No evidence for association between bipolar disorder risk gene variants and brain structural phenotypes

Journal of Affective Disorders 151 (2013) 291–297

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Research report

No evidence for association between bipolar disorder risk gene variants and brain structural phenotypes Martin Tesli a,b,n,1, Randi Egeland c,1, Ida E. Sønderby d, Unn K. Haukvik a,f, Francesco Bettella a, Derrek P. Hibar g, Paul M. Thompson g, Lars Morten Rimol a,f, Ingrid Melle a,b, Ingrid Agartz a,e,f, Srdjan Djurovic a,b,d, Ole A. Andreassen a,b a

Institute of Clinical Medicine, University of Oslo, Oslo, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway c Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark d Department of Medical Genetics, Oslo University Hospital, Oslo, Norway e Department of Clinical Neuroscience, HUBIN Project, Psychiatry Section, Karolinska Institutet and Hospital, Stockholm, Sweden f Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway g Imaging Genetics Center, Laboratory of Neuro Imaging, Departments of Neurology and Psychiatry, UCLA School of Medicine, Los Angeles, CA, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 7 February 2013 Received in revised form 7 June 2013 Accepted 8 June 2013 Available online 29 June 2013

Background: While recent genome-wide association studies have identified several new bipolar disorder (BD) risk variants, structural imaging studies have reported enlarged ventricles and volumetric reductions among the most consistent findings. We investigated whether these genetic risk variants could explain some of the structural brain abnormalities in BD. Methods: In a sample of 517 individuals (N ¼121 BD cases, 116 SZ cases, 61 other psychosis cases and 219 healthy controls), we tested the potential association between nine SNPs in the genes CACNA1C, ANK3, ODZ4 and SYNE1 and eight brain structural measures found to be altered in BD, and if these were specifically affecting the BD sample. We also assessed the polygenic effect of all these 9 SNPs on the brain phenotypes. Results: Our most significant result was an association between the risk allele A in CACNA1C SNP rs4775913 and decreased cerebellar volume (pnom. ¼ 0.0075) in the total sample, which did not remain significant after multiple testing correction (pthreshold o 0.0064). There was no evidence for diagnostic specificity for this association in the BD group. Further, no polygenic effect of these 9 SNPs was observed. Limitations: Low statistical power might increase our type II error rate. Conclusions: The present findings indicate that these risk SNPs do not explain a large proportion of the structural brain alterations in BD. Thus, these genes which are all related to neuronal functions must be involved in other pathophysiological aspects of BD development. & 2013 Elsevier B.V. All rights reserved.

Keywords: Bipolar disorder Schizophrenia MRI Genetics CACNA1C ANK3

1. Introduction Bipolar disorder (BD) is a severe psychiatric disorder with an important and complex genetic contribution. Heritability is estimated to range between 59% and 93% based on twin studies (Kendler et al., 1995; Kieseppa et al., 2004; Lichtenstein et al., 2009). Epidemiological and molecular genetic studies suggest that the disorder is polygenic (Craddock et al., 1995; Owen et al., 2009). Furthermore, indications of a genetic overlap with schizophrenia

n Corresponding author at: KG Jebsen Centre for Psychosis Research—TOP Study, Building 49, Oslo University Hospital, Ullevål, Kirkeveien 166, PO Box 4956, Nydalen, 0424 Oslo, Norway. Tel.: +47 23016277; fax: +47 23027333. E-mail address: [email protected] (M. Tesli). 1 Contributed equally.

