, et al., 2007; Torrens, Serrano, Astals, Perez-Dominguez, & Martin- Santos, 2004; Whelan-Goodinson, Ponsford, & Schonberger, 2008) and is considered as the gold- standard for diagnosis in psychiatric research, thus also being used to validate other diagnostic instruments (Cassidy, Schmitz, & Malla, 2008; Healey, et al., 2008.; Sanchez-Villegas, et al., 2008). Subjects were considered to present substance misuse if they answer positively to all three SCID/NP screening questions for alcohol abuse and/or substance dependence. Noteworthy is the fact that tobacco dependence was not investigated, as it was not part of SCID-NP. 2.2 Gene and Polymorphism (SNP) Selection:
The main KARG database pathways described above were the initial source for gene selection. Genes in key positions in the pathways or genes that had been previously associated with PG in our studies were selected. Each gene was then entered into the International Haplotype Map Project database (www.hapmap.org) and available genotypic data for each gene was downloaded and analyzed for detection of haplotypes. Haplotype detection was performed through the software Haploview version 4.2 (Barrett, Fry, Maller, & Daly, 2005). After haplotypes were detected within a gene, we proceeded to the selection of tagSNPs. TagSNPs are SNPs (variants, polymorphisms) that are in high linkage disequilibrium (LD) with other SNPs within a haplotype block and are chosen in order to reduce genotyping requirements by eliminating redundancy in the information provided by SNPs in high LD. Haploview software program finds the smallest set of tag SNPs that meets the requirements regarding minor allele frequency (MAF), minimum value of r2 (linkage disequilibrium), and minimum distance between SNPs. We selected tagSNPs with a minimum r2 of 0.8, MAF of 0.15 and a minimum distance between SNPs of 60 base-pairs was required for accurate assay design. After determining tagSNPs for all selected genes, we proceeded to test genetic assays for each tagSNP in order to ensure that the assays would perform adequately. During this process, some of the selected genes and tagSNPs had to be eliminated and substituted because it was not possible to design an assay. Our final list included 40 genes and a total of 384 tagSNPs (Table 1) that were genotyped using an Illumina® platform. Figures 1-5 represent the five signalling pathways as described by Li et al. (2008). Below is a list of genes that were selected for our study from each pathway. Please note that several of these genes are present in more than one of the addiction-related pathways. Genes selected from the Gap-junction pathway:
• dopamine receptor genes types 1 and 2 (DRD1, DRD2) • metabotropic glutamate receptor genes 1 and 5 (GRM1, GRM5) • serotonin receptor type 2 (HTR2A, HTR2C) 2 Linkage disequilibrium is a measure of nonrandom association between two or more alleles such that certain combinations of alleles are more likely to occur together on a chromosome than other combinations of alleles, i.e. the degree to which knowing the alleles of one SNP will accurately predict alleles of other SNPs. It is often parameterized as the squared correlation or r2 measure of linkage disequilibrium between two loci. Genes selected from the MAPK pathway:
• mitogen-activated protein kinase kinase 1, 2, and 3 (MAP2K1, MAP2K2, MAP2K3) • protein kinase, cAMP-dependent, catalytic – PKA family (PRKAC beta, alpha, and gamma) • tumor necrosis factor receptor subfamily, member 1b – TNF family(TNFRSF1/ NBL1) Genes selected from the GnRH pathway:
• gonadotropin-releasing hormone (GnRH1, GnRH2) • phospholipase d, phosphatidylcholine-specific (PLD1, PLD2) • protein kinase, cAMP-dependent, catalytic – PKA family (PRKAC beta, alpha, and gamma) • insulin gene (INS) Genes selected from Long-term potentiation pathway:
• calcium/calmodulin-dependent protein kinase (CAMK2A, CAMK2B, CAMK2D, CAMK2G), • protein kinase, cAMP-dependent, catalytic – PKA family (PRKAC beta, alpha, and gamma), • mitogen-activated protein kinase 1 (MAPK1 or ERK2). Genes selected from Neuroactive-ligand pathway:
• dopamine receptor genes (DRD1, DRD2, DRD3, DRD4); • metabotropic glutamate receptor genes (GRM1, GRM5), • cannabinoid receptor gene (CNR1, CNR2), • serotonin receptor genes (HTR1B, HTR2A, HTR3A, HTR3B, HTR6, HTR7). Other genes that were selected based on our preliminary results:
• serotonin transporter gene (HTT/ SLC6A4), • dopamine transporter gene (DAT/ SLC6A3), • tyrosine hydroxylase gene (TH), • tryptophan hydroxylase 2 gene (TPH2), • cocaine- and amphetamine- regulated transcript (CARTPT) • ankyrin repeat and kinase domain containing 1 gene (ANKK1) and tetratricopeptide repeat domain 12 gene (TTC12). Due to their proximity DRD2, TTC12 and ANKK1 can be considered as a cluster of genes. 2.3 Statistical Analysis:
Power calculations were performed through QUANTO power calculator (Gauderman & Morrison, 2006) and revealed that a case-control ratio of 1: 0.5, with 500 cases would have 80% power to detect associations with an odds ratio of 1.5, considering a minor allele frequency of 0.15, and a population prevalence of 0.04. Statistical significance was set at α=0.001. Haplotype analysis was performed through Golden-Helix SNP and Variation Suite version 7 (SVS v7). False Discovery Rate was applied for multiple-testing The minimum requirements to include a tagSNP in the pool of markers to be analyzed were: genotype calls >75%, HWE p ≥ 0.01, and haplotypes with a minimum 5% estimated haplotype frequency for both cases and controls. 2.4 Sequencing of DRD4 exon III region:
