Effects of a Novel Schizophrenia Risk Variant Rs7914558 at CNNM2 On

The British Journal of Psychiatry (2014) 204, 115–121. doi: 10.1192/bjp.bp.113.131359

Effects of a novel schizophrenia risk variant rs7914558 at CNNM2 on brain structure and attributional style Emma Jane Rose, April Hargreaves, Derek Morris, Ciara Fahey, Daniela Tropea, Elizabeth Cummings, Carlo Caltagirone, Paola Bossu` , Chiara Chiapponi, Fabrizio Piras, Gianfranco Spalletta, Michael Gill, Aiden Corvin and Gary Donohoe

Background A single nucleotide polymorphism (rs7914558) within the attributional style), such that risk status was associated with cyclin M2 (CNNM2) gene was recently identified as a reduced self-serving bias across groups (GG4AG4AA, common risk variant for schizophrenia. The mechanism by P50.05). Using voxel-based morphometry to investigate which CNNM2 confers risk is unknown. neuroanatomical regions putatively supporting social cognition, risk carriers had relatively increased grey matter Aims volume in the right temporal pole and right anterior cingulate To determine the impact of the rs7914558 risk ‘A’ allele on cortex (Pcorrected50.05) in the Irish healthy controls sample; measures of neurocognition, social cognition and brain neuroanatomical associations between CNNM2 and grey structure. matter volume in anterior cingulate cortex were also observed in the Italian schizophrenia and healthy controls Method samples. Patients with schizophrenia (n = 400) and healthy controls (n = 160) completed measures of neuropsychological function Conclusions and social cognition. Structural magnetic resonance imaging Although the biological role of CNNM2 in schizophrenia data were also acquired from an overlapping sample of Irish remains unknown, these data suggest that this CNNM2 risk healthy controls (n = 159) and an independent sample of variant rs7914558 may have an impact on neural systems Italian patients (n = 82) and healthy controls (n = 39). relevant to social cognition. How such effects may mediate the relationship between genotype and disease risk remains Results to be established. No effects of genotype on neuropsychological test performance were observed. However, a dosage effect of Declaration of interest the risk allele was found for an index of social cognition (i.e. None.

Schizophrenia is a major cause of global disability with a lifetime Approaches to elucidating the biological role of novel risk of approximately 1%. Perhaps because of its complex genetics, schizophrenia genetic susceptibility factors include delineating identifying the genetic variation responsible for disease risk has their effects on ‘intermediate’ or ‘endo-’phenotypes (i.e. discrete proven difficult, despite schizophrenia’s substantial heritability aspects of behaviour or brain structure and function that mediate (~80%).1–4 Genome-wide association studies (GWAS) have genetic effects on the broader phenotype).13–15 Neurocognitive identified a small number (~12) of common risk variants for intermediate phenotypes widely used in schizophrenia genetics schizophrenia.5,6 Recently, the Psychiatric GWAS Consortium include general cognitive ability, long-term and working memory, ‘mega-analysis’ of over 51 000 cases and controls identified seven and attentional control. In addition to these more ‘traditional’ risk loci, including five novel findings.7 Of the novel loci neuropsychological phenotypes, social cognition, which includes identified, one of these (on 10q 34.33) was unique in harbouring functions such as emotional recognition and perception, theory two genome-wide significant variants, including the rs7914558 of mind and attributional style, has been a recent focus of single nucleotide polymorphism (SNP) located within an intron schizophrenia gene studies. Measures of social cognition predict of the cyclin M2 gene (CNNM2, OMIM: 607803 (this gene was social and occupational function in schizophrenia independently previously known as ‘ancient conserved domain protein 2’, of neurocognitive task performance and may mediate the effects ACDP2)). Since, CNNM2 has been associated with both increased of neurocognition on functional outcome.16 Moreover, there are risk for schizophrenia in a family-based study and increased risk data to suggest that social cognitive functions may be critically for psychiatric disorders generally.8,9 mediated by genetic factors (see Skuse & Gallagher for a review17). The cyclin M2 gene is implicated in the transport of Therefore, studying the effects of schizophrenia risk variants on magnesium (Mg2+), which plays a crucial role in many biological social cognition may index aspects of disability distinct from processes, including neuronal transmission. Despite the extensive traditional neurocognitive tasks. For example, although ZNF804A evidence for unique mammalian Mg2+ transporters, CNNM2 is has not been reliably associated with general cognitive deficits, it one of only a few proteins biochemically identified as fulfilling this has been associated with poorer performance on indices of social role.10 Sequence variation within CNNM2 increases hypertension cognition, including cortical activation during a theory of mind risk and alters serum Mg2+ blood levels, a risk factor for a variety task and increased biases in social attributions.18,19 Our study of vascular diseases.11 Furthermore, CNNM2 mutations are considers the effects of SNP rs7914558 at CNNM2 on indices of associated with hypomagnesaemia risk.12 Whether or how this neurocognition and social cognition in both patients and healthy biological role in Mg2+ regulation is relevant to CNNM2’s individuals. We focused solely on rs7914558 because the frequency association with increased risk for schizophrenia is uncertain. of the second schizophrenia-associated SNP at this locus

