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. 2014 Jun 30;2(5):729–736. doi: 10.3892/br.2014.308

Meta-analyses of 10 polymorphisms associated with the risk of schizophrenia

DONGJUN DAI 1,*, YUNLIANG WANG 2,*, JIAOJIAO YUAN 1,*, XINGYU ZHOU 1, DANJIE JIANG 1, JINFENG LI 2, YUZHENG ZHANG 2, HONGLEI YIN 2, SHIWEI DUAN 1,✉
PMCID: PMC4106624  PMID: 25054019

Abstract

Schizophrenia (SCZ) is a severe complex psychiatric disorder that generates problems for the associated family and society and causes disability with regards to work for patients. The aim of the present study was to assess the contribution of 10 genetic polymorphisms to SCZ susceptibility. Meta-analyses were conducted using the data without a limitation for time or language. A total of 27 studies with 7 genes and 10 polymorphisms were selected for the meta-analyses. Two polymorphisms were found to be significantly associated with SCZ. SNAP25 rs3746544 was shown to increase the SCZ risk by 18% [P=0.01; odds ratio (OR), 1.18; 95% confidence interval (CI), 1.05–1.34] and GRIK3 rs6691840 was found to increase the risk by 30% (P=0.008; OR, 1.30; 95% CI, 1.07–1.58). Significant results were found under the dominant (P=0.001; OR, 1.36; 95% CI, 1.13–1.65) and additive (P=0.02; OR, 1.45; 95% CI, 1.06–1.98) model for the SNAP25 rs3746544 polymorphism and under the additive model for the GRIK3 rs6691840 polymorphism (P=0.03; OR, 1.73; 95% CI, 1.04–2.85). There were no significant results observed for the other eight polymorphisms, which were CCKAR rs1800857, CHRNA7 rs904952, CHRNA7 rs6494223, CHRNA7 rs2337506, DBH Ins>Del, FEZ1 rs559668, FEZ1 rs597570 and GCLM rs2301022. In conclusion, the present meta-analyses indicated that the SNAP25 rs3746544 and GRIK3 rs6691840 polymorphisms were risk factors of SCZ, which may provide valuable information for the clinical diagnosis of SCZ.

Keywords: schizophrenia, meta-analysis, polymorphism, SNAP25 rs3746544, GRIK3 rs6691840

Introduction

Schizophrenia (SCZ) is a common severe psychiatric disorder that affects <1% of the population. SCZ patients lose the ability to work or interact socially (1) and require assistance from the government (2). SCZ is a complex disorder. Environment and genetic factors play significant roles in SCZ (3–5). Environmental factors, including redox imbalance (4), inflammation (6) or obstetrical complications (7), have been reported to be associated with SCZ. Family, twin and adoption studies have shown that the genetic components increased the risk of SCZ (8,9). The lifetime risk for twins was >40%, which was much higher compared to 6.5% in first-degree relatives (10) and 1% in the general population (9). Multiple polygenic components have been shown to contribute to the risk of SCZ (11). In addition, epigenetic modification, such as DNA methylation, indicated that aberrant gene methylation may also influence the development of SCZ (12,13).

