ITIH family genes confer risk to schizophrenia and major depressive disorder in the Han Chinese population

Progress in Neuro-Psychopharmacology & Biological Psychiatry 51 (2014) 34–38

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ITIH family genes confer risk to schizophrenia and major depressive disorder in the Han Chinese population☆,☆☆ Kuanjun He a,b, Qingzhong Wang a, Jianhua Chen a,c, Tao Li a, Zhiqiang Li a, Wenjin Li a, Zujia Wen a, Yu Qiang a, Meng Wang a, Jiawei Shen a, Zhijian Song a, Jue Ji a, Guoyin Feng c, Shuguang Qi d, He Lin a,e,f, Yongyong Shi a,e,f, Zaohuo Cheng d,⁎ 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 College of Life Science, Inner Mongolia University for Nationalities, Tongliao, Inner Mongolia, PR China c Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China d Wuxi Mental Health Center, Jiangsu Wuxi 214151, PR China e Shanghai Changning Mental Health Center, 299 XieHe Road, Shanghai 200042, PR China f Institute of Neuropsychiatric Science and Systems Biological Medicine, Shanghai Jiao Tong University, Shanghai 200042, PR China b

a r t i c l e

i n f o

Article history: Received 6 August 2013 Received in revised form 7 December 2013 Accepted 9 December 2013 Available online 2 January 2014 Keywords: Association ITIH family genes Major depressive disorder Schizophrenia SNP

a b s t r a c t As a major extracellular matrix component, ITIHs played an important role in inflammation and carcinogenesis. Several genome-wide association studies have reported that some positive signals which were derived from the tight linkage disequilibrium region on chromosome 3p21 were associated with both schizophrenia and bipolar disorders in the Caucasian population. To further investigate whether this genomic region is also a susceptibility locus of schizophrenia and major depressive disorder in the Han Chinese population, we conducted this study by recruiting 1235 schizophrenia patients, 1045 major depressive disorder patients and 1235 healthy control subjects in the Han Chinese samples for a case–control study. We genotyped seven SNPs within this region using TaqMan® technology. We found that rs2710322 was significantly associated with schizophrenia (adjusted Pallele = 0.0018, adjusted Pgenotype = 0.006, OR [95% CI] = 1.278 [1.117–1.462]) while rs1042779 was weakly associated with schizophrenia (adjusted Pallele = 0.048, OR [95% CI] = 1.164 [1.040–1.303]) and major depressive disorder (adjusted Pallele = 0.042, OR [95% CI] = 1.178 [1.047–1.326]); it was also our finding that rs3821831 was positively associated with major depressive disorder (adjusted Pallele = 0.003, adjusted Pgenotype = 0.006, OR [95% CI] = 1.426 [1.156–1.760]). Furthermore, no haplotype was found to be associated with schizophrenia and major depressive disorder. Via the association analysis which combines the schizophrenia and major depressive disorder cases, we also notice that rs1042779 and rs3821831 were significantly associated with combined cases (rs1042779: adjusted Pallele = 0.012, adjusted Pgenotype = 0.018, OR [95% CI] = 1.171 [1.060–1.292]; rs3821831:adjusted Pgenotype = 0.012, OR [95% CI] = 1.193 [1.010–1.410]). Our results revealed that the shared genetic risk factors of both schizophrenia and major depressive disorder exist in ITIH family genes in the Han Chinese population. © 2014 Published by Elsevier Inc.

