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ORIGINAL ARTICLE

Association of rs17250932, rs4794067, and rs2240017 polymorphisms in the TBX21gene with autoimmune diseases: a meta-analysis

Haili Wang1, 22, Hong Wang1, 22, Yue Wu1, 22, Hua-yun Ling1, 22, Ling-ling Wu1, 22, Dong-Qing Ye1, 22*, Bin Wang1, 22*

1Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Anhui, P.R. China

2Anhui Province Laboratory of Inflammation and Immune Mediated Diseases, Anhui Medical University, Anhui, P.R. China

Abstract

Objective: To systematically evaluate the association between TBX21 gene polymorphisms (rs17250932, rs2240017, and rs4794067) and the risk of autoimmune diseases in Asian populations.

Methods: The Medline, Web of Science, and Chinese Biomedical Literature Database were used to retrieve eligible studies that were published before July 2020. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated by using the dominant model, heterozygote contrast model, and allelic contrast model. Publication bias was evaluated using contour-enhanced funnel plots and Egger’s regression test. Sensitivity analysis was conducted to assess the robustness of this meta-analysis.

Results: A total of 12 eligible studies including 3834 patients and 4824 healthy controls were recruited in this meta-analysis. The pooled data demonstrated that TBX21 rs2240017 and rs4794067 polymorphisms are significantly associated with the risk of autoimmune diseases in Asian populations in allelic contrast model (OR: 1.456, 95% CI: 1.131–1.875, p=0.004; OR: 0.766, 95% CI: 0.615–0.954, p=0.017), heterozygote comparison model (OR: 1.647, 95% CI: 1.239–2.189, p=0.001; OR: 0.796, 95% CI: 0.634–0.999, p=0.049), and dominant mode (OR: 1.572, 95% CI: 1.194–2.071, p=0.004; OR: 0.767, 95% CI: 0.607–0.970, p=0.027). The G allele of rs2240017 may be a risk factor for autoimmune diseases and the T allele of rs4794067 may increase the risk of autoimmune diseases. However, we failed to find evidence of the association between TBX21 rs17250932 polymorphism and susceptibility to autoimmune diseases. No publication bias was found in this meta-analysis.

Conclusions: This meta-analysis indicated that TBX21 rs2240017 and rs4794067 polymorphisms confer susceptibility to autoimmune diseases, but not rs17250932.

Key words: meta-analysis, TBX21, T-bet, polymorphism, autoimmune diseases

*Corresponding authors: Bin Wang and Dong-Qing Ye. Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Anhui Province Laboratory of Inflammation and Immune Mediated Diseases, Hefei, Anhui 230032, China. E-mail address: wangbin@ahmu.edu.cn (Bin Wang); ydq@ahmu.edu.cn (Dong-Qing Ye)

These authors contributed equally to this work.

Received 5 September 2020; Accepted 10 November 2020; Available online 1 July 2021

DOI: 10.15586/aei.v49i4.197

Copyright: Wang H, et al.
License: This open access article is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/

Introduction

Autoimmune diseases are a kind of chronic complex disease characterized by loss of autoimmune tolerance. About 7.6%–9.4% of the general population are suffering from autoimmune diseases,1 and the incidence is still increasing. Autoimmune diseases are now ranked within the top 10 causes of death, resulting in a great economic burden.2 Although, the mechanism of autoimmune diseases is still unclear, the combination of genetic predisposition and environmental factors are thought to play an important role in the etiology of the disease.3 Data from human genome-wide association studies (GWAs), linkage and association studies indicate that autoimmune diseases may share a common genetic background.4

T cell-specific T-box transcription factor (TBX21) gene is a member of a phylogenetically conserved family of genes that share a common DNA-binding domain, the T-box, and located on human chromosome 17q21.32. It is found that the transcription factor T-bet, encoded by TBX21 gene, is a central player in autoimmune diseases.5,6 As a key regulator of Th1 cell differentiation, T-bet can initiate naive Th precursor cells developed to Th1 lineage and play an essential role in promoting IFN-γ production.7 For example, T-bet-transgenic mice avoided developing collagen-induced arthritis,8 and the mouse model showed an ameliorated type 1 diabetes (TID) in the absence of T-bet.9 The systemic lupus erythematosus (SLE) was also found to be less susceptible in T-bet-deficient mice.10 Previous studies have shown that TBX21 was a strong candidate gene because of its role in Th1/Th2 balance.11 Collectively, these findings suggest that TBX21 may play a critical role in multiple immune-mediated diseases.