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(rs11191580, located within the neighbouring gene, NT5C2, with neurological, psychiatric or other major medical health problems, a risk allele frequency of 0.91) yielded insufficient power to and had no other contraindication for magnetic resonance examine its neurocognitive effects in our samples. We tested the imaging (MRI). In accordance with local ethics committee hypothesis that the increased risk for schizophrenia associated approval, written, informed consent was obtained for the use of with rs7914558 was mediated via deleterious effects on both these sMRI data and participants provided a saliva sample (Oragene aspects of cognition. Furthermore, we sought to investigate the DNA self-collection kits: DNA Genotek, Ontario, Canada) for impact of rs7914558 on brain structure (i.e. grey and white matter genetics analysis. Individuals (n = 159) were selected for imaging volume), via delineating the mediating impact of this SNP on analysis based on sMRI data quality and successful rs7914558 brain structure in regions underlying associated functions. genotyping. The majority were recorded as ‘Caucasian Irish’ or ‘other European’ (n = 129). Lineage was not documented in 30 Method participants; however, the relative homogeneity of the local population (92% of Dublin city’s population are of ‘Caucasian Neuropsychological study Irish’ background; www.cso.ie/census) suggests that these were We assessed cognition in 400 clinically stable patients with a also likely to be Caucasian Irish. Furthermore, this subgroup did DSM-IV diagnosis20 of schizophrenia or schizoaffective disorder not differ from other imaging participants in gender ratio, age and 160 healthy individuals (online Table DS1). Participants were and years of education or minor allele frequency. recruited from five sites across Ireland, and ethics approval was obtained from local ethics committees. Written informed consent Italian imaging sample was obtained from all participants. Participants were aged 18–65 years and had confirmed Irish lineage (i.e. Irish grandparents on Patients with a confirmed diagnosis of schizophrenia (n = 66) were both sides of the family). Healthy individuals had no history of recruited consecutively from two out-patient clinics in central significant neurological or psychiatric conditions and no first- Italy. Patient exclusion criteria included a history of: (a) DSM-IV degree relatives with a history of psychosis. These samples Axis I or II diagnosis except schizophrenia; (b) traumatic head represent a subset of the Irish samples included in the original injury with a loss of consciousness; (c) epilepsy, seizures or other study (Ripke et al7) identifying CNNM2 as a risk variant. relevant neurological or medical illness (for example cerebro- vascular disease); and/or (d) substance misuse in the 6 months Neuropsychological test measures pre-participation. Healthy participants (n = 37) were recruited from the same geographical area as patients and screened for Participants completed a neuropsychological battery that included current or past diagnosis of any DSM-IV Axis I or II disorder measures of IQ (i.e. Wechsler Adult Intelligence Scale (WAIS-III)21), using the SCID-I and SCID-II.28,29 In addition to the exclusion working and long-term memory (i.e. letter number sequencing criteria outlined for patients, individuals with a history of and logical memory subtests from the Wechsler Memory Scale schizophrenia or any other psychiatric disorder diagnosis among (WMS-III)22) and the Cambridge Neuropsychological Battery first-degree relatives were also excluded. All participants were of (CANTAB)23 spatial working memory task and attentional control White Italian ancestry. (i.e. the CANTAB intradimensional/extradimensional set-shifting task). sMRI parameters Social cognition measures Irish structural images were acquired on a Philips Intera Achieva Social cognitive function was assessed using the: (a) Hinting 3T MRI system, and involved the acquisition of a 180 slice 24 25 Task; (b) Reading the Mind in the Eyes (‘Eyes’) Task; and T1-weighted image using a turbo field echo gradient echo pulse (c) Internal, Personal and Situational Attributions Questionnaire sequence (repetition time TR) = 8.4 ms; echo time (TE) = 3.8 ms; (IPSAQ).26 The Hinting and Eyes tasks target theory of mind flip angle, 88; slice thickness, 0.9 mm; voxel size, 0.9 mm3; 180 processes. The IPSAQ, on the other hand, is concerned with slices; duration, 6 min). The Italian sMRI was conducted using a attributional style and uses subscale scores that delineate the 1.5T whole-body Siemens Vision Magnetom scanner and involved extent of internal and external attributions for hypothetical events the acquisition of T1-weighted images using a magnetisation- to calculate two indices of cognitive bias, i.e. externalising bias and prepared rapid gradient echo (MPRAGE) sequence (TR = 11.4 ms; personalising bias. Externalising bias scores index an individual’s TE = 4.4 ms; flip angle, 1518; slice thickness, 1 mm, no interslice tendency to attribute more positive than negative events to gap; voxel size, 1 mm3). oneself. As this bias might be expected to have a positive effect on one’s sense of self it is often described as a ‘self-serving’ bias. Genotyping Personalising bias is indicative of the tendency to credit external Genetics analysis was carried out using DNA extracted from blood attributions to personal (i.e. actions or omissions of another) as or saliva samples. The rs7914558 SNP was genotyped using a opposed to situational factors (i.e. circumstance or chance). These Taqman SNP genotyping assay on a 7900HT sequence detection data were collected as part of an ongoing multisite project; system. The call rate for the Taqman genotyping was 495% consequently all assessments were not available in the entire and the samples were in Hardy–Weinberg equilibrium cohort (IPSAQ: n = 387 (256 patients and 131 controls); Hinting (P40.05). Along with these samples a small number of HapMap Task: n = 408 (276 patients and 132 controls); Eyes Task: n = 201 CEU DNA samples (www.hapmap.org) were genotyped for (151 patients and 50 controls)). rs7914558 for quality control purposes and were found to be Structural imaging study concordant with available HapMap data for this SNP. Irish imaging sample Statistical analyses Structural magnetic resonance imaging (sMRI) data were acquired from healthy individuals as part of the Trinity College Institute for Neuropsychological data Neuroscience (TCIN) biobank project (see Rose et al27 for details). Behavioural data were analysed in SPSS (Release 16 for Windows Participants were right-handed and had no history of significant PC). For each measure, univariate ANOVA considered both group

