Psychiatry Research 230 (2015) 964–967

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Association of age-of-onset groups with GWAS significant schizophrenia and bipolar disorder loci in Romanian bipolar I patients Maria Grigoroiu-Serbanescu a,n, Carmen C. Diaconu b, Stefanie Heilmann-Heimbach c, Ana Iulia Neagu b, Tim Becker d a

Alexandru Obregia Clinical Psychiatric Hospital, Biometric Psychiatric Genetics Research Unit, Bucharest, Romania Stefan S. Nicolau Institute of Virology, Bucharest, Romania c Institute of Human Genetics, Dept. Genomics, Life & Brain Center, University of Bonn, Bonn, Germany d Institute for Community Medicine, Ernst Moritz Arndt University Greifswald, D-17475 Greifswald, Germany b

art ic l e i nf o

a b s t r a c t

Article history: Received 22 July 2015 Received in revised form 28 October 2015 Accepted 4 November 2015 Available online 10 November 2015

We investigated the influence of the age-of-onset (AO) on the association of 45 loci conferring risk for bipolar disorder (BP) and schizophrenia with BP-type-I in a Romanian sample (461 patients, 436 controls). The AO-analysis implicated the EGFR gene, as well as loci in other genes, in the AO variation of BPtype-I and revealed for the first time the link between BP-type-I and risk variants considered specific to schizophrenia (polymorphisms in MMP16/RIPK2 and CNNM2 genes). & 2015 Elsevier Ireland Ltd. All rights reserved.

Keywords: EGFR MMP16 CNNM2 genes

1. Introduction Bipolar disorder (BP) and schizophrenia are severe heritable psychiatric disorders. Epidemiologic (Lichtenstein et al., 2009) and molecular studies suggest that BP and schizophrenia partly share their genetic liability. The Cross-Disorder Group of Psychiatric Genomics Consortium (PGC) (2013) analyzed genome-wide data obtained for five psychiatric disorders and demonstrated high SNP-based co-heritability between schizophrenia and BP (0.68). Schizophrenia Working Group of the PGC (2014) identified 108 loci significantly associated with schizophrenia at genome-wide level; some of these loci were involved not only in schizophrenia, but also in BP (Chen et al., 2013; Mühleisen et al., 2014). Evidence is growing that the contribution of genome-wide significantly associated risk loci to the genetic liability to major psychoses in different populations might depend on disorder subphenotype, in particular on the subphenotype generated by the age-of-onset (AO) (Mathieu et al., 2010; Priebe et al., 2012; Jamain et al., 2014). Several clinical studies showed that early- and lateonset forms of BP and schizophrenia are accompanied by different morbid risk for major psychoses to first degree relatives of probands (Rice et al., 1987; Schürhoff et al., 2000; Byrne et al., 2002; n Correspondence to: Alexandru Obregia Clinical Psychiatric Hospital, Biometric Psychiatric Genetics Research Unit, 10, Sos. Berceni, R-041914 Bucharest, Romania. E-mail address: [email protected] (M. Grigoroiu-Serbanescu).

http://dx.doi.org/10.1016/j.psychres.2015.11.008 0165-1781/& 2015 Elsevier Ireland Ltd. All rights reserved.

Grigoroiu-Serbanescu et al., 2001, 2014), which might suggest a different expression of the genetic propensity. Therefore the aim of the present study was to investigate the hypothesis of a possible AO influence on the association of selected risk loci for BP and schizophrenia revealed by large-scale genome-wide association studies (GWAS) with BP-type-I (BP-I) in the Romanian population.