Abbreviations: ITIHs, the homologous heavy chains of the inter-alpha-trypsin inhibitors; GWAS, the genome-wide association study; SCZ, schizophrenia; MDD, major depressive disorder; BPAD, bipolar affective disorder; DSM-IV criteria, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; CEU, northern and western Europe in the United States; YRI, Yoruba in Ibadan, Nigeria; CHB, Han Chinese in Beijing; LD, linkage disequilibrium. ☆ Conflict of interest statement: The authors declare no conflict of interest. ☆☆ Source of funding: This work was supported by the Natural Science Foundation of China (31325014, 81130022, 81272302, 31000553, 81121001, 81171271), the 863 Program (2012AA02A515), the 973 Program (2010CB529600), Program for Changjiang Scholars and Innovative Research Team in University (IRT1025), the Foundation for the Author of National Excellent Doctoral Dissertation of China (201026), the Program for New Century Excellent Talents in University (NCET-09-0550), Shanghai Rising-Star Program, Shanghai Science and Technology Development Funds (12QA1401900), Shanghai Municipal Natural Science Foundation (11ZR1431300), the Youth Research Project of Shanghai Health and Family Planning Commission (2013Y82), “Shu Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (12SG17), Shanghai Jiao Tong University Interdisciplinary Liberal Arts and Science Funds (13JCRZ02). ⁎ Corresponding author at: Wuxi Mental Health Center, No.156 Qian Rong Road, Wuxi, Jiangsu Province 214151, PR China. Tel./fax: +86 510 83219310. E-mail address: [email protected] (Z. Cheng). 0278-5846/$ – see front matter © 2014 Published by Elsevier Inc. http://dx.doi.org/10.1016/j.pnpbp.2013.12.004

K. He et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 51 (2014) 34–38

1. Introduction Requiring long-term medical and social care, psychiatric disorders place a large burden not only on patients and their families but also on society and health services. Schizophrenia (SCZ) and major depressive disorder (MDD) are two common psychiatric disorders and have relatively high morbidity and heritability. Affecting about 1% of the world population, SCZ has approximately 60–85% individual heritability (Burmeister et al., 2008); MDD is also a severe disorder with a 8–12% prevalence in most countries and has 40–50% individual heritability (O'Donovan et al., 2009). Because genetic factor was considered to be the main factor of the etiology of the two psychiatric disorders, numerous genetic studies searching for the risk genes of the diseases were performed. Recently, several promising gene loci have been identified by applying new approaches, for example genome-wide association study (GWAS). GWAS is a powerful tool for identifying common risk factors of complex diseases. Several GWAS have been consistently reported that ITIH family genes on chromosome 3p21 were associated with both schizophrenia and bipolar disorders in the population of European ancestry. Using GWAS and meta-analysis of bipolar affective disorder (BPAD), Scott et al. (2009) reported strong association evidence of rs1042779 (Arg595Gln, P = 1.8 × 10−7) in ITIH1 in the individuals of European ancestry (Scott et al., 2009). By combining GWAS results of SCZ and BPAD, Sklar et al. (2011) found strong association at rs736408 (combined P = 8.4 × 10−9 compared to BPAD P = 2.00 × 10−7), which is located in the NEK4–ITIH1–ITIH3–ITIH4 region (Sklar et al., 2011). Ripke et al. (2011) found that another SNP, i.e. rs2239547 which was located in the ITIH3–ITIH4 region reached genome-wide significance (P = 7.8 × 10− 9) in a joint GWAS analysis with 16,374 joint cases (cases with SCZ, schizoaffective disorder or BPAD) and 14,044 joint controls in the population of European ancestry (Ripke et al., 2011). Through joint analysis with the SCZ data of Psychiatric Genome-Wide Association Study Consortium (Ripke et al., 2011), Hamshere et al. (2013) have verified again that rs2239547 has significantly associated not only with SCZ (3.62 × 10−10) but also with SCZ and BPAD in a combined analysis (Hamshere et al., 2013). Notably, ITIH1, ITIH3, and ITIH4 belong to a family of serine protease inhibitors, the inter-alpha-trypsin inhibitors (ITI). ITI are composed of light chain–bikunin (encoded by AMBP) and five homologous heavy chains (ITIHs encoded by ITIH1, ITIH2, ITIH3, ITIH4, and ITIH5). ITIH1, ITIH3, and ITIH4 are arranged in the order of H1–H3–H4 on chromosome 3p21. So far, ITIH molecules have been found to play a particularly important role in inflammation (Garcia-Gil et al., 2010; Kim et al., 2011; Opal et al., 2005, 2007) as well as in carcinogenesis and metastatic processes (Chong et al., 2010; Hamm et al., 2008; Paris et al., 2002; Veeck et al., 2008). To investigate whether the region of ITIH family genes, i.e. ITIH1, ITIH3, and ITIH4 are associated with SCZ or MDD in the Han Chinese population, we totally genotyped seven SNPs which were selected for a better coverage of this region in DNA samples of 3515 individuals of the Han Chinese origin (1235 SCZ patients, 1045 MDD patients, and 1235 normal controls). 2. Materials and methods 2.1. Subjects The sample set included 1235 unrelated SCZ cases (805 males and 430 females), 1045 unrelated MDD cases (729 males and 316 females), and 1235 normal controls (665 males and 570 females). All subjects were recruited from the Han Chinese population in Shanghai China. The mean ages of SCZ cases, MDD cases, and controls cases were 36.4 (±9.0), 34.4 (±12.1), and 30.6 (±11.4) respectively. They were all outpatients or stable in-patients. They were interviewed by two independent psychiatrists and were diagnosed strictly according to DSM-IV criteria (Diagnostic and Statistical Manual of Mental Disorders, Fourth