Sequencing of the DRD4 exon III VNTR locus was performed at the Centre for Applied Genomics at the Hospital for Sick Kids and in our laboratory using the ABI-Avant 3130 Genetic Analyzer for Sequencing Analysis. All sequencing results were analyzed using SeqScape v.2.5. The results were then reviewed for quality assurance and samples that presented dubious results were sent for re- 3. Results:
In our initial proposal, subjects could be considered as controls if their past year SOGS/PGSI scores were = 0. However, several controls were re-assessed during out project and presented lifetime
SOGS/PGSI scores of 1 to 2. Clinically, these subjects would be considered as low-risk gamblers and had a lower likelihood of progressing to moderate and high-risk gambling categories. However, in respect to their biological risk, it has been shown that genetic vulnerability for PG increases in gamblers who have presented at least one of the DSM criteria for pathological gambling in comparison to subjects who never met any DSM criteria for PG (Eisen, et al., 2001). Moreover, all controls that presented a positive screening for alcohol abuse and/ or drug abuse (lifetime) were also excluded from the analysis. Thus, our sample of control subjects was reduced to 214 subjects. Conversely, we were able to identify 514 subjects that were considered as problem gamblers (minimum SOGS/ PGSI lifetime scores ≥3). Amongst our cases, we also identified 99 PG subjects who also presented positive screening for alcohol abuse and/ or drug abuse. 3.1 PG subjects (n=514) x Control Subjects (n=214)
For the comparison between our total sample of PG and control subjects, a total of 307 haplotypes distributed along 40 genes were initially selected, based on our inclusion criteria of a minimum of 75% genotype calls and MAF ≥ 0.15 (Table 1). Haplotype blocks for analysis were defined using the confidence bounds method as described by Gabriel et al. (Gabriel, et al., 2002), in which 95% confidence bounds on D' are generated and each comparison is called "strong LD", "inconclusive" or "strong recombination". A block is created if 95% of informative (i.e. non-inconclusive) comparisons are "strong LD". This definition allows for many overlapping blocks to be valid. The next step is to sort the list of all possible blocks, start with the largest block and keep adding blocks as long as they don't overlap with an already declared block. Only haplotype blocks with an estimated frequency >5% were included in the analysis, so that the final selection comprised 303 tagSNPs distributed along 39 genes (gene excluded: GnRH2). Uncorrected p-values (≤0.001) show that haplotypes distributed across 13 genes were nominally
associated with PG (Table 2). After FDR correction for multiple testing (α=0.001), two haplotypes on
CAMK2D, four haplotypes on HTR2A, one haplotype on PLD2, and one haplotype on PRKACB
were significantly associated with PG. FDR corrected p-values (between 0.002 and 0.01) revealed
association trends with DRD3 (dopamine receptor gene, subtype 3), GRM1 (metabotropic glutamate
receptor gene, subtype 1), HTR3A (serotonin receptor gene, subtype 3A), HTT/ SLC6A4 (serotonin transporter gene), TTC12 (part of the ANKK1-DRD2-TTC12 cluster of genes), and CAMK2A (calcium/calmodulin-dependent protein kinase, subtype 2A). 3.2 PG subjects with alcohol and/or drug abuse – PG-ADA (n=99) x Control Subjects (n=214)
For the comparison between PG subjects with positive screening for alcohol and/or substance abuse (PG-ADA), a total of 298 tagSNPs across 39 genes (gene excluded: GnRH2) met our selection criteria as described above. 3 D prime (D') is a scaled measure of the difference in frequency between observed number of haplotype pairs and the expected number. This measure is an estimator of linkage disequilibrium (LD). Uncorrected p-values (≤0.001) show that 1 haplotype in: CAMK2D is nominally associated with PG-
ADA (Table 3). The CAMK2D haplotype rs2158196 _GCA is inversely associated with PG-ADA; i.e.
the haplotype might be protective, since its frequency is significantly (uncorrected p-value) lower in PG-ADA subjects compared to healthy controls. None of these associations remained significant after FDR correction for multiple testing. 3.3 PG subjects with alcohol and/or drug abuse – PG-ADA (n =99) x PG subjects without
substance abuse (n =257)
For the comparison between PG- ADA subjects and PG subjects without alcohol and/or drug abuse history, a total of 298 tagSNPs across 39 genes (gene excluded: GnRH2) met our selection criteria as described in item 3.1 Uncorrected p-values (≤0.001) show that GRM1, CAMK2D, and DRD2 (one haplotype on each
gene) genes were nominally associated with the group of PG subjects without substance abuse history, (Table 4). None of these associations remained significant after FDR correction for multiple testing. As we first hypothesized, we found genetic associations with genes located on chromosomes 4 (CAMK2D), 5 (CAMK2A), and 17 (PLD2, HTR2A), which are chromosomes that have strong evidence for harbouring susceptibility genes for addictions (M. D. Li & Burmeister, 2009), and on chromosome 1 (PRKACB). We found nominal associations with genes located on chromosome 6 (GRM1), chromosome 11 (TTC12, DRD2), and chromosome 17 (HTT / SLC6A4). Noteworthy is the
fact that across all group comparisons significant associations were found with CAMK2D, which
is part of the pathway that interlinks the other 4 pathways.
