ANKK1 TaqIA and SLC6A3 VNTR polymorphisms in alcohol dependence: Association and gene–gene interaction study in a population of Central Italy

Neuroscience Letters 522 (2012) 103–107

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DRD2/ANKK1 TaqIA and SLC6A3 VNTR polymorphisms in alcohol dependence: Association and gene–gene interaction study in a population of Central Italy Fiorenzo Mignini a,∗ , Valerio Napolioni a , Claudia Codazzo b , Francesco M. Carpi a , Mario Vitali b , Marina Romeo b , Mauro Ceccanti b a b

School of Pharmacy, Experimental Medicine Unit, University of Camerino, Italy Department of Clinical Medicine, Lazio Region of Alcoholism Center, Sapienza University, Rome, Italy

h i g h l i g h t s     

DRD2/ANKK1 TaqIA (rs1800497), SLC6A3 40 bp-VNTR SNP and gene–gene interaction analysis. 560 alcoholic and control AD patients from a population of Central Italy. SLC6A3 40 bp 3 UTR-VNTR displays no association with AD. DRD2/ANKK1 TaqIA genotype distribution is significantly associated to AD. Gene–gene interaction analysis yielded no significant result.

a r t i c l e

i n f o

Article history: Received 1 April 2012 Received in revised form 23 May 2012 Accepted 4 June 2012 Keywords: Dopamine D2 receptor Dopamine DAT transporter Polymorphism Alcohol dependence Gene–gene interaction

a b s t r a c t Dopamine is a neurotransmitter whose functions are mediated by five receptors expressed in several organs and tissues. Dopaminergic system dysfunctions are involved in the etiology or treatment of several pathological conditions, including drug addiction. Alcohol dependence (AD) is a widespread psychiatric disorder, affecting 5.4% of the general population lifetime. Family and twins studies support the role of a genetic component in AD. Since dopamine neurotransmission has been shown to be involved in drug reward, related genes are plausible candidates for susceptibility to AD. Here, we evaluated both the DRD2/ANKK1 TaqIA (rs1800497) and SLC6A3 40 bp-VNTR SNP and gene–gene interaction analysis in AD patients from a population of Central Italy. The study design was a case–control. In total, 280 alcoholic subjects (213 men and 67 woman) and 280 age- and sex-matched control subjects were recruited for this study. Case subjects met the DSM-IV criteria for AD and they are free from any psychiatric co-morbidities. Controls were subjects who had non-alcohol problem either never drank; those who have smoked at least one pack of cigarettes per day for at least 1 year were excluded. Genotyping was performed by allelespecific PCR and RFLP-PCR. SLC6A3 40 bp 3 UTR-VNTR displays no association with AD. DRD2/ANKK1 TaqIA genotype distribution is significantly associated to AD (O.R. = 1.551, p = 0.023), with A1* allele displaying an O.R. = 1.403 (p = 0.029). Gene–gene interaction analysis using three-way contingency table analysis by a log-linear model yielded no significant result. Our study in a population of Central Italy extends and confirms previous results and, for the first time, tested the gene–gene interaction between SLC6A3 and DRD2 in AD. © 2012 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Dopamine (DA) is a catecholamine neurotransmitter whose functions are mediated by five dopamine receptors expressed in several organs and tissues [5,23–25]. Dopaminergic system

∗ Corresponding author at: School of Pharmacy, Experimental Medicine Unit, Via Madonna delle Carceri n. 9, University of Camerino, 60032 Camerino, Italy. Tel.: +39 0737 403304; fax: +39 0737 403325. E-mail address: fi[email protected] (F. Mignini). 0304-3940/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neulet.2012.06.008

dysfunctions are involved in the etiology or treatment of several pathological conditions, including drug addiction. The involvement of DA in the rewarding effects of drugs of abuse is suggested by the findings that administration of those addictive drugs increases DA levels in the nucleus accumbens [33] and that blockade of DA transmission reduces the rewarding effects of psychostimulants [21]. Dopamine receptor D2 (DRD2) KO mice displayed lower response to alcohol [35], lower preference for alcohol [30] and an abolition of alcohol-induced conditioned place preference compared to littermates [6]. The results obtained with pharmacological modulation were less consistent across studies.