Dysfunction of the dopaminergic system has been accepted as an associated factor for SCZ (14). CCKAR encodes cholecystokinin type A receptor (CCKAR), which is a receptor of CCK. CCK can regulate the release of dopamine and dopamine-related behaviors (15). The activation of CCKAR in caudal nucleus accumbens can stimulate dopamine release, and therefore influence the process of SCZ (16,17). DBH encodes an enzyme that can catalyzes the conversion of dopamine to norepinephrine (18,19). The genetic association between DBH and SCZ has been shown in a previous study (20). CHRNA7 is located on chromosome 15q13–q14, which is a susceptible SCZ locus. A low expression of CHRNA7 was found in postmortem human hippocampus, reticular thalamic nucleus and frontal cortex of SCZ cases (21–23). The association between CHRNA7 and SCZ has been found in numerous studies (24–26). FEZ1 encodes fasciculation and elongation protein ζ-1 (FEZ1), which participates in the neurite extension machinery through an interaction with disrupted in schizophrenia 1, a candidate SCZ gene (27–29). A significant association has been demonstrated between FEZ1 and SCZ (30). A number of studies have indicated that oxidative stress is a risk factor for SCZ (31–33). Glutathione (GSH) is one of the key redox regulators that can protect the nervous tissue from reactive oxygen species (34). GCLM encodes glutamate-cysteine ligase modifier (GCLM), which is a key enzyme of the GSH pathway that may be associated with SCZ (35). Glutamate receptors may be involved in the pathophysiology of SCZ (36). GRIK3 encodes a protein that is a member of the glutamate receptors. A higher expression of GRIK3 has been found in SCZ cases compared to controls (37). SNAP25 encodes a protein that is implicated in the docking priming and fusion of the vesicles, which has been shown to be associated with SCZ (38,39).

Association studies between the genetic polymorphisms of the aforementioned 7 genes and SCZ have been performed in different populations (Table I). The discrepancies in the association studies of these genetic loci may be due to the different ethnic background and insufficient power. Meta-analysis can enhance the power by combining data from different individual studies and can draw a more comprehensive conclusion than a single association study. The aim of the present meta-analysis was to assess the associations between the 7 genes and the SCZ risk.

Table I.

Characteristics of the case-control studies in the current meta-analyses.

Gene Polymorphism Authors Year Country Ethnicity Cases/controls HWE Result Power (Refs.)
CCK rs1800857 Zheng et al 2012 China Asians 508/519 NA S 0.416 (50)
Minato et al 2007 Japan Asians 290/290 Yes NS 0.321 (51)
Sanjuan et al 2004 Spain Europeans 105/93 Yes NS 0.103 (52)
Tachikawa et al 2001 Japan Asians 87/100 Yes NS 0.138 (53)
CHRNA7 rs904952 Bakanidze et al 2013 German Europeans 224/224 Yes S 0.275 (54)
Bakanidze et al 2013 Georgian Europeans 50/51 Yes S 0.099 (54)
Cabranes et al 2013 Spain Europeans 152/95 Yes NS 0.166 (55)
Ancin et al 2010 Spain Europeans 508/793 Yes NS 0.618 (56)
Iwata et al 2007 China Asians 188/188 Yes NS 0.363 (57)
rs6494223 Cabranes et al 2013 Spain Europeans 153/95 Yes NS 0.161 (55)
Joo et al 2010 Korea Asians 254/349 NA S 0.426 (58)
Ancin et al 2010 Spain Europeans 510/793 Yes NS 0.613 (56)
rs2337506 Bakanidze et al 2013 German Europeans 224/222 Yes NS 0.189 (54)
Joo et al 2010 Korea Asians 254/349 NA NS 0.365 (58)
Iwata et al 2007 China Asians 188/186 Yes NS 0.206 (57)
DBH Ins>Del Hui et al 2013 China Asians 195/304 Yes NS 0.280 (59)
Zhou et al 2013 China Asians 747/625 Yes S 0.655 (60)
Yamamoto et al 2003 Canada Europeans 106/120 Yes NS 0.162 (61)
FEZ1 rs559668 Koga et al 2007 Japan Asians 1,913/1,911 Yes NS 0.688 (62)
Hodgkinson et al 2007 USA Europeans 159/173 Yes NS 0.193 (63)
Yamada et al 2004 Japan Asians 356/359 Yes NS 0.164 (30)
rs597570 Koga et al 2007 Japan Asians 1,913/1,911 Yes NS 0.697 (62)
Hodgkinson et al 2007 USA Europeans 159/170 Yes NS 0.172 (63)
Yamada et al 2004 Japan Asians 360/359 Yes NS 0.166 (30)
SNAP25 rs3746544 Lochman et al 2013 Czech Europeans 183/193 Yes S 0.212 (47)
Carroll et al 2009 British Isles Europeans 650/712 Yes S 0.615 (48)
Kawashima et al 2008 Japan Asians 372/367 NA S 0.340 (49)
GRIK3 rs6691840 Kilic et al 2010 Turkey Europeans 256/242 Yes S 0.240 (64)
Ahmad et al 2009 India Asians 100/100 Yes NS 0.138 (65)
Lai et al 2005 Taiwan Asians 160/160 Yes NS 0.086 (66)
Begni et al 2002 Italy Europeans 99/116 Yes S 0.136 (67)
GCLM rs2301022 Hanzawa et al 2011 Japan Asians 358/359 Yes NS 0.769 (68)
Ma et al 2010 China Asians 427/415 NA S 0.334 (69)
Kishi et al 2008 Japan Asians 742/817 Yes NS 0.344 (70)
Matsuzawa et al 2009 Japan Asians 214/220 Yes NS 0.623 (71)
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HWE, Hardy-Weinberg equilibrium; NA, not applicable; S, significant; NS, not significant.