3.4. DRD4 sequencing analysis
The DRD4 exon III VNTR locus was sequenced for 170 of our PG subjects. We discovered a novel
single-nucleotide deletion in two out of eleven individuals homozygous for the 7-repeat allele in our
problem gambling sample (Figure 8). The novel single-nucleotide deletion variant was found in the first repeat unit for one subject and in the third repeat unit for the second subject. In both cases the deletion was of the "A" nucleotide. Although the deletions are located in two different repeat units the deletions occur at the same position within the repeat units. In silico analysis showed that this deletion would give rise to a shift in the reading frame, causing a premature stop codon and truncated predicted amino acid sequence (Figure 9). This deletion was found in two out of eleven subjects (18%) who had the 7-7 genotype. Both subjects were pathological gamblers; however, due to the low frequency of the 7-7 genotype and of the deletion, we were not able to verify whether the deletion was associated with PG severity. No deletions were found in any of the subjects who had the 2-2 or 4-4 genotype. 4. Limitations and Changes to Original Proposal:
The main change to our original proposal is in regard to sample composition. As described in item 3 (page 14), in order to maintain a more strictly defined phenotype, the number of control subjects in our sample was decreased (initially 400 controls, actual sample: 214), while we were able to increase the number of cases (PG subjects – initially 400 subjects, actual sample: 514). This change decreased our
power to detect associations with very small odds ratio (< 1.5). Nevertheless, we were still able to
detect associations that remained significant after correction for multiple testing.
We were granted a 6-month extension for our project. Our project was delayed initially due to problems in importing genetic assays from the United States. Another important source of delay was the legal process of authorizing DNA transfer between institutions, since part of our genotyping and sequencing was done at another genetic laboratory in the University of Toronto. Finally, technical difficulties with our sequencer delayed DRD4 sequency. In regards to other limitations, we acknowledge that the sample size of our group of PG-ADA has limited the interpretation of our results. At this point, we can say that the nominal associations found (uncorrected p-values ≤ 0.001) can be regarded only as preliminary data. In this specific case, preliminary data is important since this was the first study to investigate genetic associations with PG subjects with positive screening for substance abuse. The number of genes and tagSNPs selected could also be a source of concern. Our choice of genes reflected the current knowledge on neuropathways involved in addictions per se or in brain systems that are closely related to PG and addictions. Each of these systems is composed of many parts, which in turn are synthesized by a number of genes. In fact, our choice of genes was parsimonious
compared to genome-wide association studies and reflects our concern with type I error. We
opted for a conservative α level (≤ 0.001) and applied a multiple-testing correction that is
considered as conservative for candidate gene studies.
Despite limitations, this investigation presents many methodological advantages (sample size, substantial rational for the choice of genes) compared to previously published PG genetic association We are aware of the fact that different addictions (substance or behavioural) will not completely share genetic vulnerability factors and that there is a significant environmental component in the vulnerability for addiction disorders. We recognize that we investigated only a portion of the genetic architecture of PG that overlaps with substance addiction, and that we selected only genes that are considered as common vulnerability factors for addiction across different substances of abuse. It was not our objective to investigate any environmental factors involved in PG. As mentioned on page 16, the low frequency of the deletion found in subjects homozygous for the 7-7 genotype, prevented the investigation of a possible association of this deletion with PG. It was not possible to know beforehand if we were going to find a new deletion and its frequency. Nevertheless, the finding is valid and will be reported to the scientific community through publication on a peer- reviewed scientific journal. 5. Discussion:
Is PG an addiction? If so, can a behavioural addiction affect the same brain systems as substances of abuse? These questions have been the focus of attention of many researchers in the gambling field. Recently, the Diagnostic and Statistical Manual for psychiatric disorders has acknowledged that clinical and research data support the construct of PG as an addiction, thus incorporating PG under addictive disorders in its next edition. As outlined in section 1 (Introduction), imaging research has provided important evidence suggesting that PG affects areas of the brains in a very similar fashion as addictive substances. However, to the best of our knowledge, there has been no investigation on whether PG can induce changes in the brain signalling pathways that have been traditionally regarded as induced by substances of abuse. In order to provide initial data to answer this question, we have conducted a PG genetic association study on whether genes on addiction-related signalling pathways were also associated with PG. For this project, we were able to genotype 40 out of 396 genes involved in common addiction-related signalling In summary, our results show that haplotypes on CAMK2D, PRKACB, HTR2A, and