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In humans, Blum et al. [3] first reported the association between DRD2 TaqIA single nucleotide polymorphism (SNP) and alcoholism. Several meta-analysis have confirmed this association, but the effect size is small [27,40]. The lower DRD2 binding in alcohol dependence (AD) subjects suggests that these drugs have also an effect on DRD2 density [17]. However, the degree to which these factors may alter the receptor density cannot explain the two to three-fold variability reported among healthy subjects [10]. The human dopamine transporter (DAT) is a critical regulator of dopaminergic neurotransmission throughout the brain. The SLC6A3 gene (OMIM*126455, gene map locus 5p15.3) encoding DAT, displays a polymorphic Variable Number of Tandem Repeats (VNTR) polymorphism in the 15th exon, at 3 UTR [36]. The 40-bp VNTR element is repeated 3–13 times, occurring in most human populations with greatest frequency in the 9- and 10-repeat alleles [37]. Though the VNTR resides in the 3 UTR, and therefore not affecting the protein’s amino acid sequence, regulatory factors such as mRNA stability, nuclear transport, and protein synthesis are potentially affected by such variations [12]. To date, several association studies have been conducted aiming to assess the contribution of SLC6A3 40 bp-VNTR to alcoholism liability with conflicting results [42]. Very recently, a meta-analysis have suggested a possible association between the SLC6A3 VNTR 9* allele and alcoholic subgroup with alcohol withdrawal seizure or delirium tremens [8]. Also, a gender- and paternal-effect was suggested [44]. Considering the fundamental importance of replication studies in genetic epidemiology and the marked differences of genotype frequency distribution among ethnic groups, we attempt to replicate previous findings by testing the association of the SLC6A3 40 bp-VNTR and DRD2/ANKK1 TaqIA (rs1800497) SNP with AD syndrome through a case–control study in a population of Central Italy. Notably, since AD is a complex phenotype [26], it is expected that epistasis may contribute significantly to the definition of the underlying multifaceted genetic architecture. Genetic interactions play an important role in complex traits, but they can be difficult to find unless a hypothesis-driven approach is employed that reduces the scale of the search and affect the prior probabilities involved [11,41]. To this aim, we also tested the gene–gene interaction between the two polymorphisms analyzed. 2. Materials and methods 2.1. Ethics The protocol and informed consent form were approved by the Local Ethical Committee of “Sapienza” University of Rome. Before signing the informed consent form, subjects were informed about the study in detail. The study was conducted in accordance with the Declaration of Helsinki in its revised edition, the Guidelines of Good Clinical Practice (CPMP/ICH/135/95) and with international and local regulatory requirements. Clinical unit of “Sapienza” University select samples and attribute to each one a progressive number followed by a letter indicative of the participating unit. For each case a report with indication of age and sex of patients as well as of general clinical characteristics of the case was prepared. The Camerino University Unit work in blind and know only the number and letter indicative of each sample. 2.2. Study design, subjects and clinical assessment The study design was a case–control. 280 alcoholic subjects (213 men and 67 woman) and 280 age- and sex-matched control subjects were recruited. Case subjects met the DSM-IV (American Psychiatric Association) criteria for alcohol dependence and they

Table 1 Demographic and clinical characteristics of the alcoholism sample.

Age in yrs Age at the onset Drinks per day Alcohol consumption (years at risk)

Gender Male Female M/F ratio Smoking Yes No Drug consumption Family history of alcoholism Yes No

N

Mean± S.D.

Range

280 261 90 260

44.8 18.1 15.2 18.2

± ± ± ±

23–80 3–64 4–48 2–63

10.1 8.8 8.7 11.0

N

Percent

213 67 3.2:1

76.1% 23.9%

236 31 55

88.4% 11.6% 19.6% (out of the total)

