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Association between schizophrenia and common variation in neurocan (NCAN), a genetic risk factor for bipolar disorder
Schizophrenia Research 138 (2012) 69–73
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Association between schizophrenia and common variation in neurocan (NCAN), a genetic risk factor for bipolar disorder Thomas W. Mühleisen a, b, 1, Manuel Mattheisen a, c, d, 1, Jana Strohmaier e, Franziska Degenhardt a, b, Lutz Priebe a, b, C. Christoph Schultz f, René Breuer e, Sandra Meier e, Per Hoffmann a, b, GROUP Investigators 2, Fernando Rivandeneira g, h, Albert Hofman h, André G. Uitterlinden g, h, Susanne Moebus i, Christian Gieger j, Rebecca Emeny k, Karl-Heinz Ladwig k, l, H.-Erich Wichmann m, n, Markus Schwarz o, Jutta Kammerer-Ciernioch o, Ralf G.M. Schlösser f, Igor Nenadic f, Heinrich Sauer f, Rainald Mössner p, Wolfgang Maier p, Dan Rujescu q, Christoph Lange c, d, Roel A. Ophoff r, s, Thomas G. Schulze t, Marcella Rietschel e, Markus M. Nöthen a, b, u, Sven Cichon a, b, v,⁎ a
Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany Institute of Human Genetics, University of Bonn, Bonn, Germany c Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA d Institute for Genomic Mathematics, University of Bonn, Bonn, Germany e Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Mannheim, Germany f Department of Psychiatry and Psychotherapy, Friedrich-Schiller-University Jena, Jena, Germany g Department of Internal Medicine, Genetics Laboratory, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands h Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands i Institute of Medical Informatics, Biometry, and Epidemiology, University Duisburg-Essen, Essen, Germany j Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany k Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany l Department of Psychosomatic Medicine and Psychotherapy, Klinikum Rechts der Isar, Technische Universität, Munich, Germany m Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany n Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität and Klinikum Grosshadern, Munich, Germany o Psychiatric Center Nordbaden, Wiesloch, Germany p Department of Psychiatry, University of Bonn, Germany q Department of Psychiatry, Division of Molecular and Clinical Neurobiology, Ludwig-Maximilians-University, Munich, Germany r Department of Medical Genetics and Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands s UCLA Center for Neurobehavioral Genetics, Los Angeles, CA, USA t Department of Psychiatry and Psychotherapy, University Medical Center, University of Göttingen, Göttingen, Germany u German Center for Neurodegenerative Disorders (DZNE), Bonn, Germany v Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany b
a r t i c l e
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Article history: Received 26 October 2011 Received in revised form 2 March 2012 Accepted 6 March 2012 Available online 11 April 2012 Keywords: Association Genetic overlap Manic depression Psychotic disorder Cortex Hippocampus
a b s t r a c t A recent study found genome-wide significant association between common variation in the gene neurocan (NCAN, rs1064395) and bipolar disorder (BD). In view of accumulating evidence that BD and schizophrenia partly share genetic risk factors, we tested this single-nucleotide polymorphism for association with schizophrenia in three independent patient–control samples of European ancestry, totaling 5061 patients and 9655 controls. The rs1064395 A-allele, which confers risk for BD, was significantly over-represented in schizophrenia patients compared to controls (p = 2.28 × 10 − 3; odds ratio = 1.11). Follow-up in non-overlapping samples from the Schizophrenia Psychiatric GWAS Consortium (5537 patients, 8043 controls) provided further support for our finding (p = 0.0239, odds ratio = 1.07). Our data suggest that genetic variation in NCAN is a common risk factor for BD and schizophrenia. © 2012 Elsevier B.V. All rights reserved.
⁎ Corresponding author at: Department of Genomics, Life & Brain Center, University of Bonn, Sigmund-Freud-Strasse 25, D-53105 Bonn, Germany. Tel.: +49 228 6885 405; fax: +49 228 6885 401. E-mail address: [email protected] (S. Cichon). 1 These authors contributed equally to this work. 2 For a full list of members see Contributors. 0920-9964/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2012.03.007
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1. Introduction Bipolar disorder (BD) and schizophrenia are genetically complex neuropsychiatric disorders with a life-time prevalence of around 1%. Formal genetic data have suggested a partial etiological overlap between BD and schizophrenia (Lichtenstein et al., 2009; Van Snellenberg and de Candia, 2009), and molecular data have provided evidence for the presence of overlapping genetic factors (ISC, 2009). The most robust evidence has been obtained from recent genomewide association studies (GWAS). These suggest genetic effects across diagnostic categories for the zinc finger transcription factor ZNF804A (O'Donovan et al., 2008; Williams et al., 2010; Williams et al., 2011), and the voltage-dependent calcium channel CACNA1C (Ferreira et al., 2008; Moskvina et al., 2009; Green et al., 2010; Nyegaard et al., 2010; Ripke et al., 2011). In a recent GWAS and a follow-up study of BD samples from Europe, we found genome-wide significant association for the single-nucleotide polymorphism (SNP) rs1064395 in the 3′ untranslated region of the neurocan gene (NCAN) on chromosome 19p13.11 (p= 3.02 × 10 − 8, odds ratio (OR)= 1.31; Cichon et al., 2011). We then replicated this association in independent European, US-American, and Australian BD samples (p= 2.74 × 10− 4, OR= 