Association analysis of CHRNA3 polymorphisms with schizophrenia in a Chinese Han population: A case-control study.

Schizophrenia (SCZ) is a highly heritable, chronic, severe psychiatric disorder associated with significant financial costs to families and societies. In this case-control study, we investigated the associations between seven SNPs in CHRNA3 gene and the risk of SCZ.A total of 1071 (384 cases and 687 controls) unrelated subjects were recruited for our association study. Seven candidate tagging SNPs in CHRNA3 gene (rs3743077, rs1317286, rs938682, rs12914385, rs2869546, rs3743075, rs8040868) selected in HapMap database were genotyped by Sequenom MassARRAY. Finally, association analysis was conducted under various models.According to our results, in genetic model analysis, rs12914385 and rs8040868 are associated with decreased risk of SCZ in female subgroup; rs3743075 is associated with decreased risk of SCZ in subgroup with age <45; while rs3743077 and rs2869546 are associated with increased risk of SCZ. Haplotype analysis suggested that the 3 variants comprised 1 block, and that the haplotype Ars938682Crs12914385Crs2869546 was significantly correlated with an increased risk of SCZ in the subgroup with age ≥45.Our data indicate potential associations between CHRNA3polymorphisms and SCZ susceptibility, and the significant variants identified in our study may be used as genetic biomarkers for SCZ susceptibility in Chinese Han population.

Introduction

Schizophrenia (SCZ), named by Dr Bleuler in 1908, is a highly heritable, chronic, severe psychiatric disorder with a lifetime risk of approximately 1% in the general population worldwide.[ Due to the high heterogeneity, the symptoms of schizophrenia are divided into 4 groups: positive, negative, cognitive, and mood symptoms.[ Schizophrenia is diagnosed based on criteria in either the APA fifth edition of the DSM 5 or the WHO’ ICD-10. As an enigmatic illness, schizophrenia places a substantial burden on patients, their families, and society.[ However, the exact pathogenesis of schizophrenia remains unknown and, despite large numbers of trials of potential therapies, the efficacy of pharmacological treatments is poor for many schizophrenia patients.[

Multiple genetic and environmental factors contribute to disturbances in brain function and development that result in schizophrenia.[ Risk factors for SCZ include urbanicity, migration, sex, season of birth pregnancy, and birth complications.[ The heritability of schizophrenia estimated of approximately 80% to 85% by monozygotic twin and adoption and family studies, indicating that genetic factors may play an important role in the pathophysiology of SCZ.[ Recently, several genome-wide association studies (GWASs) of schizophrenia have identified around 30 schizophrenia-associated loci, but the replication results remain controversial and ambiguous.

The associations between CHRNA3 polymorphisms and schizophrenia risk have not been investigated in the northern Chinese Han population. We, therefore, conducted an extensive association analysis to evaluate the roles of CHRNA3 gene polymorphisms and haplotypes on susceptibility to esophageal cancer in a population of northwestern Chinese patients from a single case-control study. Seven SNPs were selected and examined in the present study and our results may shed new light on the association between CHRNA3 and SCZ in Chinese Han population.

Methods

Ethics and consent

The study was approved by the Ethics Committee of the Xizang Minzu University and Northwest University, and complied with the Declaration of Helsinki. Written informed consents were obtained from all participants prior to participation in the study.

Study participants and sample collection

A total of 1071 (384 cases and 687 controls) unrelated subjects were recruited for our association study. Cases were all clinically diagnosed with SCZ by trained psychiatrist post hoc according to Diagnostic Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria.[ Patients with complicating diagnoses of mental retardation, organic brain damage, neurological disorders, autoimmune disorders, and low comprehension skills were excluded from the study. Healthy controls with no evidence of SCZ or other diseases were randomly selected from healthy people who did medical examination in hospital during the same period. Additionally, the participants are northern Chinese people who live in Xi’an city or nearby. After signing the informed consents, 5 mL venous blood samples were collected from each subject into tubes containing EDTA, then centrifuged and stored at −80°C.