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Edition). All SCZ patients are paranoid schizophrenics and have no presence of lifetime episode of mania or depression. All MDD patients are carefully selected according to the standard that all of them have suffered at least two distinct MDD episodes while showing no manic symptoms. Normal controls were randomly selected from the general public of the Han Chinese population in Shanghai. We required in written form the volunteers to respond of their medical histories with supplementary questions about psychosis, and other major complex diseases. Before collecting the blood of the volunteers, a face to face interview has been conducted and physical examinations including height, weight, blood pressure, etc. have been carried out. All informed consent have been obtained from subjects, and the study was reviewed and approved by the local ethical committee. 2.2. DNA extraction, SNP selection and genotyping Genomic DNA was extracted from peripheral blood samples using QuickGene DNA whole blood kit L (FUJIFILM) protocol. Six SNPs (rs2710322, rs3774356, rs2535629, rs17331151, rs3821831, rs4687554) were selected according to tag SNPs selecting strategies and analyzed by online software: Tagger (http://www.broadinstitute. org/mpg/tagger/server.html), while rs1042779 was selected according to the previous reports. Altogether 7 SNPS were selected and the coverage of tag SNPs was 88% (de Bakker et al., 2005, 2006). The information of 7 SNPs was shown in Table 1. We did not select rs736408 and rs2239547 which were reported to be associated with SCZ and BPAD in the Caucasian population (Hamshere et al., 2013; Ripke et al., 2011; Sklar et al., 2011) because the reports about them had not been published before we began to conduct this study. All SNPs were genotyped using TaqMan SNP Genotyping Assays on the Fludigm EP1 platform. All probes and primers were designed by the Assay-by-Design™ or Assayon-Demand™ service of Life Technologies. SNPs are determined by the genotype calls of each sample with a call rate better than 97.9% (Supplementary Tables 1 & 2). 2.3. Statistical analysis Allele and genotype frequencies, haplotype analysis, Hardy– Weinberg equilibrium analysis, association tests, and combined association analysis of cases of schizophrenia and major depressive disorder were performed by using the online SHEsis software (http://analysis2. bio-x.cn/SHEsisMain.htm) (Li et al., 2009; Shi and He, 2005). This is a user-friendly software platform which is equipped with a series of highly efficient analytic tools designed for association studies. The case–control genetic power was calculated by Genetic Power Calculator (available at http://pngu.mgh.harvard.edu/~purcell/gpc/cc2. html) (Purcell et al., 2003). All tests were two-tailed and statistical significance was assumed at the threshold of 0.05. P-values were adjusted using the Bonferroni correction. 2.4. Population stratification analysis We performed the population stratification analysis by using the STRUCTURE software (version 2.3.4, http://pritch.bsd.uchicago.edu/ structure.html) (Falush et al., 2003, 2007; Hubisz et al., 2009) and the additional genotyping data of 79 randomly selected SNPs. Additionally, we also obtained data of these 79 SNPs from 522 HapMap samples, including 174 samples from Utah residents with ancestry from northern and western Europe in the United States (CEU), 209 samples from Yoruba in Ibadan, Nigeria (YRI), and 139 samples from Han Chinese in Beijing (CHB) (HapMap public release 28 at http://hapmap.ncbi.nlm. nih.gov/ cgi-perl/gbrowse/hapmap28_B36/) (International HapMap Consortium, 2003). We use the STRUCTURE software to find the distinct populations using the genotype data of these 79 SNPs from 522 HapMap samples. We applied the admixture model and correlated frequencies model, with a burn-in length of 10,000 and MCMC repeats of 10,000.

