Schizophrenia Research 158 (2014) 255–260

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The phenotypic manifestations of rare CNVs in schizophrenia Alison K. Merikangas a,⁎, Ricardo Segurado b, Paul Cormican a, Elizabeth A. Heron a, Richard J.L. Anney a, Susan Moore a, Eric Kelleher a, April Hargreaves a, Heike Anderson-Schmidt c, Michael Gill a, Louise Gallagher a, Aiden Corvin a a b c

Department of Psychiatry & Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin 2, Ireland Centre for Support and Training in Analysis and Research, University College Dublin, Dublin 4, Ireland Psychiatric Genetics, Department of Psychiatry and Psychotherapy, University Medical Centre, Georg-August-University Göttingen, Germany

a r t i c l e

i n f o

Article history: Received 11 March 2014 Received in revised form 14 June 2014 Accepted 14 June 2014 Available online 4 July 2014 Keywords: Schizophrenia CNV Copy number variation Paternal age Family history Phenotype

a b s t r a c t There is compelling evidence for the role of copy number variants (CNVs) in schizophrenia susceptibility, and it has been estimated that up to 2–3% of schizophrenia cases may carry rare CNVs. Despite evidence that these events are associated with an increased risk across categorical neurodevelopmental disorders, there is limited understanding of the impact of CNVs on the core features of disorders like schizophrenia. Our objective was to evaluate associations between rare CNVs in differentially brain expressed (BE) genes and the core features and clinical correlates of schizophrenia. The sample included 386 cases of Irish ancestry with a diagnosis of schizophrenia, at least one rare CNV impacting any gene, and a core set of phenotypic measures. Statistically significant associations between deletions in differentially BE genes were found for family history of mental illness (decreased prevalence of all CNVs and deletions, unadjusted and adjusted) and for paternal age (increase in deletions only, unadjusted, among those with later ages at birth of patient). The strong effect of a lack of a family history on BE genes suggests that CNVs may comprise one pathway to schizophrenia, whereas a positive family history could index other genetic mechanisms that increase schizophrenia vulnerability. To our knowledge, this is the first investigation of the association between genome-wide CNVs and risk factors and sub-phenotypic features of schizophrenia beyond cognitive function. © 2014 Elsevier B.V. All rights reserved.

1. Introduction There is now compelling evidence for the role of copy number variants (CNVs) in schizophrenia (International Schizophrenia Consortium, 2008; Malhotra et al., 2011; Vacic et al., 2011; Derks et al., 2013; Rees et al., 2013a), as well as for their impact on broad domains of neurocognitive functioning (Couzin, 2008; MacLeod et al., 2012). It has been estimated that CNV burden is higher in schizophrenia cases than in controls, with up to 2–3% of schizophrenia cases carrying rare CNVs (Wilson et al., 2006; International Schizophrenia Consortium, 2008; Stefansson et al., 2008; Vrijenhoek et al., 2008; Xu et al., 2008; St Clair, 2009; Magri et al., 2010; Mulle et al., 2010; Tam et al., 2010; Levinson et al., 2011; Vacic et al., 2011; Doherty et al., 2012), and there are at least twelve CNV loci that have been reported to be associated with schizophrenia (Rees et al., 2013b; Morris et al., 2014).

⁎ Corresponding author at: Trinity College Dublin, Department of Psychiatry and Neuropsychiatric Genetics Research Group, Trinity Centre for Health Sciences, St. James Hospital, James Street, Dublin 8, Ireland. Tel.: +353 1 896 2241. E-mail address: [email protected] (A.K. Merikangas).

http://dx.doi.org/10.1016/j.schres.2014.06.016 0920-9964/© 2014 Elsevier B.V. All rights reserved.

Certain recurrent CNVs, while predisposing towards specific disorders, can also influence their broader phenotypic manifestations, such as learning disability, physical characteristics, and seizures (O'Donovan et al., 2008); other CNVs might also act as modifiers or vulnerability factors for these phenotypes rather than influencing dichotomous disease outcomes (Guilmatre et al., 2009; Moreno-De-Luca et al., 2010). Since the impact of CNVs might extend beyond the current clinical diagnostic entities in neuropsychiatry, we set out to investigate the clinical outcome of CNVs that impacted differentially brain expressed (BE) genes. The majority of CNV research in schizophrenia has focused on disorder-specific CNV identification, and to date, few studies have systematically examined the impact of CNVs on specific sub-phenotypes of schizophrenia, with the recent exception of general cognitive ability (MacLeod et al., 2012; Derks et al., 2013; van Scheltinga et al., 2013). Here, we use the term sub-phenotype to refer to underlying characteristics or clinical correlates of the disorders that may be endophenotypes, but that have not yet met the criteria (Gottesman and Gould, 2003). Lower-level disorder features, such as clinical features and symptom severity, or intellectual deficits are considered sub-phenotypes for the purposes of this research, in accordance with the NIMH Research Domain Criteria (RDoC) (Insel et al., 2010), wherein domains of function provide an alternative to traditional diagnostic entities (Simmons and Quinn, 2014).

