A new risk locus in the ZEB2 gene for schizophrenia in the Han Chinese population

Progress in Neuro-Psychopharmacology & Biological Psychiatry 66 (2016) 97–103

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A new risk locus in the ZEB2 gene for schizophrenia in the Han Chinese population Raja Amjad Waheed Khan a,b,g,1, Jianhua Chen a,b,c,1, Meng Wang a,b, Zhiqiang Li a,b, Jiawei Shen a,b, Zujia Wen a,b, Zhijian Song a,b, Wenjin Li a,b, Yifeng Xu c, Lishan Wang f, Yongyong Shi a,b,d,e,⁎ a

Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, PR China Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, PR China Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China d Shanghai Changning Mental Health Center, 299 Xiehe Road, Shanghai 200042, PR China e Institute of Neuropsychiatric Science and Systems Biological Medicine, Shanghai Jiao Tong University, Shanghai 200042, PR China f Shanghai Information Technology Co., Ltd., 951 Technology Park, Building B CANGYUAN Jianchuan Road, Minhang District, Shanghai 200240, PR China g University of Azad Jammu and Kashmir, Dept. of Chemistry, Muzaffarabad 13100, Pakistan b c

a r t i c l e

i n f o

Article history: Received 5 September 2015 Received in revised form 18 November 2015 Accepted 1 December 2015 Available online 3 December 2015 Keywords: Case–control study ZEB2 Schizophrenia Bipolar disorder Major depressive disorder Han Chinese

a b s t r a c t The ZEB2 gene encodes the Zinc Finger E-box binding protein. As a key regulator of epithelial mesenchymal differentiation, ZEB2 plays an important role in the pathogenesis of cancer, and its high level expression has been observed in glioma patients. Different mutations in this gene have been identified in patients with Mowat–Wilson syndrome. A previous genome-wide association study (GWAS) of schizophrenia conducted in Caucasians has shown a significant association of rs12991836, located near the ZEB2 gene, with schizophrenia. Thus, we conducted a case control study to further investigate whether this genomic region is also a susceptibility locus for schizophrenia in the Han Chinese population. In total, 1248 schizophrenia (SCZ) cases (mean age ± S.D., 36.44 ± 9.0 years), 1344 bipolar disorder (BPD) cases (mean age ± S.D., 34.84 ± 11.44 years), 1056 major depressive disorder (MDD) cases (mean age ± S.D., 34.41 ± 12.09 years) and 1248 healthy control samples (mean age ± S.D., 30.62 ± 11.35 years) were recruited. We genotyped 12 SNPs using the Sequenom MassARRAY platform in this study. We found that rs6755392 showed a significant association with SCZ (rs6755392: adjusted Pallele = 0.016; adjusted Pgenotype = 0.052; OR (95% CI) = 1.201 (1.073 ~ 1.344)). Additionally, two haplotypes (TCTG, TCTA) were also significantly associated with SCZ. This is the first study claiming the association of the genetic risks of rs6755392 in the ZEB2 gene with schizophrenia. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD) are three major psychiatric disorders. The population prevalence of SCZ and BPD is 1%, and heritability is 75–85% and 65– 75%, respectively; the population prevalence of MDD is approximately 17%, and its heritability is 40% (Margit et al., 2008; O'Donovan et al., 2009). The disability-adjusted life years (DALYs) are 7.4% for SCZ, 7% Abbreviations: SCZ, schizophrenia; BPD, bipolar disorder; MDD, major depressive disorder; GWAS, genome wide association study; LD, linkage disequilibrium; SCID-1, Structured Clinical Interview for DSM-IV Axis 1 Disorders; PARP, Poly ADP ribose polymerase; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; DALYs, Disability-adjusted life years; CEU, Northern and Western Europe and the United States; YRI, Yoruba in Ibandan, Nigeria; CHB, Han Chinese in Beijing; JPT, Japanese in Tokyo, Japan; OR, odd ratio; Chi2, chi squared test; ITIHS, Inter-alpha-trypsin inhibitor homologous heavy Chain; LMAN2L, Lectin, mannose binding 2-like. ⁎ Corresponding author at: Bio-X Institutes, Shanghai Jiao Tong University, Central Little White House, 1954 Huashan Road, Shanghai 200030, PR China. E-mail address: [email protected] (Y. Shi). 1 These authors contributed equally to this work.

http://dx.doi.org/10.1016/j.pnpbp.2015.12.001 0278-5846/© 2015 Elsevier Inc. All rights reserved.

