Haplotype association between GABAA receptor γ2 subunit gene (GABRG2) and methamphetamine use disorder

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Psychostimulant use disorder and schizophrenia have a substantial genetic basis. Evidence from human and animal studies on the involvement of the γ-aminobutyric acid (GABA) system in methamphetamine (METH) use disorder and schizophrenia is mounting. As we tested for the association of the human GABAA receptor gamma 2 subunit gene (GABRG2) with each diagnostic group, we used a case–control design with a set of 178 subjects with METH use disorder, 288 schizophrenics and 288 controls. First, we screened 96 controls and identified six SNPs in GABRG2, three of whom we newly reported. Next, we selected two SNPs, 315C>T and 1128+99C>A, as representatives of the linkage disequilibrium blocks for further case–control association analysis. Although no associations were found in either allelic or genotypic frequencies, we detected a haplotypic association in GABRG2 with METH use disorder, but not with schizophrenia. This finding partly replicates a recent case–control study of GABRG2 in METH use disorder, and thus indicates that GABRG2 may be one of the susceptibility genes of METH use disorder.


In recent years there has been a pronounced increase in use of psychostimulants involving methamphetamine (METH).1 Lifetime prevalence of psychostimulant use in some developed countries is found in 1–3% of the adult population,2 and psychostimulant use in any form may lead to abuse or dependence with physiological, psychological and behavioral component.3 Findings from family and twin studies suggest that the genetic contribution is important for the development of psychostimulant use disorders. Heritability estimates from a population-based twin study for METH use disorder are substantial,4, 5 for example, 66% for psychostimulant abuse.6

The dopamine system is a prime candidate for genetic influence on drug abuse, particularly METH abuse, because it is thought to be involved in the reward and reinforcing mechanism in the meso-cortico-limbic system in the nucleus accumbens.7 Moreover, the primary site of biological activity of METH is the dopamine transporter in this system.

Instead, a role for the γ-aminobutyric acid (GABA) system in drug abuse is also suggested in accumulating evidence. First, the irreversible GABA-transaminase inhibitor, γ-vinyl GABA, attenuates such increase of the dopamine release in the nucleus accumbens following acute administration of METH.8 Second, QTL mapping for acute alcohol withdrawal severity suggests that a polymorphism in the GABAA receptor γ2 subunit gene in mice is genetically correlated with this phenotype.9 A third line of evidence involves several case–control association studies, suggesting that the human GABAA receptor γ2 subunit gene (GABRG2) is marginally associated with METH use disorder,10 and is also associated with alcoholism comorbid with antisocial personality disorder,11 although there are conflicting results.12, 13 Therefore, it is possible that GABRG2 affects vulnerability to substance use disorder, including METH use disorder.

On the other hand, a number of post-mortem studies have reported an altered GABA neurotransmission in schizophrenia. These studies reported that release and uptake of GABA at synaptic terminals were reduced in schizophrenic cortex14, 15, 16 and that the activity of glutamic acid decarboxylase (GAD), the synthesizing enzyme for GABA, GAD mRNA expression, and the density of GABAergic interneurons, were reduced in the prefrontal cortex (PFC) of schizophrenics.17, 18, 19, 20 Although there was reportedly no significant change in overall mRNA levels for GABAA receptor subunits,17 expression of the alternately spliced short isoform of GABAA receptor γ2 subunit, γ2S, was markedly reduced in the PFC of schizophrenics.21 The relative over-representation of the γ2L subunit, which possesses an additional phosphorylation site within the eight amino acids inserted, should result in a functionally less active form of the receptor,22, 23 and this defective GABAergic system may be involved in the development of schizophrenia. The evidence of linkage analysis from multiple genome scans of schizophrenia within 5q31–34 where GABRG2 locates also support the involvement of this gene in the development of schizophrenia.24, 25, 26, 27

Here, we explored the possible contributions of GABRG2 in both METH use disorder and schizophrenia. We systemically searched all exons and the intronic branch sites of GABRG2 for polymorphisms, and examined haplotype-based case–control association analysis with both METH use disorder and schizophrenia.


Our screening of 96 controls in all exons and the flanking intronic splice sites of GABRG2 revealed six SNPs, which were designated ‘Asn79Ser’, ‘315C>T’, ‘588T>C’, ‘922+20G>A’, ‘1129−1482A>C’, and ‘1230C>T’. Minor allele frequencies and a schematic graph of these SNPs are presented in Table 1 and Figure 1, respectively. Of all identified SNPs, 315C>T, 588T>C (rs211037) and 922+20G>A have been reported elsewhere.