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In parallel to specific molecular genetic research streams, there has been substantial progress in the identification of other risk factors for schizophrenia (Sorensen et al., 2014). The two factors that have been most consistently studied include a positive family history of psychiatric disorders in general, and schizophrenia in particular (Gottesman, 1991), and advanced paternal age (Malaspina et al., 2008; Kong et al., 2012). Advancing paternal age is a major source of new mutations in humans, likely due to inefficient repair processes and the accumulation of replication errors during gametogenesis with increased age (Goriely et al., 2003; Heffner, 2004). The role of advanced paternal age in schizophrenia is of interest because of the high rates of de novo mutations in schizophrenia (Rees et al., 2012), although the findings on de novo mutation in schizophrenia have not been documented consistently (Buizer-Voskamp et al., 2013). It is possible that de novo point mutations or defective epigenetic regulation of paternal genes might be better understood through CNV analysis, particularly in neurodevelopmental disorders (Malaspina et al., 2008). It is notable that one study did not find increased paternal age among the de novo carriers (Kirov et al., 2012). The aim of this work is to examine the phenotypic outcome of genic copy number variation (CNV) in schizophrenia, using a case-only design. The purpose of investigating genic CNVs is to narrow the analytic focus and target functional regions of the genome. We hypothesized that (1) individuals carrying rare CNVs impacting differentially BE genes would demonstrate a more severe clinical presentation, have a lower intelligence quotient (IQ), and have impaired social cognitive functioning, and (2) that the offspring of older parents would be more likely to have CNVs impacting differentially BE genes.

Table 1 Schizophrenia phenotype variables by sample collection. Phenotype

RPGIb

No No No Yes

Yes Yes Yes Yes

PANSSc PANSS

SANSd SANS

PANSS PANSS

SAPSe SAPS

Yes No

Yes Yes

Intelligence testing Wechsler Test of Adult Reading Verbal IQf Performance IQ Full scale IQ

Yes Yes Yes Yes

Yes Yes Yes Yes

Social cognition Hinting Task Mind in the Eyes IPSAQg externalizing IPSAQ personalizing

No No No No

Yes Yes Yes Yes

Developmental & parental/family factors Developmental delay Maternal age Paternal age Family history of mental illness Clinical characteristics Negative symptoms Alogia Blunted affect Positive symptoms Delusions Hallucinations Measures of severity Age at onset Global assessment of function

a

2. Experimental materials and methods

b c

2.1. Subjects

d e f

In this case-only analysis, the sample included 386 Irish psychosis cases with rare gene-impacting CNVs (defined below), and phenotype information beyond a diagnosis, from two local clinical samples, the Genetic Association study of Schizophrenia and related Psychoses (GASP, N = 138), and the Resource for Psychiatric Genetics in Ireland (RPGI, N = 248). Each sample collection used slightly different procedures and measures (see Table 1), but in both instances cases were required to be over 18 years of age, of Irish ancestry (defined as having four Irish grandparents), and to have a diagnosis of schizophrenia or schizoaffective disorder through expert clinician evaluation (as described in Gilks et al. (2010)). Cases were screened to exclude those with substance-induced psychosis, or psychosis due to a general medical condition. All subjects provided written informed consent. 2.2. Phenotype selection The sub-phenotypes (shown in Table 1) included: developmental delay (N = 189); maternal (N = 130) and paternal (N = 135) age and family history of mental illness (N = 240); clinical symptoms and symptom severity, including age at onset (N = 345); and neuropsychological measures and social cognition (Donohoe et al., 2010). Developmental delay, parental age at birth, and family history of mental illness were collected based on self-report or collateral history where available. With respect to the family history of mental illness, participants were asked a yes/no question: “Is there any history of mental health problems in your family?”, or were asked specifically about family history of schizophrenia in first degree relatives, and then about other psychiatric disorders in first degree relatives (e.g. Bipolar Affective Disorder, Major Depressive Disorder, Substance Abuse, Alcohol Dependence Syndrome, Anorexia Nervosa, suicide). For the analyses presented here, cases were defined as having a family history of mental illness, or not having a history of mental illness in the family. The clinical assessment included: positive (delusions (N = 354) and hallucinations (N = 360)) and negative (alogia (N = 362) and blunted affect