PLD2 were significantly associated with PG when compared to control subjects in a sample of 514 PG subjects and 214 control subjects. Nominal associations (uncorrected p-values) were also found for other genes that have been traditionally considered as the main candidate genes for PG: DRD3, GRM1, HTR3A, HTT/ SLC6A4, TTC12 (part of the ANKK1-DRD2-TTC12 cluster of genes). In regards to our comparisons between PG-ADA and controls or PG subjects without substance abuse history, we found also nominal associations with genes traditionally considered as the main candidate genes for PG and addictions (DRD2, HTR2A, and GRM1), as well as with genes that had never been investigated in regards to gambling behaviour: CAMK2A and CAMK2D. Below we will provide an overview of the role of these genes in addictive processes and discuss the implications of our findings in regards to advancing knowledge and to future research on the neurobiology of PG. Finally, we will also discuss our findings regarding sequencing of the DRD4 exon A. Calcium- and calmodulin- dependent protein kinase genes (CAMK2A and CAMK2D)
• CAMK2A located on chromosome 5
• CAMK2D located on chromosome 4
The CAMK2 family of genes is responsible for the production of calcium- and calmodulin-dependent protein kinases. Protein Kinases act as regulators of cell function. Genes involved in protein kinases translation (i.e., production) constitute one of the largest and most functionally diverse gene families. Currently 518 human protein kinases have been indentified, with the vast majority of them belonging to one superfamily. Each superfamily can be clustered into groups, families and sub-families, of increasing sequence similarity and biochemical function. CAMK2 kinases (and thus CAMK2 genes) are a subfamily of the CASK family, which is part of the larger CAMK group. By adding phosphate groups to substrate proteins, they direct the activity, localization and overall function of many proteins, and serve to orchestrate the activity of almost all cellular processes. Kinases are predominantly important in signal transduction and co-ordination of complex cellular functions (Manning, Whyte, Martinez, Hunter, & Sudarsanam, 2002). CAMK2 is involved in several aspects of neuronal function, including neuroplasticity, gene expression, and neurotransmitter synthesis and release (Lee & Messing, CAMK2A and CAMK2D are part of the long-term potentiation and GnRH signalling pathways (Figures 2 and 3, respectively). Figure 2 shows that CAMK2 genes directly regulate ionotropic glutamate receptor (AMPAR/ GRIN) cell cycle, thus affecting the Neuroactive-ligand receptor interaction pathway, i.e. CAMK2 modulation affects how AMPAR will respond to glutamate. CAMK2 also modulates D1 receptor cell cycle (not represented in Figure 2). Figure 3 illustrates how CAMK2 genes ultimately regulate gene expression and secretion of gonadotropins, thus directly affecting GnRHR1 and 2 genes on the Neuroactive-ligand receptor interaction pathway. Ultimately, the 5 common pathways for addiction are integrated with each other
through fast and slow positive feedbacks loops that are all interlinked through CAMK2 (C. Y. Li, et
CAMK2 has been reported to be involved in several addictive processes: sensitization, drug tolerance and self-administration, withdrawal and relapse (Lee & Messing, 2008). B. Serotonin receptor 2A gene (HTR2A):
• HTR2A located on chromosome 13
Serotonin (5-HT) activity is associated with several behaviours such as behavioral inhibition (Coccaro, et al., 1989; Stein, Hollander, & Liebowitz, 1993), sensory reactivity (Sheard & Aghajanian, 1968) , sleep, sexual behaviour, and cognitive function (Patterson, et al., 1996; Ressler & Nemeroff, 2000). At least 14 subtypes of 5-HT receptors have been cloned and identified. The excitatory 5- HT2 receptor class is predominantly found on postsynaptic neurons, and activates phospholipase C. As can be seen on figure 5, phospholipase C (PLC) activates protein kinase C (PKC), also involved in addictive processes (see item C below). Serotonin does not directly participate in motivation-reward, but exerts influence through its effects on the dopamine system. Application of 5-HT onto dopaminergic neurons from the VTA (part of the brain's reward system) increased their firing rate in vitro, an effect that was attributed to action of 5-HT on HTR2 receptors (Pessia, Jiang, North, & Johnson, 1994). As shown in figures 1-5, serotonin receptors are part of the neuroligand-receptor interaction (figure 1) and gap junction pathways (figure 5). Even though it has become common knowledge that 5-HT receptors are involved in behavioural inhibition and cognitive functions, previous candidate gene studies had focused on the investigation of the serotonin transporter (HTT). This is the first candidate gene study in PG to investigate and report an association with serotonin receptor gene 2A (HTR2A). C. Phospholipase D2 gene (PLD2):
• PLD2 located on chromosome 17
There are two types of phospholipase genes: PLD1 and PLD2. Both genes synthesize pospholipase D, an enzyme that produces phosphatidic acid (PA) through hydrolyzation processes. PA is further metabolized into diacylglycerol (DAG), which regulates certain types of protein kinase C (PKC). Thus, PLD genes regulate PKC activity through the production of PA and DAG. Activation of PKC by calcium ions and the second messenger diacylglycerol is thought to play a central role in the induction
of cellular responses to a variety of ligand-receptor systems (glutamate ionotropic receptors,
dopamine D2 receptor, canabinoid receptor type 1, and nicotinic cholinergic receptors) and in the
regulation of cellular responsiveness to external stimuli.