116 Effects of a schizophrenia risk variant on brain structure and attributional style

(patient v. control) and rs7914558 genotype (AA v. AG. v. GG)as The effects of CNNM2 rs7914558 on cognition fixed effects, and included age, gender and IQ as covariates. General cognition

Voxel-based morphometry (VBM)30 Although patients performed worse than controls on all measures of general cognitive function (P40.001), there was no genotype- Structural MRI analysis was performed within SPM5 (www.fil.ion. related variability on indices of IQ, memory or attentional control ucl.ac.uk/spm) running under Matlab (v7.8; The MathWorks, (online Table DS2). Cambridge, UK) and utilised the VBM toolbox (v5.1, http:// dbm.neuro.uni-hen.de/vbm). Volumes passing initial data Social cognition quality control (i.e. visual inspection for scanner artifacts and gross anatomical abnormalities) were segmented into grey matter, In tests of theory of mind, patients performed significantly worse white matter and cerebrospinal fluid, without tissue priors and than healthy participants on the Eyes Task (F(2,121) = 8.11, 5 using a Hidden Markov Random Field weighting of 0.15. Seg- P 0.05) but not the Hinting Task. Performance was unaffected mented images were normalised using DARTEL,31 in which grey by genotype for both measures. In contrast, differences in matter and white matter templates were created using standard attributional style as measured by the IPSAQ scores were observed parameters. Jacobian scaled (‘modulated’) warped tissue classes between genotype groups. Specifically, we observed a main effect were subsequently created for both grey matter and white matter of genotype for externalising bias scores, in the absence of any for each participant and the resultant images smoothed with an differences between patients and controls (Fig. 1). This effect 8mm3 Gaussian kernel. was because of the linear impact of genotype on externalising 5 Structural MRI data were analysed using full factorial ANOVA bias (F(1,389) = 10.14, P 0.001), such that the non-risk ‘GG’ models in SPM5. Separate analyses were carried out for the Irish individuals scored highest (i.e. had greater externalising bias) on and Italian imaging samples. Although the imaging sequences this subscale, followed by ‘AG’ and then ‘AA’ individuals 5 used at each site would be expected to produce T -weighted (P 0.05). Although personalising bias scores differed between 1 5 images that were relatively comparable, methodological concerns patients and controls (F(2,375) = 6.12, P 0.05), a main effect of related to differences in the imaging parameters precluded genotype on this index was not observed. combining the data-sets for a single analysis. Modelling included The effects of CNNM2 rs7914558 on brain volume genotype as a fixed factor with either three (AA v. AG. v. GG) or two (i.e. risk homozygotes v. non-risk carriers) levels plus Irish sMRI/VBM sample analysis of grey matter age and gender as covariates. Although the three-group analysis There were no effects of genotype that survived correction for was designed to replicate modelling in our neuropsychological multiple comparisons at the whole brain level in either the analyses, we also carried out the two-group analysis in order to three (AA v. AG v. GG) or two (AA v. AG/GG) groups. Conversely, address potential issues in accurately modelling the data because post hoc anatomical ROI analyses showed a main effect of of the relatively small number of individuals in the homozygous rs7914558 status (Fig. 2). In the three-group analysis, there was a minor allele group (i.e. ‘GG’). To account for normal variation main effect of genotype in the right temporal pole/BA38 (Montreal in brain volume, total volume was also included as a covariate. Neurological Institute (MNI): 40, 16, 733; F(2,154) = 7.88, number Analysis of the Italian data-set also included group (patient v. of voxels/cluster extent (KE) = 217, Pcorrected for multiple control) as a fixed factor. In addition to preliminary whole brain comparisons 50.05) and anterior cingulate cortex/BA32 (MNI: analyses of grey matter and white matter, based on the outcomes 10, 39, 19; F(2,154) = 7.26, KE = 50, Pcorrected50.05). In both of our cognitive analyses (see Results) post hoc region-of-interest clusters, post hoc t-tests indicated that this main effect was because (ROI) analyses considering grey matter variation in regions of relatively greater grey matter volume in homozygous ‘A’ that support social cognitive functions (i.e. anterior cingulate individuals compared with heterozygotes (t(154) = 3.95 and cortex/BA32; superior temporal sulcus/BA22; and temporal t(154) = 3.77, Pcorrected50.05). Similarly, the two-group analysis pole/BA38)32–34 were also carried out. (AA v. AG/GG) was also indicative of a significant main effect Since the non-uniform smoothness of VBM data can influence of genotype in the right temporal pole (MNI: 39, 15, 733; the interpretation of these types of analysis,30,35 prior to the determination of significance a non-stationarity cluster extent 3.00 – correction utilising the random field theory version of cluster * inference under non-stationarity36 was implemented using the NS toolbox for SPM5 (http://fmri.wfubmc.edu/cms/NS-General). 2.00 – Results * The effects of CNNM2 rs7914558 on demographic and clinical measures 1.00 – There was no genotype-related variability in gender ratio, age or years of education in any of the samples. Moreover, in patient Mean externalising bias score samples, rs7914558 was not associated with age at onset, illness duration or medication (i.e. chlorpromazine equivalents) and there was no difference in genotype frequency between patients 0.00 – GG AG AA and controls. There was, however, a significant difference in age rs7914558 genotype and years of education between patients and healthy participants, such that patients were older (Italian VBM sample: Fig. 1 Main effect of rs7914558 genotype on Internal, Personal F = 10.79, P = 0.001; Irish neurocognition sample: F = (1,97) (1,537) and Situational Attributions Questionnaire (IPSAQ) externalising 4.86, P50.05) and had less years of education (Italian bias score. VBM sample: F(1,81) = 21.59, P50.001; neurocognition sample: Error bars show +1 standard error. *P50.05. F(1,537) = 105.61, P50.001) (Table DS1).