Sample collection GASPa

g

GASP = Genetic Association study of Schizophrenia and related Psychoses. RPGI = Resource for Psychiatric Genetics in Ireland. PANSS = Positive and Negative Syndrome Scale. SANS = Scale for the Assessment of Negative. SAPS = Scale for the Assessment of Positive Symptoms. IQ = intelligence quotient. IPSAQ = Internal, Personal and Situational Attributions Questionnaire.

(N = 360)) symptoms (via the Scale for the Assessment of Positive Symptoms (SAPS), (Andreasen, 1984), the Scale for the Assessment of Negative Symptoms (SANS) (Andreasen, 1981), and the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987) reconciled into a single scale as in Lyne et al. (2012)); measures of clinical severity (age at onset and Global Assessment of Function (GAF) (N = 194) (American Psychiatric Association, 1994)); neuropsychological assessment included the Wechsler Test of Adult Reading (WTAR) (N = 233) (as a proxy for pre-morbid IQ) (The Psychological Corporation, 2001), the Wechsler Adult Intelligence Scale (WAIS-III), using four subscales to determine verbal (similarities and vocabulary tests) (N = 228), performance (the block design and matrix reasoning tests) (N = 224) and full-scale IQ scores (N = 224) (Wechsler, 1997), the Hinting Task (N = 155) (Corcoran et al., 1995), Reading the Mind in the Eyes (N = 57) (Baron-Cohen et al., 2001), and the Internal, Personal and Situational Attributions Questionnaire (IPSAQ) (N = 148) (Kinderman and Bentall, 1996). Not all measures were included in both sample collections, so missing data rates varied by measure (e.g. parental age was only assessed in RPGI). 2.3. CNV calling CNV data were generated as part of the published Wellcome Trust Case Control Consortium 2 (WTCCC2) schizophrenia project. Genotyping was thoroughly described in Irish Schizophrenia Genomics Consortium, the Wellcome Trust Case Control Consortium (2012). Briefly, DNA was extracted from whole blood and genotypes from the Affymetrix 6.0 SNP array were generated at Sanger Institute (UK). For our local dataset, autosomal CNVs were called using the Birdsuite package (Korn et al., 2008), as described elsewhere (Morris et al., 2014). For this analysis we took a minimum CNV size of 30 kb, spanning at least

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five probes, to allow comparison with available data on CNVs in Autism Spectrum Disorders (ASD) (Pinto et al., 2010). Rare CNVs were defined as occurring in less than 1% of the full Irish WTCCC2 sample of 999 cases with schizophrenia, schizoaffective disorder or schizophreniform disorder (65.47% male), and 1104 controls (44.84% male). Control data was obtained from anonymous Irish Blood Donors from the Trinity Biobank (http://www.tcd.ie/IMM/trinity-biobank/), who were not currently taking certain prescribed medications, not remunerated, and assumed to be free from psychosis (N = 1047), and an additional group of controls as described in Donohoe et al. (2009) (N = 57). Known recurrent CNVs (Rees et al., 2013b) were not examined explicitly, and were only included in this study if they were rare (b1%). In order to narrow the analytic focus and target functional regions of the genome, CNVs were further limited to those that impacted genes. The CNVs reported in this study were not experimentally validated, but similar parameters have been used successfully in other studies (Pinto et al., 2010). 2.3.1. Gene list To permit statistical analysis of extremely rare events, these CNVs were aggregated using a candidate gene list, specifically a differentially brain expressed (BE) list. Briefly, the BE genes were defined by Raychaudhuri et al. (2010), using data from the Gene Expression Omnibus (Edgar et al., 2002), to have specific, differential expression in the brain and central nervous system as compared to other healthy body tissues. 3268 genes obtaining an expression p-value of b 0.01 were defined as differentially expressed for the analyses presented here. Consistent with a hypothesis of high penetrance for rare gene-disrupting events, the genetic trait of interest was defined as a CNV impacting one or more of the genes within the candidate list. 2.4. Statistical analyses In this case-only study the data were analyzed via linear and logistic regression examining the relationship between rare CNVs impacting BE genes and the phenotypes itemized above (Section 2.2). A pseudo case– control framework was employed wherein cases were defined as those individuals who had a CNV impacting a BE gene. In the models presented here, CNV status predicted the outcome phenotype, except in the case of parental age, where the parental age predicted CNV status. Preliminary linear and logistic regression analyses were performed unadjusted, and further analyses were adjusted for relevant covariates (age at assessment, sex, and sample collection, where applicable). The results presented here are not corrected for multiple comparisons, and all analyses were completed in SAS v9.2 (2010). 3. Results 3.1. Frequency of CNVs Table 2 shows the frequency of CNVs impacting genes in the differentially BE gene list by CNV type. Deletions and duplications are not mutually exclusive; that is, one individual can have both deletions and duplications in different parts of their genome. This results in overlapping, non-independent analyses for deletions, duplications and any CNVs. All subjects in the sample had CNVs (1 to 18 CNVs per person),