59 36

62.1% 37.9%

are free from any psychiatric co-morbidities (i.e. Bipolar Disorder, mood disorders, panic attack). Controls were subjects who had non-alcohol problem either never drank; those who have smoked at least one pack of cigarettes per day for at least 1 year were excluded. Older eligible subjects were preferred as matched controls to avoid misclassification in phenotype. Any subjects born from an inter-ethnic marriage were excluded. Related subjects were also eliminated from eligibility. The alcoholic status of the subjects was assessed by a psychiatrist or well-trained research assistants with a semi-structured clinical interview schedule for alcoholism, which includes the alcoholism section of the Schedules for Clinical Assessment in Neuropsychiatry (World Health Organization) and other relevant items. The reliability of the instrument has been reported to be satisfactory. In addition to psychiatric assessment, information on medical complications and smoking history were also collected. Demographic and clinical characteristics of the alcoholism sample are summarized in Table 1. We acknowledge that in complex disorders, genotypephenotype associations may represent false-positive results due to population stratification and/or chance. Thus, all the recruited subjects were Caucasians from Central Italy, making the possibility of population stratification remote. 2.3. Genotyping Genomic DNA extraction was carried out from peripheral blood through standardized salting-out method. Two PCR-based genotyping methods targeting the SLC6A3 40 bp 3 UTR VNTR and DRD2/ANKK1 TaqIA (rs1800497) SNP were used as previously described [15,36]. All reactions were performed using PCR Master Mix (2×) (Fermentas). PCR amplification was performed using a Tpersonal 48 thermal cycler (Whatman Biometra). The primers used to amplify the interest regions were purchased from Sigma–Aldrich (MO, USA). Briefly, for SLC6A3 40 bp 3 UTR-VNTR the forward primer sequence was 5 -TGT GGT GTA GGG AAC GGC GTG AG-3 , and the reverse primer sequence was 5 -CTT CCT GGA GGT CAC GCG TCA AGG-3 . The amplification was performed with 35 cycles with denaturation at 93 ◦ C for 30 s, annealing at 63 ◦ C for 60 s and extension at 72 ◦ C for 60 s. The PCR products were separated on 3% agarose gels with direct visualization with ethidium bromide under UV light. For DRD2/ANKK1 TaqIA (rs1800497) SNP genotyping, PCR was performed with a primer set 5 -CCG TCG ACG GCT GGC CAA GTT GTC TA-3 and 5 -CCG TCG ACC CTT CCT GAG TGT CAT CA-3 . An initial denaturation at 94 ◦ C for 4 min was followed by 35 cycles of

F. Mignini et al. / Neuroscience Letters 522 (2012) 103–107

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Table 2 SLC6A3 40 bp-VNTR (A) genotype distribution, (B) allele distribution. 3*/10*

6*/6*

6*/9*

6*/10*

9*/9*

9*/10*

9*/11*

10*/10*

10*/11*

N

2 (0.7%) 1 (0.4%) 3 (0.5%)

2 (0.7%) – 2 (0.4%)

– 1 (0.4%) 1 (0.2%)

– 2 (0.7%) 2 (0.4%)

36 (13.0%) 28 (10.0%) 64 (11.5%)

113 (40.7%) 106 (37.9%) 219 (39.2%)

1 (0.4%) 4 (1.4%) 5 (0.9%)

121 (43.5%) 134 (47.9%) 255 (45.7%)

3 (1.1%) 4 (1.4%) 7 (1.3%)

278 280 558

A Alcoholics Controls Overall

3*

6*

9*

10*

11*

N

2 (0.4%) 1 (0.2%) 3 (0.3%)

4 (0.7%) 3 (0.5%) 7 (0.6%)

186 (33.5%) 167 (29.8%) 353 (31.6%)

360 (64.7%) 381 (68.0%) 741 (66.4%)

4 (0.7%) 8 (1.4%) 12 (1.1%)

556 560 1116

B Alcoholics Controls Overall

Assuming short alleles (3* + 6* + 9*)[S*] as the risk allele. – Armitage’s trend test common O.R. = 1.181, 2 = 1.73, p = 0.189. – Short allele [S*] carriers vs. (10*/10* + 10*/11*)[L*/L*]: O.R. = 1.207 (0.865–1.684), p = 0.268. – S* vs. L*: O.R. = 1.190 (0.926–1.529), p = 0.174.