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K. He et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 51 (2014) 34–38

Table 1 The information of 7 SNPs in the ITIH1–ITIH3–ITIH4 gene region. SNP ID

rs2710322

rs3774356a

rs1042779

rs2535629

rs17331151

rs3821831

rs4687554

Position Functional Polymorphism

52817593 Intron C/T

52817891 Intron A/C

52821011 Intergenic A/G

52833219 Intron C/T

52844534 Intergenic C/T

52853401 Intron T/C

52864135 Intron C/T

a

Rs3774356 was excluded from further analysis because it was not in Hardy–Weinberg equilibrium in controls.

The results of combined association analysis of cases of SCZ and MDD are shown in Table 4. We found that rs1042779, rs17331151 and rs3821831 showed statistically significant association with combined cases before Bonferroni correction [rs1042779: Pallele = 0.002 (Chi2 = 9.72, df = 1, Fisher's P), Pgenotype = 0.003 (Chi2 = 11.85, df = 2, Fisher's P), OR [95% CI] = 1.171 [1.060–1.292]; rs17331151: Pallele = 0.029 (Chi2 = 4.77, df = 1, Fisher's P), OR [95% CI] = 1.240 [1.022–1.504]; rs3821831: Pallele = 0.038 (Chi2 = 4.31, df = 1, Fisher's P), Pgenotype = 0.002 (Chi2 = 12.53, df = 2, Fisher's P), OR [95% CI] = 1.193 [1.010–1.410]]. After Bonferroni correction, rs1042779 and rs3821831 were significantly associated with combined cases (rs1042779: adjusted Pallele = 0.012, adjusted Pgenotype = 0.018; rs3821831: adjusted Pgenotype = 0.012) (Table 4). The genetic power for each site was calculated in both the allelic (1 df) and genotypic (2 df) models, assuming the disease prevalence to be 1%, the genotype relative risk Aa to be 1.25, the genotype relative risk AA to be 2, and the high risk allele with ≥ 20% frequency. Power analysis showed that the power of each site was larger than 0.8 for significance at P = 0.05. In the different sample sets, the pairwise linkage disequilibrium (LD) values among the 6 investigated SNPs were different. SNPs with D′ N 0.95 in two disorder sample sets were classified in the same block. No haplotype blocks were identified, as shown in Supplementary Figs. 1, 3. Supplementary Fig. 2 shows the triangle chart of results of K = 3 (K is the number of assumed populations and is used as a parameter of the STRUCTURE software) in the population stratification analysis by randomly selecting 79 SNPs.