1.12; Cichon et al., 2011). In a combined analysis of these samples (8441 patients and 35,362 controls), the p-value reached 2.14 × 10− 9 (OR = 1.17). Functional data indicate that NCAN is a promising candidate gene for both BD and schizophrenia. Neurocan (OMIM 600826) is a member of the lectican family of chondroitin sulfate proteoglycans (CSPGs). As a hyaluronan-binding CSPG, neurocan is an important constituent of the brain extracellular matrix, and is thought to be involved in neuronal adhesion and neurite outgrowth (for review see Frischknecht and Seidenbecher, 2008). We found that NCAN expression in wild-type mice was localized within the cortex and the hippocampus (Cichon et al., 2011). Both brain regions are involved in cognition and the regulation of emotion, and independent functional studies in humans have correlated these areas with BD and schizophrenia (for review see Martinowich et al., 2009). To test whether NCAN also contributes to susceptibility to schizophrenia, we analyzed rs1064395 in three large data sets: (i) a combined German and Dutch GWAS sample of schizophrenia (Rietschel et al., 2011); (ii) an additional, as yet unpublished German sample of schizophrenia; and (iii) the European-American sample from the Molecular Genetics of Schizophrenia (MGS) GWAS (Shi et al., 2009). Association analysis in these samples strongly suggested that this SNP also confers a risk for schizophrenia. In further analyses, we excluded the possibility that this finding was caused by confounding factors, such as the use of control individuals from the previous BD study, and the presence of patients with schizoaffective disorder (SAD). Furthermore, we replicated our finding for rs1064395 in independent samples from the Psychiatric GWAS Consortium (PGC) study of schizophrenia (Ripke et al., 2011). 2. Methods 2.1. Subjects The BOMA/Utrecht–Rotterdam sample comprised 1169 patients with a DSM-IV diagnosis of schizophrenia and 3714 controls. These were of German (Bonn/Mannheim — BOMA) and Dutch (Utrecht– Rotterdam) ancestries. Ascertainment details have been described elsewhere (Rietschel et al., 2011). The German controls (BOMA) are those used in the GWAS step in the study of BD by Cichon et al. (2011). The second sample (BOMA2-Munich, unpublished) comprised 1208 patients and 3300 controls of German ancestry. All patients received a lifetime diagnosis of schizophrenia according to the DSMIV criteria on the basis of structured diagnostic interviews (Endicott and Spitzer, 1978; McGuffin et al., 1991; First et al., 1996) and a
consensus best-estimate procedure (Leckman et al., 1982). These patients were recruited from consecutive hospital admissions at the Central Institute of Mental Health, Mannheim; the Department of Psychiatry of the University of Bonn; and the Department of Psychiatry, Ludwig-Maximilians-University, Munich. The sample included a subgroup of 221 patients with SAD. A total of 1697 population-based controls were derived from the Heinz Nixdorf Recall Study (HNR, Schmermund et al., 2002), and 1603 from the KORA study (Wichmann et al., 2005). The third sample of 2684 schizophrenia patients and 2641 controls was derived from the Database of Genotypes and Phenotypes (dbGaP, Mailman et al., 2007) schizophrenia datasets phs000167.v1.p1 and phs000021.v3.p2. A description of the Molecular Genetics of Schizophrenia (MGS) sample of European-American ancestry is provided in Shi et al. (2009). MGS contained a subgroup of 306 patients with SAD. A total of 1323 controls overlap with the GAIN-EA/TGEN1 sample investigated in the follow-up study of BD by Cichon et al. (2011). In a subsequent step, we tested rs1064395 in the stage 1 samples of the PGC schizophrenia study (Ripke et al., 2011), after removal of all samples already tested in our previously described samples. The nonoverlapping sample consisted of 5537 patients and 8043 controls, all of European and European-American descent (see also Section 3, Results and discussion). 2.2. Genotyping and quality control For the first-step analysis, rs1064395 genotypes were extracted from the following genome-wide data sets: (i) the BOMA/Utrecht– Rotterdam sample; (ii) the new BOMA2-Munich sample, genotyped at the Department of Genomics, Life & Brain Center, Bonn; and (iii) the MGS sample. The quality control (QC) of BOMA2-Munich followed the protocol described by Rietschel et al. (2011). After QC, BOMA2-Munich consisted of 596 patients with Human610-Quad data, 612 patients with Human660W-Quad data, 669 HNR controls with HumanOmni1-Quad data, 1028 HNR controls with HumanOmniExpress data, and 1603 KORA controls with HumanOmni2.5 data (Illumina, San Diego, CA, USA). KORA data were provided by the KORA study, Helmholtz Zentrum München, Neuherberg. From the quality-controlled MGS data available through dbGaP, we removed additional subjects (population outliers, cryptically related, and duplicates) identified through our QC protocol. For the follow-up analysis, quality-controlled data of rs1064395 were extracted from stage 1 of the Schizophrenia PGC study (see also Section 3, Results and discussion). 2.3. Association analysis In the first-step analysis, association was tested in each sample using a logistic regression analysis and assumption of an additive effect. To minimize potential stratification effects, regression was performed with covariates derived from multidimensional scaling analyses (two dimensions for BOMA/Utrecht–Rotterdam and BOMA2-Munich; and three dimensions for MGS). For the combined analyses, a fixed-effects meta-analysis was performed, as based on the weighted z-score method (de Bakker et al., 2008) and implemented in PLINK (Purcell et al., 2007). The test was two-tailed in the first-step meta-analysis, and one-tailed in the follow-up meta-analysis. 3. Results and discussion Convergent lines of evidence from formal genetic and association studies of BD and schizophrenia support the hypothesis that these two disorders share a certain fraction of common etiological factors. In a recent GWAS and follow-up study we found that common genetic variation in the NCAN gene (rs1064395, allele A) is a risk factor for BD (Cichon et al., 2011). The logical next step is to test whether this factor