Materials and methods

Systemic search

A systemic search was performed using the PubMed database. The following keywords were used to identify the available studies: Schizophrenia, polymorphism and association. The studies included in the meta-analysis met certain criteria: i) The study was an original human case-control study on the association between gene polymorphisms and SCZ; ii) the study had sufficient information to obtain the odds ratios (ORs) and 95% confidence intervals (CIs); iii) genotype distribution of each polymorphism in the controls met the Hardy-Weinberg equilibrium (HWE); iv) each polymorphism contained more than three datasets from the studies; and v) there was no previous meta-analysis on the association between the selected polymorphism and SCZ. The following information was carefully extracted or calculated from each selected study: Gene name, polymorphism, first author’s name, year of publication, country, ethnicity, the numbers of cases and controls, HWE for controls, results of the association in certain polymorphism with SCZ and the power of individuals.

Statistical analysis

The Arlequin program was used to test HWE (40). The power of each study was calculated by the Power and Sample Size Calculation program. The statistical heterogeneity across the studies included in the meta-analysis was assessed by Cochran’s Q statistic and I2 test (41) to decide the type of analysis. The fixed-effects model was used for the analysis with an I2<50%, whereas the random-effects model was used for the analysis with an I2>50%. In addition to the allelic analysis model, the meta-analyses were also performed under the dominant, recessive and additive models. The statistical analyses of the meta-analyses were performed by Review Manager 5 (42). Funnel plots were generated to observe the potential publication bias.

Results

Meta-analysis and associations

As shown in Fig. 1, a search in the online PudMed database was performed. A total of 3,456 studies were retrieved by using the aforementioned keywords. Among them, 1,774 studies were removed that had a previous meta-analysis, and 1,446 studies with a limited number of studies on the same gene were subsequently excluded. Another 209 studies were excluded as they did not meet the included criteria. In total, 27 studies of 10 polymorphisms for 7 genes were involved in the meta-analyses. All the genotype distributions in the involved studies met HWE (Table I).

Figure 1.

Figure 1

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Flowchart of selection process in the meta-analyses. SCZ, schizophrenia.

No significant heterogeneity was observed between SCZ and rs1800857 of CCKAR (I2=31%), rs904952 (I2=6%) and rs2337506 (I2=0%) of CHRNA7, rs559668 (I2=0%) and rs597570 (I2=0%) of FEZ1, rs3746544 of SNAP25 (I2=0%), rs6691840 of GRIK3 (I2=16%), rs2301022 of GCLM (I2=53%). Significant heterogeneity was found in the meta-analyses for rs6494223 of CHRNA7 (I2=84%) and DBH Ins>Del (I2=61%) with SCZ. No publication bias was found in all the meta-analyses due to the symmetrical shape of the funnel plots (Fig. 3).