Several published case-control studies have been conducted to evaluate the association between TBX21 rs17250932, rs4794067, and rs2240017 polymorphisms and autoimmune diseases, including SLE,12,13 rheumatoid arthritis (RA),14 T1D,15 Behcet’s disease,16 and autoimmune hepatitis (AIH),17 etc. However, the results were controversial and inconsistent. This discrepancy might be due to studies with inadequate statistical power, ethnic differences, publication bias, etc. Therefore, the aim of this meta-analysis is to evaluate the association between the TBX21 rs17250932, rs4794067, and rs2240017 polymorphisms and susceptibility to autoimmune diseases.

Materials and methods

Search strategy

A well-conducted search was performed by two investigators independently to retrieve all literature works examining the association between TBX21 single nucleotide polymorphisms (rs17250932, rs4794067, and rs2240017) and autoimmune disease. We used three bibliographic databases (Medline, Web of Science, and Chinese Biomedical Literature Database) to retrieve eligible studies, the combination of keywords, such as “TBX21”, “t-bet”, “polymorphism”, “autoimmune disease”, and the names of individual diseases, served as Medical Subject Heading (MeSH) terms and/or text words. Additional studies were supplemented by the references of relevant original research reports and related articles.

Inclusion and exclusion criteria

We made some restrictions on the retrieved literature works: (1) published in English or Chinese; (2) published up to November 2018. Studies were included based on the following: (3) unrelated case-control or cohort design; (4) evaluating the association between TBX21 polymorphism and autoimmune disease; (5) containing available and sufficient data for comparison and calculating odds ratios (OR) and 95% confidence intervals (CI). Duplicated datasets or studies that containing family members were excluded. In addition, the autoimmune disease should be diagnosed according to the respective classification criteria.

Data extraction

First author, year of publication, country and ethnicity of the studied population, demographic information, numbers of cases and controls, and the allele and genotype frequency of rs17250932, rs4794067, and rs2240017 were extracted from each literature by two authors independently. When the two authors were at odds, the third author was consulted. Those studies which contained more than one disease or single nucleotide polymorphism were treated as a separate study.

Evaluation of statistic association

The meta-analysis aims to evaluate the strength of association between TBX21 rs17250932, rs4794067, and rs2240017 polymorphisms and autoimmune diseases by calculating the total OR and 95% CI. Three genetic models were performed, including the dominant model, heterozygote contrast model, and allelic contrast model. χ2 tests were used to examine the existence of Hardy–Weinberg equilibrium (HWE), p<0.05 was considered to be statistically different.

Heterogeneity was assessed with Cochran’s Q-test and I2 statistic. p<0.10 indicated a significant Q-statistic and the existence of within- and between-study variation I2 statistic values from 0 to 100%; 25%, 50%, and 75% represent low, moderate, and high heterogeneity, respectively.18 The pooled ORs and their 95% CIs were obtained by using a random effect model in case of significant heterogeneity (p<0.10 or I2>50%). Otherwise, a fixed-effect model (p<0.10 or I2>50%) was used in this meta-analysis.

Contour-enhanced funnel plots were carried out to evaluate the potential publication bias intuitively.19 Begg’s and Egger’s tests were also performed to quantify the evidence for asymmetry and p<0.05 was considered to be a statistically significant representation of publication bias. By removing one study each time and repeating the analysis, a sensitivity analysis was performed to value the stability of this meta-analysis. All statistical analyses were carried out by STATA 12.0 software (Stata Corp, College Station, TX, USA).

