SCHRES-05794; No of Pages 5 Schizophrenia Research xxx (2014) xxx–xxx

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Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants Andrew Kenneth Martin a,⁎, Gail Robinson b, David Reutens c, Bryan Mowry d,e a

University of Queensland, Queensland Brain Institute, St Lucia, Queensland, 4072, Australia University of Queensland, School of Psychology, Australia University of Queensland, Centre for Advanced Imaging, Australia d University of Queensland, Queensland Brain Institute, Australia e Queensland Centre for Mental Health Research, Australia b c

a r t i c l e

i n f o

Article history: Received 29 October 2013 Received in revised form 4 February 2014 Accepted 5 March 2014 Available online xxxx Keywords: Schizophrenia Copy number variants Genetics Cannabis Substance abuse Age at onset

a b s t r a c t Background: Large deletions are found to a greater extent in patients with schizophrenia compared with healthy controls. This study aims to investigate clinical symptomatology and substance abuse rates in patients with large (N500 kb), rare (b 1% of cohort) deletions and duplications compared with schizophrenia patients in general. Methods: 633 schizophrenia patients, including 60 with large (N500 kb), rare (b 1% of cohort) deletions and 74 with large, rare duplications, who formed part of a large genome-wide association study, were assessed for alcohol and cannabis abuse rates as well as a range of symptom measures using the Diagnostic Interview for Genetic Studies (DIGS), Family Interview for Genetic Studies (FIGS), and medical records. Results: Patients with large, rare deletions had significantly less cannabis abuse rates but comparable alcohol abuse rates, with an age at onset later than those without large, rare deletions. There was no significant difference in any substance abuse or clinical symptom rates between patients with and without large, rare duplications, but an interaction did exist between cannabis abuse, duplication status, and age at onset, with cannabis abuse resulting in an earlier age at onset only in those without a large, rare duplication. Similarly, patients with a large, rare duplication had a later onset age for cannabis abuse/dependence. Conclusions: Schizophrenia patients with large, rare deletions were less likely to have comorbid cannabis abuse over their lifetime. This provides support for a threshold model of risk with those carrying a schizophreniaassociated copy number variation less reliant on environmental insults. Patients with large, rare duplications were protected against earlier onset of schizophrenia in the presence of comorbid cannabis abuse in addition to later onset of cannabis abuse itself. © 2014 Elsevier B.V. All rights reserved.

1. Introduction The aetiology of schizophrenia is likely multifactorial with both genetic and environmental contributions. Heritability is approximately 0.8 (Cardno and Gottesman, 2000; Sullivan et al., 2003) with genome wide association studies (GWAS) providing evidence for polygenic risk contributions from common single nucleotide polymorphisms (SNPs), as well as rare (b1%) copy number variants (CNVs) (Mowry and Gratten, 2012), especially large (N500 kb) deletions (Owen et al., 2010). Substance abuse is higher in patients with schizophrenia, particularly cannabis abuse (Barnett et al., 2007), with longitudinal studies suggesting that cannabis use increases the risk for the disorder (Arseneault et al., 2004; Moore et al., 2007). In order to make sense of

⁎ Corresponding author. Tel.: +61 41 7 3346 3340. E-mail address: [email protected] (A.K. Martin).

the number of genetic and environmental risk factors for schizophrenia, a threshold model has been proposed (McGue et al., 1983; Tsuang et al., 2001; McGuffin, 2004). Those with the strongest genetic predisposition for schizophrenia may need little or no environmental insult, whereas those with relatively low genetic vulnerability may require a greater environmental insult. Evidence from large GWA studies suggests that large, rare deletions but not duplications are seen at an excess in cases compared with controls (Owen et al., 2010; Levinson et al., 2011) and may result in increased neurodevelopmental instability (Yeo et al., 1999) lowering the threshold for polygenic or environmental risk factors to direct development towards psychosis. Environmental factors in risk of schizophrenia have been comprehensively reviewed (van Os et al., 2010) with a consistent association found between cannabis abuse and schizophrenia (Minozzi et al., 2010). Studies have also shown that exposure to cannabis is not confounded by indices of genetic risk, with little of the association between cannabis abuse and psychosis explained by genetic confounds (Veling

http://dx.doi.org/10.1016/j.schres.2014.03.004 0920-9964/© 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Martin, A.K., et al., Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.03.004