Table 1 SNPs in GABRG2 and minor allele frequencies
Figure 1

Schematic presentation of identified and reported GABRG2 SNPs. Solid box represents exons. The SNPs in bold type were used to evaluate LD structure.

To evaluate the linkage disequilibrium (LD) in the 96 screened samples using several widely used measures (D′, Δ2 and P-value), we genotyped five SNPs in GABRG2 (two SNPs (315C>T, 588T>C) of identified SNPs, two SNPs (rs2268583, rs2284780) from the dbSNP database, and one SNP (1128+99C>A) reported as BamHI RFLP previously11). These SNPs were selected because they showed sufficient heterozygosity (a frequency of minor allele>0.1) to detect a small effect of a susceptibility gene presumed to underlie complex disorders, and they were distributed almost evenly on the entire exonic regions of the gene (Figure 1).

Estimation of LD between each pairwise SNP is presented in Table 2. These results show that the first three and the last two consecutive SNPs were in complete or nearly complete LD with each other. Therefore, we selected two SNPs (315C>T and 1128+99C>A) as representatives of these nearly complete LD regions for further case–control association analysis.

Table 2 Pairwise linkage disequilibrium in controls

In addition to screened 96 samples, we genotyped 178 subjects with METH use disorder, 288 schizophrenics, and 288 controls in all. Two representative SNPs were in moderate LD with each other in METH use disorder (D′=0.72), schizophrenia (D′=0.51) and control subjects (D′=0.61). Genotypic and allelic frequencies of two SNPs in each population are summarized in Table 3. The genotypic distributions of each SNP did not significantly deviate from the Hardy–Weinberg equilibrium in either METH use disorder, schizophrenia or control subjects (P=0.98, 0.84 and 0.70 at 315C>T and P=0.15, 0.62 and 0.06 at 1128+99C>A, respectively). The distributions of each SNP did not differ significantly between each diagnostic group and controls in both allele and genotype frequencies (Table 3).

Table 3 Genotypic and allelic distributions of the GABRG2 SNPs in patients with METH use disorder and schizophrenia vs controls

The distributions of haplotypic frequencies estimated using the expectation-maximization algorithm implemented in the Arlequin 2.0 significantly differed between METH use disorder and control subjects (P=0.044). In contrast, there was no significant difference in haplotypic distributions between schizophrenic and control subjects (P=0.356, Table 4). From examining at-risk haplotypes predisposed to METH use disorder, only two haplotypes, T–C and T–A (defined by 315C>T−1128+99C>A), were found to confer the significant susceptibility to this disorder. By applying the Bonferroni correction, this finding becomes nonsignificant for haplotype T–A (corrected P=0.120) and remains significant for haplotype T–C (corrected P=0.028). The presumed at-risk haplotype T–C has an estimated frequency of 18.6% among controls and 26.2% among METH use disorder subjects. The estimated odds ratio of haplotype T–C was 1.55 (95% CI (1.13–2.13)).

Table 4 Haplotypic distributions of the GABRG2 gene in patients with METH use disorder and schizophrenia vs controls


Our results provide supportive evidence for a haplotypic association in GABRG2 with METH use disorder, but not with schizophrenia. This association suggests that the susceptibility variant for METH use disorder may lie within the region in positive LD with the at-risk haplotype reconstructed in this study. The patterns of LD were shown to be two block like, the first block represented by 315C>T (covering rs2268583 at intron 1 to rs211037 at exon 5), and the second block represented by 1128+99C>A (covering rs2284780 at intron 7 to 1128+99C>A at intron 8). Since we found no association between each representative SNP and METH use disorder in either allelic or genotypic frequencies, the possibility arises that susceptibility variant can be located outside of these block-like regions. The second block includes the splicing regulatory elements surrounding the spliced exon, which bind to the polypyrimidine tract binding protein, the splicing regulator.28, 29, 30 Actually, we screened this regulatory region thoroughly through direct sequencing of the 96 samples, however, could not find any variant in these elements. Other splicing regulatory elements that bind to another splicing regulator Nova-1 were located in intron 8, about 3.5 kb downstream of 1128+99C>A.31, 32 If the second block does not cover the latter splicing regulatory elements, these regions can be a susceptible candidate. Recently, a significant association was reported between rs4480617 at the 5′-UTR of GABRG2 and METH use disorder in females.10 Therefore, this SNP or other variants in the promoter region also can be another candidate. Given that the sample size of 96 used to identify SNPs in this study provides more than 80% power to detect SNPs with about 1% minor allele frequency,33 we are almost unlikely to overlook common nonsynonymous SNPs predisposed to METH use disorder.

