Department of Psychological Medicine, Cardiff University, Cardiff, UK
Correspondence: Dr Stanley Zammit, Department of Psychological Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK. Tel: +44 (0)2920 743058; fax: +44 (0)2920 747839; email: zammits{at}Cardiff.ac.uk
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Aims To examine whether variants within the cannabinoid receptor
(CNR1) and
7 nicotinic receptor (CHRNA7)
genes are associated with schizophrenia, and whether these effects vary
according to cannabis or tobacco use. We also examined a putative interaction
between cannabis and Val158Met within the
catechol-O-methyltransferase gene (COMT).
Method Genotype effects of CHRNA7 and CNR1were studied in a case–control sample of 750 individuals with schizophrenia and 688 controls, with interactions for these genes studied in small subsamples. A case-only design of 493 ofthe schizophrenia group was used to examine interactions between cannabis use and COMT.
Results There was no evidence of association between schizophrenia and CNR1 (OR=0.97, 95% CI 0.82–1.13) or CHRNA7 (OR=1.07, 95% CI 0.77–1.49) genotypes, or of interactions between tobacco use and CHRNA7, or cannabis use and CNR1or COMT genotypes.
Conclusions Neither CNR1 nor CHRNA7 variation appears to alter the risk of schizophrenia. Furthermore, our results do not support the presence of different effects of cannabis use on schizophrenia according to variation within COMT.
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7 nicotinic acetylcholine receptor (CHRNA7)
(Gray et al, 1996;
Stevens et al, 1998).
An association between schizophrenia and a putative functional variant,
–86C/T, within the CHRNA7 gene (CHRNA7) has been reported
(Leonard et al, 2002)
and warrants further exploration.
The main psychoactive compound within cannabis is
delta-9-tetrahydrocannabinol (
9-THC), which acts through the
CNR1 cannabinoid receptor. An increased incidence of psychotic disorders in
people using cannabis has been observed
(Arseneault et al,
2002; Zammit et al,
2002) and a putative interaction between cannabis use and
variation within the catechol-O-methyltransferase (COMT)
gene on risk of psychosis has also been reported
(Caspi et al, 2005).
Findings from relatively small studies examining association between
CNR1 genetic variation – most commonly at the single nucleotide
polymorphism (SNP) rs1049353 – and schizophrenia have been inconsistent,
and it was considered worth while to examine this in a substantially larger
sample than has been studied thus far.
The main aims of our study were to investigate whether variations at –86C/T within CHRNA7 and at rs1049353 within CNR1 were associated with schizophrenia, and whether these relationships differed according to use of tobacco or cannabis. We also investigated whether there was any evidence of an interaction between cannabis use and the Val158Met polymorphism (SNP rs4680) within COMT, as previously reported (Caspi et al, 2005), as well as with SNPs rs737865 and rs165599 within this gene. The SNP rs4680 alters enzyme activity of COMT (Chen et al, 2004), whereas the GGG haplotype of SNPs rs737865–4680–165599 has been reported to be associated with lower expression of COMT messenger RNA in human brain tissue (Bray et al, 2003) and with increased risk of schizophrenia (Shifman et al, 2002). Main genotype and haplotype effects of COMT in this sample have been previously reported, with no evidence found for any association with schizophrenia (Williams et al, 2005).
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>0.8) were achieved between raters for diagnoses and
rating scale items. Controls were unrelated blood donors ascertained from the
same regions as the majority of the patients. Given the prevalence of
schizophrenia and the fact that people taking regular medication cannot be
blood donors in the UK, it was not deemed necessary to screen the control
group for schizophrenia to retain statistical power
(Owen et al, 1997).
Ethical approval was granted for this study and informed consent was obtained
from all participants. All study participants were White, with both parents born in the UK or Ireland. All cases of schizophrenia satisfied DSM–IV criteria (American Psychiatric Association, 1994) for consensus lifetime diagnosis of the disorder, made by two independent raters. The following phenotypes, determined a priori, were examined in relation to –86C/T and rs1049353 genotype:
Data on tobacco and cannabis use were obtained from interview and case-note records for 657 of the schizophrenia group. Questions regarding the age at which the person first started using cannabis or tobacco were only introduced during the latter part of the sample recruitment, and these data were therefore available for only 22% of cases in which the person reported ever using these substances. Substance use data were not collected initially for the control group, and unfortunately most members of the control group were not asked at the initial interview for permission to contact them again for further information. As a result of this, cannabis use data were available for only 116 controls and tobacco use data for 49 controls.