denaturation at 94 ◦ C for 30 s, annealing at 58 ◦ C for 30 s and extension at 72 ◦ C for 30 s, with a final extension at 72 ◦ C for 5 min. PCR products were digested with 5 units of TaqI restriction enzyme at 65 ◦ C overnight. The resulting products were analyzed by electrophoresis and visualized under UV light. 2.4. Statistical analysis Hardy–Weinberg Equilibrium (HWE) was checked by chisquare test. Pearson’s chi-square or Fisher’s exact test (when expected N < 5) were used to evaluate the differences in genotypic and allelic distributions between the groups and the strength of association was expressed as odds ratios (ORs) with 95% confidence intervals. Gene-gene interaction was assessed using three-way contingency table analysis by a log-linear model. Statistical analyses were conducted with SPSS 16.0 (SPSS Inc., Chicago, IL). Differences were considered statistically significant when the probability (p) was less than 0.05. Power calculations were performed using Quanto ver. 1.2.4. (available at http://hydra.usc.edu/gxe/), taking into account sample sizes and minor allele frequencies (MAF), genetic log-additive model, prevalence of AD in Italy [38], and type I error rate (0.05). Considering these parameters, we had a sufficient power (0.80) to detect an association with a genotypic relative risk (or OR) ≤0.7 and/or ≥1.4 for SLC6A3 40 bp 3 UTR-VNTR, and ≤0.6 and/or ≥1.5 for DRD2/ANKK1 TaqIA (rs1800497) SNP. 3. Results Genotype and allele frequencies at SLC6A3 40 bp 3 UTRVNTR and DRD2/ANKK1 TaqIA (rs1800497) SNP are reported in Tables 2 and 3, respectively. For both study groups, genotype frequencies did not deviate significantly from HWE (p > 0.05). Control frequencies are very similar to that previously reported for Italian population [37]. No significant differences in genotype and allele distributions were detected between males and females. For SLC6A3 40 bp 3 UTR-VNTR, we detected 3 rare alleles in the whole dataset (3*, N = 3; 6*, N = 7; 11*, N = 12) (Table 2). Therefore, according to the quantitative effect of the SLC6A3 40 bp 3 UTR-VNTR on DAT1 expression [43], we included these individuals in the statistical analysis as carriers of short allele [S*] (3*, 6*, with 9*) and carriers of long allele [L*] (11* with 10*), respectively. SLC6A3 40 bp 3 UTR-VNTR displays no association with AD, although there is a trend toward higher frequency of SLC6A3*S allele among patients [O.R. = 1.190 (0.926–1.529, 95% C.I.); p = 0.174] (Table 2). For DRD2/ANKK1 TaqIA (rs1800497) SNP, the genotype distribution is significantly associated to AD (Armitage’s trend test common O.R. = 1.551, 2 = 5.15, p = 0.023), with A1* allele displaying an

Table 3 DRD2/ANKK1 TaqIA (rs1800497) polymorphism (A) genotype distribution, (B) allele distribution.

Alcoholics Controls Overall

Alcoholics Controls Overall

A1*/A1*

A1*/A2*

A2*/A2*

N

10 (3.6%) 3 (1.1%) 13 (2.4%)

98 (35.6%) 83 (30.4%) 181 (33.0%)

167 (60.7%) 187 (68.5%) 354 (64.6%)

275 273 548

A1*

A2*

N

118 (21.5%) 89 (16.3%) 207 (18.9%)

432 (78.5%) 457 (83.7%) 889 (81.1%)

550 546 1096

Assuming A1* as the risk allele. – Armitage’s trend test common O.R. = 1.551, 2 = 5.15, p = 0.023. – A1* carriers vs. A2*/A2*: O.R. = 1.406 (0.989–1.999), p = 0.057. – A1* vs. A2*: O.R. = 1.403 (1.034–1.903), p = 0.02.

O.R. = 1.403 (1.034–1.903), p = 0.029 (Table 3). No significant association was detected when smoking and drug addiction status were analyzed according to both polymorphisms. This result excludes possible association bias due to the presence of poly-substance abusers. Gene–gene interaction analysis using three-way contingency table analysis by a log-linear model yielded no significant result (Table 4).