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A.K. Martin et al. / Schizophrenia Research xxx (2014) xxx–xxx

et al., 2008; GROUP Investigators, 2011; van Winkel, 2011). In healthy controls, cannabis abuse is associated with schizophrenia-like cognitive and behavioural changes (Morrison et al., 2009), and exacerbates the existing symptoms in patients with schizophrenia (D'Souza et al., 2005). Recent evidence, however, suggests that first-episode psychosis patients with comorbid cannabis abuse may actually perform better on a wide range of cognitive tests than those without comorbid cannabis abuse (Yucel et al., 2012). This could reflect a subgroup who only become psychotic due to cannabis abuse, with cognition remaining largely intact, because prior to the environmental insult, development was unperturbed by deleterious genetic factors. Given the evidence for large deletions as genetic risk factors and cannabis abuse as an environmental risk factor, the following study will investigate rates of cannabis abuse in schizophrenia patients with and without large (N500 kb), rare (frequency b1% of the sample) copy number variants (both deletions and duplications). It is hypothesized that patients with large deletions will show lower rates of cannabis abuse than those without but similar levels of alcohol abuse, offering support for a threshold model of genetic and environmental influence on risk for schizophrenia. As large, rare duplications are not associated with schizophrenia, cannabis abuse should not differ according to duplication status. As previous studies have found that age at onset is earlier in patients with comorbid cannabis abuse (Weller et al., 1988; DeQuardo et al., 1994; Hambrecht and Hafner, 1996; Addington and Addington, 1998; Rabinowitz et al., 1998), an exploratory study into clinical factors, including age at onset, and patients with large, rare CNVs will be conducted. 2. Methods This Australian sample was recruited as part of two consecutive collaborative US/Australian Molecular Genetics of Schizophrenia studies: (i) MGS1, a genomewide linkage study which included families with a proband with schizophrenia, and one or more siblings with schizophrenia or schizoaffective disorder (Suarez et al., 2006). Only the proband from each family was included in the current study; (ii) MGS2, a genome-wide association study that included unrelated individuals with schizophrenia or schizoaffective disorder plus a sample of healthy controls (Shi et al., 2009). Eligible individuals were recruited from a range of sources, including local treatment facilities, physician referrals, community organizations, supported accommodation facilities and advertisements. Probands and relatives with a diagnosis of Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (APA, 1994) schizophrenia or schizoaffective disorder were included. In order to standardize schizoaffective diagnoses, we operationalized the mood syndrome duration criterion at ≥30% of total illness duration (Suarez et al., 2006). Individuals were included if they met self-reported European Caucasian ancestry, later confirmed by genetic analysis. Exclusion criteria were: (i) inability to give informed consent to all aspects of the study; (ii) psychosis judged to be secondary to substance use or a known neurological disorder such as epilepsy; and (iii) severe intellectual disability (any impairment that precluded informed consent, and any individual with an IQ assessed below 55 according to formal testing/medical record evidence). 2.1. Clinical ascertainment Individuals were comprehensively ascertained by trained clinicians using: (i) the Diagnostic Interview for Genetic Studies (DIGS) (Nurnberger et al., 1994) (ii) Family Interview for Genetic Studies (FIGS) (Gershon et al., 1988; Maxwell, 1992); (iii) information extracted from all available medical records; (iv) Narrative summary prepared by the interviewer and based on all information obtained from the DIGS, FIGS and medical records. The narrative summary was invaluable in recording the first-hand impressions of the interviewer. This facilitated diagnostic assessment by augmenting the DIGS interview information,