0165-0327/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.06.008

(SZ) come from epidemiological and genetic associations studies (Lichtenstein et al., 2009; Moskvina et al., 2009). There has been recent progress in gene discovery in BD (Sklar et al., 2011), with a range of new findings. However, the underlying functional mechanisms of the new susceptibility genes are still elusive. It has been hypothesized that the effect of gene variants on brain biology may improve the understanding of how the genes affect disease development (Perrier et al., 2011). The genetic markers recently associated with BD are single nucleotide polymorphisms (SNP)s in the genes CACNA1C (Ferreira et al., 2008; Sklar et al., 2008, 2011), ANK3 (Ferreira et al., 2008; Schulze et al., 2009; Scott et al., 2009; Sklar et al., 2008; Smith et al., 2009; Tesli et al., 2011), ODZ4 (Sklar et al., 2011) and SYNE1 (Green et al., 2012; Sklar et al., 2011). Variants in CACNA1C (Green et al., 2010; Sklar et al., 2011) and ANK3 (Athanasiu et al., 2010)

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have also been implicated in SZ pathology, thus supporting the hypothesis of genetic overlap. Based on these findings, it has been suggested that ion channel dysregulation is involved in BD pathology, as both CACNA1C and ANK3 encode proteins related to ion channel functioning (Ferreira et al., 2008). Animal and stem cell studies also show abnormal characteristics in neurons derived from individuals with mutations and deficiencies in these genes (Hedstrom et al., 2008; Pasca et al., 2011; Sobotzik et al., 2009). Further, CACNA1C is implicated in neuronal excitability, synaptic plasticity, and gene expression hypothesized to take part in neurodevelopment (Calin-Jageman and Lee, 2008). But we still lack knowledge on the mechanisms by which these gene variants ultimately give rise to the clinical picture observed in BD. One strategy to enhance the insight into the underpinnings of BD, is to combine information from identified risk genetic variants with evidence from neuroimaging studies. As for the brain morphological alterations found in imaging studies of BD, some of the most consistent findings are ventricular enlargement, white matter hyperintensities, subcortical volumetric reductions and cortical thickness reductions. In one metaanalysis of 98 individual studies enlarged ventricles (N ¼BD 201 cases and 285 controls) and deep white matter hyperintensities (N ¼ 394 cases and 456 controls) were the most robust findings (Kempton et al., 2008). Our group recently compared SZ and BD subjects to healthy controls in a large structural magnetic resonance imaging (sMRI) study (N ¼ 173 SZ cases, 139 BD cases, and 207 healthy controls), demonstrating ventricular enlargement, subcortical and cerebellar volume reductions in both BD and SZ, frontotemporal cortical thinning in bipolar disorder type 1 (BDI) and SZ, thus showing similarities in brain structural change between the two disorders. Even so, all volume alterations were significantly larger in SZ patients (Rimol et al., 2010). A more detailed report of these cortical abnormalities found that cortical thinning was more pronounced than cortical area reduction in BD (Rimol et al., 2012). sMRI imaging genetic studies of BD risk variants have focused mainly on the CACNA1C SNP rs1006737. This SNP has been related to increased gray matter volume (N ¼77 healthy adults) (Kempton et al., 2009), brainstem volume alterations (N ¼ 585 healthy individuals) (Franke et al., 2010), increased gray matter density in right amygdala and right hypothalamus (N ¼ 41 euthymic BD subjects and 40 controls) (Perrier et al., 2011), as well as a total cortical volume increase (N ¼ 55 healthy subjects) (Wang et al., 2011). To the best of our knowledge, there are no studies on brain volumetric measures and SNPs in the genes ANK3, ODZ4 or SYNE1. In the current study, we investigated how some of the most consistently reported BD risk genetic variants may affect the brain volumetric reductions reported in this disorder.