Figure 3.

Figure 3

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Funnel plots of 10 relative polymorphisms with schizophrenia. SE, standard error; OR, odds ratio.

The meta-analyses demonstrated a significant association between rs6691840 of GRIK3 and SCZ at the allelic level (P=0.008; OR, 1.30; 95% CI, 1.07–1.58; Table II and Fig. 2) and additive model (P=0.03; OR, 1.73; 95% CI, 1.04–2.85; Table II; Fig. 2). A significant association was also found in rs3746544 of SNAP25 in the allelic analysis (P=0.01; OR, 1.18; 95% CI, 1.05–1.34; Table II and Fig. 2), and under the dominant (P=0.001; OR, 1.36; 95% CI, 1.13–1.65; Table II; Fig. 2) and additive models (P=0.02; OR, 1.45; 95% CI, 1.06–1.98; Table II; Fig. 2). No significant association was demonstrated in the meta-analyses of the other polymorphisms (P>0.05; Table II).

Table II.

Meta-analyses of 10 relative polymorphisms with schizophrenia.

Genetic model Polymorphism Cases/controls S OR (95% CI) P-value I2 (%) Power
Overall CCKAR rs1800857 990/1,002 4 0.93 (0.80–1.07) 0.29 31 0.736
CHRNA7 rs904952 1122/1351 5 0.97 (0.87–1.09) 0.63 6 0.891
CHRNA7 rs6494223 916/2055 3 1.10 (0.80–1.53) 0.56 84 0.881
CHRNA7 rs2337506 665/1304 3 1.00 (0.86–1.18) 0.95 0 0.737
DBH Ins>Del 1048/1049 3 1.16 (0.92–1.46) 0.20 61 0.832
FEZ1 rs559668 2428/2443 3 0.96 (0.84–1.09) 0.54 0 0.827
FEZ1 rs597570 2432/2440 3 0.98 (0.86–1.11) 0.73 0 0.819
SNAP25 rs3746544 1205/1272 3 1.18 (1.05–1.34) 0.006a 0 0.850
GRIK3 rs6691840 615/618 4 1.30 (1.07–1.58) 0.008a 16 0.471
GCLM rs2301022 1741/1811 4 1.02 (0.86–1.20) 0.83 53 0.921
Dominant CCKAR rs1800857 482/483 3 1.03 (0.79–1.33) 0.85 0 0.514
CHRNA7 rs904952 1122/1351 5 1.07 (0.89–1.28) 0.50 0 0.800
CHRNA7 rs6494223 663/888 2 0.90 (0.73–1.11) 0.33 0 0.664
CHRNA7 rs2337506 412/408 2 1.17 (0.81–1.68) 0.40 0 0.362
DBH Ins>Del 1048/1049 3 1.24 (0.87–1.77) 0.23 63 0.798
FEZ1 rs559668 2428/2443 3 0.94 (0.81–1.08) 0.38 0 0.960
FEZ1 rs597570 2432/2440 3 0.94 (0.81–1.09) 0.40 0 0.958
SNAP25 rs3746544 833/905 2 1.36 (1.13–1.65) 0.001a 0 0.759
GRIK3 rs6691840 615/618 4 1.68 (0.86–3.28) 0.13 84 0.593
GCLM rs2301022 1314/1396 3 0.91(0.79–1.07) 0.25 0 0.917
Recessive CCKAR rs1800857 482/483 3 1.20 (0.82–1.78) 0.35 0 0.260
CHRNA7 rs904952 1122/1351 5 0.71 (0.46–1.11) 0.13 68 0.795
CHRNA7 rs6494223 663/888 2 1.00 (0.75–1.33) 0.99 0 0.459
CHRNA7 rs2337506 412/408 2 0.95 (0.68–1.34) 0.78 0 0.426
DBH Ins>Del 1048/1049 3 1.11 (0.89–1.38) 0.36 0 0.645
FEZ1 rs559668 2428/2443 3 1.13 (0.73–1.75) 0.57 47 0.260
FEZ1 rs597570 2432/2440 3 1.40 (0.89–2.21) 0.15 39 0.188
SNAP25 rs3746544 833/905 2 1.09 (0.60–1.98) 0.78 66 0.403
GRIK3 rs6691840 615/618 4 1.14 (0.50–2.59) 0.75 57 0.197
GCLM rs2301022 1314/1396 3 0.92 (0.67–1.26) 0.60 0 0.407
Additive CCKAR rs1800857 297/288 3 1.17 (0.77–1.77) 0.47 0 0.242
CHRNA7 rs904952 537/700 5 0.91 (0.72–1.16) 0.44 45 0.612
CHRNA7 rs6494223 341/442 2 0.94 (0.68–1.28) 0.68 0 0.387
CHRNA7 rs2337506 241/251 2 1.18 (0.67–2.11) 0.56 0 0.291
DBH Ins>Del 543/567 3 1.20 (0.94–1.54) 0.15 48 0.537
FEZ1 rs559668 2010/1990 3 1.15(0.74–1.87) 0.54 45 0.222
FEZ1 rs597570 2016/1985 3 1.38 (0.87–2.18) 0.17 39 0.188
SNAP25 rs3746544 425/512 2 1.45 (1.06–1.98) 0.02a 22 0.457
GRIK3 rs6691840 391/432 4 1.73 (1.04–2.85) 0.03a 0 0.186
GCLM rs2301022 840/869 3 0.89 (0.65–1.22) 0.47 0 0.394
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a