for BPD and 40.5% for MDD, as indicated by the Global Burden of Disease report 2010 (Whiteford et al., 2013). Some of the genetic risk factors responsible for psychiatric disorders have been identified by GWASs (genome-wide association studies). The single nucleotide polymorphisms (SNPs) based on heritability and sharing of risk variants are observed markedly between SCZ and BPD and moderately between SCZ and MDD and between BPD and MDD (Lee et al., 2013). Meanwhile, the ZEB2 gene has been demonstrated to be significantly associated with SCZ in Caucasians by a GWAS plus meta-analysis and replication (Ripke et al., 2013). The gene ZEB2 is located on the long arm of chromosome 2q22.3 (http://genome.ucsc.edu/), and contains nine exons, of which exons 5 to 9 code for DNA-binding motifs. The mouse gene lacks exon 3 (Sekido et al., 1996). ZEB2 or ZFHX1B encodes Smad-interacting protein 1 (SIP1), a member of the δEF-1 or ZEB protein family. This group of proteins is characterized by a homeodomain flanked by two separated, highly conserved zinc finger clusters: an N-terminal one containing four zinc fingers and a C terminal one containing three zinc fingers (Kristin et al., 1999; Nam et al., 2013). Each zinc finger cluster can

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bind independently to CACCT (G) sequences present in the promoter regions of genes involved in differentiation and development, such as Xenopus Xbra2, the human a4-integrin promoter and the E-cadherin promoter. The integrity of the two zinc finger clusters of SIP1 is necessary for its binding as a monomer to the target promoter sequences (Remacle et al., 1999). The role of ZEB2 in the progression of different forms of tumors has been highlighted in several studies (Imamichi et al., 2006; Rosivatz et al., 2002; Yoshihara et al., 2009). A high level of ZEB2 expression is positively correlated with the pathological classification grade of glioma patients. Knockdown of ZEB2 can inhibit the expression, proliferation, differentiation and invasion of glioma cells. It is reported that a lower expression of ZEB2 not only enhances Ecadherin expression but also inhibits N-cadherin, b-catenin, Vimentin and Snail expression. Several regulators related to apoptosis such as Caspase-3, Caspase-6, Caspase-9, and Cleaved-PARP have been found to be activated, while PARP (Poly ADP ribose polymerase) is inhibited after ZEB2 siRNA treatment (Songtao et al., 2012). Currently, mutation in ZEB2 is known to cause Mowat–Wilson syndrome, which is characterized by moderate to severe mental retardation, Hirsch sprung disease (HSCR), epilepsy, multiple congenital anomalies including genital anomalies (Hypospadia in males), congenital heart defects, agenesis of the corpus callosum and eye defects (Garavelli et al., 2009). A missense mutation in the highly conserved C-ZF domain of ZEB2 is the cause of a new form of Mowat–Wilson syndrome (Ghoumid et al., 2013) and patients with this disorder have moderate intellectual disability (ID), but no microcephaly and congenital deformation (e.g., heart and renal defects and HSCR). More than 30% of those with Mowat–Wilson syndrome display a clinically significant level of behavioral or emotional disturbances (Evans et al., 2012). The behavioral abnormalities associated with Mowat–Wilson syndrome comprise an increased rate of repetitive behaviors, such as a high rate of oral behaviors and an under-reaction to pain, as well as happy and sociable behaviors. A previous GWAS study plus meta-analysis and replication conducted in Caucasians has demonstrated rs12991836 (P value = 1.19 × 10−8), which is located near the ZEB2 gene, as one of the new risk loci for SCZ (Ripke et al., 2013). In our study, a case–control analysis was designed to investigate whether common variants in ZEB2 are not only associated with SCZ but also with BPD and MDD, and furthermore to reveal whether there is a cross-disorder association in the Han Chinese population. Twelve SNPs covering the ZEB2 gene were selected for genotyping in the present study.

SCID-1. All MDD patients were selected carefully only when they met the following criteria: they had experienced two MDD episodes and displayed no signs of BPD during the two-year period after the onset of depression; and they were out-patients or sometimes stable in-patients, Shanghai in origin and living in Shanghai China. Written information concerning the medical histories as well as supplementary questions about psychosis and other complex disease was obtained from the volunteers. Before blood collection from the volunteers, a face-to-face interview was conducted, and physical measurements, such as height, weight, blood pressure, were also recorded. Informed consent was obtained from all of the subjects and the study was reviewed and approved by the local ethics committee. 2.2. DNA extraction, SNP selection and genotyping

2. Materials and methods

Genomic DNA was extracted from the leucocytes of the peripheral blood sample of each participant using the Quick Gene DNA whole blood kit L (FUJIFILM, Life Sciences Products, Tokyo, Japan) according to the manufacturer's instructions. Eleven tagged SNPs (rs10197694, rs6430057, rs10928212, rs1427298, rs13389578, rs13403907, rs13396535, rs6717029, rs13410341, rs6755392, rs6740731) were selected using haploview software (v4.2) with pair-wise tagging, r2 ≥ 0.6 and a minor allele frequency (MAF) ≥0.05 (Barrett et al., 2005). In addition, rs12991836, which was reported to have a significant association with SCZ in the Caucasian population, was also investigated in this study. The relative position of these 12 SNPs obtained from Vector NTI (www.invitrogen.com/VectorNTI) is shown in Fig. 1. The details of 12 SNPs are also given in Table 1. The MassARRAY Assay design software package (v4.0) was used to design the specific SNP filtering. The PCR quality and primer specificity were checked prior to running the reaction. For the dephosphorylation of residual nucleotide, shrimp alkaline phosphate phosphatase was used prior to the iPLEX Gold reaction. Following single-base extension, the reaction products were desalted with spectro clean resin (Sequenom), and 10 nL was spotted onto the SpectroCHIP using the MassARRAY Nanodispenser. All of the selected SNPs were genotyped to collect the mass spectra using the Sequenom MassARRAY matrix-assisted laser desorption ionization-time of flight mass spectrometry platform (Sequenom, San Diego, CA, USA). To further clarify the uncertain genotype calls, a manual review was performed. An assay with a call rate less than 80% within the same SpectroChip was considered to have failed. The overall call rate in our assay was more than 97%.