2. Materials and methods

potentially interfering with brain function (hypothyroidism, uncontrolled hypertension and diabetes), or an illicit drug abuse/ addiction diagnosis were also exclusion criteria. Patients were recruited from psychiatric in- and out-patient hospital units in the Oslo area and had been diagnosed with the Structural Clinical Interview for DSM-IV (SCID) (Spitzer et al., 1992). The patients were divided into three groups after DSM-IV diagnoses: (I) bipolar spectrum disorders (N ¼ 121), in the following referred to as bipolar disorder (BD): bipolar disorder type 1 (N ¼71), bipolar disorder type 2 (N ¼45), bipolar disorder not otherwise specified (N ¼5); (II) schizophrenia spectrum disorders (N ¼116), in the following referred to as schizophrenia (SZ): schizophrenia (N ¼ 89), schizoaffective disorder (N¼ 18), schizophreniform disorder (N¼ 9); and (III) other psychotic disorders (N ¼ 61), classified here as ‘other psychosis’: psychosis NOS (N ¼34), brief psychotic disorder (N ¼8), delusional disorder (N ¼5) and psychotic major depression (N ¼14). Diagnostic assessment was performed by experienced psychologists and psychiatrists, of whom all participated regularly in diagnostic meetings supervised by professors in psychiatry. Reliability measures of the diagnostic evaluation in the TOP study were performed, and the overall agreement for the DSM-IV diagnostic categories tested was 82% and the overall Kappa 0.77 (95% CI: 0.60–0.94). Information on age of onset, education, current clinical state, medication status and alcohol use was obtained during an initial clinical interview. A 3-h neuropsychological test battery, comprising Wechsler Abbreviated Scale of Intelligence (WASI), was performed by clinical psychologists (Simonsen et al., 2011). Clinical evaluation of the patients and healthy controls participating in the TOP study is described in details in a previous report (Welander-Vatn et al., 2009). The healthy control subjects (N ¼ 219) were from the same catchment area as the patient group, selected randomly from the national statistics records (www.ssb.no). Demographic and clinical data are presented in Table 1. 2.2. Genotyping All participants were genotyped at Expression Analysis Inc. (Durham, NC, USA) using the Affymetrix Genome-Wide Human SNP Array 6.0 (Affymetrix Inc, Santa Clara, CA, USA). Quality control was performed using PLINK (version 1.07; http://pngu. mgh.harvard.edu/purcell/plink/) (Purcell et al., 2007). As a quality control, exclusions of individuals based on genotyping were made of (I) one of two duplicates, (II) one of two relatives (identity by descent (IBD) 40.1875), (III) individuals with a recorded gender differing from that determined by X chromosome marker homozygosity, (IV) mixup-samples (calculated by pairwise genomewide identity by state (IBS)), (V) individuals with non-European ancestry (calculated with HapMap3 and MDS) and (VI) individuals with more than 5% missing genotype data. SNPs were excluded based on (I) deviation from the Hardy–Weinberg equilibrium p o0.0056 (Bonferroni corrected for 9 SNPs), (II) minor allele frequency below 1% and (III) low yield (o95% in controls).

2.1. Sample characteristics 2.3. SNP selection All participants in the current study were of Norwegian origin and part of the ongoing TOP (Thematically Organized Psychosis) Study. To be eligible for the study, patients had to be between 18 and 65 years, fulfilling the DSM-IV criteria for a bipolar spectrum disorder or schizophrenia spectrum disorder, and be willing to participate and able to provide written informed consent. Individuals were excluded if they had an IQ score below 70, or reported a history of head injury or neurological disorder. Subjects in the healthy control group were excluded if they, or their close relatives, had a lifetime history of a severe psychiatric disorder (SZ, BD or major depression). A history of a medical condition

Based on the largest BD GWA studies conducted (Ferreira et al., 2008; Sklar et al., 2008, 2011), primarily the PGC study by Sklar et al., 2011, the genome-wide significance threshold (o 5.0  10−8) and later replications (Schulze et al., 2009; Scott et al., 2009; Smith et al., 2009; Tesli et al., 2011), we selected nine SNPs as candidates for our study (ANK3—rs9804190 (genotyped), rs10994336 (imputed), rs10994397 (imputed), and rs1938526 (genotyped); CACNA1C—rs1006737 (genotyped), and rs4765913 (imputed); ODZ4—rs12576775 (imputed), and rs2175420 (genotyped); and SYNE1—rs9371601 (genotyped)).