The prefrontal cortex (PFC) is a brain region that regulates thought, behavior, and emotion using representational knowledge, operations often referred to as working memory. Birnbaum et al. (Birnbaum, et al., 2004) have tested the influence of PKC on intracellular signaling on PFC cognitive function and showed that high levels of PKC activity in prefrontal cortex (for instance, induced by stress) result in significant impairment of measures of working memory. These data suggest that excessive PKC activation can disrupt PFC regulation of behavior and thought, possibly contributing
to signs of distractibility, impaired judgment, impulsivity, and thought disorder. Distractibility,
impaired judgment, and impulsivity are well recognized features of PG (Cavedini, Riboldi, Keller, D'Annucci, & Bellodi, 2002; Fuentes, Tavares, Artes, & Gorenstein, 2006; Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2005; Petry, 2001), and this finding raises the possibility that working
memory deficits in PG and substance addiction occur through the same pathway (PLD inducing
PKC activation; PKC regulation cellular response to dopamine, glutamate receptors).
Similarly to CAMK2, PKC has also been involved in sensitization, drug tolerance, and drug self- administration and withdrawal (Lee & Messing, 2008). D. Protein kinase, cAMP-dependent, catalytic, beta gene (PRKACB)_
• PRKACB located on chromosome 1
The PRKACB gene synthesizes the β-catalytic isoform of the c-AMP dependent protein kinase A (PKA). PKA is inactive in its natural state, and is activated when c-AMP (produced by adenylyl cyclase) binds to its regulatory region (Lee & Messing, 2008). It has been shown that addictive
substances promote an acute increase in extracellular levels of dopamine in the nucleus accumbens,
which stimulates adenylyl cyclase (producer of c-AMP) and PKA via D1 receptors (dopamine
receptor type 1).
Substances that act as agonists in the D1 receptor together with PKA activation, increase the cell
surface expression of glutamate receptor 1 (ionotropic glutamate receptor 1, AMPA 1) in the nucleus
accumbens and hippocampus of rats (Gao, Sun, & Wolf, 2006). Increasing the expression of the glutamate receptor increases synaptic strength. Thus, PKA activation may provide a mechanism for
substance-induced neuroplasticity. Similarly to CAMK2 and PKC, animal models have demonstrated
PKA's involvement in sensitization, drug tolerance, and drug self-administration and withdrawal (Lee & Messing, 2008). E. DRD4 exon III VNTR sequencing:
Our sequencing efforts on DRD4 have revealed a deletion in the 7-repeat allele of DRD4 exon III VNTR. Moreover, our results suggest that this deletion can interrupt the synthesis of the receptor, which could result in a "defective" D4 receptors (on 7-7 allele carriers) or in a decreased production of D4 receptors. This is the first time that a deletion that would alter gene transcription has been
described for the 7-repeat allele of DRD4 exon III VNTR.
The relatively low frequency of the 7-7 genotype in the general population (3%) highlights the need for heterozygotes to be sequenced. As of yet, PG subjects who are heterozygous for the 7-repeat allele (2- 7, 4-7) were not sequenced due to cost limitations. Our future plans include sequencing 7-repeat heterozygotes and cloning sequences that present this deletion. After cloning, in vitro functional studies would provide experimental evidence of the effect of this deletion on the D4 receptor. In conclusion, this is the largest genetic association study conducted on PG until now, and the first to investigate the association between PG and genes involved in addiction signalling pathways. Our original proposal indicated two major applications for this investigation: 1. to provide better understanding of the neurobiology of PG, and 2. to provide information in regards to processes that are amenable to pharmacological intervention. As discussed above, our results provide further insight into the neurobiology of PG. By taking a systems approach in selecting candidate genes we were able to identify new genes that had not been previously investigated in PG and that provide clues regarding shared genetic pathways between PG and substance addictions. As discussed above, CAMK2A,
PRKACB, HTR2A, and PLD2 genes have important roles in synthesizing and or regulating proteins,
enzymes, and second messengers that interact with each other and induce addiction-related
neurochemical changes. These genetic findings will likely inform future neurobiological investigation