especially when the participant's responses lacked clarity; (v) Best Estimate Final Diagnosis (BEFD) (Leckman et al., 1982) was assigned by two experienced research psychiatrists independently reviewing all available information then conferring to assign a consensus diagnosis; one of us (BM) reviewed every Australian case. Diagnostic inter-rater reliability was assessed using standard procedures (Suarez et al., 2006). 2.2. Coding of clinical variables Sources of data were audited (both electronic and hard copy), and all potential cases were identified for whom diagnostic information was available. Data were extracted from diagnostic interview databases, where possible, then responses were checked, corrected, and missing values retrieved from all available sources. Positive, negative/disorganized, and mood symptoms were scored using the Lifetime Dimensions of Psychosis Scale (LDPS) (Levinson et al., 2002) with severity and duration ratings totaled in line with the factor analysis carried out by Fanous et al. (2012). Age at onset of psychosis was defined as age when patients had their first documented psychotic symptoms. Alcohol and cannabis abuse/dependence were coded according to DSM-IV criteria with information ascertained through interview with the patient using DIGS, with a family member using FIGS, and through a detailed search of available medical records. The criteria for lifetime cannabis or alcohol abuse was a maladaptive pattern of use, leading to significant impairment or distress, as manifested by at least one of the following occurring within a 12-month period: • Recurrent use resulting in failure to fulfill major role obligations at work, school, or home • Recurrent use in situations in which it is physically hazardous • Recurrent substance-related legal problems • Continued use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of the substance The criteria for lifetime cannabis or alcohol dependence was a maladaptive pattern of use, leading to clinically significant impairment or distress, as manifested by three or more of the following occurring at any time in the same 12-month period: • Need for markedly more amounts to achieve intoxication or desired effect; or markedly diminished effect with continued use of the same amount • The characteristic withdrawal syndrome • Using larger amounts or over a longer period than intended • Persistent desire or one or more unsuccessful attempts to cut down or control use • Important social, occupational, or recreational activities given up or reduced because of use • A great deal of time spent in activities necessary to obtain, to use, or to recover from the effects of drinking • Continued use despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to be caused or exacerbated by use Cannabis abuse/dependence will be referred to as cannabis abuse from this point onwards for ease of reading. Age at onset for cannabis and alcohol abuse/dependence was the first documented or selfreported use that reached the DSM-IV-TR criteria for abuse or dependence. 2.3. Copy number variant identification 2.3.1. Original MGS study Quality control, identification and analytic methods have been described previously (Levinson et al., 2011). Briefly, DNAs were assayed using Affymetrix 6.0 genotyping arrays, which included approximately 900,000 single-nucleotide polymorphisms (SNPs) and approximately

Please cite this article as: Martin, A.K., et al., Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.03.004

A.K. Martin et al. / Schizophrenia Research xxx (2014) xxx–xxx

900,000 copy number probes. CNVs were detected with the Birdseye module of the Birdsuite software package (Korn et al., 2008). Quality control steps for CNV calls included: duplicate assays to develop narrow and broad call criteria, exclusion of calls involving telomeres and centromeres, immunoglobulin genes, and occurrence on one/two plates only. DNA samples were also subject to quality control steps. Plots of “regions of interest” calls were visually inspected with confirmation by a second calling algorithm. Quantitative polymerase chain reaction (qPCR) confirmed the presence of selected CNVs. PLINK (Purcell et al., 2007) pointwise analyses were conducted for all rare CNVs (with b 1% frequency) and those of more than 100,000 bp. 2.3.2. Australian MGS SCZ sub-set Most MGS DNAs were extracted from Epstein–Barr virus transformed lymphoblastic cell lines, and because EB transformation can create CNVs (Wang et al., 2007) we sought fresh blood samples from Australian MGS participants and extracted DNA from whole blood for confirmation of the CNVs documented in MGS. A proportion of the CNVs were confirmed for the purposes of another study using TaqMan Copy Number assays (Applied Biosystems) following recommended protocols on a StepOnePlus real-time PCR instrument (Applied Biosystems). Target assays were run simultaneously with reference assays that detect sequence that is known to have two copies in viable diploid human cells. Copy number for the targets was determined using the comparative CT (ΔΔCT) method in which the CT difference (ΔCT) between target and reference sequences for each individual is compared to the ΔCT value for control individuals that are known to have two copies of the target sequence. All CNVs were confirmed. The nature of the DNA extraction did not differ across groups. In order to calculate the frequency of an individual event, CNVs were deemed the same if the overlap was greater than or equal to 50% of the union of the two events. Only deletions and duplications occurring in less than 1% of the sample were considered rare. 2.4. Statistical analysis All analyses were carried out using SPSS v20. T-tests were used to assess differences between clinical symptoms and deletion or duplication status. Age at onset was normalized using a log transformation before the t-test was performed. Data is presented as raw scores for ease of