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Table 1 Demographics and clinical data. 1—BD (n¼ 121)

Sex, n male (%) Handedness, n right (%) Ethnicity, n european (%) Age, yearsa Education, yearsb WASI, IQ Age at onset, yearsc Duration of illness, yearsd Duration of antipsychotic medicatione No. of admissions PANSS GAF-S GAF-F Alcohol, unitsf

50 98 121 35.8 13.6 109.5 22.4 13.3 3.0 1.5 45.8 57.3 54.6 12.5

Medication

N (%)

Antipsychotics Lithium Antiepileptics Antidepressives Sedatives

61 18 53 42 17

(41.3) (81.0) (100.0) (11.5) (2.2) (11.4) (9.5) (9.9) (5.6) (2.1) (10.6) (10.4) (12.6) (26.4)

2—SZ (n¼ 116)

69 96 116 34.75 13.4 107.1 24.9 10.0 16.8 2.5 60.3 43.6 44.3 6.6

(59.5) (82.8) (100.0) (9.3) (2.4) (12.8) (8.0) (7.8) (29.5) (3.8) (17.1) (11.3) (10.2) (13.1)

N (%) (51) (15) (44) (35) (14)

102 3 23 35 14

3—MIX (n¼ 61)

32 52 61 30.8 12.6 107.1 22.7 7.5 5.0 2.0 53,3 50,2 52,6 15.1

(52.5) (85.2) (100.0) (10.6) (2.8) (11.4) (7.9) (6.6) (12.1) (2.6) (14.3) (13.9) (14.4) (19.3)

4—CTRL (n¼ 219)

117 (53.4) 199 (90,9) 218 (99.5) 35.9 (9.7) 14.3 (2.2) 115.2 (9.2) – – – – – – – 10.0 (10.0)

ANOVA/Chi square analysis F/χ2

p

Post hoc (Tukey)

χ2 ¼8.3 χ2 ¼5.4 – F¼ 4.5 F¼ 9.6 F¼ 18.7 F¼ 2.7 F¼ 10.3 F¼ 15.7 F¼ 3.1 F¼ 29.6 F¼ 41.6 F¼ 23.1 F¼ 4.0

0.04 0.5 – 0.006 o 0.001 o 0.001 0.07 o 0.001 o 0.001 0.05 o 0.001 o 0.001 o 0.001 0.008

– – – 1,2,44 3 4 41,2,3 4 41,2,3 – 1 42,3 2 41,3 – 2 4341 1 4342 1,3 42 3 41,4 42

N (%) (87,9) (3) (20) (30) (12)

49 (80) 0 9 (15) 26 (43) 4 (7)

Abbreviations: BD: Bipolar disorder, SZ: Schizophrenia, MIX: other psychosis, CTRL: Control subjects, GAF-S/F: Global Assessment Functioning-Symptom/Function (scale from 1 to 100, from low to high functioning), PANSS: Positive And Negative Syndrome Scale (scale of 30–210, from mild to serious symptoms), WASI: Wechsler Abbreviated Scale of Intelligence, IQ: Intelligence quotient and ANOVA: analysis of variance. Means and SD are reported unless otherwise specified. ANOVA analysis is univariate. All analyses of demographic and clinical data were performed using SPSS. a

Age at MRI scanning. Years of completed education, reported by the participant. c Age at first SCID-verified episode of primary symptom. d Years from age at onset to age at MRI. e Years of treatment with antipsychotic medication f Units of alcohol consumed in the last 2 weeks, reported by the participant. b