into shared protein and enzymatic pathways between PG and substance addictions. It is important to acknowledge that our results do not invalidate previous genetic associations with PG and substance addiction, since our sample had the power to detect genes with higher effect sizes. Genes, for which nominal associations were found, especially in the comparisons with PG-ADA, deserve further investigation in larger samples where comorbidity with substance use disorders has been documented. Our findings can also help interpret results from previous genetic association studies in PG. For instance, our group had previously reported an association of PG with the DRD1 gene in a family sample (da Silva Lobo, et al., 2007), and trends for association on the DRD2/ ANKK1 TaqIA/rs1800497 polymorphism (p=0.01) and the haplotype flanking DRD2 (G/C/A rs11604671/rs4938015/rs2303380) (Lobo, et al., 2010). Other studies had reported also associations with DRD2/ ANKK1 TaqIA in smaller samples of PG (Comings, et al., 1997; Comings, et al., 1996; da Silva Lobo, et al., 2007). Our results show that genes that regulate or are regulated by DRD1 and DRD2 are significantly associated with PG. This suggests that, although previously reported associations with DRD1 and DRD2 are valid, genes involved in up-stream and down-stream dopamine signalling have a greater effect (have a stronger association) on PG. The same can be said in regards to other neurotransmitter receptor genes such as glutamate receptor genes. Also, and perhaps more importantly, our results have provided information in regards to processes that are amenable to pharmacological intervention. The association of PG with CAMK2D and PRKACB
and PLD2 (directly or indirectly involved in protein kinases synthesis) indicates that protein-kinase
inhibitors could potentially be used in the treatment of PG. The recent availability of an animal model
for gambling behaviour allows pre-clinical testing of protein-kinase inhibitors that are currently under development, which would be the appropriate next step in the investigation of our findings. 6. References:
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Health correlates of pathological gambling in a methadone maintenance clinic. Exp Clin Psychopharmacol, 14(1), 87-93. Welte, J., Barnes, G., Wieczorek, W., Tidwell, M. C., & Parker, J. (2001). Alcohol and gambling pathology among U.S. adults: prevalence, demographic patterns and comorbidity. J Stud Alcohol, 62(5), 706-712. Whelan-Goodinson, R., Ponsford, J., & Schonberger, M. (2008). Validity of the Hospital Anxiety and Depression Scale to assess depression and anxiety following traumatic brain injury as compared with the Structured Clinical Interview for DSM-IV. J Affect Disord. Table 1: List of genes and SNPs selected for genotyping within their respective chromosomes.
TNFRSF1 rs4285653 TNFRSF1 rs2294630 Table 1 (cont'd List of genes and SNPs selected for genotyping within their respective chromosomes.
Table 1 (cont'd List of genes and SNPs selected for genotyping within their respective chromosomes. Table 1 (cont'd List of genes and SNPs selected for genotyping within their respective chromosomes. Table 2: Haplotype comparisons between problem gamblers (PG) with healthy control subjects, modified qui-squared tests, Golden
Helix, SVS v. 7. Only results with p>0.001 are shown 1.
Haplotype Haplotype
Gene Haplotype
Frequency Frequency
Corrected
Odds Ratio 95% CI
p-valued
Controls
rs919741 CAMK2A
GG
0.25
1.73
rs12514354 CAMK2A GA
0.61
0.63
rs12514354 CAMK2A GG
0.12
2.30
rs919740 CAMK2A
AC
0.53
0.66
rs2241695 CAMK2A CAMK2D CA
0.48
1.89
CAMK2D AA
0.24
1.99
rs10003275 CAMK2D TA
0.29
0.61
rs4834354 CAMK2D AA
0.32
0.64
rs10003275 CAMK2D AG
0.25
1.76
rs4834349 CAMK2D GA
0.25
0.63
rs17446418 CAMK2D AA
0.33
1.63
rs1524998 CAMK2D GA
0.36
0.65
rs17531026 CAMK2D AG
0.24
0.64
rs2293323 CAMK2D CG
0.37
0.66
rs6836139 CAMK2D AA
0.36
0.67
rs7660775 CAMK2D AT
0.69
0.63
rs10515115 CARTPT AG rs2134655 DRD3
GG
0.43
1.66
rs7633291 DRD3
AA
0.58
0.67
rs6280 DRD3 AC
rs7755078 GRM1
CC
0.27
1.76
rs362854 GRM1 TC
0.24
1.67
rs2648640 GRM5 AC rs672981 GRM5 CG 1 Genes highlighted in yel ow were significant after FDR correction (p ≤0.001). Genes in bold and italicized were significant at uncorrected p ≤0.001. Table 2 (Cont'd): Haplotype comparisons between problem gamblers (PG) with healthy control subjects, modified qui-squared tests,
Golden Helix, SVS v. 7. Only results with p>0.001 are shown1.