Results

Characteristics of eligible studies

A total of 73 studies were retrieved after a systematic research in above-mentioned bibliographic databases until July 31, 2020. Among these articles, 20 duplicate articles were excluded. Twenty studies were excluded for not mentioning single nucleotide polymorphisms and autoimmune disease after a title and abstract screening. Four animal experimental studies, one non-case control study, and seven reviews were excluded. After excluding three articles that mainly investigate irrelevant SNPs and six articles whose raw data are unavailable, 12 articles met the inclusion criteria. However, after a careful examination, we found that two articles should be excluded because one of them is a pedigree study and the other is deviating from Hardy–Weinberg equilibrium (HWE). Finally, 10 articles were identified as eligible studies. It is worth noting that one article only provided the frequency of rs17250932 T/C allele in the controls, but did not provided data on TT/TC/CC genotypes. Therefore, we only included the rs4794067 and rs2240017 in this article into the meta-analysis. Of these 10 articles, two articles investigated two kinds of autoimmune diseases respectively, so each autoimmune disease was considered to be a separate study. Therefore, a total of 12 eligible studies including 3834 patients and 4824 healthy controls were recruited in this meta-analysis. The literature selection process is shown in Figure 1.

Figure 1 Flowchart for identification of studies in the meta-analysis.

The recruited 12 eligible studies contained nine studies for rs17250932, three studies for rs2240017, and 10 studies for rs4794067. Five genotyping methods were used, including polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP), PCR–restriction fragment length polymorphism assay (PCR-RFLP), Taqman, Sequenom MassARRAY, and PCR. The characteristics of each study including first author, year of publication, journal, disease, ethnicity, genotype method, and demographic characteristics of case and control are summarized in Table 1.

Table 1 Characteristics of the individual studies included in the meta-analysis.

SNP First author Year Disease Country Genotyping methods Sample size (case/control) Genotypes (case/control) Allele (case/control) HWE
MM Mm mm M m
rs17250932                        
  You Y 2010 SLE China PCR-RFLP 248/261 226/210 20/50 2/1 472/470 24/52 Yes
Chae SC 2009 RA Korean PCR-RFLP 367/572 332/546 33/26 0/0 697/1118 33/26 Yes
  Chen S 2010 AIH China PCR-RFLP 84/318 72/267 12/50 0/1 156/584 12/52 Yes
  Morita M 2012 HD Japan PCR 90/79 58/60 3/9 0/0 119/129 3/9 Yes
  Morita M 2012 GD Japan PCR 113/79 81/60 3/9 0/0 165/129 3/9 Yes
  Zhang D 2013 ITP China PCR-RFLP 275/261 241/224 34/37 0/0 516/485 34/37 Yes
  Liao D 2015 BD China PCR-RFLP 401/613 338/508 65/78 0/6 741/1094 65/90 Yes
  Liao D 2015 VKH China PCR-RFLP 401/613 347/508 50/78 2/6 744/1094 54/90 Yes
  Ge ML 2013 AA China PCR-RFLP 202/195 177/167 25/28 0/0 379/362 25/28 Yes
rs2240017                        
  Chae SC 2009 RA Korean PCR-RFLP 367/572 247/457 71/81 4/6 565/995 79/93 Yes
  Sasaki Y 2004 T1D Japan PCR-RFLP 153/200 108/162 43/34 2/4 259/358 45/38 Yes
  Takahashi Y 2013 RS Japan PCR 29/44 25/35 3/6 1/3 53/76 5/12 Yes
rs4794067                        
  You Y 2010 SLE China PCR-RFLP 248/261 202/192 43/63 3/6 447/447 49/75 Yes
  Chen S 2010 AIH China PCR-RFLP 84/318 79/247 5/67 0/4 163/561 5/75 Yes
  Leng RX 2016 SLE China Sequenom Massarray 1466/2266 1118/1732 314/494 34/40 2550/3958 382/574 Yes
  Morita M 2012 HD Japan PCR 90/79 57/43 13/14 0/3 127/100 13/20 Yes
  Morita M 2012 GD Japan PCR 113/79 75/43 21/14 1/3 171/100 23/20 Yes
  Zhang D 2013 ITP China PCR-RFLP 275/261 228/193 46/66 1/2 502/452 48/70 Yes
  Takahashi Y 2013 RS Japan PCR 29/113 23/96 6/16 0/1 52/208 6/18 Yes
  Liao D 2015 BD China PCR-RFLP 406/613 303/464 92/116 8/13 698/1044 108/142 Yes
  Liao D 2015 VKH China PCR-RFLP 406/613 319/464 75/116 6/13 713/1044 87/142 Yes
  Ge ML 2012 AA China PCR-RFLP 202/195 172/149 30/46 0/0 374/344 30/46 Yes

SLE: Systemic lupus erythematosus; RA: Rheumatoid arthritis; AIH: Autoimmune hepatitis; HD: Hashimoto’s disease; GD: Graves’ disease; ITP: Primary immune thrombocytopenia; BD: Behcet’s disease; VKH: Vogt-Koyanagi-Harada; AA: Aplastic anemia; RS: Rasmussen syndrome; HWE: Hardy–Weinberg equilibrium; PCR: polymerase chain reaction; PCR-SSCP: PCR-single strand conformation polymorphism; PCR-RFLP: PCR-restriction fragment length polymorphism assay.