2.1. Study subjects

2.3. Statistical analysis

In total, 1248 unrelated SCZ patients (845 males and 403 females), 1344 BPD patients (584 males and 760 females), 1056 unrelated MDD patients (737 males and 319 females) and 1248 healthy controls (672 males and 576 females) were recruited in this study. The mean ages of the SCZ patients, BPD patients, MDD patients and healthy controls were 36.44 years (±9.0), 34.84 years (±11.44), 34.41 years (±12.09) and 30.62 years (±11.35), respectively. The patients included in this study were interviewed by two independent psychiatrists and were diagnosed strictly according to DSM-IV criteria (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) based on SCID-1 (structured Clinical Interview for DSM-IV Axis 1 Disorders). Normal controls were selected from the general public of the Han Chinese population in Shanghai. The control subjects were also interviewed by two independent psychiatrists according to DSM-IV criteria based on the non-patient edition (SCID-1)(American Psychiatric Association, 1994). All of the included SCZ patients in this study were paranoid schizophrenics with no history of depression or mania. BPD patients were also diagnosed according to criteria mentioned in DSM-IV and

Hardy–Weinberg equilibrium (HWE) analysis, allele and genotype frequency analysis, and haplotype analysis, for SCZ, BPD and MDD were performed using online SHEsisPlus software available at (http:// shesisplus.bio-x.cn/SHEsis.html) (Li et al., 2009; SHI and He, 2005), which is a user-friendly software platform equipped with a series of highly efficient analytic tools designed for association studies. All of the tests were two tailed, and statistical significance was assumed at the threshold of 0.05. A permutation-based correction was performed with 1000 shuffles. The permutation is max (T) based so that it does not drop any SNP while permuting and gives the exact permutations that are specified. Hence, the empirical values of two data sets are calculated. The Sidak step-down (SD) p-values correction was also performed for haplotypes. In Sidak step-down method p-values are examined in order, from smallest to largest. Once a p-value is found that is large according to criteria based on threshold that is assigned and p-value's position in the list, that p values and all larger p-values are accepted (Sidak, 1967). The genetic power was calculated by the genetic power calculator available at (http://pngu.mgh.harvard.edu/ ~purcell/gpc/) (Purcell et al., 2003).

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Fig. 1. Relative position of the SNPs in the ZEB2 gene obtained from Vector NTI (Invitrogen, Carlsbad, CA, USA).

2.4. Genetic power calculation The genetic power against each site was computed in both the genotypic (2df) and allelic (1df) models. The following parameters were considered while calculating the power: disease prevalence, 1%; genotype relative risk Aa, 1.25; genotype relative risk AA, 2; and high risk allele frequency ≥ 20%. Genetic power analysis revealed that SNPs rs12991836, rs10197694, rs6430057, rs1427298 and rs6755392 showed a power N 0.8 for both the genotype (2df) and allele (1df), whereas genetic power values for SNPs rs10928212, rs13389578 rs13396535, rs6717029 and rs13410341 in both the genotype (2df) and allele (1df) were 0.26, 0.05, 0.35, 0.22, 0.48 and 0.34, 0.06, 0.45, 0.29 and 0.58, respectively. For each individual haplotype, we treated the related block as a biallelic site, i.e., the haplotypes were divided into two groups: the specific type (e.g., TAAGT) and non-specific type (e.g., non- TAAGT). Thus the power calculation is similar to the single site analysis. Genetic power for the two haplotypes TAAGT, TCTG, was N0.8, whereas that for the haplotype TCTA was approximately 0.70.

2.5. Population stratification To avoid a false-positive association, STRUCTURE software (version 2.3.4, http://pritch.bsd.uchiacago.edu/structure.html) was used to analyze the potential population stratification in our samples (Pritchard et al., 2000). This software assumes that there are K populations in the data set (i.e., K is the number of assumed populations), and then it tries to identify the distinct populations using the genotype data. Table 1 Information on 12 SNPs in the ZEB2 gene region. SNP ID

Position

Functional

rs12991836 rs10197694 rs6430057 rs10928212 rs1427298 rs13389578 rs13403907 rs13396535 rs6717029 rs13410341 rs6755392 rs6740731

145141541 145164666 145171912 145181758 145214421 145216048 145222038 145223872 145225885 145227258 145242122 145270592

Downstream Intronic Regulatory Intronic Regulatory Downstream upstream Upstream Upstream Intronic Intronic Regulatory

Polymorphism A/C C/T A/T A/C A/G C/T A/G C/T C/T C/T A/G A/G

rs13403907 and rs6740731 were excluded for further analysis due to their failure to meet Hardy–Weinberg equilibrium in the cases and controls.