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(a)

02468

(b)

04 8 1216

AG/GG AA T

0.0 0.9 1.8 2.7 3.6

Fig. 2 The impact of rs7914558 genotype on grey matter volume in post hoc anatomical region-of-interest (ROI) analysis in regions supporting social cognitive function (anterior cingulate cortex, superior temporal gyrus and temporal pole).

(a) Irish healthy individuals (AA v. AG v. GG); (b) Irish healthy individuals (AA v. AG/GG); and (c) Italian patients and healthy individuals (AA v. AG/GG) (i) 3-plane view of grey matter volume differences, (ii) statistical parametric mapping (SPM) contrast. Note: clusters showing an impact of genotype are shown rendered on the ch2better brain template from MRIcron (www.nitrc.org/projects/mricron).

F(1,155) = 14.92, KE = 948, Pcorrected50.05) and right anterior function of genotype, irrespective of whether rs7914558 was cingulate cortex (MNI: 13, 41, 18; F(1,155) = 11.47, KE = 118, considered as a fixed factor with three or two levels. Pcorrected50.05). Again this was because of relatively greater volume in ‘A’ homozygous individuals (AA v. AG/GG: Italian sMRI/VBM sample analysis of grey matter t(155) = 3.39 and t(155) = 3.86, Pcorrected50.05). Both two- and three-genotype group analyses were indicative of a main effect of group (Pcorrected<0.05) on grey matter volume in a Irish sMRI/VBM sample analysis of white matter wide range of regions (online Table DS3). Whole brain analysis Following correction for multiple comparisons at the whole brain modelling genotype as a fixed factor with two or three levels failed level there were no white matter regions where volume varied as a to note effects of genotype on grey matter volume (either main