Genotyping
The CHRNA7 promoter polymorphism –86C/T was genotyped as a
restriction-fragment length polymorphism using the restriction enzyme
Hph1 (New England Biolabs, Ipswich, Massachusetts, USA). The primers
were 5'-agtacctcccgctcacacctcg-3' and
5'-atgttgagtcccggagctg-3' as used by Leonard et al
(2002). The product was
amplified using the GC-RICH PCR System (Roche Diagnostics, Basel,
Switzerland), and the 272 base pairs fragment was digested with Hph1
resulting in two fragments of 79 bp and 193 bp with the T allele. The products
were run out on a 1.5% agarose gel and visualised using ethidium bromide.
The CNR1 polymorphism rs1049353 was genotyped by fluorescence polarisation using an AcycloPrime kit (PerkinElmer, Waltham, Massachusetts, USA) and the output was read on an LJL Biosystems (Sunnyvale, California, USA) plate reader. A 297 bp amplimere was amplified using primers 5'-ttccctcttgtgaaggcact-3' and 5'tcattgagcatggtaaagtt-3'. The SNP was at position 125. The extension primer used in the fluorescence polarisation assay was 5'-catggttaccttggcaatcttgac-3'. The COMT markers were genotyped using SNaPshot (Applied Biosystems, Foster City, California, USA) using an ABI3100 sequencer. Details of primers and reaction conditions are provided in Appendix 1 at http://www.cardiff.ac.uk/medicine/psychological_medicine/pub_data/comt.htm.
Analysis
The reference participants for the analyses were those with genotypes that
were CC homozygous for –86C/T, GG homozygous for rs1049353, AA
homozygous for rs737865, AA homozygous for rs165599, and homozygous for the
Met allele at Val158Met within COMT. Only 0.4% of our
participants were homozygous for the T allele at the –86C/T locus, and
they were therefore grouped with the C/T heterozygotes.
Logistic regression was used to examine associations between dichotomous outcomes and genotypes. A dominance genetic model, as described above, was examined for –86C/T, whereas additive models were used for the CNR1 and COMT variants (Lewis, 2002). For the study of continuous phenotypic outcomes, linear regression was used. However, for age at onset, where assumptions of normality were not met, data were ln-transformed prior to regression modelling. Statistical interactions on a multiplicative scale between substance use and genotype on risk of schizophrenia were investigated using a likelihood ratio test within the logistic regression models. For Val158Met, however, as no association was observed between this SNP and cannabis use in the Dunedin cohort (Caspi et al, 2005), we used a case-only approach to investigate possible gene–environment interactions because this is statistically more powerful (Khoury & Flanders, 1996). The case-only analysis was also used for rs737865 and rs165599 within COMT. Haplotypes for COMT were examined using UNPHASED, version 3.0 (Dudbridge, 2003).
This study had greater than 95% power to detect an additive genetic effect
with an odds ratio of 1.4 or above at
=0.05 for the CNR1 and
COMT variants examined. This study also had greater than 95% power to
find an association between –86C/T variation and schizophrenia based on
frequencies of CC genotype of 0.91 in the control group and 0.84 in the
schizophrenia group, as observed by Leonard et al
(2002). The interaction odds
ratio previously reported for cannabis and Val158Met was 3.5
(Caspi et al, 2005),
and our case-only approach had more than 90% power to detect an interaction
odds ratio of as low as 1.5, at
=0.05.
Sensitivity analysis
Some participants were likely to have started using tobacco or cannabis
after the onset of schizophrenia and it is possible that this could obscure
and complicate interpretation of results from this study. Examination of the
association between schizophrenia and genotypes was therefore repeated with
analyses restricted to cases where the onset of substance use was reported to
be at least 1 year prior to age at schizophrenia onset.
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CHRNA7
The –86C/T genotypes were in Hardy–Weinberg equilibrium in both
the schizophrenia group (
2=0.01, P=0.76) and the
control group (
2=0.01, P=0.92). As shown in
Table 1, there was no evidence
for any association between –86C/T geno-type and schizophrenia (CT/TT
genotypes OR= 1.07, 95% CI 0.77–1.49; P=0.70). There was little
evidence of any difference in the effect of genotype on schizophrenia between
those who smoked (schizophrenia group n=473, controls n=24;
OR=3.0, 95% CI 0.4–22.9) and those who did not (schizophrenia group
n=186, controls n=25; OR=1.7, 95% CI 0.4–7.7;
interaction likelihood ratio test
2=0.21, d.f.=1,
P=0.65). As tobacco use data were available only for a small
proportion of the control group, a more powerful case-only analysis was also
used, and this also failed to provide any evidence for interaction
(n=659; odds ratio for tobacco use by CHRNA7 genotype 0.89,
95% CI 0.53–1.48).