Considering immigration and geographical genetic isolation, the admixture model was suitable for our sample. The correlated frequency model was selected because the samples of the three disorders were recruited from the same population. We applied the admixture model and correlated the frequency model with the following parameters: burn-in length of 10,000, and MCMC (Markov chain Monte Carlo) repeats of 10,000. We ran the program several times at each K (from 2 to 7) to ensure consistent results. To determine whether the 83 SNPs employed can reflect the stratification status, we first analyzed a combined population comprising 174 HapMap CEU individuals, 176 Hap Map YRI individuals, 86 CHB individuals, and 89 JPT individuals (The International HapMap Consortium, 2003). The results are presented in the triangle chart produced by the STRUCTURE software (Fig. 3). 3. Results 3.1. Single-site analysis The p-values of Hardy–Weinberg equilibrium (HWE) for two SNPs exceeded 0.05 in both the healthy controls and cases; thus, these two SNPS (rs13403907 and rs6740731) were excluded in the following study. The allele and genotype frequencies of the remaining 10 SNPs in the three patient samples and normal controls were determined. For SCZ, three SNPs showed statistically significant associations before the permutation correction; however, only one SNP remained significant after the permutation correction (rs6755392: Pallele = 0.001, Chi2 = 10.279, OR (95% CI) = 1.201 (1.073 ~ 1.344); Pgenotype = 0.006, Chi2 = 10.066)) (Table 2). For BPD, only rs6755392 showed a significant association before correction (rs6755392: Pallele = 0.039, Chi2 = 4.226, OR (95% CI) = 1.121 (1.005 ~ 1.251); Pgenotype = 0.025, Chi2 = 7.332) (Table 2). For MDD, only rs6755392 showed a significant association with the genotype before correction (rs67355392: Pallele = 0.604, Chi2 = 0.268, OR (95% CI) = 1.031 (0.917 ~ 1.159); Pgenotype = 0.048, Chi2 = 6.063) (Table 2). Overall, rs6755392 was significantly associated with SCZ, BPD or MDD before correction, whereas it was merely significantly associated with SCZ after a 1000-permutation correction (rs6755392: adjusted Pallele = 0.016, adjusted Pgenotype = 0.052) (Table 2). 3.2. Linkage disequilibrium The pairwise linkage disequilibrium (LD) among the 10 finally investigated SNPs was very similar among different sample sets in this study. Adjacent SNPs with pairwise D’ ≥ 0.75 were classified into the same blocks. Therefore, two haplotype blocks of SNPs were identified in

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Table 2 Allelic and genotypic frequencies of ten SNPs in SCZ, BPD, MDD and controls respectively. Bold numbers represent P value b 0.05. SNP

Allele

rs12991836 SCZ BPD MDD Control rs10197694 SCZ BPD MDD Control rs6430057 SCZ BPD MDD Control rs10928212 SCZ BPD MDD Control rs1427298 SCZ BPD MDD Control rs13389578 SCZ BPD MDD Control rs13396535 SCZ BPD MDD Control rs6717029 SCZ BPD MDD Control rs13410341 SCZ BPD MDD Control rs6755392 SCZ BPD MDD Control

C 1316(0.533) 1445(0.539) 1058(0.515) 1238(0.519) C 1532(0.641) 1685(0.632) 1356(0.651) 1566(0.638) T 2294(0.934) 2457(0.92) 1873(0.916) 2184(0.915) A 1935(0.78) 2133(0.797) 1649(0.807) 1988(0.797) A 1509(0.629 1653(0.619) 1292(0.621) 1558(0.634) T 2290(0.918) 2472(0.924) 1881(0.917) 2294(0.919) T 1857(0.756) 2039(0.763) 1532(0.762) 1926(0.773) C 1908(0.791) 2149(0.806) 1668(0.801) 2021(0.819) T 1808(0.729) 1995(0.745 1545(0.752) 1787(0.748) G 1150(0.477) 1320(0.494) 1073(0.515 1292(0.523)

OR [95% CI] A 1152(0.466) 1231(0.46) 994(0.484) 1146(0.48) T 856(0.358) 981(0.367) 726(0.348) 888(0.361) A 162(0.065) 211(0.079) 171(0.083) 202(0.084) C 545(0.219) 541(0.202) 393(0.192) 506(0.202) G 887(0.37) 1015(0.38) 788(0.378) 896(0.365) C 202(0.081) 202(0.075) 169(0.082) 200(0.08) C 599(0.243) 631(0.236) 478(0.237) 564(0.226) T 502(0.208) 517(0.193) 414(0.198) 445(0.18) C 670(0.27) 681(0.254) 507(0.247) 599(0.251) A 1260(0.522) 1350(0.505) 1009(0.484) 1178(0.476)

Chi2

Chi2

P value Permutation p Genotypes

0.945 [0.844 ~ 1.058] 0.92 [0.823 ~ 1.027] 1.014 [0.901 ~ 1.142]