SNP selection and genotyping

Seven candidate tagging SNPs in CHRNA3 gene (rs3743077, rs1317286, rs938682, rs12914385, rs2869546, rs3743075, rs8040868) were selected in HapMap database with a minor allele frequency (MAF)>5%. Genomic DNA was extracted from whole-blood sample by using the GoldMag-Mini Whole Blood Genomic DNA Purification Kit (GoldMag Ltd, Xi’an. China) in accordance with manufacturer's protocol.[ Then, the concentration of DNA was measured by NanoDrop 2000, and qualified samples were stored at −80°C for the next genotyping. MassARRAY Assay Design 3.0 software (Sequenom, San Diego, CA) was used to design multiplexed MassEXTEND assay.[ Genotyping was performed using Sequenom MassARRAY RS1000 (Sequenom, Inc) in accordance with the manufacturer's protocol.[ Finally, SequenomTyper 4.0 Software (Sequenom Inc) was used to manage and analyze the data.[

Statistical analysis

Statistical analyses in this study was conducted by Microsoft Excel and the SPSS 19.0 statistical package (SPSS, Chicago, IL) as previously described.[ Hardy–Weinberg equilibrium (HWE) was assessed for the frequency of each SNP using a goodness-of-fit χ2 test on the control subjects. The distribution differences of demographic characteristics (gender and age) and genotype were analyzed by χ2 tests.[ ORs and 95% CI were calculated by using unconditional logistic regression analysis with adjustment for age and gender. The genetic models (codominant, dominant, recessive, log-additive) analyses were conducted by a web-based software SNPStats.[ Finally, we used Haploview software package (version 4.2) and the SHEsi software platform (http://www.nhgg.org/analysis/) to perform linkage disequilibrium analysis and haplotype-based associations adjusted by gender and age. In addition, all P values presented in this study are 2-sided, and P ≤ .05 was considered statistically significant.[

Results

Study population

The demographic information of this study was shown in Table 1. A total of 1071 (384 cases and 687 controls) participants were recruited in this case-control study. There were 201 men (52.3%) and 183 women (47.7%) in the case group, and 387 men (56.3%) and 300 women (43.7%) in the control group. The mean age of the cases was 36.58 ± 13.73 years and that of the controls was 48.56 ± 9.56 years. In addition, age occurred significant difference between cases and controls (P < .0001) while gender did not (P = .208).

Table 1

The demographic information of this study.

Association between CHRNA3 polymorphisms and SCZ risk

Table S1 summarizes the basic information of polymorphisms we selected, including alleles (A/B), MAF of case and control, and the association between alleles and SCZ risk. All of the 7 SNPs were in HWE among the control subjects (P>.05). However, no associations were observed between the alleles and SCZ risk in an allele model. We then assessed an association between each SNP and SCZ risk in an unconditional logistic regression analysis, which was performed using four models: codominant, dominant, recessive, and log-additive model. But unfortunately, we still didn’t find any associations (Table S2).

Stratified analysis of CHRNA3 polymorphisms and the risk of SCZ adjusted by gender or age

We further conducted association analysis stratified by gender and age. The information of SNPs after stratification was listed in Table 2. SNPs in all 4 subgroups are in Hardy–Weinberg equilibrium in the control subjects (P > .05). In the subgroup with age ≥45, 2 SNPs were found to be significantly associated with an increased risk of SCZ (rs3743077T/C, OR = 1.62, 95% CI: 1.17–2.24, P = .004; rs2869546, OR = 1.54, 95% CI: 1.11–2.15, P = .01). We also compared the genotype frequencies between cases and controls. For rs3743077 and rs2869546, results indicated that the genotype frequency distributions differed between the cases and controls in the subgroup with age ≥45 (rs3743077, P = .007; rs2869546, P = .025), whereas there was no significant difference in other 3 subgroups (Table 3).

Table 2

Association analysis between SNPs in CHRNA3 with SCZ stratified by gender and age.

Table 3

Comparison of genotype frequencies between cases and controls.

Next, genetic models were used to further identify the associations between the SNPs and the risk of SCZ. As shown in Table 4, there was no association between rs12914385 or rs8040868 and the risk of SCZ among males, whereas the associations between those SNPs and the risk of SCZ among females under models were stronger than those in the nonstratified analysis. In female subgroup, rs12914385 was found to be associated with decreased risk of SCZ in recessive model (OR = 0.82, 95% CI: 0.54–1.26, P = .026); and rs8040868 is associated with decreased risk of SCZ in codominant model (OR = 0.94, 95% CI: 0.6–1.46, P = .018 for the C/T genotype), recessive model (OR = 0.33, 95% CI: 0.15–0.75, P = .005) and additive model (OR = 0.70, 95% CI: 0.51–0.98, P = .033). As shown in Table 5, the results indicated that rs3743075 was associated with decreased risk of SCZ in recessive model in the subgroup with age <45 (OR = 0.57, 95% CI: 0.34–0.94, P = .027). In subgroup with age ≥45, rs3743077 and rs2869546 were found to be associated with increased risk of SCZ in codominant model (OR = 1.94, 95% CI: 1.25–3.01, P = .0096; OR = 1.75, 95% CI: 1.12–2.71, P = .033), dominant model (OR = 1.93, 95% CI: 1.26–2.96, P = .0023; OR = 1.76, 95% CI: 1.15–2.69, P = .0091) and additive model (OR = 1.61, 95% CI: 1.15–2.26, P = .0056; OR = 1.53, 95% CI: 1.09–2.14, P = .014).