2.4. Imputation of SNPs Following the above mentioned quality control, the candidate SNPs were imputed with MaCH (Li et al., 2010) (http://www.sph. umich.edu/csg/abecasis/MACH/download/1000G-PhaseI-Interim. html) using the European samples in the Phase I release of the 1000 Genomes Project (SNPs not present in the 1000 Genomes reference, and SNPs with ambiguous strand alignments (A/T and G/C SNPs), were removed from the sample data sets). Imputation was a three stage process, involving (I) ChunkChromosome where the data set was broken into 2500 SNP pieces with 500 SNP overlap (http://genome.sph.umich.edu/wiki/ChunkChromosome), (II) MaCH where each piece was phased (40 rounds and 400 states) (http://www.sph.umich.edu/csg/abecasis/MaCH/download/), and (III) Minimac where each phased piece was imputed to the 1000 Genomes European reference panel (20 rounds and 400 states) (http://genome.sph.umich.edu/wiki/Minimac). In the third stage, all imputed SNPs were provided with an estimated r2 score as quality metric. Exclusions were made of SNPs with an r2 score o0.5, leaving 9,584,802 SNPs. 2.5. Image acquisition and post processing The sample of 517 participants underwent MRI scanning at Oslo University Hospital, Oslo, Norway between 2003 and 2009 on the same 1.5 T scanner (Siemens Magnetom Sonata skanner, Siemens Medical Solutions, Erlangen, Germany) equipped with a standard head coil (Rimol et al., 2010). After a conventional 3-plane localizer, two sagittal T1-weighted magnetization-prepared rapid gradient echo volumes were acquired with the Siemens tfl3d1_ns

pulse sequence (TE¼3.93 ms, TR ¼2730 ms, TI 1000 ms, flip angle ¼71; FOV ¼ 24 cm, voxel size ¼1.33  0.94  1 mm3, and number of partitions ¼160). Acquisition parameters were optimized for increased gray/white matter image contrast. All brain scans were read by a specialist in neuroradiology and found to be free from organic brain pathology (i.e. tumors, hemorrhagia, infarcts, and organic hydrocephalus). There was no scanner upgrade during the study period, and patients and controls were scanned consecutively. The FreeSurfer software (version 3.0.2) (http://surfer.nmr.mgh. harvard.edu) was used to calculate volume measures of subcortical structures of interest and measures of cortical thickness and areal expansion (surface area). For the subcortical segmentation, a neuroanatomical label was automatically assigned to each voxel in the MRI volume based on spatial location and image intensity (Fischl et al., 2002). For the cortical surface area and thickness measures, a 3D reconstruction of the gray/white matter boundary and pial surface was created, and the surfaces were averaged across participants using a non-rigid high-dimensional spherical averaging method to align cortical folding patterns (Dale et al., 1999; Fischl et al., 1999). Each hemisphere surface consisted of approximately 160,000 vertices arranged in a triangular grid. Estimates of cortical surface area were obtained by computing the area of each triangle in a standardized, spherical atlas space surface tessellation. Vertexwise estimates of relative area expansion for each individual subject in atlas space were then computed by assigning 1/3 of the area of each triangle to each of its vertices. Cortical thickness was measured as the distance between the gray/white matter boundary and the pial surface at each vertex. The maps produced