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is also implicated in schizophrenia. We therefore analyzed SNP rs1064395 in a series of published and unpublished samples of schizophrenia. An overview of the results is provided by Table 1. The SNP passed all filters of our QC protocol. In all three samples, we observed an over-representation of the risk allele A in patients compared to controls: BOMA/Utrecht–Rotterdam (15.8% vs. 14.7%, OR = 1.07, p = 0.268); BOMA2-Munich (16.9% vs. 15.2%, OR = 1.11, p = 0.096); and MGS (17.1% vs. 15.1%, OR = 1.14, p = 0.0164). However, this only reached statistical significance in the MGS sample. This result may have been explained by the small effect size conferred by this variant and the relatively limited power of each individual sample to reach significance. The result is therefore not unexpected, given that MGS had the greatest power of the three investigated samples, comprising 52.7% of all patients (BOMA2-Munich: 24.0%; BOMA/ Utrecht–Rotterdam: 23.3%). To combine the effects from each sample, we tested the SNP in a meta-analysis and observed a p-value of 2.28 × 10 − 3 and an OR of 1.11. BOMA2-Munich and MGS contained small subgroups of patients with SAD (18% and 12%). To exclude the possibility that the observed association signal was largely driven by SAD patients (11% of all patients), we re-analyzed both samples without these patients. The results were essentially the same (data not shown). No separate analysis of SAD patients was performed in view of the small sample size. Some of the controls used in the first meta-analysis were also used in the GWAS and follow-up study of BD by Cichon et al. (2011; for details see Section 2.1, Subjects). To assess whether this overlap had a confounding effect on the present finding for schizophrenia, we performed a meta-analysis without these controls (reduction in sample size of ~27%). This generated p = 4.98 × 10 − 3 and OR = 1.11. The OR indicates the same effect size as that observed in the overall metaanalysis, but with a slightly larger p-value, as would be expected for a reduced sample size. In the replication step, we tested the marker in further 5537 patients and 8043 controls originating from different European populations (Sweden, Norway, Denmark, Germany, UK, Ireland, Bulgaria, Portugal), from the USA, and from Australia. These subjects are included in the PGC schizophrenia study (Ripke et al., 2011) which is one of the largest GWAS of schizophrenia analyzed to date. To avoid any overlap between our first-step and our follow-up, we excluded three samples from stage 1 of the PGC study (SGENE-Bonn, SGENEUCLA, MGS) and investigated only 14 of the available 17 PGC samples. Of these 14 samples, 11 showed an over-representation of the A-
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allele in patients compared to controls (data not shown). The overrepresentation of the rs1064395 risk allele in patients is consistent with the result of our first-step analysis and our previous BD study. The meta-analysis showed a significant p-value of 0.0239 (OR = 1.07). The effect size of rs1064395 in the present study of schizophrenia (first-step, OR = 1.11; follow-up, OR = 1.07) was smaller than the effect observed in the original study of BD (OR = 1.14 without GWAS step; Cichon et al., 2011, Table 1). This suggests that the variant may have a stronger impact on the development of BD than on schizophrenia. However, a precise estimation of the effect size (OR) may be hampered by various confounding factors, such as ascertainment bias between the studies and random allele frequency fluctuation. Although our studies have investigated >8000 BD patients and >10,000 schizophrenia patients, additional follow-up studies are needed to allow a more precise estimation of the small effect sizes of this common variant in BD and schizophrenia. In conclusion, the present study found significant association between NCAN rs1064395 and schizophrenia, and with the same risk allele as observed previously for BD. Our molecular genetic results strengthen the hypothesis of a genetic overlap between BD and schizophrenia. Functional studies are warranted to elucidate which patho-physiologically relevant changes in the brain are mediated by the risk allele. Role of funding source This study was supported by the German Federal Ministry of Education and Research (BMBF), within the context of the German National Genome Research Network plus (NGFNplus), and the Integrated Genome Research Network (IG) MooDS (grant 01GS08144 to Sven Cichon and Markus M. Nöthen, grant 01GS08147 to Marcella Rietschel). Igor Nenadic was supported by a Junior Scientist Grant of the IZKF, Medical School, Jena University Hospital. Igor Nenadic, Heinrich Sauer, Markus M. Nöthen, and Sven Cichon were additionally supported through an EU grant (EUTwinsS network, RTN, FP6). Jana Strohmaier was supported by the German Research Foundation (GRK 793). Christoph Lange and Manuel Mattheisen were supported by grants U01 HL089856, R01 MH087590 and R01 MH081862. The HNR study was conducted with the support of the Heinz Nixdorf Foundation, part of the genetic analyses was financed by the Medical Faculty of the University Hospital of Essen. The KORA study was initiated and financed by the Helmholtz Zentrum München — National Research Center for Environmental Health which is funded by the BMBF and by the State of Bavaria. Part of KORA was financed by the NGFN (NGFN-2 and NGFNplus grant 01GS0823) and supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. Genotyping of the Dutch samples from Utrecht was sponsored by NIMH funding, R01 MH078075. Funding support for the Molecular Genetics of Schizophrenia (MGS) sample was provided by the National Institute of Mental Health (R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01