P≤0.05.

S, number of studies; OR, odds ratio; CI, confidence interval.

Figure 2.

Figure 2

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Forest plots of SNAP25 rs3746544 and GRIK3 rs6691840 polymorphisms with schizophrenia. CI, confidence interval.

Power analyses

All the power analyses in the meta-analyses were tested under a moderate risk of SCZ (OR, 1.2) (Tables I and II). The results showed that the power of the meta-analyses was much higher compared to the previous studies (Tables I and II). The power of the majority of the meta-analyses was sufficient (Power>0.730; Table II), except for the meta-analysis of rs6691840 (Power=0.471).

Discussion

The present meta-analyses performed a systemic overview of the association between gene polymorphisms and SCZ. A total of 7 selected genes (CCKAR, CHRNA7, DBH, FEZ1, SNAP25, GRIK3 and GCLM) and 10 polymorphisms (rs1800857, rs904952 rs6494223, rs2337506, DBH Ins>Del, rs559668, rs597570, rs3746544, rs6691840 and rs2301022) were used to identify the association between the genetic factors and SCZ. rs6691840 was demonstrated to be a risk factor for SCZ on the allelic level. rs3746544 was found to increase the SCZ risk by 18% on the allelic level, 34% under the dominant model and 45% under the additive model. The meta-analyses could not identify the significant associations between the remaining polymorphisms and SCZ (Table II). To the best of our knowledge, this is the first meta-analyses for all the 10 polymorphisms.

Glutamate receptors in the frontal cortex play a significant role in the memory system that may be associated with SCZ (43). GRIK3 encodes a key subtype of glutamate receptors that is expressed with a higher level in SCZ cases compared to controls (37). The GRIK3 rs6691840 polymorphism can affect the primary structure of human ionotropic glutamate by changing serine to alanine (Ala) at position 310 in extracellular N-terminus (44,45). Previous case-control studies showed that rs6691840-Ala may increase the risk of SCZ in Turkish, Italian and Indian populations. By contrast, there was no association between rs6691840 and SCZ in the Chinese population. The present meta-analysis of GRIK3 rs6691840 combined the data from the four studies and demonstrated that rs6691840-Ala increased the SCZ risk by 30% (P=0.008). There was no ethnic difference evaluated in rs6691840 [fixation index (Fst), 0.053; HapMap-CEU, 0.757; HapMap-HCB, 0.952; HapMap-GIH, 0.784] and low heterogeneity was also observed (allelic level, I2=16%; additive model, I2=0%). Notably, the power of the meta-analysis was relatively small (Power=0.471), indicating that larger scale replication studies are required to confirm the strong association in the present meta-analysis.