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View this table: [in a new window] | Table 1 Association between CHRNA7 (–86C/T) and CNR1 (rs1049353) genotypes and schizophrenia |
There were 123 in the schizophrenia group with data relating to age of
first using tobacco, and 104 (85%) of these claimed to have started using
tobacco prior to the onset of schizophrenia. In the sensitivity analysis there
was similarly little evidence of any difference in the effect of genotype on
schizophrenia between non-smokers and those smoking prior to illness onset
(n=110; OR=2.7, 95% CI 0.3–22.3; interaction
2=0.1, d.f.=1, P=0.73).
Another way of presenting these data is to examine the relationship between tobacco use and schizophrenia stratified by –86C/T genotype. Tobacco use was strongly associated with schizophrenia in the whole sample (OR=4.4. 95% CI 3.3–6.0; P<0.001), with no evidence of any interaction when stratified by genotype (CC genotype OR=2.6, 95% CI 1.4–4.7; CT/TT genotypes OR=4.6, 95% CI 0.4–53.0; interaction likelihood ratio test as above, P=0.65). Tobacco use was not associated with –86C/T genotype (OR=0.9, 95% CI 0.5–1.5).
Results for associations between –86C/T genotype and various phenotypes within schizophrenia are presented in Table 2. There was weak evidence (P=0.07) that participants with the CT/TT genotypes had a younger age of onset, by approximately 2 years on average, than those homozygous for the C allele.
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View this table: [in a new window] | Table 2 Effect estimates for phenotype characteristics according to CHRNA7 (–86C/T) and CNR1 (rs1049353) genotypes in participants with schizophrenia |
CNR1
Genotypes at rs1049353 were in Hardy–Weinberg equilibrium in both the
schizophrenia group (
2= 0.56, P=0.44) and controls
(
2=1.0, P=0.36). As shown in
Table 1, there was no evidence
for any association between rs1049353 genotype and schizophrenia (odds ratio
for linear trend of genotypes 0.97, 95% CI 0.82–1.13;
P=0.66).
There was little evidence of any difference in the effect of rs1049353
genotype on schizophrenia between those who did not use cannabis
(schizophrenia group n=445, controls n=93; OR=1.04, 95% CI
0.73–1.47) and those who did (schizophrenia group n=261,
controls n=23; OR=0.92, 95% CI 0.48–1.75; interaction
2=0.11, d.f.=1, P=0.74). As cannabis use data were
again available for only a small proportion of the control group, a case-only
analysis was used, and this also failed to provide any evidence for
interaction (n=706; odds ratio for cannabis use by CNR1
genotype 0.83, 95% CI 0.65–1.05).
As part of the sensitivity analysis, there were 71 individuals in the
schizophrenia group with data relating to age of first using cannabis, and 64
(90%) of these reported first use prior to onset of schizophrenia. As in the
main analysis, there was little evidence of any difference in the effect of
rs1049353 genotype on schizophrenia between those who did not use cannabis and
those who claimed to have used cannabis at least 1 year prior to illness onset
(n=614; OR=0.84, 95% CI 0.40–1.78; interaction
2=0.26, d.f.=1, P=0.61).
Presenting these data in another way, there was a strong association between cannabis use and schizophrenia in this sample (OR=2.6, 95% CI 1.8–3.7; P<0.001), with no evidence of any difference when stratified by rs1049353 genotype (GG genotype OR=2.3, 95% CI 1.2–4.4; GA genotype OR=3.1, 95% CI 1.3–7.2; AA genotype OR=1.1, 95% CI 0.2–4.6; interaction likelihood ratio test as above, P=0.33). There was no evidence for any association between rs1049353 genotype and various phenotypes within schizophrenia (Table 2).