Table 1 NCAN rs1064395 shows evidence for association with schizophrenia in three independent samples and replicates in a follow-up study; the original finding in BD is provided for comparison. MAF Patients First-step BOMA/Utrecht–Rotterdam (Rietschel et al., 2011) BOMA2/Munich (unpublished) MGS GAIN and nonGAIN with EA (Shi et al., 2009) Meta-analysis Follow-up Meta-analysis in Schizophrenia PGC (Ripke et al., 2011)c Original BD finding (Cichon et al., 2011)d
1169 1208 2684 5061
5537 8441 (7759)
Controls
a
b
MA
Patients
Controls
OR [95%CI]
3714 3300 2641 9655
A A A
0.158 0.169 0.171
0.147 0.152 0.151
1.07 [0.95; 1.22] 1.11 [0.98; 1.26] 1.14 [1.02; 1.26] 1.11
L2COV, 0.2680 L2COV, 0.0964 L3COV, 0.0164 FEM, 2.28 × 10− 3
8043 35,362 (34,062)
A A
0.179
0.164
1.07 1.17 (1.14)
FEM, 0.0239 FEM, 2.14 × 10− 9 (FEM, 1.30 × 10− 6)
Test, p-value
Abbreviations: 95%CI, lower and upper borders of the 95% confidence interval; BD, bipolar disorder; FEM, fixed-effect meta-analysis; L2COV or L3COV, logistic regression with covariates derived from multidimensional scaling analyses (first two or three dimensions) to minimize potential stratification effects; MA, minor allele; MAF, minor allele frequency; OR, odds ratio. Results of the meta-analysis of the first-step samples are highlighted in bold. a OR refers to the MA which is the risk allele. b In the first-step analysis in schizophrenia and in the original analysis in BD, p-values are two-sided. In the follow-up analysis in schizophrenia, the p-value is one-sided. c The PGC investigated 17 samples in their stage 1. In the follow-up of the present study, only 14 PGC samples were considered because BOMA/Utrecht–Rotterdam and MGS overlap with SGENE-Bonn, SGENE-UCLA and MGS investigated in the original PGC study. The MAF values given here are mean values across the 14 samples. d Meta-analysis of the GWAS and all follow-up samples. To account for inflation of the OR due to a possible winner's curse effect in the GWAS step, we re-calculated the p-value and the OR (in brackets) using all samples except the GWAS sample (682 patients, 1300 controls).
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MH46318, U01 MH79469, and U01 MH79470) and the genotyping of the sample was provided through the Genetic Association Information Network (GAIN). The datasets used for the analyses described in this manuscript were obtained from the database of Genotypes and Phenotypes (dbGaP) found at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession numbers phs000167.v1.p1 and phs000021.v3.p2. Samples and associated phenotype data for the MGS sample were provided by the MGS Collaboration (PI: Pablo V. Gejman, Evanston Northwestern Healthcare (ENH) and Northwestern University, Evanston, IL, USA). The above mentioned funding sources had no involvement in the study design, the analysis and interpretation of data, the writing of the report, or the decision to submit the paper for publication. Contributors Thomas W. Mühleisen, Manuel Mattheisen, Marcella Rietschel, Markus M. Nöthen, and Sven Cichon designed the study. Marcella Rietschel, Thomas G. Schulze, Jana Strohmaier, Dan Rujescu, Rainald Mössner, Wolfgang Maier, Markus Schwarz, Jutta Kammerer-Ciernioch, C. Christoph Schultz, Igor Nenadic, Ralf G.M. Schlösser, and Heinrich Sauer recruited and diagnosed the patients from Germany. Data from the Dutch patients were kindly provided by Roel A. Ophoff, and the GROUP (Genetic Risk and Outcome in Psychosis) Investigators René S Kahn1, Don H Linszen2, Jim van Os3, Durk Wiersma4, Richard Bruggeman4, Wiepke Cahn1, Lieuwe de Haan2, Lydia Krabbendam3, and Inez Myin-Germeys3. Susanne Moebus, Christian Gieger, Rebecca Emeny, Karl-Heinz Ladwig, H.-Erich Wichmann recruited the German controls, and Fernando Rivandeneira, Albert Hofman, and André G. Uitterlinden kindly provided the Dutch controls. Rene Breuer and Sandra Meier compiled the clinical data. Franziska Degenhardt, Lutz Priebe, and Per Hoffmann prepared the DNA and performed the genotyping. Manuel Mattheisen and Christoph Lange performed the statistical analysis. Thomas W. Mühleisen, Manuel Mattheisen, Markus M. Nöthen, and Sven Cichon analyzed and interpreted the data. Thomas W. Mühleisen, Manuel Mattheisen, Markus Nöthen, Marcella Rietschel, and Sven Cichon prepared the manuscript, with feedback from the other authors. 1 Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. 2 Academic Medical Centre University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands. 3 Maastricht University Medical Centre, South Limburg Mental Health Research and Teaching Network, Maastricht, The Netherlands. 4 University Medical Center Groningen, Department of Psychiatry, University of Groningen, Groningen, The Netherlands. Conflict of interest The authors declare that they have no competing financial or other interests that might be perceived to influence the results and discussion reported in this paper. Acknowledgments We are grateful to all of the patients and control probands who contributed to this study. We thank the schizophrenia group of the Psychiatric GWAS Consortium (PGC) for providing access to the relevant data for SNP rs1064395. We also thank Christine Schmäl for carefully reading the manuscript.