2. Methods 2.1. Patient and control samples The study was approved by the grant committee of the Romanian Ministry for Education and Research. All participants provided written informed consent following a detailed explanation of the study aims and procedures. All patients and controls were of Romanian descent. Genealogical information about parents and all four grandparents was obtained through direct interview of the subjects. 461 unrelated BP-I patients were recruited from consecutive hospital admissions. The diagnosis of BP-I was assigned according to DSM-IV criteria (APA, 1994) on the basis of both the Diagnostic Interview for Genetic Studies (DIGS) (NIMH, 1995) and medical records. Patients were included in the sample if they had at least two documented hospitalized illness episodes (one manic/mixed and one depressive or two manic episodes) and no residual mood

M. Grigoroiu-Serbanescu et al. / Psychiatry Research 230 (2015) 964–967

incongruent psychotic symptoms during remissions. This information was confirmed by first degree relatives for 64% of the cases. Detailed clinical-demographic information about patients is presented in Table S_1. AO was defined as the age at which the proband first met DSMIV criteria for a manic, mixed, or major depressive episode. Population-based controls were screened with DIGS for lifetime history of any major affective or schizoaffective disorders, schizophrenia and any other psychosis, obsessive-compulsive disorder, eating disorders, alcohol/drug addiction. 438 unaffected controls were included in the study. There were no significant differences between cases and controls in terms of age and sex distribution (Table S_1). 2.2. SNP selection and genotyping Fifty genome-wide significant SNPs in SCZ and BP studies with more than 10,000 subjects (Psychiatric GWAS Consortium Bipolar Disorder Working Group, 2011; The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium, 2011; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013), as well as SNPs considered relevant for these disorders in the review by Sullivan et al. (2012) were selected for genotyping in our samples. Primer molecules for the multiplex reaction were designed using the Assay Design Suite tool (www.mysequenom. com, Sequenom, San Diego, CA, USA). Genotyping was performed in 2013 at the Department of Genomics, Life & Brain Center (University of Bonn, Germany) using Sequenom's Mass Array System and iPlex Gold reagents in accordance with the manufacturer's instructions. A detailed description of the method can be found in Blondal et al. (2003). 2.3. Quality control Quality control (QC) was performed using INTERSNPv.1.11 software (Herold et al., 2009). Only SNPs with call rate Z90% and no significant deviation from Hardy–Weinberg–Equilibrium at p ¼0.05 in controls and patients were included in the analysis. Individuals were removed from the analysis if their DNA call rate was o90% or there was discrepancy between genotyped and phenotypic sex. After quality control, six patients, nine controls and five genotyped SNPs were excluded from the downstream analysis. The list of SNPs that passed the QC is shown in Table S_2. The final samples consisted of 455 cases (267 females and 188 males) and 429 controls (256 females; 173 males). 2.4. Statistical analysis The association between disease status and SNP-genotypes, as well as the AO-influence on this association were analyzed using both logistic regression and linear regression as implemented in INTERSNP software (Herold et al., 2009). Two AO-groups (early-onset, late-onset) were defined for the logistic regression using commingling (admixture) analysis performed with the SEGREG program of S.A.G.E.v6.1 software (S.A.G. E., 2012). The model best fitting the data was chosen based on the smallest AIC-value. Since there was no significant difference between cases and controls in terms of age and sex distribution, two variables that might have influenced the regression results, they were not included as covariates in the analysis. The Bonferroni correction for multiple testing (45 SNPs) was applied to the nominal p-values. This resulted in a corrected p-value of 0.001 for determining significance of results. The sample power to detect an allelic association at alpha ¼0.05 (uncorrected for multiple testing) was 45% for a minor

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allele frequency (MAF) of 0.20 (average MAF-value in our samples) and 36% for a MAF of 0.10 (lowest MAF-value in our samples). The expected effect size was 1.3. The computation was performed with the Genetic Power Calculator (http://pngu.mgh.harvard.edu/pur cell/gpc).