COMT
There was no evidence for any association between Val158Met
genotype and cannabis use in our sample of 493 persons with schizophrenia
(OR=0.98, 95% CI 0.76–1.27, P=0.89). Results were almost
identical when restricting the analysis to participants who first used
cannabis at least 1 year prior to their illness onset and who had first used
it by age 18 years or earlier (n=338; OR=0.76, 95% CI
0.41–1.40; P=0.38). Similarly, there was no evidence that
variation at rs737865 or rs165599 was associated with cannabis use in the
case-only analysis, even when restricting the analysis to first use of
cannabis at least 1 year prior to illness onset and first use by age 18 years
or earlier (rs737865, OR=1.09, 95% CI 0.56–2.00; rs165599, OR=1.09, 95%
CI 0.57–2.08). There was no evidence of overall haplotype association
with cannabis use (
2=4.7, d.f.=7, P=0.69) or of
specific association with the rs737865–4680–165599 GGG haplotype
(
2=0.001, d.f.=1, P=0.98).
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CHRNA7 and tobacco use
There have been few association studies of polymorphisms within
CHRNA7 and schizophrenia to date. Leonard et al
(2002) screened the core
promoter region of the full-length gene and reported an association between
schizophrenia and variant –86C/T. Although we found no evidence for an
association between the promoter SNP –86C/T and schizophrenia, CT/TT
genotypes occurred slightly more frequently in participants with schizophrenia
than in controls, in a direction consistent with the findings by Leonard
et al (2002).
However, we observed a much smaller difference in CC frequency of less than
1%, as opposed to the 7% reported in the original study
(Leonard et al,
2002).
People with schizophrenia commonly display evidence of sensory attention
impairments (Adler et al,
1982; Leonard et al,
1996), including deficits in pre-pulse inhibition and P50 gating
response (Braff & Saccuzzo,
1985; Braff et al,
1992; Waldo et al,
1994). Improvements in such neurophysiological deficits in people
with schizophrenia following cigarette smoking have been reported
(Adler et al, 1993;
Olincy et al, 1998),
with similar improvements observed following nicotine administration in animal
models (Bickford & Wear,
1995; Stevens et al,
1996,
1998). Specific agonists of
the
7 receptor (CHRNA7) normalise sensory gating deficits in
animal models (Stevens et al,
1998), whereas evidence for genetic linkage to the P50 deficit
and, to a lesser extent, to schizophrenia, has been reported for chromosome
band 15q14, an area that contains CHRNA7
(Coon et al, 1993;
Freedman et al, 1997;
Leonard et al,
1998).
Despite this support, from a variety of sources, that CHRNA7 is a good candidate gene for schizophrenia, there is weak evidence at present that variation within this gene is associated with the disorder (Riley et al, 2000; Xu et al, 2001; Leonard et al, 2002; Gault et al, 2003; Li et al, 2004; Fan et al, 2006). However, given the findings from experimental studies of the effect of nicotine on neurophysiological deficits in both animal models and humans, as described earlier, it may be that any association between CHRNA7 and schizophrenia is mediated by impairments in sensory gating or other related physiological responses. In the study by Leonard et al (2002), presence of the T allele at –86C/T was also associated with reduced inhibition of the P50 response in the control group, and although two other studies did not replicate this finding (Gault et al, 2003; Houy et al, 2004), one reported an association between P50 sensory gating response and another promoter variant, –194G/C (Houy et al, 2004). There is a clear need for research into CHRNA7 variation in relation to neurophysiological deficits in well-designed and adequately powered studies to address this further.
CNR1, COMT and cannabis
We found no evidence of association between the CNR1 locus
rs1049353 and schizophrenia, consistent with the overall findings previously
reported for this variant from two much smaller studies
(Leroy et al, 2001;
Ujike et al, 2002),
although one of these reported an association in a subgroup analysis
(Leroy et al, 2001).
Two studies have reported associations between schizophrenia and variation
within an (AAT)n microsatellite approximately 20 kb
upstream of the translational start site of CNR1
(Ujike et al, 2002;
Martinez-Gras et al,
2006). However, different alleles were associated with increased
risk in these two studies, and the association in one of the studies was again
observed only for a subgroup of participants, this time with hebephrenic
schizophrenia.
The CNR1 gene is located on 6q14–15, a region of replicated linkage for schizophrenia (Lewis et al, 2003). There are four SNPs within CNR1 on HapMap that have a heterozygosity in European populations greater than 0.1; three of these are in the 3' untranslated region whereas rs1049353 is a synonymous SNP found within exon 1. The relatively small size of CNR1, the limited variation within the gene and its linkage disequilibrium structure mean it is unlikely that a substantial effect on schizophrenia risk is conferred by variation within this gene, given our findings and the lack of other consistent associations reported to date.