3. Results Hardy–Weinberg equilibrium testing (HWE) P-values for 6 SNPs were larger than 0.05 in the healthy controls and in all patients group (Supplementary Tables 1 & 2). Rs3774356 was excluded from further analysis because it was not in Hardy–Weinberg equilibrium in controls, with the threshold of P value being 0.05. The allelic and genotypic frequencies of six SNPs in those two patient sample sets and the healthy control sets were listed in Tables 2 & 3. For SCZ, we found that rs2710322 and rs1042779 showed statistically significant association with SCZ before Bonferroni correction [rs2710322: Pallele = 0.0003 (Chi2 = 12.80, df = 1, Fisher's P), Pgenotype = 0.001 (Chi2 = 13.85, df = 2, Fisher's P), OR [95% CI] = 1.278 [1.117–1.462]; rs1042779: Pallele = 0.008 (Chi2 = 6.98, df = 1, Fisher's P), Pgenotype = 0.017 (Chi2 = 8.21, df = 2, Fisher's P), OR [95% CI] = 1.164 [1.040– 1.303]]; rs17331151 and rs3821831 also showed statistically significant association with SCZ before Bonferroni correction [rs17331151: Pallele = 0.015 (Chi2 = 5.87, df = 1, Fisher's P), OR [95% CI] = 1.322 [1.054–1.658]; rs3821831: Pgenotype = 0.040 (Chi2 = 6.45, df = 2, Fisher's P), OR [95% CI] = 1.044 [0.866–1.258]] (Table 2). For MDD, we found that rs2710322, rs1042779, and rs3821831 were associated with MDD before Bonferroni correction [rs2710322: Pallele = 0.01 (Chi2 = 6.62, df = 1, Fisher's P), OR [95% CI] = 0.840 [0.735–0.959]; rs1042779: Pallele = 0.007 (Chi2 = 7.39, df = 1, Fisher's P), Pgenotype = 0.012 (Chi2 = 8.93, df = 2, Fisher's P), OR [95% CI] = 1.178 [1.047– 1.326]; rs3821831: Pallele = 0.0005 (Chi2 = 11.06, df = 1, Fisher's P), Pgenotype = 0.001 (Chi2 = 13.66, df = 2, Fisher's P), OR [95% CI] = 1.426 [1.156–1.760]] (Table 3). After Bonferroni correction, rs2710322 and rs1042779 were associated with SCZ (rs2710322: Pallele = 0.0018, Pgenotype = 0.006; rs1042779: Pallele = 0.048) (Tables 2, 3). We also found that rs3821831 and rs1042779 were positively associated with MDD after Bonferroni correction (rs3821831: adjusted Pallele = 0.003, adjusted Pgenotype = 0.006, OR [95% CI] = 1.426 [1.156–1.760]; rs1042779: adjusted Pallele = 0.042, OR [95% CI] = 1.178 [1.047–1.326]) (Tables 2, 3).

4. Discussion The study's major finding was the significant association of common SNPs within the ITIH1–ITIH3–ITIH4 genomic region with both SCZ and MDD in the Han Chinese population. We found that rs2710322 was associated with SCZ (adjusted Pallele = 0.0018, adjusted

Table 2 Allele and genotype frequency of the six SNPs in SCZ. Bold numbers represent P-values b 0.05. SNP ID

Alleles

rs2710322 Case Control rs1042779 Case Control rs2535629 Case Control rs17331151 Case Control rs3821831 Case Control rs4687554 Case Control

C (freq) 1930 (0.793) 1813 (0.750) A (freq) 1374 (0.563) 1281 (0.525) A (freq) 1034 (0.427) 1022 (0.424) C (freq) 2316 (0.942) 2270 (0.925) C (freq) 2192 (0.901) 2188 (0.897) C (freq) 911 (0.372) 890 (0.366)

OR [95% CI] T (freq) 504 (0.207) 605 (0.250) G (freq) 1066 (0.437) 1157 (0.475) G (freq) 1388 (0.573) 1388 (0.576) T (freq) 142 (0.058) 184 (0.075) T (freq) 240 (0.099) 250 (0.103) T (freq) 1539 (0.628) 1544 (0.634)

P-value

P-Bonferroni

1.278 [1.117–1.462]

0.0003

0.0018

1.164 [1.040–1.303]

0.008

0.048

1.012 [0.903–1.134]

0.841

1.322 [1.054–1.658]

0.015

1.044 [0.866–1.258]

0.654

1.027 [0.914–1.154]

0.654

0.090

Genotypes C/C (freq) 755 (0.620) 674 (0.557) A/A (freq) 378 (0.311) 341 (0.280) A/A (freq) 217 (0.179) 222 (0.184) C/C (freq) 1091 (0.888) 1051 (0.857) C/C (freq) 982 (0.808) 987 (0.810) C/C (freq) 165 (0.135) 152 (0.125)

C/T (freq) 420 (0.345) 465 (0.385) A/G (freq) 619 (0.510) 599 (0.491) A/G (freq) 600 (0.495) 578 (0.480) C/T (freq) 134 (0.109) 168 (0.137) C/T (freq) 228 (0.188) 214 (0.176) C/T (freq) 581 (0.474) 586 (0.482)

T/T (freq) 42 (0.035) 70 (0.058) G/G (freq) 217 (0.179) 279 (0.229) G/G (freq) 394 (0.325) 405 (0.336) T/T (freq) 4 (0.003) 8 (0.007) T/T (freq) 6 (0.005) 18 (0.015) T/T (freq) 479 (0.391) 479 (0.394)