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Association between schizophrenia and common variation in neurocan (NCAN), a genetic risk factor for bipolar disorder Thomas W. Mühleisen a, b, 1, Manuel Mattheisen a, c, d, 1, Jana Strohmaier e, Franziska Degenhardt a, b, Lutz Priebe a, b, C. Christoph Schultz f, René Breuer e, Sandra Meier e, Per Hoffmann a, b, GROUP Investigators 2, Fernando Rivandeneira g, h, Albert Hofman h, André G. Uitterlinden g, h, Susanne Moebus i, Christian Gieger j, Rebecca Emeny k, Karl-Heinz Ladwig k, l, H.-Erich Wichmann m, n, Markus Schwarz o, Jutta Kammerer-Ciernioch o, Ralf G.M. Schlösser f, Igor Nenadic f, Heinrich Sauer f, Rainald Mössner p, Wolfgang Maier p, Dan Rujescu q, Christoph Lange c, d, Roel A. Ophoff r, s, Thomas G. Schulze t, Marcella Rietschel e, Markus M. Nöthen a, b, u, Sven Cichon a, b, v,⁎ a
Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany Institute of Human Genetics, University of Bonn, Bonn, Germany c Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA d Institute for Genomic Mathematics, University of Bonn, Bonn, Germany e Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Mannheim, Germany f Department of Psychiatry and Psychotherapy, Friedrich-Schiller-University Jena, Jena, Germany g Department of Internal Medicine, Genetics Laboratory, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands h Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands i Institute of Medical Informatics, Biometry, and Epidemiology, University Duisburg-Essen, Essen, Germany j Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany k Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany l Department of Psychosomatic Medicine and Psychotherapy, Klinikum Rechts der Isar, Technische Universität, Munich, Germany m Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany n Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität and Klinikum Grosshadern, Munich, Germany o Psychiatric Center Nordbaden, Wiesloch, Germany p Department of Psychiatry, University of Bonn, Germany q Department of Psychiatry, Division of Molecular and Clinical Neurobiology, Ludwig-Maximilians-University, Munich, Germany r Department of Medical Genetics and Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands s UCLA Center for Neurobehavioral Genetics, Los Angeles, CA, USA t Department of Psychiatry and Psychotherapy, University Medical Center, University of Göttingen, Göttingen, Germany u German Center for Neurodegenerative Disorders (DZNE), Bonn, Germany v Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany b
a r t i c l e
i n f o
Article history: Received 26 October 2011 Received in revised form 2 March 2012 Accepted 6 March 2012 Available online 11 April 2012 Keywords: Association Genetic overlap Manic depression Psychotic disorder Cortex Hippocampus
a b s t r a c t A recent study found genome-wide significant association between common variation in the gene neurocan (NCAN, rs1064395) and bipolar disorder (BD). In view of accumulating evidence that BD and schizophrenia partly share genetic risk factors, we tested this single-nucleotide polymorphism for association with schizophrenia in three independent patient–control samples of European ancestry, totaling 5061 patients and 9655 controls. The rs1064395 A-allele, which confers risk for BD, was significantly over-represented in schizophrenia patients compared to controls (p = 2.28 × 10 − 3; odds ratio = 1.11). Follow-up in non-overlapping samples from the Schizophrenia Psychiatric GWAS Consortium (5537 patients, 8043 controls) provided further support for our finding (p = 0.0239, odds ratio = 1.07). Our data suggest that genetic variation in NCAN is a common risk factor for BD and schizophrenia. © 2012 Elsevier B.V. All rights reserved.
⁎ Corresponding author at: Department of Genomics, Life & Brain Center, University of Bonn, Sigmund-Freud-Strasse 25, D-53105 Bonn, Germany. Tel.: +49 228 6885 405; fax: +49 228 6885 401. E-mail address: [email protected] (S. Cichon). 1 These authors contributed equally to this work. 2 For a full list of members see Contributors. 0920-9964/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2012.03.007
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1. Introduction Bipolar disorder (BD) and schizophrenia are genetically complex neuropsychiatric disorders with a life-time prevalence of around 1%. Formal genetic data have suggested a partial etiological overlap between BD and schizophrenia (Lichtenstein et al., 2009; Van Snellenberg and de Candia, 2009), and molecular data have provided evidence for the presence of overlapping genetic factors (ISC, 2009). The most robust evidence has been obtained from recent genomewide association studies (GWAS). These suggest genetic effects across diagnostic categories for the zinc finger transcription factor ZNF804A (O'Donovan et al., 2008; Williams et al., 2010; Williams et al., 2011), and the voltage-dependent calcium channel CACNA1C (Ferreira et al., 2008; Moskvina et al., 2009; Green et al., 2010; Nyegaard et al., 2010; Ripke et al., 2011). In a recent GWAS and a follow-up study of BD samples from Europe, we found genome-wide significant association for the single-nucleotide polymorphism (SNP) rs1064395 in the 3′ untranslated region of the neurocan gene (NCAN) on chromosome 19p13.11 (p= 3.02 × 10 − 8, odds ratio (OR)= 1.31; Cichon et al., 2011). We then replicated this association in independent European, US-American, and Australian BD samples (p= 2.74 × 10− 4, OR= 1.12; Cichon et al., 2011). In a combined analysis of these samples (8441 patients and 35,362 controls), the p-value reached 2.14 × 10− 9 (OR = 1.17). Functional data indicate that NCAN is a promising candidate