We also failed to find any supporting evidence for a differential effect of cannabis use on psychosis risk according to variation at Val158Met within COMT. In the Dunedin study evidence for an interaction was observed only for people first using cannabis by age 18 years, but not for those using it after this age (Caspi et al, 2005). One explanation proposed for this was that there may exist a sensitive or even critical period of risk when the influence of cannabis exposure is moderated by COMT genotype. In our study we failed to find evidence for an interaction between cannabis use and COMT genotype even when restricting the analysis to participants who claimed to have first used cannabis by the same cutoff period of age 18 years, despite more than adequate statistical power to replicate the original findings. Furthermore, in contrast to the findings by Caspi et al (2005), cannabis use by age 18 years was actually less common in participants with schizophrenia homozygous for the Val allele compared with those heterozygous for this allele or homozygous for Met (5.3%, 6.4% and 8.7% respectively), although this was not significantly different.
Limitations to the interpretation of our results
This study was adequately powered to examine main effects on risk of
schizophrenia, suggesting it is unlikely that variations in CNR1 or
CHRNA7 are important risk factors for schizophrenia. Furthermore,
this study was adequately powered for studies of interactions using a
case-only design, but this approach is dependent on the assumption of no
genotype–exposure association in the population. For COMT this
assumption is likely to be a reasonable one, given that no association with
cannabis use was observed in the Dunedin cohort
(Caspi et al, 2005).
However, this assumption may be less likely to hold true for CNR1 or
CHRNA7, given that cannabis and nicotine act through receptors coded
for by these genes, and also given the sporadic reports of associations
between cannabis and tobacco dependence and CNR1/CHRNA7
genotypes (Greenbaum et al,
2006; Hopfer et al,
2006). For that reason we also conducted studies of interactions
between CNR1 and cannabis as well as between CHRNA7 and
tobacco using a more traditional case–control approach, although
statistical power to exclude anything other than large interaction effects for
these two genes using this latter approach was limited.
Although we genotyped three SNPs in COMT that together form a haplotype reported to be significantly associated with schizophrenia (Shifman et al, 2002), we only genotyped one SNP in each of CNR1 and CHRNA7. It is not possible therefore to rule out causal effects of variants within these genes that are not in strong linkage disequilibrium with the SNPs we tested. However, a strong effect of CNR1 on risk of schizophrenia seems unlikely, given the linkage disequilibrium structure within this gene. Our confidence in ruling out such an effect for CHRNA7 is lower, although we did not feel that the evidence we obtained was strong enough to warrant further genotyping of CHRNA7 SNPs, especially given the problems resulting from the partial duplication of this gene, which makes such studies inherently more difficult.
A final limitation of our study is that, unlike the longitudinal data collection in the Dunedin cohort, our case–control design relied on people recalling age of first use of cannabis and relating this in time to the date of their first contact with psychiatric services. Such data seem inherently more likely to be misclassified than prospectively collected data. It is unclear to what extent any such misclassification might have resulted in an underestimate of the association between cannabis use and genotype in our case-only analysis, and therefore obscured any true interaction effect. It would, however, presumably require a substantial amount of misclassification to obscure an interaction effect as strong as that reported by Caspi and colleagues, whereby cannabis use was associated with a 10-fold increase in risk of psychotic disorder in those homozygous for valine but had no effect in those homozygous for methionine (Caspi et al, 2005). This finding of an interaction effect in the Dunedin cohort was observed only in a subgroup of participants – those using cannabis by age 18 years. Similarly, supportive evidence of a putative interaction between cannabis use and COMT on psychotic symptoms, following administration of cannabis in an experimental setting was again observed only in a subgroup of participants with schizophrenia, this time those with evidence of pre-existing psychotic traits (Henquet et al, 2006). Although such findings are biologically plausible and seem intuitively appealing, substantially more evidence from replication of these findings is required. Our study, although providing adequate power to observe even a relatively small association between cannabis use and COMT genotype in participants with schizophrenia, may not be the ideal design to examine such a relationship, and other longitudinal studies may be able to investigate this with greater confidence in the future.
In summary, we failed to find any evidence that variation at the CHRNA7 or CRN1 locus was associated with schizophrenia, or that the effect of variation at these loci was modified by use of tobacco or cannabis respectively. Cannabis use was not associated with presence of the valine allele at Val158Met within COMT in our sample, therefore our findings do not not support a previous report of a putative gene–environment interaction between COMT genotype and cannabis use on risk of schizophrenia.
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