Table 4 Gene–gene interaction according to 2 × 2 × 2 contingency tables by log-linear model (x = DRD2/ANKK1 TaqIA; y = SLC6A3 40 bp-VNTR; z = case–control). DRD2/ANKK1 TaqIA

SLC6A3 40 bp-VNTR

N

L*/L*

S* carriers

Alcoholics A1* carriers A2*/A2* N

49 (46.2%) 74 (44.3%) 123 (45.1%)

57 (53.8%) 93 (55.7%) 150 (54.9%)

DRD2/ANKK1 TaqIA

SLC6A3 40 bp-VNTR

Controls A1* carriers A2*/A2* N

xyz xy(z) independence xz(y) independence yz(x) independence

106 167 273 N

L/L

S* carriers

43 (50.0%) 90 (48.1%) 133 (48.7%)

43 (50.0%) 97 (51.9%) 140 (51.3%)

86 187 273

G2

d.f.

p

4.14 0.18 3.28 0.8

4 2 2 2

0.387 0.914 0.194 0.670

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4. Discussion Alcohol is known to activate the dopaminergic system, which in turn is associated with positive reinforcement [2]. In this context, genetic variants in dopamine neurotransmission pathway might account for the variability in alcohol response and might contribute to individual variation in vulnerability to alcohol abuse [31]. The aim of the present work was to estimate by single- and twolocus analysis, the influence of two important candidate genes on AD liability. Importantly, both polymorphisms are functionally relevant and thus they could also be useful to dissect and explore the complex biological underpinnings of AD. As we were dealing with a case–control association study, we paid particular attention to the quality of the sampling in order to avoid false-positive results due to population stratification. In fact, the study was carried out in a quite genetically homogeneous population, all individuals being Caucasians from Central Italy [4,29]. In the present study we demonstrated that: (i) SLC6A3 40 bp 3 UTR-VNTR displays no association with AD, (ii) A1* allele at DRD2/ANKK1 TaqIA polymorphism is positively associated with AD, (iii) there is no evidence for a gene–gene interaction between SLC6A3 and DRD2/ANKK1 in AD. DAT is responsible for the reuptake of extracellular synaptic dopamine into pre-synaptic neurons, hereby terminating dopaminergic neurotransmission [14]. The mechanism by which DAT availability is regulated in the brain is still not exactly understood. Neuro-imaging studies show that DAT levels may be affected by chronic alcohol use and that DAT levels were significantly lower in the striatum of alcohol-dependent humans and monkeys than in controls, returning to control levels after a period of abstinence [22]. To date, it is not exactly clear whether high or low levels of DAT are likely to predispose to alcoholism. Both the 9- and the 10-repeat alleles have been repeatedly associated with increased DAT expression and, as such, both may be treated as risk alleles [13,19,43]. In a general case–control design, the vast majority of studies did not find significant differences in SLC6A3 40 bp 3 UTR-VNTR distribution between alcohol dependent subjects and controls [42]. Data reported in the current study, first in the Italian population of such proportions, reinforces and confirms the mentioned above case–control association studies. To our knowledge, only Köhnke and co-workers found that the 9-repeat allele was significantly more frequent in German alcoholics than in controls [20]. All the above-described studies are based on a case–control design in which all alcohol-dependent individuals are treated as a single group. As such, identifying subgroups in which the clinical heterogeneity of alcohol dependence is taken into account may more genuinely represent reality and increase power of finding associations. In addition, the definition of subgroups or endophenotypes closely associated with a specific genetic factor than a general alcohol-dependent phenotype, may result in more solid and replicable results if the subgroups are of considerable size. The TaqIA SNP rs1800497, located in the gene coding for the putative kinase ANKK1 near the termination codon of the DRD2 gene (OMIM*126450; gene map locus 11q22–q23), is the most studied genetic variation in a broad range of psychiatric disorders and personality traits [34]. Several authors demonstrate that DRD2 gene is responsible for alcohol drinking in rats [28,32]. Furthermore, the DRD2 gene encodes an inhibitory dopamine receptor subtype. The striatopallidal mediumspiny neurons, cells involved in psychostimulants reward pathways, predominantly express the DRD2 subtype [23]. A large number of individual genetic association studies have found that the TaqIA SNP is linked to alcoholism and antisocial traits [9]. In particular, the TaqI A1* allele appears to be over-represented in alcohol-dependent individuals. Meta-analyses clearly highlight the existence of a significant, but modest, excess of the A1* allele frequency in alcohol-dependent patients with an attributable risk