gene for both BD and schizophrenia. Neurocan (OMIM 600826) is a member of the lectican family of chondroitin sulfate proteoglycans (CSPGs). As a hyaluronan-binding CSPG, neurocan is an important constituent of the brain extracellular matrix, and is thought to be involved in neuronal adhesion and neurite outgrowth (for review see Frischknecht and Seidenbecher, 2008). We found that NCAN expression in wild-type mice was localized within the cortex and the hippocampus (Cichon et al., 2011). Both brain regions are involved in cognition and the regulation of emotion, and independent functional studies in humans have correlated these areas with BD and schizophrenia (for review see Martinowich et al., 2009). To test whether NCAN also contributes to susceptibility to schizophrenia, we analyzed rs1064395 in three large data sets: (i) a combined German and Dutch GWAS sample of schizophrenia (Rietschel et al., 2011); (ii) an additional, as yet unpublished German sample of schizophrenia; and (iii) the European-American sample from the Molecular Genetics of Schizophrenia (MGS) GWAS (Shi et al., 2009). Association analysis in these samples strongly suggested that this SNP also confers a risk for schizophrenia. In further analyses, we excluded the possibility that this finding was caused by confounding factors, such as the use of control individuals from the previous BD study, and the presence of patients with schizoaffective disorder (SAD). Furthermore, we replicated our finding for rs1064395 in independent samples from the Psychiatric GWAS Consortium (PGC) study of schizophrenia (Ripke et al., 2011). 2. Methods 2.1. Subjects The BOMA/Utrecht–Rotterdam sample comprised 1169 patients with a DSM-IV diagnosis of schizophrenia and 3714 controls. These were of German (Bonn/Mannheim — BOMA) and Dutch (Utrecht– Rotterdam) ancestries. Ascertainment details have been described elsewhere (Rietschel et al., 2011). The German controls (BOMA) are those used in the GWAS step in the study of BD by Cichon et al. (2011). The second sample (BOMA2-Munich, unpublished) comprised 1208 patients and 3300 controls of German ancestry. All patients received a lifetime diagnosis of schizophrenia according to the DSMIV criteria on the basis of structured diagnostic interviews (Endicott and Spitzer, 1978; McGuffin et al., 1991; First et al., 1996) and a
consensus best-estimate procedure (Leckman et al., 1982). These patients were recruited from consecutive hospital admissions at the Central Institute of Mental Health, Mannheim; the Department of Psychiatry of the University of Bonn; and the Department of Psychiatry, Ludwig-Maximilians-University, Munich. The sample included a subgroup of 221 patients with SAD. A total of 1697 population-based controls were derived from the Heinz Nixdorf Recall Study (HNR, Schmermund et al., 2002), and 1603 from the KORA study (Wichmann et al., 2005). The third sample of 2684 schizophrenia patients and 2641 controls was derived from the Database of Genotypes and Phenotypes (dbGaP, Mailman et al., 2007) schizophrenia datasets phs000167.v1.p1 and phs000021.v3.p2. A description of the Molecular Genetics of Schizophrenia (MGS) sample of European-American ancestry is provided in Shi et al. (2009). MGS contained a subgroup of 306 patients with SAD. A total of 1323 controls overlap with the GAIN-EA/TGEN1 sample investigated in the follow-up study of BD by Cichon et al. (2011). In a subsequent step, we tested rs1064395 in the stage 1 samples of the PGC schizophrenia study (Ripke et al., 2011), after removal of all samples already tested in our previously described samples. The nonoverlapping sample consisted of 5537 patients and 8043 controls, all of European and European-American descent (see also Section 3, Results and discussion). 2.2. Genotyping and quality control For the first-step analysis, rs1064395 genotypes were extracted from the following genome-wide data sets: (i) the BOMA/Utrecht– Rotterdam sample; (ii) the new BOMA2-Munich sample, genotyped at the Department of Genomics, Life & Brain Center, Bonn; and (iii) the MGS sample. The quality control (QC) of BOMA2-Munich followed the protocol described by Rietschel et al. (2011). After QC, BOMA2-Munich consisted of 596 patients with Human610-Quad data, 612 patients with Human660W-Quad data, 669 HNR controls with HumanOmni1-Quad data, 1028 HNR controls with HumanOmniExpress data, and 1603 KORA controls with HumanOmni2.5 data (Illumina, San Diego, CA, USA). KORA data were provided by the KORA study, Helmholtz Zentrum München, Neuherberg. From the quality-controlled MGS data available through dbGaP, we removed additional subjects (population outliers, cryptically related, and duplicates) identified through our QC protocol. For the follow-up analysis, quality-controlled data of rs1064395 were extracted from stage 1 of the Schizophrenia PGC study (see also Section 3, Results and discussion). 2.3. Association analysis In the first-step analysis, association was tested in each sample using a logistic regression analysis and assumption of an additive effect. To minimize potential stratification effects, regression was performed with covariates derived from multidimensional scaling analyses (two dimensions for BOMA/Utrecht–Rotterdam and BOMA2-Munich; and three dimensions for MGS). For the combined analyses, a fixed-effects meta-analysis was performed, as based on the weighted z-score method (de Bakker et al., 2008) and implemented in PLINK (Purcell et al., 2007). The test was two-tailed in the first-step meta-analysis, and one-tailed in the follow-up meta-analysis. 3. Results and discussion Convergent lines of evidence from formal genetic and association studies of BD and schizophrenia support the hypothesis that these two disorders share a certain fraction of common etiological factors. In a recent GWAS and follow-up study we found that common genetic variation in the NCAN gene (rs1064395, allele A) is a risk factor for BD (Cichon et al., 2011). The logical next step is to test whether this factor