Table 4

Associations between SNPs and the risk of SCZ under genetic models (stratified by gender).

Table 5

Associations between SNPs and the risk of SCZ under genetic models (stratified by age).

Association of CHRNA3 haplotypes with the risk of SCZ

In the subgroup with age ≥45, linkage disequilibrium analysis revealed a block in CHRNA3 (Fig. 1), including rs938682, rs12914385, and rs2869546. Further analyses of associations between CHRNA3 haplotypes and SCZ risk showed that haplotype “ACC” in the block was associated with a significantly increased risk of SCZ (Table 6). Additionally, we did not find any other meaningful associations between the haplotypes and SCZ risk.

Figure 1

Haplotype block map for subgroup with age ≥45.

Table 6

Associations between CHRNA3 haplotypes and SCZ risk in the subgroup with age ≥45.

Discussion

In the present case-control study, we investigated the association between 7 SNPs and the risk of SCZ in Chinese Han population. In the overall analysis, we did not find any significant associations between SNPs and the risk of SCZ. Interestingly, however, some associations were found in analysis stratified by age or gender. In female subgroup, rs12914385 and rs8040868 are associated with decreased risk of SCZ. In analysis stratified by age, rs3743075 is associated with decreased risk of SCZ in subgroup with age <45; while rs3743077 and rs2869546 are associated with increased risk of SCZ. Additionally, in the subgroup with age ≥45, haplotype Ars938682Crs12914385Crs2869546 was found to be associated with increased risk of SCZ.

Schizophrenia is a complex psychiatric disorder associated with significant financial costs to families and societies. According to a prevalence study of SCZ in China between 1990 and 2010, the prevalence of schizophrenia in China has more than doubled over the past 20 years.[ Schizophrenia spectrum disorder has been reported to present sensorimotor-gating deficits (commonly measured by prepulse inhibition, PPI).[ And PPI can be enhanced by nicotine, therefore it has been proposed that schizophrenia patients smoke to ameliorate their early attentional deficits.[ This evidence is consistent with the idea that schizophrenia patients have a strongly increased likelihood of smoking.[ CHRNA3 (cholinergic receptor nicotinic alpha 3 subunit) is an alpha-type subunit encoded by a locus located on chromosome 15q. In 2010, Petrovsky et al reported that sensorimotor gating is influenced by variations of the CHRNA3 gene, which might also have an impact on the course and severity of schizophrenia.[ They found that rs1317286 was associated with PPI in schizophrenia in Caucasian population. It might be due to the ethnic differences, however, no associations were observed between rs1317286 and SCZ in the present study.

Despite this study showing some SNPs associated with SCZ susceptibility in stratified analysis, some limitations should be considered. First, our sample size was relatively small. Therefore, our findings must be confirmed in studies with larger sample sizes as well as in a meta-analysis. Second, the association between genetic polymorphisms and SCZ was not evaluated. Further studies with larger sample size are required to characterize the function of CHRNA3 and elucidate the mechanisms underlying the association between the CHRNA3 and SCZ susceptibility.

To our knowledge, it is first time to report for rs12914385 and rs8040868, a number of studies have reported associations between these 2 variants and lung cancer risk, and we found that these 2 variants are associated with decreased risk of SCZ in female subgroup.[ Rs3743075 and rs2869546 are also previously reported to be associated with lung cancer in Americans and Chinese populations, respectively.[ In addition, rs3743077 are reported to be associated with dizziness at first inhalation of cigarette.[ In this study, we found that the 5 variants above are associated with SCZ in stratified analysis. However, due to the small sample size in our work, further study is needed to pin down the exact relationship among CHRNA3, smoking, and SCZ.

Author contributions

Conceptualization: Li Wang, Yongjun He, Tianbo Jin.

Data curation: Chenghao Guo.

Formal analysis: Tianbo Jin.

Investigation: Guixin Li, Mei Bai, Linhao Zhu, Yongjun He.

Methodology: Li Wang.

Software: Linhao Zhu.

Validation: Dongya Yuan.

Visualization: Dongya Yuan.

Writing – original draft: Chenghao Guo.

Writing – review & editing: Chenghao Guo.