0.943 0.331 2.166 0.141 0.06 0.805

0.985 [0.876 ~ 1.108] 1.026 [0.916 ~ 1.15] 0.944 [0.835 ~ 1.066]

0.06 0.805 0.205 0.65 0.85 0.356

0.763 [0.615 ~ 0.946] 0.928 [0.759 ~ 1.135] 0.987 [0.797 ~ 1.221]

6.086 0.013 0.521 0.47 0.014 0.904

1.106 [0.965 ~ 1.268] 0.996 [0.87 ~ 1.141] 0.936 [0.808 ~ 1.084]

2.123 0.145 0.002 0.959 0.768 0.38

1.022 [0.909 ~ 1.148] 1.067 [0.953 ~ 1.195] 1.06 [0.939 ~ 1.196]

0.134 0.713 1.282 0.257 0.908 0.34

1.011 [0.825 ~ 1.24] 0.937 [0.764 ~ 1.148] 1.03 [0.832 ~ 1.276]

0.012 0.91 0.388 0.532 0.076 0.782

1.101 [0.965 ~ 1.256] 1.056 [0.928 ~ 1.202] 1.065 [0.927 ~ 1.224]

2.078 0.149 0.698 0.403 0.798 0.371

1.194 [1.036 ~ 1.377] 1.092 [0.949 ~ 1.257] 1.127 [0.971 ~ 1.307]

6.038 0.013 1.525 0.216 2.492 0.114

1.105 1.018 0.978

2.355 0.124 0.078 0.778 0.093 0.76

1.201 1.121 1.031

10.279 0.001 4.226 0.039 0.268 0.604

0.102

0.106

0.016 0.288

C/A 598(0.484) 667(0.498) 530(0.516) 590(0.494) C/C 498(0.417) 539(0.404) 426(0.409) 503(0.409) T/T 1072(0.872) 1135(0.85) 858(0.839) 1003(0.84) A/A 751(0.605) 851(0.636 681(0.666) 795(0.637) A/A 474(0.395) 501(0.375) 381(0.366) 493(0.401) T/T 1054(0.845) 1143(0.854) 866(0.844) 1052(0.843) T/T 705(0.574) 780(0.584) 577(0.574) 755(0.606) C/C 759(0.629) 866(0.649) 659(0.633) 824(0.668) C/T 476(0.384) 501(0.374) 399(0.388) 457(0.383) A/G 590(0.489) 696(0.521) 559(0.536) 600(0.485)

A/A 277(0.224) 282(0.21) 232(0.226) 278(0.233) C/T 536(0.448) 607(0.455) 504(0.484) 560(0.456) T/A 150(0.122) 187(0.14) 157(0.153) 178(0.149) A/C 433(0.349) 431(0.322) 287(0.281) 398(0.319) A/G 561(0.468) 651(0.488) 530(0.509) 572(0.466) C/T 182(0.146) 186(0.139) 149(0.145) 190(0.152) C/T 447(0.364) 479(0.358) 378(0.376) 416(0.334) C/T 390(0.323) 417(0.312) 350(0.336) 373(0.302) T/T 666(0.537) 747(0.558) 573(0.558) 665(0.557) A/A 335(0.278) 327(0.244) 225(0.216) 289(0.234)

P value Permutation P

C/C 359(0.29) 1.122 0.57 389(0.29) 2.253 0.324 264(0.257) 1.067 0.586 324(0.271) T/T 160(0.134) 0.15 0.927 187(0.14) 0.255 0.88 111(0.106) 4.319 0.115 164(0.133) A/A 6(0.004) 6.18 0.045 12(0.008) 0.505 0.776 7(0.006) 0.732 0.693 12(0.01) C/C 56(0.045) 2.743 0.253 55(0.04 0.093 0.954 53(0.051) 4.323 0.115 54(0.043) G/G 163(0.136) 0.136 0.934 182(0.136) 1.862 0.393 129(0.124) 4.299 0.116 162(0.132) C/C 10(0.008) 1.84 0.398 8(0.005) 1.374 0.502 10(0.009) 2.999 0.223 5(0.004) 1.84 0.398 C/C 76(0.061) 2.735 0.254 76(0.056) 1.731 0.42 50(0.049) 4.704 0.095 74(0.059) T/T 56(0.046) 7.074 0.029 50(0.037) 1.879 0.39 32(0.03) 3.136 0.208 36(0.029) C/C 97(0.078) 3.542 0.17 90(0.067) 0.72 0.697 54(0.052) 0.513 0.773 71(0.059) G/G 280(0.232) 10.066 0.006 312(0.233) 7.332 0.025 257(0.246) 6.063 0.048 346(0.28)

0.345

0.225

0.052 0.225 0.349

SCZ, schizophrenia; BPD, bipolar disorder; MDD, major depressive disorder; CI, confidence interval; OR, odd ratio for minor allele in controls; Chi2, chi-squared test.

SCZ, BPD and MDD, respectively, which included haplotype block 1 with the following sequence of SNPs: rs10197694-rs6430057-rs10928212rs1427298-13,389,578 (51.38 Kb) and haplotype block 2 with the following sequence of SNPs: rs13396535-rs6717029-rs13410341rs6755392 (18.25 Kb) (Fig. 2).