Table 2 Frequency of copy number variants (CNVs) by list and type (N = 386). Gene list

CNV type

Number of cases (%)

Any gene

Any Deletions Duplications Any Deletion Duplication

386 (100.00) 198 (51.30) 278 (72.02) 161 (41.71) 63 (16.32) 103 (26.68)

Brain expressed gene

257

with a mean of 1.7 CNVs per person (standard deviation (SD) 1.21, median 1, interquartile range (IQR) 1–2). There was a mean of 0.68 deletions (SD 1.09, median 1, IQR 0–1) and 1.01 duplications (SD 0.91, median 1, IQR 0–1) per person respectively. The mean number of genes impacted by CNVs was 3.9 (S.D. 7.84, range 1 to 129 per person). Forty-one percent of the sample had a CNV that impacted a gene (or genes) in the BE list. 3.2. Association analyses The results of the associations between CNVs, developmental and parental/family factors, clinical correlates, IQ and social cognition are summarized in Table 3. For categorical variables the percentage represents the proportion of the group of patients with that CNV type that were positive for the phenotype under investigation; for continuous variables, the mean of the group is shown. No statistically significant associations between CNVs impacting differentially BE genes and developmental delay, clinical severity, pre-morbid intelligence and intelligence quotient, nor social cognition phenotypes were found; however, associations were found for paternal age (see Table 4; deletions: OR = 1.08, p = 0.03, adj OR = 1.04, adj p = 0.46), and family history of mental illness (see Table 5; all CNVs: OR = 0.56, p = 0.04; adj OR = 0.53 adj p = 0.04; deletions: OR = 0.42, p = 0.01, adj OR = 0.33, adj p b 0.01). Note that the parental age analyses consider parental age as the independent variable because unlike other sub-phenotypes that result from CNVs, parental age may be a predictor of genetic mutations. The parental age analyses were adjusted for the opposite parent's age, the subject's age and sex. In Table 5, odds ratios that are lower than one indicate that those people with CNVs or deletions impacting differentially BE genes are less likely to have a positive family history of mental illness than those who do not have these variants. In summary, statistically significant associations between deletions in differentially BE genes predicting the phenotypes under investigation were found for family history of mental illness (decreased prevalence of all CNVs and deletions, unadjusted and adjusted) and for paternal age (increase in deletions only, unadjusted among those with later ages at birth of patient). 4. Discussion The two major findings from these analyses of the impact of CNVs in schizophrenia are: (1) BE CNVs are less common among those with a positive family history of psychiatric disorders; and (2) advanced paternal age is associated with deletions in BE genes. This apparent inverse association between family history and CNVs (principally deletions) lends support to theories of both rare de novo risk and to common inherited risk for schizophrenia. The differential effects of deletions and duplications demonstrate the importance of distinguishing between these CNV types in examining their potential mechanisms in the etiology of schizophrenia. To our knowledge, this is the first investigation of the association between genome-wide CNVs and risk factors and sub-phenotypic features of schizophrenia beyond cognitive function. The association between deletions in differentially BE genes and family history of mental illness is especially interesting because those cases with BE deletions were less likely to have a family history of mental illness. This association supports the conclusions of Kirov et al. (2012), who reported higher rates of family history negative cases in an investigation of de novo CNVs in schizophrenia. Another study further showed that the de novo CNV mutation rate in cases lacking a family history was eight times higher in cases than in controls (Xu et al., 2008). Our findings broadly confirm the latter findings; however, our data are not directly comparable, because we could not assess inheritance status of the CNVs in our study. This finding could indicate that there are two pathways to schizophrenia; one through direct genetic vulnerability indexed by a positive family history, and another through