118 Effects of a schizophrenia risk variant on brain structure and attributional style

effects or interactions with group). Similarly, post hoc ROI example increased depressive symptoms.40 However, a post hoc analyses that considered all three genotype groups did not reveal comparison of symptom severity scores in our patients (i.e. Scale any impact of genotype on grey matter. However, when ‘A’ for Assessment of Positive Symptoms (SAPS) and Scale for homozygotes were compared with the combined group of Assessment of Negative Symptoms (SANS) scores; collected and hetero- and homozygous carriers of the rs7491558 non-risk ‘G’ analysed as previously reported)41 revealed no association between allele in ROI analyses, there was a significant interaction between clinical profiles (positive and negative symptoms) and rs7914558. group and genotype in the left anterior cingulate (MNI: 77, 7, 52; This result combined with the observation that CNNM2’s effects t(97) = 3.80, KE = 135, Pcorrected50.05). Within this cluster, patients on attributional style were consistent across patients and controls who were homozygous for the risk ‘A’ allele showed reduced grey suggests that variability in attributional bias associated with matter volume (t(97) =3.49, KE = 101, Pcorrected50.05), whereas CNNM2 is not necessarily indicative of clinical pathology. Rather, control homozygous risk carriers had greater grey matter volume although CNNM2 may be associated with variation in how social (t(97) = 3.79, KE = 69, Pcorrected50.05), compared with carriers of attributions are formed, these effects may not be deleterious. the ‘G’ allele. Further studies that include measures more sensitive to depressive symptomatology (for example the Beck Depression Inventory42 or Hamilton Rating Scale for Depression43) will be helpful in Italian sMRI/VBM sample analysis of white matter elucidating this hypothesis further. As with the grey matter analyses, there was a main effect of group on white matter volume in a number of clusters (Pcorrected50.05; Table DS3), but no impact of rs7149558 at CNNM2 genotype on CNNM2 rs7914558 and brain structure white matter volume in either three- or two-genotype group Anatomical ROI analyses of sMRI data were indicative of whole brain analyses. CNNM2-related variability in grey matter volume in putative social cognition regions (i.e. right anterior cingulate cortex and Discussion temporal pole) in both imaging samples. In the context of evidence of CNNM2’s influence on attributional style reported This study sought to ascertain the effects of the putative above, these findings are intriguing, although the mechanism by schizophrenia risk variant rs7914558 at CNNM2 on measures of which these behavioural and volumetric findings relate to each neuropsychological performance, social cognition and brain other are unclear. A functional imaging investigation, which volume. In Irish patients and healthy participants the risk ‘A’ allele may speak to this issue, found that activity in dorsal anterior was associated with response variation on a measure of cingulate cortex was associated with self-serving bias in causal 40 attributional style, in the absence of genotype-related effects on attribution. This study is not designed to establish a causal neuropsychological performance. Specifically, carriers of the risk relationship between brain structure and function in the anterior allele showed a lower externalising bias than other genotype cingulate cortex, or between this genotype and brain function. groups. The same CNNM2 variant was also associated with However, dorsal anterior cingulate cortex volume has been variation in grey matter volume in putative ‘social cognition’ consistently associated with cognitive reappraisal in emotion 44 regions (i.e. anterior cingulate cortex and temporal pole), regulation, and altered volume has consistently been reported compared with non-risk individuals (AG and GG). Collectively, in psychiatric disorders involving emotional dysregulation 44–46 these data suggest that CNNM2 exerts significant but subtle effects (including depression). One possibility therefore is that the on social cognition and its neural underpinnings. CNNM2 risk variant rs7914558 studied here may increase risk for schizophrenia and changes in attributional style via an impact on emotional processing mediated by alterations in grey matter CNNM2 rs7914558 and social cognition volume in regions regulating these functions, such as the anterior Altered attributional style is commonly reported in psychiatric cingulate cortex. conditions. Instead of representing an impairment in social Although this notion is intriguing, it is curious that distinct cognition such as mental state decoding or mental state reasoning, genotype effects on grey matter were noted in healthy individuals attributional style refers instead to an individual’s relatively stable v. patients. That similar effects were seen in Irish and Italian tendency towards making inferences about the causes of social controls (for example AA4AG/GG in anterior cingulate cortex), events as related to self (something I am responsible for) others despite the latter sample being relatively underpowered, suggests (something another person intended) or situation (contextual that in unaffected individuals the rs7914558 risk allele is reliably factors). In paranoia, attributional style is characterised by the associated with increased volume. By comparison, patients who tendency to attribute negative events to external, global and stable were homozygous carriers of the risk allele had relatively reduced factors.37 In depression, attributional style is often characterised grey matter volume in this same region. This finding was not by a tendency to attribute negative events internally.38 Such hypothesised in our study, and may be the result of multiple tendencies towards bias in social attributions and how they factors, including CNNM2 interactions with other schizophrenia contribute to psychiatric disease phenotypes have been widely genetic risk factors and/or environmental influences that are studied. For example, whereas the tendency to attribute external specific to the disease phenotype. Furthermore, observed effects negative events to people rather than situations (i.e. personalising of CNNM2 did not indicate neurocognitive deficits, but rather bias) has been associated with paranoia,39 a predisposition variability in attributional style: whether these effects should be towards attributing positive events to internal factors and attribute associated with relative increases or decreases in brain volume is negative events to external factors (i.e. externalising bias/self- unclear. To our knowledge, this is the first neurocognitive serving bias) is more typical of healthy individuals than of patients investigation of this variant to be reported, and these inter- with paranoid or depressive symptoms. In the present study, we pretations are inevitably speculative; further investigation of the observed an association between CNNM2 and variation in influence of CNNM2 genotype on anterior cingulate cortex externalising bias scores, suggesting that risk allele carriers were less volume and function is clearly warranted to more fully ascertain inclined towards such self-serving attributions. These differences its effects. In this context it is interesting to note precedents for might be expected to reflect differences in symptomatology, for differences between patients and controls with regard to other