For rs2240017, MM, Mm, and mm stand for TT, TC, and CC, respectively. For rs2240017, MM, Mm, and mm stand for CC, CG, and GG, respectively.

The association of TBX21 rs17250932 polymorphism and autoimmune disease susceptibility

The findings indicated that no relationship exists between rs17259032 polymorphism and autoimmune disease in the above-mentioned three genetic contrast model, and the pooled OR (95% CI) was 0.842 (0.619−1.144) in allelic contrast model, 0.832 (0.580−1.195) in heterozygote comparison, and 0.830 (0.588−1.171) in dominant model (all p>0.05). The results are summarized in Table 2 and Figure 2.

Table 2 Meta-analysis of the association between the TBX21 rs17250932, rs2240017, and rs4794067 polymorphism and autoimmune diseases.

SNP Comparison No. of studies Test of association Model Test of heterogenity Begg’s test Egger’s test
OR (95% CI) Z p Q p I2 (%) Z p T p
rs17250932 Allelic contrast model 9 0.842 (0.619–1.144) 1.10 0.271 R 22.58 0.004 64.6 1.15 0.251 0.383 0.232
  Heterozygote comparison 9 0.832 (0.580–1.195) 0.99 0.320 R 28.22 0.000 71.7 1.36 0.175 −1.63 0.147
  Dominant model 9 0.830 (0.588–1.171) 1.06 0.288 R 26.02 0.001 69.3 1.56 0.118 −1.49 0.180
rs2240017 Allelic contrast model 3 1.456 (1.131–1.875) 2.91 0.004 F 2.79 0.248 28.4 0.00 1.000 0.220 0.359
  Heterozygote comparison 3 1.647 (1.239–2.189) 3.44 0.001 F 1.59 0.452 0.0 0.00 1.000 −1.09 0.473
  Dominant model 3 1.572 (1.194–2.071) 2.86 0.004 F 2.24 0.326 10.7 0.00 1.000 −1.50 0.374
rs4794067 Allelic contrast model 10 0.766 (0.615–0.954) 2.38 0.017 R 27.82 0.001 67.6 1.25 0.210 −2.90 0.020
  Heterozygote comparison 10 0.796 (0.634–0.999) 1.97 0.049 R 23.45 0.005 61.6 1.07 0.283 −1.72 0.125
  Dominant model 10 0.767 (0.607–0.970) 2.22 0.027 R 26.27 0.002 65.7 1.25 0.210 −2.26 0.054

R: random effect model; F: fixed effect model.

Significant P-value (<0.05) is highlighted in bold.

Figure 2 Forest plots regarding the association between TBX21 rs17250932, rs2240017, and rs4794067 polymorphism and autoimmune disease susceptibility in the allelic contrast model. (A) rs17250932; (B) rs4794067; (C) rs2240017.

The association of TBX21 rs2240017 polymorphism and autoimmune disease susceptibility

A significant association between TBX21 rs2240017 polymorphism and autoimmune diseases was revealed in allelic contrast model (OR: 1.456, 95% CI: 1.131−1.875, p=0.004), heterozygote comparison model (OR: 1.647, 95% CI: 1.239−2.189, p=0.001), and dominant model (OR: 1.572, 95% CI: 1.194−2.071, p=0.004) in Asian population. A summary of meta-analysis findings regarding the association of TBX21 rs2240017 polymorphism and autoimmune disease susceptibility is given in Table 2 and Figure 2.

The association of TBX21 rs4794067 polymorphism and autoimmune disease susceptibility

A significant association was found between TBX21 rs4794067 polymorphism and autoimmune diseases by using allelic contrast model (OR: 0.766, 95% CI: 0.615–0.954, p=0.017), heterozygote comparison model (OR: 0.796, 95% CI: 0.634–0.999, p=0.049), and dominant model (OR: 0.767, 95% CI: 0.607–0.970, p=0.027) in Asian population. However, it should be noted that the marginal value was approximately 1 in the heterozygote comparison model. Meta-analysis results concerning the association of TBX21 rs4794067 polymorphism and autoimmune disease susceptibility are shown in Table 2 and Figure 2.