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is also implicated in schizophrenia. We therefore analyzed SNP rs1064395 in a series of published and unpublished samples of schizophrenia. An overview of the results is provided by Table 1. The SNP passed all filters of our QC protocol. In all three samples, we observed an over-representation of the risk allele A in patients compared to controls: BOMA/Utrecht–Rotterdam (15.8% vs. 14.7%, OR = 1.07, p = 0.268); BOMA2-Munich (16.9% vs. 15.2%, OR = 1.11, p = 0.096); and MGS (17.1% vs. 15.1%, OR = 1.14, p = 0.0164). However, this only reached statistical significance in the MGS sample. This result may have been explained by the small effect size conferred by this variant and the relatively limited power of each individual sample to reach significance. The result is therefore not unexpected, given that MGS had the greatest power of the three investigated samples, comprising 52.7% of all patients (BOMA2-Munich: 24.0%; BOMA/ Utrecht–Rotterdam: 23.3%). To combine the effects from each sample, we tested the SNP in a meta-analysis and observed a p-value of 2.28 × 10 − 3 and an OR of 1.11. BOMA2-Munich and MGS contained small subgroups of patients with SAD (18% and 12%). To exclude the possibility that the observed association signal was largely driven by SAD patients (11% of all patients), we re-analyzed both samples without these patients. The results were essentially the same (data not shown). No separate analysis of SAD patients was performed in view of the small sample size. Some of the controls used in the first meta-analysis were also used in the GWAS and follow-up study of BD by Cichon et al. (2011; for details see Section 2.1, Subjects). To assess whether this overlap had a confounding effect on the present finding for schizophrenia, we performed a meta-analysis without these controls (reduction in sample size of ~27%). This generated p = 4.98 × 10 − 3 and OR = 1.11. The OR indicates the same effect size as that observed in the overall metaanalysis, but with a slightly larger p-value, as would be expected for a reduced sample size. In the replication step, we tested the marker in further 5537 patients and 8043 controls originating from different European populations (Sweden, Norway, Denmark, Germany, UK, Ireland, Bulgaria, Portugal), from the USA, and from Australia. These subjects are included in the PGC schizophrenia study (Ripke et al., 2011) which is one of the largest GWAS of schizophrenia analyzed to date. To avoid any overlap between our first-step and our follow-up, we excluded three samples from stage 1 of the PGC study (SGENE-Bonn, SGENEUCLA, MGS) and investigated only 14 of the available 17 PGC samples. Of these 14 samples, 11 showed an over-representation of the A-
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allele in patients compared to controls (data not shown). The overrepresentation of the rs1064395 risk allele in patients is consistent with the result of our first-step analysis and our previous BD study. The meta-analysis showed a significant p-value of 0.0239 (OR = 1.07). The effect size of rs1064395 in the present study of schizophrenia (first-step, OR = 1.11; follow-up, OR = 1.07) was smaller than the effect observed in the original study of BD (OR = 1.14 without GWAS step; Cichon et al., 2011, Table 1). This suggests that the variant may have a stronger impact on the development of BD than on schizophrenia. However, a precise estimation of the effect size (OR) may be hampered by various confounding factors, such as ascertainment bias between the studies and random allele frequency fluctuation. Although our studies have investigated >8000 BD patients and >10,000 schizophrenia patients, additional follow-up studies are needed to allow a more precise estimation of the small effect sizes of this common variant in BD and schizophrenia. In conclusion, the present study found significant association between NCAN rs1064395 and schizophrenia, and with the same risk allele as observed previously for BD. Our molecular genetic results strengthen the hypothesis of a genetic overlap between BD and schizophrenia. Functional studies are warranted to elucidate which patho-physiologically relevant changes in the brain are mediated by the risk allele. Role of funding source This study was supported by the German Federal Ministry of Education and Research (BMBF), within the context of the German National Genome Research Network plus (NGFNplus), and the Integrated Genome Research Network (IG) MooDS (grant 01GS08144 to Sven Cichon and Markus M. Nöthen, grant 01GS08147 to Marcella Rietschel). Igor Nenadic was supported by a Junior Scientist Grant of the IZKF, Medical School, Jena University Hospital. Igor Nenadic, Heinrich Sauer, Markus M. Nöthen, and Sven Cichon were additionally supported through an EU grant (EUTwinsS network, RTN, FP6). Jana Strohmaier was supported by the German Research Foundation (GRK 793). Christoph Lange and Manuel Mattheisen were supported by grants U01 HL089856, R01 MH087590 and R01 MH081862. The HNR study was conducted with the support of the Heinz Nixdorf Foundation, part of the genetic analyses was financed by the Medical Faculty of the University Hospital of Essen. The KORA study was initiated and financed by the Helmholtz Zentrum München — National Research Center for Environmental Health which is funded by the BMBF and by the State of Bavaria. Part of KORA was financed by the NGFN (NGFN-2 and NGFNplus grant 01GS0823) and supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. Genotyping of the Dutch samples from Utrecht was sponsored by NIMH funding, R01 MH078075. Funding support for the Molecular Genetics of Schizophrenia (MGS) sample was provided by the National Institute of Mental Health (R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01