As has been widely discussed, a spurious association can arise because of confounding such as population stratification and clinical heterogeneity, given the problems of reliability due to no use of structured interviews. However, our data are partly in agreement with a recent report10 that found the significant association between GABRG2 and METH use disorder in females. This provides further corroboration that our haplotypic association with METH uses disorder is not spurious, although potential sources of bias such as ascertainment bias still remain possible. For example, subjects suffering from not only METH use disorder but also METH-induced psychosis are more likely to seek medical care and thus to be ascertained. Such ‘spurious comorbidity’34 of psychosis may account for the apparent association in this study. In the present study, we did not stratify the METH use disorder sample according to the comorbidity of METH-induced psychosis because the sample size was too small for reliable analysis. Although the precise prevalence of the comorbid METH-induced psychosis remains unknown, the data in the late 1940s and early 1950s in Japan indicating that about 10% of METH users had METH-induced psychosis35 would suggest that comorbid METH-induced psychosis is over-represented in our clinically ascertained sample with METH use disorder.

As no association exists between GABRG2 and schizophrenia in our sample, association between GABRG2 and METH use disorder would not likely be attributable to spurious comorbid METH induced-psychosis, which may share the pathophysiology of susceptibility with schizophrenia, the so-called sensitization phenomena.35 On the contrary, the comorbid polysubstance-related disorder over-represented in our sample with METH use disorder can account for the apparent association in this study. Indeed, previous findings suggesting nonspecific substance dependence vulnerability5 supported the existence of such a ‘misattributed’ association in our study. In addition to concurrent comorbidity, we also cannot deny the possibility of spurious comorbid bias caused by the past comorbid diseases because of not examining the past history of any mental diseases systematically. METH use subjects in our study included a large number of patients who experienced first psychotic symptoms after METH use for a relatively short duration and participants in the special program designed for drug use disorder, in which they could not participate if they suffered from other psychiatric problems. The low levels of comorbidity in METH use subjects may reflect such biased ascertainment.

There is indeed a neuroscientific framework to link GABRG2 and METH use disorder. First, several lines of investigation7 implicate the mesolimbic dopamine system in psychostimulant-induced motor activity. Furthermore, it was shown in a pharmacological study36 that a GABAergic system in PFC modulated the motor response to psychostimulants by inhibiting PFC pyramidal neurons. Second, a tentative association was found for a GABRG2 SNP and the frontally located event-related potential (ERP) complex N100/P200 after auditory stimuli..37 Thus, the prefrontal activation difference may reflect the differential GABRG2 activities derived from variants of the gene. Accordingly, GABRG2 activities in PFC could affect the modulation of mesolimbic reward circuitries, which might be associated with vulnerability of METH use disorder.

Overall our results indicate that GABRG2 may play a role in the risk of METH use disorder development in this population. Analysis of the promoter region or the splicing regulatory elements in intron 8 in a future study would be a logical next step in searching for a susceptible variant of GABRG2 in METH use disorder. However, it remains uncertain whether the associated phenotype may reflect the vulnerability of METH-specific abuse or nonspecific substance abuse.



All patients in this study were unrelated and recruited from three medical institutes participating the Japanese Genetics Initiative for Drug Abuse (JGAIDA).38 They were diagnosed according to DSM-IV criteria by the consensus of at least two experienced psychiatrists on the basis of unstructured interviews and review of the medical records prior to genotyping.

The number of the patients with METH uses disorder, comprised of 164 METH-dependent subjects, and 14 METH abuse subjects, and schizophrenia were 178 (144 males and 34 females) and 288 (140 males and 148 females), respectively. The ages of each patient group were 18–69 years old (mean±SD; 36.7±12.0) and 15–75 (39.6±14.0), respectively. No patient with schizophrenia had severe physical complications or other Axis-I disorders according to DSM-IV when enrolled in this study, because seven schizophrenic subjects with METH use disorder were excluded based on the criteria that restricted a comorbid diagnosis of any psychotic disorder other than METH-induced psychosis. Among the subjects with METH use disorder, 150 (124 males and 25 females) have a comorbid diagnosis of METH-induced psychosis, three of anorexia nervosa, one of obsessive-compulsive disorder, and one of major depressive disorder. Additionally, 119 subjects with METH use disorder have abuse or dependence on drugs other than METH. The past history of any mental illness was not examined. The ages of METH-induced psychotic subgroup were 19–69 years old (37.7±12.3). No patient with METH use disorder had any severe physical complications when enrolled in this study. The 288 unrelated healthy volunteers (152 males and 136 females), aged 19–65 years (33.6±13.0), were comprised of hospital staff members and medical students at Fujita Health University. All healthy controls were also psychiatrically screened based on unstructured interviews. After complete description of the study to each subject, written informed consent was obtained. This study was approved by the ethics committee of each JGAIDA institute.