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schizophrenia risk variants (for example ZNF804A, DARRP32), relevant to social cognition. Whether the effects observed here at which have since been replicated. the level of behaviour and cortical volumes related to social cognition represent pleiotropic effects, or may mediate the Bioinformatic analysis of rs7914558 relationship between genotype and increased disease risk remains to be established. The SNP under consideration here, rs7914558, is located on chromosome 10q24 in a large region of high and complex linkage Emma Jane Rose, PhD, Neuropsychiatric Genetics Group, Department of Psychiatry disequilibrium. Analysis of HapMap Phase II + III data (release 28 and Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland, and NCBI build 36) indicates that rs7914558, positioned at TSTPP/MEGEH, RTI International, Baltimore, Maryland, USA; April Hargreaves, MSc, 2 Derek Morris, PhD, Ciara Fahey, MSc, Daniela Tropea, PhD, Elizabeth 104 765 898(hg18), is in high linkage disequilibrium (r 40.8) Cummings, MD, Neuropsychiatric Genetics Group, Department of Psychiatry, Trinity with 42 other SNPs in this region (including 22 SNPs at r2 = 1). College Dublin, Ireland; Carlo Caltagirone, MD, IRCCS Santa Lucia Foundation, Department of Clinical and Behavioural Neurology and Department of Neuroscience, These SNPs span a 322 kb region from rs11191438 at position Tor Vergata University, Rome, Italy; Paola Bossu` , PhD, Chiara Chiapponi, PhD, 104 627 854 to rs4307650 at position 104 949 842. This region Fabrizio Piras, PhD, Gianfranco Spalletta, MD, PhD, IRCCS Santa Lucia Foundation, Department of Clinical and Behavioural Neurology, Rome, Italy; contains four RefSeq genes: C10orf32, AS3MT, CNNM2 and Michael Gill, MD, Aiden Corvin, MD, Neuropsychiatric Genetics Group, Department NT5C2. Analysis of SNP6gene expression databases including of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland; Gary Donohoe, PhD, Neuropsychiatric Genetics Group, Department of brain (see online supplement) does not identify a direct functional Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, link between rs7914558 (or proxy) and altered expression of any and School of Psychology, National University of Ireland, Galway, Ireland

of the four genes in this region; due to long-range linkage Correspondence: Gary Donohoe, School of Psyhology, National University disequilibrium in this region all four remain candidate of Ireland, Galway, Ireland. Email: [email protected]

schizophrenia risk genes. It is worth noting that new data First received 25 Apr 2013, accepted 18 Jul 2013 identify a putative functional interaction between CNNM2 and ZNF804A, an established schizophrenia risk gene. Hill and colleagues47 recently reported that a reduction in CNNM2 expression was observed following small interfering RNA (siRNA) Funding

knockdown of the ZNF804A protein in human neural progenitor The Science Foundation Ireland, the Health Research Board (Ireland) and the Italian Ministry cells. This is interesting because ZNF804A is the only other gene – of Health provided financial support for these studies. schizophrenia-implicated or otherwise – to have been associated with variation in social attributional styles, the phenotype Acknowledgements implicated in our study.19 We thank our collaborators in the TCIN Biobank Project (Drs Ian H. Robertson, Hugh Garavan, John O’Doherty, Fiona N. Newell, Jane McGrath, Arun Bokde and their staff and Study limitations students) and those who volunteered to participate in these studies. We are also grateful to Professors John Waddington, Ted Dinan, Eadbhard O’Callaghan and Kieran Murphy, and Interpretation of these data is limited by a number of factors. First, Dr F. Anthony O’Neil for their role in the recruitment of the Irish neuropsychological patient little is known regarding the functional pathways of the rs7914558 sample. The authors also acknowledge data management support from the Trinity Centre for High Performance Computing. The data management system used for this work was SNP considered here, either in general or with regard to BC|SNPmax v. 3.5-121 (Biocomputing Platforms Ltd, Finland). schizophrenia specifically. Certainly, our understanding of how or why this particular variant contributes to social cognitive References processes involved in attributional style and variability in brain

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120 Effects of a schizophrenia risk variant on brain structure and attributional style

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121 The British Journal of Psychiatry 1. doi: 10.1192/bjp.bp.113.131359