of 11% [27,40]. In two recent studies, A1* allele has also been associated with a highly increased mortality rate in alcohol-dependent individuals and with a substantially increased relapse rate [1,7]. The lack of univocal results can be attributable to the composition of samples such as differences in ethnicity, age-range of the sample, sex differences, matching of cases to controls, recruitment of patients and the presence of withdrawal symptoms. On the one hand, there continue to be new results supporting this association. However, the biological mechanisms through which rs1800497 SNP influences alcohol-related behaviors are still largely unclear and do not permit further speculations. Lastly, our study fails to demonstrate a gene–gene interaction between DRD2 and SLC6A3 genes in influencing AD liability. This is in line with previous evidence in heroin dependence [18] and in serious/violent delinquency [16], but not with smoking cessation [39]. Gene–gene and gene–environment interaction could play an important role in such a complex clinical trait as AD and ignoring them may underestimate its pathogenetic component. To our knowledge, this is the first study reporting a gene–gene interaction analysis between DRD2 and SLC6A3 in AD. Research focused on biological mechanisms underlying the supposed link between genotype and phenotype are highly warranted. In addition, a way to reduce the distance between the genotype and the phenotype is to focus our attention on endophenotypes (biological, neurological or cognitive variables). It might help in identifying special subgroups of patients, which, in turn, could have important clinical implications for more successful interventions, paving the way to the personalized medicine and to a possible pharmacogenomic approach. Acknowledgements The authors thank the Italian Lazio Region and the Camerino University for their support grants. References [1] U. Berggren, C. Fahlke, K.J. Berglund, K. Wadell, H. Zetterberg, K. Blennow, D. Thelle, J. Balldin, Dopamine D2 receptor genotype is associated with increased mortality at a 10-year follow-up of alcohol-dependent individuals, Alcohol and Alcoholism 45 (2010) 1–5. [2] K.C. Berridge, T.E. Robinson, What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience, Brain Research Reviews 28 (1998) 309–369. [3] K. Blum, E.P. Noble, P.J. Sheridan, A. Montgomery, T. Ritchie, P. Jagadeeswaran, H. Nogami, A.H. Briggs, J.B. Cohn, Allelic association of human dopamine D2 receptor gene in alcoholism, Journal of the American Medical Association 263 (1990) 2055–2060. [4] F. Brisighelli, A. Blanco-Verea, I. Boschi, P. Garagnani, V.L. Pascali, A. Carracedo, C. Capelli, A. Salas, Forensic Science International: (2012), http://dx.doi.org/10.1016/j.fsigen.2012.03.003 Genetics Epub ahead of print. [5] C. Cavallotti, M. Mancone, P. Bruzzone, M. Sabbatini, F. Mignini, Dopamine receptor subtypes in the native human heart, Heart and Vessels 25 (2010) 432–437. [6] C.L. Cunningham, M.A. Howard, S.J. Gill, M. Rubinstein, M.J. Low, D.K. Grandy, Ethanol-conditioned place preference in reduced in dopamine D2 receptor deficient mice, Pharmacology Biochemistry and Behavior 67 (2000) 693–699. [7] A. Dahlgren, H.L. Wargelius, K.J. Berglund, C. Fahlke, K. Blennow, H. Zetterberg, L. Oreland, U. Berggren, J. Balldin, Do alcohol-dependent individuals with DRD2 A1 allele have an increased risk of relapse? A pilot study, Alcohol and Alcoholism 46 (2011) 509–513. [8] Y. Du, Y. Nie, Y. Li, Y.J. Wan, The association between the SLC6A3 VNTR 9-repeat allele and alcoholism – a meta-analysis, Alcoholism, Clinical and Experimental Research 35 (2011) 1625–1634. [9] C. Esposito-Smythers, A. Spirito, C. Rizzo, J.E. McGeary, V.S. Knopik, Associations of the DRD2 TaqIA polymorphism with impulsivity and substance use: preliminary results from a clinical sample of adolescents, Pharmacology Biochemistry and Behavior 3 (2009) 306–312. [10] L. Farde, H. Hall, S. Pauli, C. Halldin, Variability in D2-dopamine receptor density and affinity: a PET-study with 11 C-raclopride in man, Synapse 20 (1995) 200–208. [11] J. Flint, T.F. Mackay, Genetic architecture of quantitative traits in mice, flies, and humans, Genome Research 19 (2009) 723–733.

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