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interpretation. Chi-square tests were used to compare sex and substance abuse rates with deletion or duplication status. A two-by-two analysis of variance (ANOVA) was carried out to test for interactions between cannabis abuse and age at disease onset. Multiple testing corrections were not required as the hypothesis was formed a priori and correction would have been overly conservative.

3. Results The cohort was divided into those with deletions and duplications that were 500 kb or greater and occurred in less than 1% of the Australian MGS1-2 sample. This resulted in 60 patients in the deletion group and 74 in the duplication group. Ten patients had both a deletion and a duplication and were included in both groups. Clinical ratings were comparable regardless of deletion or duplication status (see Table 1). There were no significant sex differences in either the deletion or duplication groups and sex did not explain the difference in cannabis abuse rates or age at onset across the groups. The deletions group had less reported cannabis abuse than those without (31.7% v 47.5%, χ2 = 5.5, p = 0.02). The patients with deletions had a later age at onset than those without (24.1 yrs v 22.1 yrs, t = −2.7, p = 0.01). There were no significant differences between those with and without duplications on reports of substance abuse or age at onset. Cannabis abuse was associated with earlier age at onset (21.0 yrs v 23.4 yrs, t = 4.7, p = 0.00) (see Table 2). In the deletions group, the interaction between cannabis abuse and age at onset was nonsignificant, F(1,628) = 0.2, p = 0.66, suggesting an independent role for deletions. For the duplications group the interaction between cannabis abuse and age at onset was significant, F(1,628) =4.2, p = 0.04. Cannabis abuse resulted in an earlier age at onset regardless of deletion status. Conversely, age at onset was earlier for cannabis abusers only in the no duplication group (see Table 2). Although in the total sample the majority of cannabis abusers were male (80.6%), the age at onset was comparable for males and females (22.1 yrs v 22.8 yrs, t = −1.3, p = 0.20) and sex did not significantly alter the results. Age at onset for alcohol abuse was comparable for patients with and without deletions (20.4 yrs v 19.2 yrs, t = −0.9, p = 0.36) as was age at onset for cannabis abuse (19.3 yrs v 18.4 yrs, t = −0.7, p = 0.49). Patients with duplications had a trend towards later age at onset for alcohol abuse (20.9 yrs v 19.1 yrs, t = −1.9, p = 0.05) with significantly

Table 1 Clinical characteristics of schizophrenia patients with large (N500 kb), rare (b1%) deletions and duplications.

Male Substance abuse Cannabis abuse Alcohol abuse

Cannabis abuse AAO#⁎ Alcohol abuse AAO+⁎ Symptoms Positive Neg/Dis Mood SZ AAO*

Deletion

Non-Del

(N = 60)

(N = 573)

N (%)

N (%)

Stat

Sig

Duplication

Non-Dup

(N = 74)

(N = 559)

N (%)

N (%)

Stat

Sig

40 (66.7%)

414 (72.3%)

χ2 = 0.8

0.36

56 (75.7%)

398 (71.2%)

χ2 = 0.6

0.42

19 (31.7%) 22 (36.7%)

272 (47.5%) 244 (42.9%)

χ2 = 5.5 χ2 = 0.9

0.02 0.35

29 (39.2%) 35 (47.3%)

262 (46.9%) 231 (41.6%)

χ2 = 1.6 χ2 = 0.9

0.21 0.35

Mean (sd)

Mean (sd)

Mean (sd)

Mean (sd)

19.3 (4.9) 20.4 (5.7)

18.4 (4.7) 19.2 (5.2)

t = −0.7 t = −0.9

0.46 0.36

20.3 (6.0) 20.9 (6.5)