Data supplement 0.001 0.05 0.001 5 = 0.001 5 5 P tients P P P patients patients patients 4 4 5 = 4.86, = 10.79, = 21.59, = 105.61, (1,537) (1,97) (1,81) F (1,537) F F F AA AG rs7914558 GG Total sample 159103 66 21 37 23 17 6 67 45 23 22 71 35 26 9 ns 560 400 160 89 66 23 258 174 84 213 160 53 Total Patients Controls Total Patients Controls Total Patients Controls Total Patients Controls Comparison Participant demographics Years of education, mean (s.d.) 10.98 (4.02) 15.38 (3.20) 11.00 (3.93) 15.60 (4.93) 9.65 (3.14) 15.28 (3.10) 12.58 (4.59) 15.50 (2.26) Controls Age at onset, years: meanDuration, (s.d.) years: mean (s.d.)CPZeq, mg/day: mean (s.d) 24.11 (8.34) 18.18 (10.41) 507.64 (606.42) n/a n/a n/a 24.00 (6.24) 517.50 15.12 (297.92) (8.85) n/a n/a 23.05 611.95 (7.55) (912.49) 19.55 n/a (10.21) n/a 384.05 (309.31) 25.42 (10.69) n/a n/a 19.16 (11.76) n/a ns ns Years of education, mean (s.d.) 13.09 (2.55) 15.98 (2.42) 13.86 (2.47) 16.13 (2.63) 12.95 (2.45) 16.17 (2.60) 13.11 (2.71) 15.61(2.00) Controls Age at onset, years: meanCPZeq, (s.d.) mg/day: mean (s.d)n 22.79 (7.32) 582.84 (543.76) n/a n/a 21.57 (5.52) 549.22 (478.55) n/a n/a 570.17 (511.79) 23.28 (8.22) n/a n/a 597.11 (618.99) 21.87 (6.03) n/a n/a ns ns Gender, F:MAge, years: mean (s.d.)Years of education, mean (s.d.)Gender, F:MAge, years: mean (s.d.) 17.00 (2.30) 27.86 (12.42) 40.67 (11.67) 32.28 (12.58) 87:72 25:41 38.41 (10.83) 18:19 31.00 (17.54) 17.06 (3.06) 31.33 (16.19) 41.65 (12.01) 34.09 (12.89) 5:12 14:7 41.27 (12.15) 3:3 29.11 (7.89) 17.02 (2.11) 26.75 (10.91) Controls 6:17 37:30 16.97 9:13 27.87 (2.26) (12.51) ns ns 14:12 36:35 6:3 ns ns n Gender, F:MAge, years: mean (s.d.) 41.45 (12.25) 37.14 (12.70) 111:288 39.96 76:84 (10.45) 39.61 (14.14) 42.52 (11.86) 37.13 22:44 (12.33) 12:11 42.32 (11.79) 36.12 (13.02) Controls

1 Table DS2 Summary of performance on neuropsychological (cognitive and social cognitive) measures. Note: ** p<0.001; *p<0.05; LNS=letter number sequence; LM=logical memory; IDED=intradimensional/extradimensional set shifting task; HT = the hinting task; RME = reading the mind in the eyes; IPSAQ = internal, personal and situational attributions questionnaire; EB = externalising bias; PB = personalising bias.

Group x GG AG AA Main effect Main effect Function Test or Subscale Cohort N Genotype of Group of Genotype (mean (sd)) (mean (sd)) (mean(sd)) Interaction

IQ Patients 305 94.6(20.5) 88.9(16.98) 90.2(17.7) Abbreviated Full Scale IQ F=275.3** F=0.246 F=1.835 Controls 158 119.7(13.4) 122.7(13.6) 121.8(16.8)

Working Patients 373 8.26(3.69) 7.6(3.3) 7.4(3.25) Memory LNS F=220.1** F=2.42 F=0.129 Controls 156 13.6(2.89) 12.8(3.08) 12.4(3.4)

Patients 369 -.96(1.56) -.86(1.26) -1.06(1.25) CANTAB SWM F=90.4** F=0.268 F=0.807 Controls 151 .403(.72) .169(.813) .266(.742)

Episodic Patients 385 6.9(3.89) 6.3(3.27) 6.1(3.360 Memory LM - Immediate F=309.2** F=0.986 F=0.054 Controls 156 13.1(2.8) 12.6(2.89) 12.5(2.75)

Patients 382 8(3.42) 7.1(3.1) 7.03(3.2) LM – Delayed F=311.6** F=0.864 F=0.449 Controls 156 13.4(2.74) 13.2(2.7) 13.3(2.3)

Attentional Patients 324 11.6(11.2) 12.4(10.7) 12.5(10.9) Control IDED (8 shapes) F=11.9** F=0.006 F=0.355 Controls 149 9.4(10.6) 8.9(9.3) 8(9.07)

Patients 327 .94(1.8) .77(1.51) 1.01(2.52) IDED (6 shapes) F=0.307 F=0.914 F=1.24 Controls 148 1.25(2.42) .67(2.3) .41(.64)

Social Patients 276 15.6(3.3) 15.5(3.5) 15(3.41) Cognition HT F=15.8** F=1.58 F=0.055 Controls 132 17.2(1.4) 16.8(1.7) 16.3(2.1)