Soluble N-ethylmaleimide-sensitive factor attachments receptor (SNARE) is involved with the pathophysiology of SCZ, as it is associated with the neurotransmitter exocytotic machinery (46). SNAP25 encodes a protein that is a key part of the SNARE complex. SNAP25 can deliver neurotransmitter-containing vesicles to the inner plasma membrane. Human and animal studies indicate that SNAP25 is a risk factor for mental illness, such as SCZ (38,39). For the SNAP25 rs3746544 polymorphism, there have been two previous studies with positive results (47,48) in Europeans (Czechs and British populations) and one negative result (49) in Asians (Japanese). The present meta-analysis of rs3746544 found a strong association with SCZ on the allelic level (P=0.006), and under the dominant (P=0.001) and additive models (P=0.02). The power was sufficient for the allelic level (Power=0.850) and dominant model (Power=0.759), and no significant heterogeneity was found on the allelic levels and under the dominant model (I2=0%). Due to the limited study number, more studies are required to confirm the positive findings in other ethnic populations, including Asian and African populations.

The present meta-analyses did not find a significant association of other polymorphisms with SCZ (P>0.05; Table II). A low heterogeneity and ethnic difference were found in the meta-analyses for rs904952 of CHRNA7 (I2=6%, Fst=0.06), rs559668 (I2=0%, Fst=0.0054) and rs597570 (I2=0%, Fst=0.0059) of FEZ1. This indicated the stability of the meta-analyses. Additionally, although a high heterogeneity was found for the rs6494223 of CHRNA7 (I2=84%) and rs2301022 of GCLM (I2=53%) with SCZ, a low ethnic difference was observed (rs6494223, Fst=0.018; rs2301022, Fst=0.010). No significant heterogeneity was found in the two single-nucleotide polymorphisms (rs1800857, I2=31%; rs2337506, I2=0%) and no publication bias was found according to the symmetrical shapes in the funnel plots.

There are certain limitations of the study that require clarification. Firstly, the amount of studies was limited. Thus, a subgroup analyses by ethnicity could not be performed, and further studies in different ethnic background are required. Secondly, publication bias may exist, as the studies with a negative result are harder to publish than those with a positive result. Thirdly, there are numerous polymorphisms for each gene (CCKAR, n=1,049; CHRNA7, n=11,139; DBH, n=4,673; FEZ1, n=4,133; SNAP25, n=11,694; GRIK3, n=18,554; and GCLM, n=2,348). The meta-analyses only focused on 10 polymorphisms among those 7 genes, which may not fully represent the function of the genes. Future studies with more polymorphisms are required. Fourthly, SCZ is a complex disorder that a number of factors may participate in. Different statuses of SCZ patients, such as redox imbalance and inflammation, may influence the result. More genes with a larger number of polymorphisms should be considered, although 7 genes were analyzed that participate in several mechanisms, including the dopamine system, neurite extension machinery, oxidative stress and the GSH pathway.

In conclusion, the present meta-analyses indicated that the SNAP25 rs374654 and GRIK3 rs6691840 polymorphisms are risk factors for SCZ. Future studies with larger scale sample sizes and different ethnicities are required to confirm the present findings.

Acknowledgements

The present study was supported by grants from the National Natural Science Foundation of China (grant nos. 31100919 and 81371469), Natural Science Foundation of Zhejiang Province (grant no. LR13H020003), K.C. Wong Magna Fund in Ningbo University and Ningbo Social Development Research Projects (grant no. 2012C50032).

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