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were not restricted to the voxel resolution of the original data and were thus capable of detecting submillimeter differences between groups. 2.6. Statistical analysis We included eight morphometric summation measures shown to be significantly altered in our own sample, where we compared BD subjects (N ¼121) with healthy controls (N ¼219) (Table S1). These analyses were performed with the statistical software package R (http://www.r-project.org/) for 16 structural measures derived from FreeSurfer (in addition to amygdala volume). The significantly altered measures (pthreshold o0.0029) were: frontal, parietal, temporal, total cortical thickness, cerebellar volume, hippocampal volume, ventricular volume and total brain volume. Ventricular volume comprised lateral, inferior lateral, third and fourth ventricles. For details, see Table S2. Comparable results for SZ subjects vs. healthy controls are shown in Table S5. Associations between SNPs and morphometric summation measures were tested in the whole sample (N ¼517) through linear regression for each SNP and each morphometric measure under an additive model in PLINK (version 1.07; http://pngu.mgh. harvard.edu/purcell/plink/) (Purcell et al., 2007). Each analysis controlled for sex, age at scanning, and diagnostic group (BD, SZ, other psychosis or control), as well as intracranial volume (ICV) for subcortical structures. As disease duration, duration of antipsychotic medication and alcohol consumption have been shown to influence brain volumetric measures in earlier studies, the effects of these variables on the eight brain regions were assessed with a linear regression model in SPSS Statistics Version 20.0 (IBMs SPSSs Statistics 20, www.spss.com). Of these variables, alcohol consumption was negatively associated with total brain volume (total sample and healthy control subgroup), parietal cortical thickness (total sample and BD subgroup) and duration of illness was negatively associated with ventricular volume (total sample, SZ subgroup). These variables were entered as covariates in the model for the respective brain regions and diagnostic groups. In order to reduce the number of tests, we applied the following algorithm for the main analyses: only nominally significant findings in the overall sample (N ¼517) were analyzed in the diagnostic subgroups (BD, SZ and healthy controls). Then, only for potentially nominally significant findings in the BD sample, interaction analyses were performed against the healthy control group, to determine diagnostic specificity. Demographic data were analyzed using SPSS. Numerical variables were analyzed through univariate analysis of variance (ANOVA) with subsequent Tukey post hoc testing of significant findings, and categorical variables using Chi squared analysis. To keep experiment-wide type I error less than 5%, we estimated the total number of independent tests in our analysis from our set of SNPs accounting for the non-independence of SNPs due to linkage disequilibrium. The threshold to control type I error rate was estimated using Matrix Spectral Decomposition

(matSpD) and set to p o0.0064 (http://gump.qimr.edu.au/general/ daleN/matSpD/) (Li and Ji, 2005; Nyholt, 2004). Additionally, we assessed the combined effect of all the nine risk SNPs on the eight brain regions, to evaluate the potential polygenic effect. These analyses were performed with the method described by Purcell et al. (2009), where the beta coefficients from the original studies were used to determine the cumulative risk load for these nine SNPs in each individual in the sample. We used results from the PGC study (Sklar et al., 2011) and results from a large collaborative BD case-control study (Ferreira et al., 2008) for those of our candidate SNPs which were not included in the publicly available PGC study results (http://www.broadinstitute. org/mpg/ricopili/). Beta coefficients were multiplied by the number of risk alleles for each SNP, and summed up for each individual in our sample. These individual polygenic risk scores were entered as independent variables and brain phenotypes as dependent variables in a linear regression model in R, with the same covariates as in the single SNP analyses. Polygenic analyses were undertaken both in the total sample and in the BD subgroup. We also performed case-control association analyses for these SNPs, for the BD sample vs. healthy controls and for the combined BD and SZ sample vs. healthy controls. 2.7. Ethics statement The Norwegian Scientific-Ethical Committees and the Norwegian Data Protection Agency approved the study. All subjects gave written informed consent prior to inclusion in the project.

3. Results The most significant finding was an association between the risk allele A in CACNA1C SNP rs4775913 and decreased cerebellar volume in the total sample (N ¼ 517) (pnom. ¼ 0.0075). The same allele was also associated with temporal thickness reduction (pnom. ¼ 0.045) (Table 2). None of the findings remained significant after correction for multiple testing (pthreshold o0.0064). When performing diagnostic sub-group analyses for these two associations, none were significant after multiple testing correction, although there were nominal associations for the healthy control group (rs4765913 and reduced cerebellar volume, P¼ 0.0067; rs4765913 and reduced temporal thickness, P¼ 0.01) (Table S3). As none of these tests were nominally significant in the BD sample, interaction analyses were not undertaken, in accordance with our algorithm described in the Materials and methods section. Thus, there was no evidence of diagnostic specificity in the BD subgroup. No case-control single SNP associations remained significant at a multiple-testing-corrected pthreshold (0.0064), neither for the BD sample nor for the combined BD and SZ sample (Table S6). Further, there were no significant associations between polygenic BD risk score for these nine SNPs and the eight brain phenotypes, neither in the total sample (Table S7) nor in the BD subgroup.