Haplotype Haplotype
Corrected
Gene Haplotype
Frequency Frequency
p-valued
Odds Ratio 95% CI
Controls
GA
0.36
0.50
25.9
0.56
0.44
0.70
AA
0.15
0.26
25.6
0.50
0.38
0.66
AA
0.33
0.47
23.4
0.57
0.46
0.72
AA
0.14
0.23
19.9
0.53
0.40
0.70
rs2296973 HTR2A CA
0.16
0.26
18.3 1.9E-05 0.0020 0.56
0.42
0.73
rs6314 HTR2A GG
0.55
0.43
17.5 2.9E-05 0.0025 1.61
1.29
2.02
rs6313 HTR2A
GA
0.38
0.49
16.4 5.1E-05 0.0035 0.63
0.50
0.79
rs9534512 HTR2A AC
0.13
0.21
15.2 9.6E-05 0.0050 0.56
0.42
0.75
rs6313 HTR2A
GC
0.20
0.12
14.8 1.2E-04 0.0059 1.90
1.36
2.64
rs9534512 HTR2A CA
0.20
0.12
12.2 4.7E-04 0.0123 1.79
1.29
2.48
rs1923885 HTR2A AG
0.42
0.52
11.9 5.5E-04 0.0133 0.67
0.54
0.84
rs4942577 HTR2A GA
0.28
0.37
10.8 1.0E-03 0.0222 0.67
0.53
0.85
rs7997012 HTR2A GA 10.5 1.2E-03 0.0229 1.57 rs582854 HTR2A AA 10.5 1.2E-03 0.0229 0.62 rs1379170 HTR3A AA 14.0 1.8E-04 0.0074 1.77 10.2 1.4E-03 0.0247 0.69 PLD2
CG
0.49
0.36
23.0
1.75
1.39
2.21
rs2286670 PLD2
CA
0.39
0.49
12.1 5.1E-04 0.0130 0.67
0.54
0.84
rs2286671 PLD2
GA
0.15
0.08
11.0 9.3E-04 0.0207 1.89
1.29
2.76
rs12404263 PRKACB
GA
0.36
0.24
20.2
1.78
1.38
2.29
PRKACB GA
0.42
0.31
15.7 7.5E-05 0.0049 1.61
1.27
2.05
PRKACB AA
0.15
0.08
15.4 8.7E-05 0.0051 2.16
1.46
3.20
PRKACB AG
0.34
0.23
15.4 8.9E-05 0.0049 1.67
1.29
2.16
PRKACB AG
0.40
0.51
14.0 1.8E-04 0.0077 0.65
0.52
0.82
PRKACB GG
0.41
0.51
12.4 4.3E-04 0.0115 0.67
0.53
0.84
1 Genes highlighted in yel ow were significant after FDR correction (p ≤0.001). Genes in bold and italicized were significant at uncorrected p ≤0.001. Table 2 (Cont'd): Haplotype comparisons between problem gamblers (PG) with healthy control subjects, modified qui-squared tests,
Golden Helix, SVS v. 7. Only results with p>0.001 are shown1.

Haplotype Haplotype
Corrected
Gene Haplotype
Frequency Frequency
p-valued
Odds Ratio 95% CI
Controls
rs140700 SLC6A4
GC
0.12
0.06
13.0 3.0E-04 0.0099
2.19
1.42
3.39
rs17564182 TACR1
CA
0.16
0.23
10.8 1.0E-03 0.0222
0.63
0.48
0.83
rs11688000 TACR1 10.6 1.1E-03 0.0230 10.3 1.3E-03 0.0242 rs2303380 TTC12
AG
0.25
0.17
12.9 3.3E-04 0.0104
1.69
1.27
2.25
1 Genes highlighted in yel ow were significant after FDR correction (p ≤0.001). Genes in bold and italicized were significant at uncorrected p ≤0.001. Table 3: Haplotype comparisons between problem gamblers with alcohol and/or drug abuse (PG-ADA) with control subjects;
modified qui-squared tests, Golden Helix, SVS v. 7. Association trends with uncorrected p ≤0.01 are shown1.

Haplotype Haplotype
Corrected
Odds Ratio
Gene Haplotype
Frequency Frequency
χ2 Nominal
p-valued
Controls
GCA 0.16 0.29 10.1
0.001 0.20 0.48 0.30 0.76
HTR2A AGC 0.17 0.08 9.9 0.002 0.18 2.47 1.39 4.37 AGC 0.33 0.20 9.9 0.002 0.17 1.94 1.28 2.93 HTR2A AAG 0.14 0.26 9.4 0.002 0.20 0.48 0.30 0.77 GGG 0.12 0.05 9.3 0.002 0.20 2.89 1.43 5.85 HTR2A AGG 0.14 0.06 9.1 0.003 0.21 2.63 1.38 5.01 HTR2A AGA 0.09 0.03 9.1 0.003 0.20 3.30 1.46 7.45 HTR2A GAA 0.15 0.26 8.7 0.003 0.23 0.50 0.31 0.79 rs6313 HTR2A GCA 0.20 0.11 8.5 0.004 0.24 2.12 1.27 3.53 AGA 0.17 0.28 8.3 0.004 0.26 0.51 0.33 0.81 AAA 0.25 0.15 8.3 0.004 0.25 1.95 1.23 3.08 CAA 0.25 0.15 7.9 0.005 0.28 1.91 1.21 3.01 CNR2 AGA 0.28 0.18 7.8 0.005 0.29 1.86 1.20 2.87 TACR3 ACA 0.11 0.05 7.6 0.006 0.30 2.58 1.29 5.17 AAC 0.22 0.34 7.6 0.006 0.30 0.56 0.37 0.85 TACR3 AGC 0.36 0.25 7.3 0.007 0.34 1.73 1.16 2.57 GGG 0.12 0.05 7.0 0.008 0.37 2.42 1.24 4.73 TTC12 AAA 0.08 0.16 6.9 0.008 0.37 0.45 0.25 0.83 TACR3 GCA 0.36 0.25 6.9 0.009 0.36 1.70 1.14 2.52 TTC12 AAT 0.08 0.16 6.9 0.009 0.35 0.45 0.25 0.83 GCG 0.25 0.16 6.9 0.009 0.35 1.82 1.16 2.85 HTR2A AAA 0.14 0.23 6.7 0.010 0.37 0.53 0.32 0.86 HTR2A AAA 0.14 0.23 6.7 0.010 0.36 0.52 0.32 0.86 TTC12 GAA 0.08 0.16 6.6 0.010 0.38 0.46 0.25 0.84 1. Genes in bold and italicized were significant at uncorrected p ≤0.001 Table 4: Haplotype comparisons between problem gamblers with alcohol and/or drug abuse (PG-ADA) with PG subjects without
substance abuse history; modified qui-squared tests, Golden Helix, SVS v. 7. Association trends with uncorrected p ≤0.01 are
shown1.