P-value

P-Bonferroni

0.001

0.006

0.017

0.102

0.739

0.052

0.040

0.768

0.24

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Table 3 Allelic and genotypic frequencies of the six SNPs in MDD. Bold numbers represent P-values b0.05. SNP ID

Alleles

rs2710322 Case Control rs1042779 Case Control rs2535629 Case Control rs17331151 Case Control rs3821831 Case Control rs4687554 Case Control

C (freq) 1470 (0.716) 1813 (0.750) A (freq) 1157 (0.566) 1281 (0.525) A (freq) 876 (0.425) 1022 (0.424) C (freq) 1936 (0.934) 2270 (0.925) C (freq) 1922 (0.926) 2188 (0.897) C (freq) 797 (0.385) 890 (0.366)

OR [95%CI] T (freq) 584 (0.284) 605 (0.250) G (freq) 887 (0.434) 1157 (0.475) G (freq) 1186 (0.575) 1388 (0.576) T (freq) 136 (0.066) 184 (0.075) T (freq) 154 (0.074) 250 (0.103) T (freq) 1273 (0.615) 1544 (0.634)

P-value

P-Bonferroni

0.840 [0.735–0.959]

0.010

0.06

1.178 [1.047–1.326]

0.007

0.042

1.003 [0.891–1.130]

0.959

1.154 [0.9170–1.452]

0.222

1.426 [1.156–1.760]

0.0005

1.086 [0.962–1.226]

0.181

0.003

Pgenotype = 0.006) in the Han Chinese population. Meanwhile, we also found rs3821831 was significantly associated with MDD (adjusted Pallele = 0.003, adjusted Pgenotype = 0.006). Rs1042779 which was reported to be associated with BPAD in the individuals of European ancestry (Scott et al., 2009) was found to be associated with SCZ (Pallele = 0.048) and MDD (Pallele = 0.042) in our samples after Bonferroni correction. Via the association analysis combining the SCZ and MDD cases, we found that rs1042779 was significantly associated with compound cases (adjusted Pallele = 0.012, adjusted Pgenotype = 0.018); and rs3821831 was associated with compound cases (adjusted Pgenotype = 0.012), but rs2710322 was not associated with combined cases. We did not detect obvious population stratification, and therefore the results should not be strongly affected by this confounding factor. Previous GWAS have consistently reported that ITIH family genes on chromosome 3p21 were associated with both BPAD and SCZ. Scott et al. (2009)reported that rs1042779 in ITIH1 was associated with both SCZ and BPAD in the population of European ancestry (Scott et al., 2009). Though the weak degree of association, we have successfully reconfirmed the association of rs1042779 with SCZ and MDD. At the same time, by analyzing the compound cases consisting of SCZ and MDD cases, we found that rs1042779 was significantly associated with the compound cases. The association degree significance is higher than that of the separate analysis of every case.

Genotypes C/C (freq) 516 (0.502) 674 (0.557) A/A (freq) 318 (0.311) 341 (0.280) A/A (freq) 177 (0.172) 222 (0.184) C/C (freq) 907 (0.875) 1051 (0.857) C/C (freq) 887 (0.855) 987 (0.810) C/C (freq) 142 (0.137) 152 (0.125)

C/T (freq) 438 (0.426) 465 (0.385) A/G (freq) 521 (0.510) 599 (0.491) A/G (freq) 522 (0.506) 578 (0.480) C/T (freq) 122 (0.118) 168 (0.137) C/T (freq) 148 (0.143) 214 (0.176) C/T (freq) 513 (0.496) 586 (0.482)

T/T (freq) 73 (0.071) 70 (0.058) G/G (freq) 183 (0.179) 279 (0.229) G/G (freq) 332 (0.322) 405 (0.336) T/T (freq) 7 (0.007) 8 (0.007) T/T (freq) 3 (0.003) 18 (0.015) T/T (freq) 380 (0.367) 479 (0.394)