3.3. Haplotype analysis According to haplotype analysis, three haplotypes, one for block rs10197694-rs6430057-rs10928212-rs1427298-13,389,578 (TAAGT = P = 0.005, P corrected = 0.022) and two for block rs13396535rs6717029-rs13410341-rs6755392 (TCTG: P = 1.88 × 10− 5, P corrected = 7.53 × 10− 5; TCTA: P = 1.41 × 10− 5, P corrected = 7.05 × 10−5), were found to be significant for SCZ (Table 3). Similarly, two haplotypes, one for block rs10197694-rs6430057-rs10928212rs1427298-13,389,578 (CTAGT: P = 0.007, P corrected = 0.036) and one for block rs13396535-rs6717029-rs13410341-rs6755392 (TCCA:

P = 1.56 × 10−4, P corrected = 7.79 × 10−4), were significantly associated with BPD (Table 3). 4. Discussion SCZ, BPD and MDD are three severe mental disorders; each greatly affects both individuals and their families as well as poses a great challenge to society and health services. Genetic factors play an important role in the etiology of these disorders. Ripke et al. (2013) identified an SNP (rs12991836) near the ZEB2 gene as one of the new risk loci for SCZ in Caucasians in a GWAS plus meta-analysis and replication study (Ripke et al., 2013). In our study, rs6755392 was found to be significantly associated with SCZ. Previously, Ripke et al. (2013) reported the significant association between rs12991836 located near ZEB2 gene and SCZ in Caucasians; however, we did not detect the association between them in the Han Chinese population. The possible reason for this discrepancy is that SCZ is genetically heterogeneous with ethnic differences between

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Fig. 2. Linkage disequilibrium (LD) plot of the ten SNPs in the samples of the three major mental disorders. (a), (b) and (c) are the LD plots obtained from the SHesis platform for schizophrenia, bipolar disorder and major depressive disorder cases with controls, respectively. Adjacent SNPs with D’ ≥ 0.75 were placed in one block.

Caucasian and Asian populations. The allelic frequency differs between these two populations: the minor allele frequency in the Han Chinese population is (0.48), whereas that in the Caucasian population is (0.366). The minor allele (A) in our study is a major allele (A) in the Caucasian population, whereas the major allele (C) in our study is the minor allele in the Caucasian population. The previous study in the Caucasian population has no sample overlap with the current report. Apart from this, rs10496964 in the chromosomal region 2q22.3 near the gene ZEB2 was also demonstrated to be associated with genetic generalized epilepsy (Steffens et al., 2012). In another case–control study based on a GWAS and meta-analysis of a Caucasian population, rs12105918 located in intron 2 of the ZEB2 gene was identified as a susceptibility locus for renal cell carcinoma (Henrion et al., 2012). The shared epidemiology and involvement of common genes in these three psychiatric disorders have been mentioned in many studies (Chen et al., 2012; He et al., 2013; O'Donovan et al., 2008). Recently, He et al. (2014) reported ITIHs as a risk gene for SCZ and MDD in the Han Chinese population (He et al., 2014). Yu et al. (2014) concluded in hypothesis-driven pathway-based study that myelin-related pathways also contribute to the risk of both SCZ and BPD, and furthermore, that substantial overlap in nominally associated SNPs and genes in these pathways hint that these SNPs could contribute to the

susceptibility for both disorders (Yu et al., 2014). Lim et al. (2014) also reported an association between LMAN2L (rs6746896) and both SCZ and BPD (Lim et al., 2014). We searched the Psychiatric Genetics Consortium (PGC) data base for schizophrenia available at (https://www.broadinstitute.org/mpg/ ricopili/) (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) to trace out the availability of SNPs and their corresponding p values in this data base against the SNPs we have investigated in the Han Chinese population. The PGC data revealed that out of 10 SNPs which we investigated in the Han Chinese population, only two SNPs (rs10928212 and rs1427298) seem to have significant p values for SCZ. One SNP (rs12991836) has also been found with significant pvalue in this data base that was reported previously by Ripke et al. (2013) to be significantly associated with SCZ in Caucasian. The SNP (rs6755392), was not found with significant p value for SCZ in the PGC data base. There might be two possible reasons: (i) difference in linkage disequilibrium (LD) in this region between CEU and CHB populations; (ii) the allelic frequency also differs between these two populations, which may be the alternate reason. The minor allele frequency of rs6755392 in the Chinese population is (0.47), whereas that in the Europeans is (0.29). The LD plot obtained in this gene region by using haploview software (version 4.2) for both the CHB and CEU populations

Fig. 3. Identification of population stratification using STRUCTURE 2.3.4. (A) A total of 525 HapMap genotype data clusters. Red: CEU; green: CHB + JPT; blue: YRI. (B) Triangle plot of our own genotype data of 83 SNPs when K = 3. Red: controls; green: schizophrenia cases; yellow: bipolar disorder cases; blue: major depressive cases. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