Table 2 Nominally significant results in the total sample. Gene

SNP

A1

A2

FRQ

BETA

SE

P

Region

CACNA1C CACNA1C

rs4765913 rs4765913

A A

T T

0.2238 0.2238

−2190.7541 −0.0183

816.0707 0.0091

0.0075 0.04484

Cerebellar vol. Temp. cort. th.

SNP—Single nucleotide polymorphism; A1—Minor allele; A2—Major allele; FRQ—minor allele frequency; SE—Standard error; Vol.—volume; Ventr.—ventricular; and Temp.—temporal. Results are presented only for tests where the risk allele is associated with the brain alteration reported in BD.

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4. Discussion Our main result was no association between BD susceptibility gene variants and brain morphology, except for a trend-significant association between the risk allele A in CACNA1C SNP rs4765913 and reduced cerebellar volume. There were also nominally significant associations between this risk allele and reduced temporal thickness. However, none of these associations remained significant after multiple testing correction. Moreover, the combined polygenic risk of all nine SNPs was not significantly associated with the brain volumetric alterations observed in BD. One possible explanation of these findings is low statistical power, as described in BD case-control studies (Sklar et al., 2011). The effect size of each single marker has been shown to be very low at diagnosis level, and this might also be the case at brain volumetric level. It seems plausible that the brain volumetric changes in BD have a polygenic basis, like the disorder itself, and that cumulative genetic risk scores could explain a larger part of the variance than single SNPs. However, we found no such polygenic effect of these nine BD risk SNPs in our sample. Another interpretation of our present findings is that the brain volumetric changes are a result of disease mechanisms, rather than a cause of the disorder. In our sample, ventricular volume was significantly increased as a result of duration of illness. Atypical antipsychotics have also been suggested as a cause of cortical thinning (Moncrieff and Leo, 2010), but this has not been found in our sample (Rimol et al., 2010, 2012). A third possibility is that the brain abnormalities in BD result from environmental factors. Alcohol consumption, which has been found increased in BD (Farren et al., 2012), was associated with reduced total brain volume as well as parietal cortical thinning in our cohort. Recent studies have related environmental factors, like childhood trauma, to brain volumetric reductions (Aas et al., 2012a), and a gene-environment interplay to neurocognitive changes in psychotic disorders (Aas et al., 2012b), but these interactions need to be further explored. However, we cannot exclude the possibility that there are other BD risk variants not yet discovered, as the missing heritability of BD is still high (Sklar et al., 2011). When performing group-wise analyses, none of the two currently reported nominally significant associations were specific to the BD group, potentially indicating that the effects of BD risk genes on reduced brain volume are unspecific with respect to diagnostic category. This interpretation is in accordance with the polygenic architecture of BD (Sklar et al., 2011), the diagnostically unspecific sMRI findings (Ellison-Wright and Bullmore, 2010; Rimol et al., 2010), and the hypothesized continuum model of severe psychiatric disorders (Craddock and Owen, 2010). It might also be due to relatively lower statistical power in the BD sample than in the healthy control group, but this is unlikely, as the effect size is not higher in the BD group than in the controls (Table S3). In the context of other imaging genetic studies on BD risk genes and brain volumetric measures, our study adds knowledge on the potential impact of some of the most significant genetic variants and brain alterations reported in BD. But our findings do not replicate any prior results, as the design of our study is different. Thus, we still lack replication with regard to the actual impact of BD risk genes on cerebral structures. Nevertheless, it is worth mentioning that we did not find any associations between the previously studied CACNA1C SNP rs1006737 and brain volumetric alterations (Table S4), indicating that this variant is not a major contributor to some of the most robust brain structural findings in BD, including enlarged ventricles, subcortical volume reductions and cortical thickness reductions. In the light of recent functional imaging studies, the identified BD risk variants in CACNA1C may act by mechanisms not readily observable with structural imaging. Functional MRI studies of the CACNA1C SNP rs1006737 report increased amygdala reactivity in