Test of heterogeneity

As shown in Table 2, heterogeneity was found when analyzing the TBX21 polymorphisms rs17250932 and rs4794067 and autoimmune diseases in allelic contrast model, heterozygote comparison model, and the dominant model (all p<0.05), but no heterogeneity was found in rs2240017 in all three genetic models (all p>0.05). Subgroup analyses and meta-regression were conducted to explore the potential heterogeneity sources. The results show that year of publication and genotyping methods were not able to explain the heterogeneity for rs17250932 (all p>0.05) and genotyping methods and the country were not statistically correlated with heterogeneity for rs4794067. But we found that the country of Korea may be responsible for the heterogeneity in rs17250932 and the year of publication may statistically correlate with the heterogeneity in rs4794067.

Publication bias

Contour-enhanced funnel plots and Begg’s and Egger’s tests were carried out to evaluate the potential publication bias in this meta-analysis. The results revealed that non-statistically significant asymmetry was found in the Asian population for rs17250932 (Egger’s test: t=0.383, p=0.232>0.05; Begg’s test: z=1.15; p=0.251>0.05) and rs2240017 (Egger’s test: t=0.220, p=0.359>0.05; Begg’s test: z=0.00; p=1.000>0.05). Although there seems to be no significant asymmetry in the Asian population for rs4794067 based on the intuitive observation of the contour-enhanced funnel plots, and the results of the Berger test also supported that no significant publication bias was found (Begg’s test: z=1.25; p=0.210>0.05); however, a statistical significance was found in Egger’s test (Egger’s test: t=-2.90, p=0.020<0.05). Therefore, we performed a trim and fill funnel plot for further verification (data are not shown), and the results show that there is no evidence of missing studies, thus confirming the absence of publication bias (Figure 3).

Figure 3 Contour-enhanced funnel plot assessing the publication bias of the included studies that evaluating the association between rs17250932, rs2240017, and rs4794067 and autoimmune diseases in allelic contrast model. (A) rs17250932; (B) rs2240017; (C) rs4794067.

Sensitivity analysis

After the single study was excluded in turn, a non-statistically significant change was observed in the recalculated pooled ORs in the Asian population for rs4794067 in the allelic contrast model, indicating that the results of this meta-analysis were stable. For rs17250932, after excluding the study of Chae et al., a marginal statistical significance (p=0.046) was shown and the pooled OR (95% CI) changed from 0.842 (0.619−1.144) to 0.772 (0.599−0.995) by using the allelic contrast model. Since only three articles about rs2240017 were included in the meta-analysis, we did not include them in the sensitivity analysis (Figure 4).

Figure 4 Sensitivity analysis plot assessing the stability of the included studies that evaluate the association between rs17250932 and rs4794067 and autoimmune diseases in the allelic contrast model. (A) rs17250932; (B) rs4794067.

Discussion

Autoimmune diseases are a group of diseases with similar pathogenesis and characterized by abnormal immunity and tissue destruction.20 Although there are many hypotheses about the pathogenesis of autoimmune diseases, increasing data have shown that the interaction of environmental factors and genetic factors may have an important impact on the occurrence and development of autoimmune diseases.21 As one of the most common genetic variations in the human genome, single nucleotide polymorphism may play an important role in autoimmune diseases.22 Previous genetic association studies have shown that autoimmune diseases may share susceptibility genes.23

T-bet, encoded by TBX21 gene, is an important Th1 transcription factor, which is closely related to the occurrence and treatment of autoimmune diseases.24 More specifically, two important polymorphisms rs17250932 and rs4794067 in the promoter region of the TBX21 gene and rs2240017 in the non-promoter region are considered to be one of the possible genetic risk factors for autoimmune diseases, and many studies have reported that the above SNPs may be associated with genetic susceptibility to autoimmune diseases in Asian population. However, the results of some studies may be contradictory. For example, You et al.13 found that rs17250932 T allele was a risk factor for SLE, while Chae et al.14 showed that rs17250932 C allele was a risk factor for RA, but Leng et al.12 failed to find a relationship between rs17250932 and SLE. Therefore, in order to comprehensively evaluate the relationship between TBX21 rs17250932, rs2240017, and rs4794067 polymorphisms and the risk of autoimmune diseases, we conducted this meta-analysis. To the best of our knowledge, this is the first meta-analysis to evaluate the relationship between TBX21 gene polymorphism and genetic susceptibility to autoimmune diseases and is more effective than any previous case-control study.