0.036 7.79 × 10−4

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0.985 0.887 0.345 0.86 0.007 1.56 × 10−4 0.727 0.056 0.574 0.644 1.001[0.897 ~ 1.116] 1.01[0.87 ~ 1.173] 0.89[0.698 ~ 1.133] 1.019[0.824 ~ 1.259] 1.526 [1.117 ~ 2.084] 1.685 [1.282 ~ 2.214] 0.98 [0.879 ~ 1.093] 1.149 [0.996 ~ 1.327] 0.925 [0.706 ~ 1.212] 1.033 [0.898 ~ 1.188] 1369(0.587) 392(0.168) 141(0.06) 177[0.075] 66(0.028) 84(0.035) 1170(0.496) 413(0.175) 110(0.046) 462(0.196) 7.05 × 10−5

7.53 × 10−5

84(0.035) 1170(0.496) 413(0.175) 110(0.046) 462(0.196) 80(0.034) 1020(0.433) 451(0.191) 182(0.077) 445(0.189)

SCZ, schizophrenia; BPD, bipolar disorder; CI, confidence interval; OR, odd ratio; freq, frequenc.

0.75 1.88 × 10−5 0.155 1.41 × 10−5 0.532

1370(0.587) 392(0.168) 141(0.06) 177(0.075)

rs10197694-rs6430057-rs10928212-rs1427298-rs13389578 rs13396535-rs6717029-rs13410341-rs6755392

CTAAT TTCGT TTAGC TAAGT CTAGT TCCA TCTG TTTA TCTA CCCA

1330(0.564) 398(0.169) 125(0.053) 130(0.055)

0.95 [0.696 ~ 1.298] 0.783 [0.7 ~ 0.876] 1.112 [0.96 ~ 1.288] 1.706 [1.337 ~ 2.176] 0.955 [0.827 ~ 1.103]

0.022

1475(0.559) 426(0.161) 136(0.051) 194(0.073] 107(0.04) 149(0.056) 1247(0.472) 499(0.189) 110(0.041) 511(0.193) 0.255 0.816 0.313 0.005

Pearson's P value OR[95%CI] Control (freq) Case (freq) Haplotype

SCZ

0.937 [0.838 ~ 1.047] 1.018 [0.874 ~ 1.185] 0.88 [0.687 ~ 1.127] 0.719 [0.569 ~ 0.909]

OR[95%CI] Control (freq) Case (freq)

BPD

Corrected P Sidak SD SNPs Blocks With D’ ≥ 0.75

Table 3 Haplotype analysis of the ZEB2 gene across SCZ and BPD cases. Haplotypes with a frequency b 0.03 in the cases or controls were not included. Bold letters represent a significant P value.

Pearson's P value

Corrected P Sidak SD

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is presented in the Supplementary Figs. 1 and 2 respectively. We also searched PGC data base for BPB, which revealed that none of the SNPs we investigated seems to have significant p-value in this data base (Psychiatric GWAS Consortium Bipolar Disorder Working Group, 2011). To determine the possible regulatory role of the SNPs included in our study, we searched the Regulome database available at (http:// regulomedb.org/) (Boyle et al., 2012) and ENCODE data base available at (http://www.genome.ucsc.edu/ENCODE/). Both data sets revealed that only one SNP (rs1427298) seems to have a regulatory role. We also searched the eQTL data base available at (http://genenetwork.nl/ bloodeqtlbrowser/) to figure out the possible role of the SNPs (Westra et al., 2013). The eQTL data search revealed that none of the SNPs seems to alter the regulatory sites or acts as a QTL. The presence of such SNPs could corroborate the hypothesis that the locus we identified may be a SCZ risk factor. Furthermore, two haplotypes for block rs13396535-rs6717029rs13410341-rs6755392 (TCTG: P = 1.88 × 10− 5, P corrected = 7.53 × 10−5; TCTA: P = 1.41 × 10−5, P corrected = 7.05 × 10−5) containing the corresponding positive SNP were found to be significantly associated with SCZ. Additionally, one haplotype for block rs13396535-rs6717029-rs13410341-rs6755392 (TCCA: P = 1.56 × 10−4, P corrected = 7.79 × 10−4) was also found to be significantly associated with BPD in this study; thus, highlighting the possible role of this SNP in BPD as well. ZEB2 encodes a protein that mediates epithelial differentiation, and different mutations in this gene cause Mowat–Wilson syndrome (Garavelli et al., 2009). ZEB2 is also required to repress the Nkx2-1 homeobox transcription factor in the generation of γ-aminobutyric acid (GABA)ergic cortical interneurons. The lack of ZEB2 masks the repression of homeobox transcription factor Nkx2-1, which affects the differentiation of progenitor cells in striatal neurons rather than cortical neurons (McKinsey et al., 2013). Keverne (1999) proposed that GABAergic interneurons play an important role in synchronizing brain activity in distinct regions of the brain, and abnormality in these interneurons may cause psychosis (Keverne, 1999). Thus, we suggest that ZEB2 could be a casual factor whose abnormality may affect the GABAergic cortical interneurons for the subsequent development of schizophrenia. To address the potential influence of population stratification, we analyzed 71 additional SNPs distributed on different chromosomes using STRUCTURE software (version 2.3.4). However, we did not detect any population stratification in our samples. In the present study, rs6755392 located in the intronic region of ZEB2 was observed to be associated with SCZ; however, this has never been reported previously. Furthermore, the association of two haplotypes with SCZ further highlights our claim regarding the association of this gene with SCZ. This study has provided additional evidence for the epidemiology of SCZ. To embark on new insight, further studies are required to validate the association of this SNP across SCZ and BPD in the Chinese population in addition to other population. It is also required to study the potential roles played by this gene in correlated pathways. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.pnpbp.2015.12.001. Conflict of interest The authors declare no conflict of interest. Contributors Author Raja Amjad Waheed Khan and Jianhua Chen co-deigned this study, wrote the protocol, carried on all experiments and managed the literature searches and analyses. Meng Wang and Zujia Wen conducted the sample collection and verification. Zhiqiang Li and Jiawei Shen undertook the statistical analysis. Zhijian Song, Wenjin Li and Yifeng Xu