295

risk allele carriers (Bigos et al., 2010; Jogia et al., 2011; Wessa et al., 2010; Tesli et al., 2013), and there is additional support for inefficient prefrontal activation (Bigos et al., 2010; Jogia et al., 2011). These findings are in accordance with the proposed model of dysregulated neuronal circuitry in BD, with an amygdalaanterior paralimbic dysfunctional neural system as one of the most consistently reported characteristics (Blond et al., 2012). Thus, one could speculate that BD is caused by aberrant affective neuronal networks to a larger extent than impaired neurodevelopment, which is a potentially important underlying mechanism in SZ (Demjaha et al., 2012). If the risk allele in CACNA1C SNP rs4765913 really exerts some of its effect on BD by reducing brain volume, particularly in the cerebellum, the potential mechanisms are not entirely clear. As this gene encodes an alpha-1 subunit of a voltage-dependent calcium (Cav1.2) channel, it has been hypothesized that ion channel dysregulation is an underlying mechanism in BD. However, to the best of our knowledge, the exact nature of this proposed ion channel dysregulation has not been demonstrated, and neither has its potential relation to reduced brain volume. But there are some interesting findings on CACNA1C and the neurobiological link between genetic variants and brain structures and relationship clinical phenotype. With regards to gene expression, a recent post mortem brain study reported higher levels of CACNA1C mRNA in healthy carriers of the CACNA1C risk-associated SNP rs1006737 than in carriers of the protective allele (Bigos et al., 2010). Further, mutations in the exons of CACNA1C have been found to give rise to the Timothy syndrome, a rare and lethal disorder characterized by somatic symptoms like cardiac arrhythmia, and psychiatric symptoms like autism and cognitive disability (Splawski et al., 2004). In patients with the Timothy syndrome mutations, there is an impaired Cav1.2 channel inactivation (Bidaud and Lory, 2011). Moreover, it was recently reported that cortical neuronal precursor cells and neurons from induced pluripotent stem cells in patients with the Timothy syndrome had several abnormal features, including defective calcium signaling and abnormal differentiation, as well as increased production of norepinephrine and dopamine (Pasca et al., 2011). However, as the CACNA1C SNP rs4765913 is situated in one of the introns, its effect on the encoded protein is probably related to the expression levels rather than structural changes, as is the case with the Timothy syndrome (Splawski et al., 2005). The main limitation of this study is probably low statistical power. This might increase the rate of false negative findings, although an appropriate correction was implemented. However, one advantage of our study set-up is the evidencebased approach, with which we selected only the most consistently reported SNPs and sMRI findings in this disorder. With this design, we probably reduced the chance of type II errors, a phenomenon often occurring when lacking a clear hypothesis and performing gene/genome-wide and whole-brain analyses, resulting in false negative findings due to too stringent multiple testing correction. In summary, none of nine previously identified BD risk variants were statistically significantly associated with reported brain volumetric alterations after multiple testing correction. Further, there was no significant polygenic effect of these variants. Thus, these variants are unlikely to explain a major part of these brain alterations. It is also possible that the current study is statistically underpowered, as has been shown to be the case for BD casecontrol GWA studies (Sklar et al., 2011). To address these questions, there is a need for larger studies on BD risk genes and brain structural measures. Other directions for future research might include whole-genome polygenic risk score analyses, and polygenic pathway analyses, assessing the impact of risk genetic variants clustered into functional groups (Mattingsdal et al., 2012). With such strategies, the statistical power would increase, and

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the need for much larger samples would thereby decrease. Also, studies using other imaging methods may help in explaining some of the underlying mechanisms, and bridging the gap between genotype and clinical phenotype in this highly heritable and severe psychiatric disorder.

Role of funding source We are not concerned that our author agreement may be incompatible with archiving requirements specified by our funding body that supports our research.

Conflict of interest All authors declare that they have no conflicts of interest.

Acknowledgments We thank patients and controls for their participation in the study, and the health professionals who facilitated our work. We also thank Thomas D. Bjella for assistance with the database.

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jad.2013.06.008.

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