3. Results Following the results of commingling/admixture analysis of AO in our sample (Fig. S_1) we chose the age 25 as cut-off point for the early- and late-onset (for details see Grigoroiu-Serbanescu et al. (2014)). 220 patients had an early-onset (AOo 25 years) and 235 patients had a late-onset (AOZ 25 years). None of the 45 SNPs passing the QC was significantly associated with BP-I in our total patient sample after Bonferroni correction of the p-values. But two SNPs reached nominal significance in the total sample (Table 1-panel A) replicating the discovery studies: rs11764590 (intronic, MAD1L1 gene) and rs9834970 downstream of TRANK1/LBA genes. Two other SNPs reached nominal significance in the total sample (rs4939921, MYO5B gene, p ¼ 0.026 and rs3818253, TRPC4AP gene, p ¼0.025), but the opposite alleles were the risk alleles compared to the discovery studies. The logistic regression (Table 1-panel A) showed that two SNPs reached nominal significance in the early-onset group [rs7004633 (intergenic, MMP16/RIPK2), rs9834970 (TRANK1/LBA)], while in the late-onset group seven SNPs showed nominally significant associations with BP-I: rs11191454 (AS3MT), rs11764590 (NT5C2), rs17172438 (EGFR), rs729969(EGFR), rs11764590 (MAD1L1), rs7914558 (CNNM2), and rs1064395 (NCAN). The SNPs in the genes AS3MT, CNNM2, NT5C2 are in LD. An additional SNP, rs10503253, located in CSMD1, showed a trend towards association (p ¼0.074). The linear regression of the AO in the total sample replicated the involvement of most of these SNPs in the AO variability of BP-I (Table 1-panel B); a positive Beta indicates an association of the risk allele with a later AO and a negative Beta supports an association with an earlier AO. The SNP rs17172438 (EGFR gene) withstood the Bonferroni correction and a new SNP (ANK3 gene) displayed nominally significant association with AO.

4. Discussion Our study is the first one to examine the possible association between SNPs significant in large-scale GWAS of schizophrenia and BP and AO in BP-I, the most severe form of BP disorder. It provided suggestive evidence that AO might contribute to the specificity of the molecular overlap between schizophrenia and BP-I and that two new loci (rs7004633 in MMP16/RIPK2 and rs7914558 in CNNM2), previously linked only to schizophrenia, might be involved in BP-I too. The linear regression of the investigated SNPs on the disease AO evidenced a SNP in the EGFR gene that withstood the Bonferroni correction and contributed to the variability of the AO in our total sample, being mainly associated with later onset. Our study is the first one that replicates the association between EGFR (epidermal growth factor receptor) and BP, first described by Sklar et al. (2008). The EGFR is involved in neural stem cell proliferation and in circadian rhythm alterations through increasing phosphorylation activity of GSK3b (Kramer et al., 2001; Foltenyi et al., 2007). This is consistent with the effects of mood stabilizers that target the inhibition of GSK3b activity in BP-I patients. Weber et al. (2011) considered EGFR and its regulators candidate molecules for psychiatric disorders. Several SNPs, nominally significant in the AO analysis in our sample (AS3MT, NT5C2, CNNM2, MAD1L1, TRANK1, NCAN, CSMD1,

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M. Grigoroiu-Serbanescu et al. / Psychiatry Research 230 (2015) 964–967

Table 1 SNP association with BP-I age-of-onset in the Romanian sample. Panel A – Risk allele and odds ratio in the discovery sample and in the Romanian sample (logistic regression). Panel B – linear regression in the Romanian sample. Panel A SNP_ID/Gene

Discovery sample Risk allele

OR*

Romanian sample Risk allele Risk allele cases

rs11764590 (MAD1L1) rs9834970 (TRANK1/LBA1) rs7004633 (Near MMP16)

T C G

1.47 1.17 1.05

T C G

rs7004633 (Near MMP16) rs9834970 (TRANK1/LBA)

G C

1.05 1.17

rs11191454(AS3MT) rs11191580 (NT5C2) rs17172438(EGFR) rs729969(EGFR) rs11764590 (MAD1L1) rs7914558(CNNM2) rs1064395 (NCAN) rs10503253 (CSMD1)

A T C T T G A A

Panel B SNP_ ID rs17172438

Risk allele controls

OR (95%CI)$

p-Value**

Total sample 0.28 0.50 0.27

0.23 0.45 0.24

OR ¼1.28 (1.04–1.59) OR ¼1.24 (1.03–1.49) OR ¼1.20 (0.97–1.48)

0.019 0.024 0.085

G C

Early onset group 0.31 0.52

0.24 0.45

OR ¼1.41 (1.09–1.82) OR ¼1.33 (1.05–1.67)