R.A.W. Khan et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 66 (2016) 97–103

were responsible for platform coordination and management. Lishan Wang provided technical guidance. Author Raja Amjad Waheed Khan wrote the first draft of the manuscript, while Yongyong Shi revised the manuscript, supervised the whole research process and also provided funding for research. All authors contributed to and have approved the final manuscript. Acknowledgments We thank and acknowledge all the participants, healthy volunteers and psychiatrists who participated in this study. We are also thankful to Psychiatric Genetics Consortium (PGC), for their large-scale data being accessible for interpretation of our results. This work was supported by the 973 Program (2015CB559100), the National 863 project (2012AA02A515), the Natural Science Foundation of China (31325014, 81130022, 81272302, 81121001, 81171271), Shanghai Key Laboratory of Psychotic Disorders (13dz2260500), the Research Project of Shanghai Health and Family Planning Commission (20134315), “Shu Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (12SG17), the Shanghai Jiao Tong University Liberal Arts and Sciences Cross-Disciplinary Project (13JCRZ02) and “the Program of Shanghai Academic Research Leader (15XD1502200)”. References American Psychiatric Association, 1994. Diagnostic and Statistics Manual of Mental Disorders. fourth ed American Psychiatric Association. Barrett, J.C., Fry, B., Maller, J., Daly, M.J., 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265. Boyle, A.P., Hong, E.L., Hariharan, M., Cheng, Y., Schaub, M.A., Kasowski, M., et al., 2012. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797. Chen, P., Chen, J., Huang, K., Ji, W., Wang, T., Li, T., et al., 2012. Analysis of association between common SNPs in ErbB4 and bipolar affective disorder, major depressive disorder and schizophrenia in the Han Chinese population. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 36, 17–21. Evans, E., Einfeld, S., Mowat, D., Taffe, J., Tonge, B., Wilson, M., 2012. The behavioral phenotype of Mowat–Wilson syndrome. Am. J. Med. Genet. A 158A, 358–366. Garavelli, L., Zollino, M., Mainardi, P.C., Gurrieri, F., Rivieri, F., Soli, F., et al., 2009. Mowat– Wilson syndrome: facial phenotype changing with age: study of 19 Italian patients and review of the literature. Am. J. Med. Genet. A 149A, 417–426. Ghoumid, J., Drevillon, L., Alavi-Naini, S.M., Bondurand, N., Rio, M., Briand-Suleau, A., et al., 2013. ZEB2 zinc-finger missense mutations lead to hypomorphic alleles and a mild Mowat–Wilson syndrome. Hum. Mol. Genet. 22, 2652–2661. He, K., An, Z., Wang, Q., Li, T., Li, Z., Chen, J., et al., 2013. CACNA1C, schizophrenia and major depressive disorder in the Han Chinese population. Br. J. Psychiatry 204, 36–39. He, K., Wang, Q., Chen, J., Li, T., Li, Z., Li, W., et al., 2014. ITIH family genes confer risk to schizophrenia and major depressive disorder in the Han Chinese population. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 51, 34–38. Henrion, M., Frampton, M., Scelo, G., Purdue, M., Ye, Y., Broderick, P., et al., 2012. Common variation at 2q22.3 (ZEB2) influences the risk of renal cancer. Hum. Mol. Genet. 22, 825–831. Imamichi, Y., König, A., Gress, T., Menke, A., 2006. Collagen type I-induced Smadinteracting protein 1 expression downregulates E-cadherin in pancreatic cancer. Oncogene 26, 2381–2385. Keverne, E.B., 1999. GABA-ergic neurons and the neurobiology of schizophrenia and other psychoses. Brain Res. Bull. 48, 467–473. Kristin, V., Jacques, E.R., Clara, C., Harry, K., Betty, S.B., Przemko, T., et al., 1999. SIP1, a novel zinc finger/homeodomain repressor, interacts with Smad proteins and binds to 5-CACCT sequences in candidate target genes. J. Biol. Chem. 274, 10. Lee, S., Ripke, S., Neale, B.M., Faraone, S.V., Purcell, S.M., Perlis, R.H., et al., 2013. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994.

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