P-value

P-Bonferroni

0.029

0.174

0.012

0.072

0.443

0.397

0.001

0.006

0.386

In recent years, many studies have verified that there were some shared genetics among major psychiatric disorders. This viewpoint provided us with more valuable information. Rs736408 and rs2239547 which are located in the ITIH1–ITIH3–ITIH4 region were found to have a strong association with SCZ and BPAD (Ripke et al., 2011; Sklar et al., 2011) or SCZ (Hamshere et al., 2013). We analyze the pairwise LD parameters between these two SNPs and the seven selected SNPs. Considering the r2 and D′ values, we found that rs1042779 can be used as a proxy of rs736408 (rs1042779 v. rs736408: r2 = 0.954; D′ = 1) and rs4687554 can be used as a proxy of rs2239547 (rs4687554 v. rs rs2239547: r2 = 0.984; D′ = 1) (Supplementary Table 3 and Supplementary Fig. 4). Rs4687554 has been found to have no association with SCZ, MDD and the compound cases of SCZ and MDD in the Han Chinese population (P N 0.05). We checked whether common SNPs in the genomic region containing ITIH family genes were also involved in the pathogenesis of mental disorders in the Han Chinese population. The risk of mental disorders in this region was detected in both populations and our results supported that this genomic region was involved in the development of SCZ and MDD. We also provide strong evidence that overlapping genetic risk between SCZ and MDD exist in ITIH family genes. Further studies in independent sample sets, especially in Eastern Asian samples, are suggested to confirm the shared risk between this region and different kinds of psychiatric disorders.

Table 4 Allelic and genotypic frequencies of the six SNPs in SCZ and MDD combined cases. Bold numbers represent P-values b 0.05. SNP ID

Alleles

rs2710322 Case Control rs1042779 Case Control rs2535629 Case Control rs17331151 Case Control rs3821831 Case Control rs4687554 Case Control

C (freq) 3400 (0.758) 1813 (0.750) A (freq) 2531 (0.564) 1281 (0.525) A (freq) 1910 (0.426) 1022 (0.424) C (freq) 4252 (0.939) 2270 (0.925) C (freq) 4114 (0.913) 2188 (0.897) C (freq) 1708 (0.378) 890 (0.366)

OR [95% CI] T (freq) 1088 (0.242) 605 (0.250) G (freq) 1953 (0.436) 1157 (0.475) G (freq) 2574 (0.574) 1388 (0.576) T (freq) 278 (0.061) 184 (0.075) T (freq) 394 (0.087) 250 (0.103) T (freq) 2812 (0.622) 1544 (0.634)

P-value

P-Bonferroni

1.043 [0.9210–1.169]

0.473

1.171 [1.060–1.292]

0.002

1.008 [0.912–1.114]

0.879

1.240 [1.022–1.504]

0.029

0.174

1.193 [1.010–1.410]

0.038

0.228

1.054 [0.951–1.167]

0.315

0.012

Genotypes C/C (freq) 1271 (0.566) 674 (0.557) A/A (freq) 696 (0.310) 341 (0.280) A/A (freq) 394 (0.176) 222 (0.184) C/C(freq) 1998 (0.882) 1051 (0.857) C/C (freq) 982 (0.808) 987 (0.810) C/C (freq) 307 (0.136) 152 (0.125)

P-value C/T (freq) 858 (0.382) 465 (0.385) A/G (freq) 1139 (0.508) 599 (0.491) A/G (freq) 1122 (0.500) 578 (0.480) C/T(freq) 256 (0.113) 168 (0.137) C/T (freq) 228 (0.188) 214 (0.176) C/T (freq) 1094 (0.484) 586 (0.482)

T/T (freq) 115 (0.051) 70 (0.058) G/G (freq) 407 (0.182) 279 (0.229) G/G (freq) 726 (0.324) 405 (0.336) T/T(freq) 11 (0.005) 8 (0.007) T/T (freq) 6 (0.005) 18 (0.015) T/T (freq) 859 (0.380) 479 (0.394)

P-Bonferroni

0.68

0.003

HDW 0.05 0.76

0.018

0.116 0.608

0.506

0.269 0.532

0.093

0.368 0.650

0.002

0.576

0.012

0.030 0.107 0.160 0.185

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