18.3 (4.5) 19.1 (5.0)

t = −2.2 t = −1.9

0.03 0.05

26.6 (7.0) 17.1 (5.8) 5.8 (5.7) 24.1 (5.7)

27.0 (7.4) 17.2 (6.0) 5.4 (5.6) 22.1 (6.5)

t t t t

0.74 0.93 0.59 0.01

25.9 (7.5) 16.3 (6.2) 5.0 (5.5) 22.8 (6.9)

27.1 (7.3) 17.3 (6.0) 5.5 (5.6) 22.2 (6.4)

t t t t

0.18 0.15 0.47 0.44

= 0.3 = 0.1 =−0.5 = −2.7

= = = =

1.3 1.4 0.7 −0.8

⁎Calculations for age at onset conducted on transformed data (log transformation). Cannabis Abuse AAO missing for 1 patient in the non-deletion group and 4 patients in the non-duplication group. + Alcohol Abuse AAO missing for 3 patients with deletions, 4 in the non-deletion group, 2 patients with duplications, & 8 patients in the non-duplication group. Neg/Dis = Negative and Disorganized. SZ = Schizophrenia. AAO = Age at onset. #

Please cite this article as: Martin, A.K., et al., Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.03.004

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A.K. Martin et al. / Schizophrenia Research xxx (2014) xxx–xxx

Table 2 Age at onsets for schizophrenia patients dependent on cannabis abuse and CNV status. N

AAO (yrs)

N

AAO (yrs)

Mean (sd) All SZ patients

633

Cannabis abuse Deletions* Duplications*

291 60 74

Deletions No deletions Duplications No duplications

41 272 29 262

22.3 (6.4) Yes 21.0 (4.7) 24.1 (5.7) 22.8 (6.9)

Sig





t = 4.7 t = −2.7 t = −0.8

0.00 0.01 0.44

t t t t

0.04 0.00 0.53 0.00

No 342 573 559

23.4 (7.5) 22.1 (6.5) 22.2 (6.4) − Cannabis abuse

+ Cannabis abuse 21.8 (4.8) 20.9 (7.6) 23.5 (6.4) 20.7 (4.4)

Stat

Mean (sd)

19 301 45 297

25.2 (5.9) 23.1 (4.7) 22.4 (7.2) 23.5 (7.5)

= = = =

2.1 4.1 0.6 5.3

AAO = Age at Onset. ⁎Calculations conducted on transformed data (log transformation).

later onset of cannabis abuse (20.3 yrs v 18.3 yrs, t = −2.2, p = 0.03) (see Table 1). 4. Discussion To the authors' knowledge, this is the first study to show reduced cannabis abuse rates in a subgroup of schizophrenia patients with large deletions. Lower substance abuse rates have been documented in patients with schizophrenia and 22q11.2 Deletion Syndrome (Bassett et al., 2003), but the current study suggests that this may be a common feature of patients with large, rare deletions in general. It provides tentative support for the threshold model of risk for schizophrenia, with patients carrying a large, rare deletion associated with less lifetime cannabis abuse, suggesting less reliance on environmental components in the pathway to schizophrenia. However, other possibilities must be considered. Large deletions have also been observed to a greater extent in intellectual disability (Girirajan et al., 2011), and patients with schizophrenia and large deletions are likely to also show cognitive deficits compared with schizophrenia patients in general. Comorbid cannabis abuse is associated with higher cognitive functioning, especially at first clinical presentation (Yucel et al., 2012) and the current findings suggest that some of this difference may be due to a subgroup of patients who have large deletions. Although the patients with higher cognitive functioning may only become psychotic following the effects of cannabis abuse, it is possible that patients with lower cognitive functioning were less exposed to substances due to absence or withdrawal from social situations or relationships necessary for acquiring illegal substances. The effect of deletions on later age at onset was observed to be independent of cannabis abuse, suggesting that deletions are a separate factor influencing the onset of symptoms. The developmental instability hypothesis (Yeo et al., 1999) posits that genetic perturbations such as mutations, or environmental insults threaten the precise expression of developmental design and render the individual more susceptible to disease. Therefore, patients with large, rare deletions may have greater developmental instability and may require fewer polygenic risk variants or environmental insults to reach the diagnostic threshold. Therefore, the later age at onset in the deletions group may be a reflection of a lower polygenic and environmental risk loading. Alternatively, large CNVs have been associated with developmental delay (Cooper et al., 2011). If the schizophrenia patients with large deletions were more likely to have had developmental delay, this may have delayed all aspects of neurodevelopment resulting in a later onset of psychotic symptoms.