In this meta-analysis, we demonstrated that TBX21 rs2240017 and rs4794067 polymorphisms are significantly associated with the risk of autoimmune diseases in Asian population. The results show that the G allele of rs2240017 may be a risk factor for autoimmune diseases and the rs4794067 T allele may increase the risk of autoimmune diseases. However, we failed to find evidence of the association between TBX21 rs17250932 polymorphism and susceptibility to autoimmune diseases. T-bet is considered to be the main regulator of CD4+ Th1 cell differentiation and can promote the production of IFN-γ by Th1 cells and NK cells.25 At the same time, T-bet also has a regulatory effect on IgG2a produced by B cells, although this effect may be based on the action of IFN-γ.26 These functions of T-bet are considered to affect the process of autoimmunity. Both rs17250932 and rs4794067 are located in the promoter region of TBX21. These SNP variants may be related to the translation of mRNA, thus regulating the expression or function of T-bet. Therefore, the polymorphism of TBX21 gene may change the expression level of mRNA, resulting changes in protein expression or the production of autoantibodies or other immune diseases.27 All these make TBX21 an interesting candidate for autoimmune diseases. However, future studies will need to confirm this hypothesis. It is worth mentioning that there is heterogeneity in our study. In the meta-analysis of rs17250932, we found strong heterogeneity, but after excluding the study of Chae et al., the heterogeneity disappeared, and there was a significant association between rs17250932 and autoimmune diseases and the T allele was a potential risk factor. However, after a careful review of Chae et al., we did not find the difference with other articles, so we concluded that rs17250932 polymorphism has nothing to do with autoimmune diseases, and more related studies need to be included to confirm this view.

However, due to the possible limitations of this meta-analysis, it is necessary to be cautious in drawing conclusions based on this study. First of all, autoimmune disease is a complex disease caused by the interaction of genetic and environmental factors. The current polymorphism may affect the occurrence of autoimmune diseases to some extent, which is difficult to be detected by meta-analysis. Second, the article retrieval in this meta-analysis is limited to three electronic databases (Medline, Web of Science, and Chinese Biomedical Literature Database), search languages are limited to English and Chinese. Due to the limited search database and language, there is a possibility of publication bias. Third, all the original studies were based on the Asian population. Although two articles have reported the association between rs17250932 and rs2240017 and autoimmune diseases in the Caucasian population, however, neither of them was included in this study because one of them is a family study and the other is not conformed to HWE. Fourth, the number of studies included in this meta-analysis is small, and lack of sufficient research to carry out subgroup analysis according to the type of disease. Fifth, significant heterogeneity was found in the analysis for rs17250932 and rs4794067 by using the three genetic models. According to meta-regression analysis, the year of publication is considered to be the main source of rs4794067 heterogeneity, while the study of Chen et al. is the main reason for rs17250932 heterogeneity. Sixth, due to data limitations or the unavailability of data, this meta-analysis does not take into account the impact of gender, age, and other environmental factors on autoimmune diseases.

Overall, our meta-analysis results show that TBX21 rs2240017 and rs4794067 polymorphisms may be associated with susceptibility to autoimmune diseases in Asian population, but we failed to found an association between rs17250932 and autoimmune diseases. In Asian population, the T allele of rs4794067 in the promoter region may be associated with increased susceptibility to autoimmune diseases, while the G allele of rs2240017 is shown to be a potential risk factor. However, this study has some limitations in exploring the exact mechanism of TBX21 gene polymorphism affecting the susceptibility to autoimmune diseases. Therefore, further studies, including larger sample sizes and well-designed case-control studies in different ethnic groups are needed to reveal the exact role of TBX21 SNPs in the pathogenesis of autoimmune diseases.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant number 81573217). We also appreciate the efforts of all the researchers whose articles are included in this study.

Conflicts of interest

All authors declare that they have no conflicts of interest.

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