0.005 0.013

1.13 1.15 1.32 1.36 1.47 1.10 1.53 1.08

A T C T T G A A

Late onset group 0.93 0.93 0.19 0.14 0.28 0.59 0.21 0.23

0.88 0.89 0.14 0.10 0.23 0.53 0.17 0.19

OR ¼1.68 OR ¼1.62 OR ¼1.45 OR ¼1.39 OR ¼1.31 OR ¼1.25 OR ¼1.31 OR ¼1.28

0.007 0.015 0.015 0.049 0.03 0.052 0.053 0.074

Romanian Sample Chr 7p11.2

Position 55151537

Gene EGFR

Risk allele C

rs729969

7p11.2

55128207

EGFR

T

p** p ¼0.00081 p correct¼ 0.035 p ¼0.015

rs11191454

10q24.32

104660004

AS3MT

A

p ¼0.008

rs11191580

10q24.33

104906211

NT5C2

T

p ¼0.022

rs7004633

8q21.3

89760311

MMP16

G

p ¼0.028

rs9804190

10q21.2

61839831

ANK3

T

p ¼0.035

rs7914558

10q24.32

104775908

CNNM2

G

p ¼0.055

Beta (SE)/ 95%CI 3.08 (0.91) (1.29/4.87) 2.51 (1.03) (0.48/4.54) 3.00 (1.14) (0.76/5.23) 2.65 (1.16) (0.37/4.92)  1.70 (0.77) (  3.21/  0.18) 1.68 (0.79) (0.12/3.24) 1.24 (0.66) (0.05/2.54)

(1.11–2.52) (1.07–2.45) (1.07–1.96) (0.99–1.95) (1.01–1.68) (1.0–1.57) (0.98–1.74) (0.97–1.69)

*

OR ¼ odds ratio. Uncorrected P-values for multiple testing. $ CI ¼ confidence interval. **

and MMP16), are located in genes associated with schizophrenia in the recent study of Schizophrenia Working Group of PGC (2014). Although we investigated SNPs different from those involved in this study, but located in the same genes, we may speculate that the SNPs studied by the Schizophrenia Working Group of PGC might also be influenced by AO, since SNPs do not act independently. The study has several limitations: limited power to detect small effects due to its relatively small sample size, the small number of SNPs investigated, and lack of a replication sample. In conclusion, our exploratory study suggests that early- and late-onset BP-I might represent, at least partially, genetically distinct forms. The AO analysis led to the replication of the association between EGFR-SNPs and BP-I and implicated two schizophrenia-risk variants in BP-I for the first time.

Author contribution M.G.S. designed the study, supervised the patient recruitment, investigation, and phenotyping, wrote the manuscript, got funding; S.H.H.. conducted the genotyping; T.B. conducted the statistical analysis. C.C.D. and A.I.N. performed DNA extraction, quality control, managed biological databases.

Conflict of interests The authors have no conflicts of interest to declare.

Funding agencies The study was funded by the Executive Agency for Higher Education, Research, Development, and Innovation Funding (UEFISCDI) of the Romanian Ministry for National Education, Bucharest, Romania (grant number 89/2012 to Maria Grigoroiu-Serbanescu). The study was also supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Genome Research Network (IG) MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia; grant 01GS08144 to M.M. Nöthen and S. Cichon) under the auspices of the National Genome Research Network plus (NGFNplus). These funding sources had no involvement in the study design, the collection, analysis, and interpretation of data, the writing of the report or the decision to submit the paper for publication.

Acknowledgments The first author is grateful to prof. M.M. Nöthen (Institute of

M. Grigoroiu-Serbanescu et al. / Psychiatry Research 230 (2015) 964–967

Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Germany) for supporting the work, to Dr. Franziska Degenhardt (Institute of Human Genetics and Department of Genomics, Life & Brain Center, University of Bonn, Germany) for designing the SNP selection and supervising the genotyping. We thank all patients and controls for their participation in this study, as well as Drs. Dan Prelipceanu, Anca Tanase, Marina Codreanu, Dorina Sima, Dana Cojocaru, Carmen Udrea, Anca Talasman, Mihail Gherghel, and the research assistants Elvira Marinescu and Mihaela Hrestic from the Obregia Clinical Psychiatric Hospital, who helped with patient recruitment and management of phenotypic databases.

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.psychres.2015.11.008.

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