Haplotype Haplotype
Gene Haplotype
Frequency Frequency
Corrected Odds
Odds Ratio
p-valued
Controls
rs7660775 CAMK2D
CAG
0.11
0.24
12.7 0.0004
0.23
0.39
0.23
0.66
rs1011973 CAMK2D GCA rs3815072 CAMK2D AAA rs2285704 CAMK2D AAA rs11098195 CAMK2D AAA rs17531026 CAMK2D AAA rs3815072 CAMK2D GAG rs6533690 CAMK2D GGA rs17531554 CAMK2D ACG rs10009286 CAMK2D ACA rs2293323 CAMK2D CGA rs2285704 CAMK2D GAA rs17446418 CAMK2D AGG rs11098198 CAMK2D AGC rs13144613 CAMK2D CAC rs2189364 CAMK2D AAG rs686 DRD1 GA 0.03 0.08 6.6 0.010 0.36 0.29 rs4648318 DRD2
AGA
0.06
0.17
10.8 0.001
0.42 0.33 0.17 0.66
1 Genes in bold and italicized were significant at uncorrected p ≤0.001 Table 4 (Cont'd): Haplotype comparisons between problem gamblers with alcohol and/or drug abuse (PG-ADA) with PG subjects
without substance abuse history; modified qui-squared tests, Golden Helix, SVS v. 7. Association trends with uncorrected p ≤0.01
are shown1.

Haplotype Haplotype
Gene Haplotype
Frequency Frequency
Nominal Corrected Odds
Odds Ratio
p-valued
Controls
rs362854 GRM1 TCC
0.07
0.20 12.9 0.0003 0.42 0.33 0.18 0.62
rs7755078 GRM1 CCG 9.2 0.002 0.38 0.47 0.28 0.77 rs1125462 GRM1 ACG 8.2 0.004 0.35 0.30 0.13 0.71 rs7755078 GRM1 ACG 7.0 0.008 0.41 1.79 1.16 2.75 rs672981 GRM5 AGG 0.19 9.8 0.002 0.43 0.39 0.21 0.71 rs2648640 GRM5 GAG 8.4 0.004 0.34 0.41 0.22 0.76 rs11021034 GRM5 GAA rs1923885 HTR2A AAC rs9607272 MAPK1 ACA MAPK1 CA 0.47 0.60 6.9 0.008 0.38 0.60 rs5755099 MAPK1 CCA rs2294630 NBL1 AGC 7.5 0.006 0.43 1.80 1.18 2.73 rs3771827 TACR1 AAA rs12641703 TACR3 rs3822292 TACR3 ACA 0.08 8.8 0.003 0.35 0.20 0.06 0.65 rs7927508 TTC12 AAA 9.2 0.002 0.34 0.40 0.21 0.73 rs7130072 TTC12 GAA 8.8 0.003 0.39 0.41 0.22 0.75 1 Genes in bold and italicized were significant at uncorrected p ≤0.001
Figure 6: Diagrammatic Representation of the DRD4 gene and DRD4 exon III VNTR.

Representation of the DRD4 gene and the alleles found in the DRD4 exon III VNTR
polymorphism. Each allele is named after the number of repetitions of a 48bp sequence present
in that allele, i.e. the 4-repeat allele has 4 repetitions of the 48bp sequence. Percentages represent
frequency of the alleles in Caucasian population. (Reproduced with permission from: Ding et al.,
PNAS January 8, 2002 vol. 99 (1): 309-314)

Figure 7: Dopamine D4 receptor graphical representation:
The DRD4 gene synthesizes the D4 receptor. Repeated sequences in the DRD4 gene (more specifically located in the exon III of the gene) are responsible for synthesizing a loop in the receptor (loop synthesized by the 4-repeat allele represented in orange). If the gene has more repeats (e.g. 7-repeat allele), the receptor loop will be longer (loop synthesized by the 7-repeat allele represented in pink). Figure 8: Screenshot of the sequence alignment (DRD4 exon III)

Figure 9: Screenshot of the deletion on DRD4 exon III from one subject in our sample

Source: https://www.problemgambling.ca/EN/Documents/InvestigationofSelectedSignallingSystem%20Genes.pdf