SNP Identification

Genomic DNA was isolated from whole blood using PUREGNER (Gentra system, Minneapolis, MN 55447, USA). For denaturing high-performance liquid chromatography (DHPLC) analysis, we designed specific primer sets amplifying all GABRG2 exons and the flanking intronic splice sites, based on GenBank sequence (NM000816 and NT023133) (primer sequences are available on request).

Polymerase chain reaction (PCR) was performed in a 10-μl volume containing 10 ng sample DNA, 0.4 M of each primer, 200 μM each dNTP, 1 × PCR Gold Buffer, 1.5 mM MgCl2 and 0.25 U of Amplitaq Gold™ (Applied Biosystems Japan Ltd, Tokyo, Japan), using GeneAmp™ PCR system 9700 (Applied Biosystems Japan Ltd). PCR cycling conditions consisted of an initial denaturation step at 95°C for 9 min, followed by 45 cycles of 95°C for 15 s, 60°C for 20 s, 72°C for 30 s, and ending with a final extension step at 72°C for 7 min.

To screen for nucleotide variants, the obtained PCR products from all screened samples were analyzed by DHPLC with the WAVE™ system (Transgenomics Japan Ltd, Tokyo, Japan). The PCR products showing variant chromatograms were amplified again and then sequenced with an ABI PRISM™ 3100 Genetic Analyzer (Applied Biosystems Japan Ltd). Furthermore, to screen for any kinds of nucleotide variants in the splicing regulatory elements surrounding the spliced exon, we performed direct sequencing of the 96 controls. The conditions for DHPLC analysis and direct sequencing were reported previously.39

SNP Genotyping

To confirm the sequencing result and to genotype the variants in additional samples, the DHPLC analysis using the primer extension methods were developed for genotyping 588T>C by modifying the method of Hoogendoorn et al,40 as reported previously.39 All the remaining SNPs examined were genotyped using PCR-restriction fragment length polymorphism (PCR-RFLP) methods. Of four RFLP sites selected, the BamHI restriction site in the eighth exon was genotyped as described by Loh et al,12 while for the rest of the three SNPs, PCR-RFLP methods were developed (detailed information on experimental procedures is available upon request).

Statistical Analysis

Tests for Hardy–Weinberg equilibrium, the calculation of LD measures such as D′, Δ2 and P-value and the estimation of haplotypic frequencies were carried out using Arlequin software 2.0.41 The haplotypic frequencies between each patient group and controls were also compared using Arlequin software 2.0. The genotypic and allelic frequencies among each patient group and control group were compared with an exact test, using SPSS (version 10). A two-tailed level of 5% was chosen for the type I error rate. We have not corrected for multiple testing so as to avoid false negative findings.

Following Ohashi and Tokunaga,40 we estimated the power of association analysis for our sample size of 178 subjects with METH use disorder, 288 schizophrenics and 288 controls under multiplicative model of inheritance, assuming a population susceptibility allele frequency of 0.30 at 315C>T and 0.48 at 1128+99C>A, the value in our screened samples. Setting the type I error rate at 5% and Genotype relative risk at more than 1.4 and 1.5, we obtained more than 80% power for direct association analysis of METH use disorder and schizophrenia, respectively.


None declared.





γ-aminobutyric acid


The human GABAA receptor gamma 2 subunit gene


glutamic acid decarboxylase


prefrontal cortex


linkage disequilibrium


denaturing high-performance liquid chromatography


polymerase chain reaction-restriction fragment lengthpolymorphism


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We gratefully acknowledge the helpful discussions with Dr J Ohashi on several points in the paper. We thank Ms Y Zusho and Ms M Miyata for their technical support. This work was supported in part by research grants from the Ministry of Education, Culture, Sports, Science and Technology, and the Ministry of Health, Labor and Welfare.

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Correspondence to N Iwata.

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  • GABAA 2 subunit gene
  • methamphetamine
  • substance use disorder
  • polymorphism
  • haplotype
  • schizophrenia

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