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Table 3 Summary of findings on associations of brain expressed copy number variants with clinical phenotypes. Variable

Developmental, parental & family factors

Clinical characteristics & severity

Intelligence testing

Social cognition

Developmental delay (%) Maternal age (mean) Paternal age (mean) Family history (%) Alogia (%) Blunted affect (%) Delusions (%) Hallucinations (%) Age at onset (mean) GAF (mean) WTAR (mean) Verbal IQ (mean) Performance IQ (mean) Full scale IQ (mean) IPSAQ externalizing (%) IPSAQ personalizing (%) Mind in the Eyes (mean) Hint Task (mean)

Phenotype positive

Total

Brain expressed deletion

Brain expressed duplication

Any brain expressed CNV

% or mean

N

Yes

No

Yes

No

Yes

No

15.34% 30.09 34.36 71.25% 53.59% 68.61% 78.25% 52.22% 23.98 60.93 96.35 92.41 89.12 90.4 33.11% 43.92% 22.3 14.55

189 130 135 240 362 360 354 360 345 194 233 228 224 224 148 148 57 155

14.29% 31.80 38.001 55.00%1,2 56.90% 71.93% 73.68% 53.45% 24.02 58.61 93.23 89.08 85.11 85.71 33.33% 47.62% 19.40 14.54

15.53% 29.78 33.79 74.50% 52.96% 67.99% 79.12% 51.99% 23.97 61.46 96.98 93.07 89.94 91.36 33.07 43.31 22.91 14.55

17.65% 29.35 33.18 67.16% 52.69% 66.3% 74.19% 50.00% 23.25 59.90 95.97 93.24 90.56 91.53 33.33% 43.59% 23.71 15.45

14.49% 30.39 34.81 72.83% 53.90% 69.40% 79.69% 52.99% 24.25 61.30 96.50 92.09 88.56 89.97 33.03% 44.04% 21.84 14.21

15.79% 30.09 34.74 64.42%1,2 54.42% 68.97% 73.97% 52.05% 23.57 59.41 95.05 91.61 88.44 89.37 30.36% 42.86% 22.00 15.03

15.04% 30.09 34.10 76.47% 53.02% 68.37% 81.25% 52.34% 24.28 62.11 97.30 92.98 89.61 91.16 34.78% 44.57% 22.50 14.23

Statistically significant findings (α = 0.05) in unadjusted models are noted with a 1, and statistically significant findings in models adjusted for age at assessment, sex, and sample collection (where applicable) are noted with a 2. Parental age analyses were also adjusted for the opposite parent's age. The sample size varies with each measure due to different levels of phenotype missingness. GAF = Global Assessment of Function, WTAR = Wechsler Test of Adult Reading, IQ = intelligence quotient, IPSAQ = Internal, Personal and Situational Attributions Questionnaire.

environmental influences that disrupt genes or pathways underlying the phenotypic manifestations of schizophrenia, or some combination of both. Future studies that discriminate genetic markers and sub-phenotypes in sporadic and familial cases of schizophrenia could address this hypothesis. The association between paternal age and deletions in BE genes in this sample adds support to the recent findings regarding general effects of paternal age on de novo mutations (Kong et al., 2012), specifically in a sample of people with schizophrenia. Associations between advanced parental age and chromosomal aberrations have been wellestablished, and numerous studies have implicated parental age in the etiology of schizophrenia (e.g. Malaspina et al. (2008); Miller et al. (2011a,b)). This work further establishes a link between deletions and paternal age in schizophrenia. Contrary to other studies (LopezCastroman et al., 2010), we found no association with advanced maternal age, and the paternal age association was no longer statistically significant after we controlled for maternal age. However, the advanced parental age effect could also be non-specific because it has been shown to influence a wide range of neurodevelopmental disorders such as ASD (Frans et al., 2013; Lampi et al., 2013); moreover maternal grandfather's age has also been implicated in schizophrenia, which would not support the theory of transmitted de novo mutations in the development of schizophrenia (Frans et al., 2011). It is important to replicate this finding in an independent sample, and to examine the joint influence of maternal age, which has also been shown to confer increased risk of neurodevelopmental disorders (Saha et al., 2009). In addition, parental age is only one of numerous risk factors that have been implicated in