RME Patients 151 25(4.7) 38(116.6) 24.3(4.8) F=0.022 F=0.254 F=0.148 Controls 50 28.4(3.04) 26.3(4.1) 24.5(5.02)

Patients 256 IPSAQ (EB) 1.6(2.9) 1.17(3.7) .26(2.8) F=0.167 F=5.72* F=1.53 Controls 131 3.2(3.8) .78(4.25) .38(3.6)

Patients 256 IPSAQ (PB) .48(.26) .48(.29) .51(.27) F=2.17 F=2.17 F=2.27 Controls 131 .61(.21) .64(.25) -1.4(13.2)

Table DS3 Summary of group (patients vs. control) effects on gray matter and white matter volume in Italian sMRI analysis. In all clusters volume in patients < controls. Note: since a vast number of clusters were significantly different (i.e. patients < controls) at pCORRECTED<0.05, in the interests of simplicity only those clusters surviving at pCORRECTED<0.001, minimum cluster extent = 100 voxels are noted in the table (full list is available on request). *closest anatomical landmark.

Cluster Extent MNI Co-ordinates Tissue Type Region F (1,95) (voxels/mm3) x y z Gray Matter Left Insula 1001 -38 12 -6 49.76 Right Medial Frontal Gyrus 235 4 47 14 46.87 Left Inferior Frontal Gyrus 270 -36 5 25 46.54 Left Anterior Cingulate 552 -6 39 17 44.73 Right Cingulate Gyrus 1182 9 -12 36 44.28 Right Insula 1703 34 19 7 43.75 Right Middle Temporal 451 58 -22 -4 42.28 Gyrus Left Middle Frontal Gyrus 105 -30 33 -10 41.03 Right Cingulate Gyrus 459 8 20 27 41.01 Left Middle Temporal Gyrus 534 -48 -32 0 40.80 Right Fusiform Gyrus 287 48 -49 -14 40.51 Left Posterior Cingulate 213 -6 -51 24 39.78 Right Parahippocampal 174 21 -39 -9 39.11 Gyrus White Matter Left Cingulate Gyrus* 478 -19 1 46 43.24 Left Anterior Cingulate* 1224 -21 36 8 40.32 Left Fusiform Gyrus* 129 -40 -6 -20 39.00

Online supplement

Bioinformatic analysis of rs7914558 rs7914558 is located on chromosome 10q24 in a large region of high and complex linkage disequilibrium (LD) that contains four RefSeq genes: C10orf32, AS3MT, CNNM2 and NT5C2. Analysis of three SNP x gene expression databases (mRNA by SNP Browseri, SNPExpressii and Genevariii provides consistent evidence that the risk/major allele of rs7914558 is associated with increased expression of both the C10orf32 and AS3MT genes across multiple cell and tissue types including brain. C10orf32 and AS3MT are separated by a distance of just 5.24kb and are both encoded on the same strand. RefSeq does report a read- through transcript that combines C10orf32 and AS3MT together but it is noted as a candidate for nonsense-mediated mRNA decay (NMD), and is therefore unlikely to produce a protein product. Beyond rs7914558 and its multiple high-LD proxy SNPs (n=42) in this region, there is evidence for other SNPs that also have a cis-effect on transcription of C10orf32 and AS3MT. A group of 8 SNPs in complete LD (r2=1), indexed by rs9537, also show consistent evidence of a cis-acting effect on expression of both C10orf32 and AS3MT and the effect is much stronger than for rs7914558. rs9537 and rs7914558 are in LD (r2=0.53) and their respective major alleles that are associated with increased expression of C10orf32 and AS3MT are on the same haplotype. It’s likely that the observation that rs7914558 is associated with differential expression of C10orf32 and AS3MT is a spurious one caused by LD and is indirectly due to the functional effect of rs9357 or one of its proxies. Teasing out the effect of either SNP on local gene expression would require data from a population with a different LD structure. Given that rs9357 (or proxy) was not reported to be more strongly associated with schizophrenia, these data do not prove that rs7914558 (or proxy) contributes to schizophrenia risk via increased expression of C10orf32 and/or AS3MT and thus all four genes in the large region remain candidate loci.

i http://www.sph.umich.edu/csg/liang/asthma/ ii http://people.genome.duke.edu/~dg48/SNPExpress/ iii http://www.sanger.ac.uk/resources/software/genevar/ Effects of a novel schizophrenia risk variant rs7914558 at CNNM2 on brain structure and attributional style Emma Jane Rose, April Hargreaves, Derek Morris, Ciara Fahey, Daniela Tropea, Elizabeth Cummings, Carlo Caltagirone, Paola Bossù, Chiara Chiapponi, Fabrizio Piras, Gianfranco Spalletta, Michael Gill, Aiden Corvin and Gary Donohoe BJP 2014, 204:115-121. Access the most recent version at DOI: 10.1192/bjp.bp.113.131359

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