One aspect to consider is sampling strategy. We needed to recruit individuals with CNVs from schizophrenia clinics rather than identify people with large CNVs from the population and determine who of these have schizophrenia. Patients with large deletions may show neurodevelopmental impairments earlier and receive alternative diagnoses and hence not be included in schizophrenia cohorts. The interaction between cannabis abuse and duplication status, such that age at onset is earlier for cannabis abusers only in patients without a duplication, points towards large, rare duplications being a protective factor against cannabis abuse in relationship to age at onset. Recent research suggests that duplications at the 22q11 locus are protective against schizophrenia (Rees et al., 2013) and the finding that large, rare duplications in general protect against earlier age at onset for patients with comorbid cannabis abuse suggests that duplications in general may act as a protective buffer against environmental risk factors and slow or possibly prevent the manifestation of illness. It is also of interest that patients with duplications have an increased age at onset for cannabis abuse. Although the onset of cannabis use was not available in the current study, the finding of increased age at onset of DSM-IV diagnosed cannabis abuse or dependence suggests that duplications may be protective against cannabis use progressing to abuse. However, further research, specifically longitudinal studies, will be needed to identify the effects that CNVs exert over development and the pathways to substance abuse and schizophrenia. The heterogeneity of the CNVs is the main limitation of the current study. However, as these events are rare, extremely large samples will be needed to assess the effects of individual deletions or duplications. Moreover, CNVs are included in the current study that have an, as yet, unknown effect on the phenotype and the threshold of 1% occurrence in the current cohort will include CNVs that are not directly related to schizophrenia risk, but may interact with other, as yet, unknown genetic and environmental factors. Another limitation concerns the lack of temporal data on lifetime cannabis abuse or a more refined measure incorporating aspects such as duration and frequency of cannabis use. Notwithstanding these caveats, this is the only study to date to examine differences in clinical presentation and cannabis rates in patients with large, rare CNVs in general. As large, rare deletions are found at a greater rate in patients than controls, the finding of less cannabis abuse and later age at onset offers an interesting starting point to understanding the role of CNVs in schizophrenia. Further research should investigate the cognitive and neuroimaging characteristics of patients with large deletions. This study suggests that interactions with substance abuse, especially cannabis abuse, may provide an interesting avenue of research, enabling a greater

Please cite this article as: Martin, A.K., et al., Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.03.004

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understanding of the interaction between genetic and environmental risk factors in the aetiology of schizophrenia. Role of funding source This work was supported by the Australian National Health and Medical Research Council (grant number 631671) and the United States National Institute of Mental Health (grant number RO1 MH59588). Contributors AM, GR, DR, & BM conceptualized the study. AM performed the analysis and wrote the manuscript. GR, DR, and BM supervised the project and contributed to the interpretation of results and editing of the manuscript. Conflict of interest The authors report no conflicts of interest. The authors alone are responsible for the content of this manuscript. Acknowledgments We would like to acknowledge all the participants and their families. We acknowledge the MGS Australian recruitment team and the MGS consortium. We thank Jake Gratten, Heather Smith, Cheryl Fillipich, Kalpana Patel and Lauren Simpson for confirming the Australian subset of CNVs. We thank Deborah Nertney and Duncan McLean for their assistance in collecting the clinical data.

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Please cite this article as: Martin, A.K., et al., Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.03.004

Cannabis abuse and age at onset in schizophrenia patients with large, rare copy number variants.

Large deletions are found to a greater extent in patients with schizophrenia compared with healthy controls. This study aims to investigate clinical s...
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