schizophrenia such as pre- and perinatal factors, viral exposures and nutritional deficiencies (Brown, 2011). The main strength of this study is the large sample of cases with rigorous clinical assessment beyond diagnosis, which enabled the examination of sub-phenotypes and clinical characteristics. An important limitation of this study is the lack of consistent data on family history and paternal age across the two clinical samples, which precluded our ability to investigate the joint influence of advanced paternal age and family history on the development of CNVs. This underscores the importance of harmonization of sub-phenotypic measures across genetic studies as future projects investigate links between genetic factors and sub-phenotypes of schizophrenia. Another possible influence on the results of this study is application of the candidate gene list to group the CNV carriers for meaningful analysis, as any candidate list may include irrelevant genes, but may also omit important genes that are relevant to the disorder. However, this challenge may be diminished by the systematic evaluation of gene-expression data used to generate the differentially BE gene list. Finally, the small size and use of computational CNV calling without experimental validation are additional weakness of the study. In conclusion, these analyses examine the associations between types of CNVs and sub-phenotypes of schizophrenia. The findings documenting the associations between BE deletions and parental age, and lack of family history may have important implications for defining the sources of heterogeneity of genetic and environmental influences on schizophrenia. This suggests that there is a difference between sporadic cases and those cases with a family history. Our results should

Table 4 Logistic regression results for paternal age at birth predicting differentially brain expressed genes, unadjusted and adjusted for age, sex and maternal age at birth (statistically significant findings at α = 0.05 noted with a 1).

Table 5 Logistic regression results for differentially brain expressed genes predicting family history of mental illness, unadjusted and adjusted for age, sex and sample collection (statistically significant findings at α = 0.05 noted with a 1).

Paternal age

Unadjusted (N = 135) OR

All BE CNVs BE deletions BE duplications

1.01 1.08 0.97

LCL 0.97 1.01 0.92

UCL 1.06 1.16 1.02

2

Χ

0.25 4.99 1.37

Adjusted (N = 124) p 0.61 0.031 0.24

OR 0.99 1.04 0.96

LCL 0.92 0.94 0.88

UCL 1.06 1.14 1.04

Family history 2

Χ

0.09 0.55 1.12

p 0.76 0.46 0.29

Unadjusted (N = 240) OR

All BE CNVs BE deletions BE duplications

0.56 0.42 0.76

LCL 0.32 0.21 0.42

UCL 0.98 0.84 1.40

2

Χ

4.13 5.97 0.76

Adjusted (N = 209) p 1

0.04 0.011 0.38

OR

LCL

UCL

Χ2

0.53 0.33 0.84

0.29 0.15 0.44

0.98 0.71 1.63

4.16 0.041 8.01 b0.011 0.26 0.61

p

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be followed up in a larger sample with a more comprehensive assessment of family history. Future studies should explore the interrelationships of CNV deletions with these risk factors and subphenotypes and their specific and non-specific influences on schizophrenia vulnerability. Role of funding source Funding for this study was provided by the Irish Research Council, the Wellcome Trust, (072894/Z/03/Z, 090532/Z/09/Z and 075491/Z/04/B) and Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z) Science Foundation Ireland (08/IN.1/B1916), the National Institute of Mental Health (MH 41953 and MH083094) and the Meath Foundation (Ireland). Contributors AKM designed the study, managed the literature searches, undertook the statistical analysis, and wrote the first draft of the manuscript. AC, LG, and RS contributed to the design of the study, and revised the manuscript. PC completed the CNV calling. RS, EH and RJLA contributed to the statistical analytic plan and analyses. SM abstracted the family history data. EK and HAS collated and interpreted the symptom severity data. AH completed the neuropsychological evaluation and advised on interpretation of the assessments. All authors contributed to and have approved the final manuscript. Conflict of interest All authors disclose that they have no conflict of interest in relation to the publication of this manuscript and its contents. Acknowledgments The authors sincerely thank all of the patients who contributed to this study and all of the staff who facilitated their participation. The authors would like to thank Derek W. Morris for managing the Neuropsychiatric Genetics Research Group Laboratory. Neuropsychological assessment was completed under the direction of Prof. Gary Donohoe. We acknowledge use of the Trinity Biobank sample from the Irish Blood Transfusion Service, the Trinity Centre for High Performance Computing, and the Wellcome Trust Case Control Consortium 2.

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