Table 1 NCAN rs1064395 shows evidence for association with schizophrenia in three independent samples and replicates in a follow-up study; the original finding in BD is provided for comparison. MAF Patients First-step BOMA/Utrecht–Rotterdam (Rietschel et al., 2011) BOMA2/Munich (unpublished) MGS GAIN and nonGAIN with EA (Shi et al., 2009) Meta-analysis Follow-up Meta-analysis in Schizophrenia PGC (Ripke et al., 2011)c Original BD finding (Cichon et al., 2011)d
1169 1208 2684 5061
5537 8441 (7759)
Controls
a
b
MA
Patients
Controls
OR [95%CI]
3714 3300 2641 9655
A A A
0.158 0.169 0.171
0.147 0.152 0.151
1.07 [0.95; 1.22] 1.11 [0.98; 1.26] 1.14 [1.02; 1.26] 1.11
L2COV, 0.2680 L2COV, 0.0964 L3COV, 0.0164 FEM, 2.28 × 10− 3
8043 35,362 (34,062)
A A
0.179
0.164
1.07 1.17 (1.14)
FEM, 0.0239 FEM, 2.14 × 10− 9 (FEM, 1.30 × 10− 6)
Test, p-value
Abbreviations: 95%CI, lower and upper borders of the 95% confidence interval; BD, bipolar disorder; FEM, fixed-effect meta-analysis; L2COV or L3COV, logistic regression with covariates derived from multidimensional scaling analyses (first two or three dimensions) to minimize potential stratification effects; MA, minor allele; MAF, minor allele frequency; OR, odds ratio. Results of the meta-analysis of the first-step samples are highlighted in bold. a OR refers to the MA which is the risk allele. b In the first-step analysis in schizophrenia and in the original analysis in BD, p-values are two-sided. In the follow-up analysis in schizophrenia, the p-value is one-sided. c The PGC investigated 17 samples in their stage 1. In the follow-up of the present study, only 14 PGC samples were considered because BOMA/Utrecht–Rotterdam and MGS overlap with SGENE-Bonn, SGENE-UCLA and MGS investigated in the original PGC study. The MAF values given here are mean values across the 14 samples. d Meta-analysis of the GWAS and all follow-up samples. To account for inflation of the OR due to a possible winner's curse effect in the GWAS step, we re-calculated the p-value and the OR (in brackets) using all samples except the GWAS sample (682 patients, 1300 controls).
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MH46318, U01 MH79469, and U01 MH79470) and the genotyping of the sample was provided through the Genetic Association Information Network (GAIN). The datasets used for the analyses described in this manuscript were obtained from the database of Genotypes and Phenotypes (dbGaP) found at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession numbers phs000167.v1.p1 and phs000021.v3.p2. Samples and associated phenotype data for the MGS sample were provided by the MGS Collaboration (PI: Pablo V. Gejman, Evanston Northwestern Healthcare (ENH) and Northwestern University, Evanston, IL, USA). The above mentioned funding sources had no involvement in the study design, the analysis and interpretation of data, the writing of the report, or the decision to submit the paper for publication. Contributors Thomas W. Mühleisen, Manuel Mattheisen, Marcella Rietschel, Markus M. Nöthen, and Sven Cichon designed the study. Marcella Rietschel, Thomas G. Schulze, Jana Strohmaier, Dan Rujescu, Rainald Mössner, Wolfgang Maier, Markus Schwarz, Jutta Kammerer-Ciernioch, C. Christoph Schultz, Igor Nenadic, Ralf G.M. Schlösser, and Heinrich Sauer recruited and diagnosed the patients from Germany. Data from the Dutch patients were kindly provided by Roel A. Ophoff, and the GROUP (Genetic Risk and Outcome in Psychosis) Investigators René S Kahn1, Don H Linszen2, Jim van Os3, Durk Wiersma4, Richard Bruggeman4, Wiepke Cahn1, Lieuwe de Haan2, Lydia Krabbendam3, and Inez Myin-Germeys3. Susanne Moebus, Christian Gieger, Rebecca Emeny, Karl-Heinz Ladwig, H.-Erich Wichmann recruited the German controls, and Fernando Rivandeneira, Albert Hofman, and André G. Uitterlinden kindly provided the Dutch controls. Rene Breuer and Sandra Meier compiled the clinical data. Franziska Degenhardt, Lutz Priebe, and Per Hoffmann prepared the DNA and performed the genotyping. Manuel Mattheisen and Christoph Lange performed the statistical analysis. Thomas W. Mühleisen, Manuel Mattheisen, Markus M. Nöthen, and Sven Cichon analyzed and interpreted the data. Thomas W. Mühleisen, Manuel Mattheisen, Markus Nöthen, Marcella Rietschel, and Sven Cichon prepared the manuscript, with feedback from the other authors. 1 Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. 2 Academic Medical Centre University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands. 3 Maastricht University Medical Centre, South Limburg Mental Health Research and Teaching Network, Maastricht, The Netherlands. 4 University Medical Center Groningen, Department of Psychiatry, University of Groningen, Groningen, The Netherlands. Conflict of interest The authors declare that they have no competing financial or other interests that might be perceived to influence the results and discussion reported in this paper. Acknowledgments We are grateful to all of the patients and control probands who contributed to this study. We thank the schizophrenia group of the Psychiatric GWAS Consortium (PGC) for providing access to the relevant data for SNP rs1064395. We also thank Christine Schmäl for carefully reading the manuscript.
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