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Research Papers: Immunology:

A single nucleotide polymorphism in the interferon-γ gene (IFNG +874 T/A) is associated with susceptibility to tuberculosis

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Oncotarget. 2017; 8:50415-50429. https://doi.org/10.18632/oncotarget.17304

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Zhang Wei _, Shen Wenhao, Mi Yuanyuan, Li Yang, Zhou Daming, Xian Jiangchun and Jiang Jijun

Abstract

Zhang Wei1,*, Shen Wenhao2,*, Mi Yuanyuan3, Li Yang1, Zhou Daming1, Xian Jiangchun1 and Jiang Jijun1

1 Department of Infectious Disease, Taizhou People’s Hospital, Taizhou, China

2 Department of Oncology, Taizhou People’s Hospital, Taizhou, China

3 Department of Urology, Third Affiliated Hospital of Nantong University, Wuxi, China

* These authors have contributed equally to this work

Correspondence to:

Zhang Wei, email:

Keywords: Interferon-γ; pulmonary tuberculosis; extra-pulmonary tuberculosis; polymorphism; meta-analysis; Immunology and Microbiology Section; Immune response; Immunity

Received: January 07, 2017 Accepted: March 30, 2017 Published: April 20, 2017

Abstract

Interferon-γ (Interferon gamma, IFNG) is an important cytokine involved in providing resistance to mycobacterial diseases. Common variants of IFNG, such as IFNG +874 T/A(rs2430561), may be related to tuberculosis susceptibility, but this association has not been consistently observed. We performed an updated meta-analysis to evaluate the association between the IFNG +874 T/A (rs2430561) polymorphism and tuberculosis susceptibility. PubMed and SinoMed databases were searched up to October 2016, and odds ratios (OR) and 95% confidence intervals (CI) were used to assess the association strength. Based on search criteria for manuscripts reporting tuberculosis susceptibility and its relationship with the IFNG +874 T/A(rs2430561)polymorphism, 42 case-control studies from 39 different articles were retrieved. Significantly positive, decreased, and protective associations were found between the IFNG +874 T/A(rs2430561)polymorphism and tuberculosis risk in five genetic models. Moreover, in the stratified subgroup analysis, a protective relationship was detected in four different ethnicities and sources of the control groups. Furthermore, the IFNG +874 T/A(rs2430561)polymorphism played an important role in protecting individuals from both pulmonary tuberculosis and extra-pulmonary tuberculosis. Our meta-analysis suggests that the IFNG +874 T/A(rs2430561)polymorphism is potentially associated with tuberculosis susceptibility and may be used as a predictive biomarker. Further studies with larger sample sizes and consideration of gene-environment interactions should be conducted to elucidate the role of IFNG +874 T/A(rs2430561) polymorphism in tuberculosis susceptibility.


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Introduction

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (MTB). Data from the global tuberculosis report estimated a worldwide incidence of 8.7 million cases, most of which occurred in Asia and Africa [1]. One-third of the world’s population is potentially infected with M. tuberculosis, but only 10% of the infected individuals eventually develop clinical TB, indicating that the host’s genetic and other factors (such as alcohol consumption, malnutrition, and human immunodeficiency virus infection) may play essential and complex roles in determining susceptibility and progression to tuberculosis [2-4]. An increasing number of studies have focused on the relationship between genetic variations of several genes and TB susceptibility [5, 6].

Currently, the gene encoding interferon-γ (IFNG) is the most extensively studied gene regarding tuberculosis susceptibility. Human IFNG is located on chromosome 12 (12q14) and has four exons spanning approximately 6 kb. Interferon-γ (IFN-γ), which is encoded by IFNG and produced by T helper 1 (Th1) lymphocytes, is upregulated and secreted as a major cytokine to activate macrophages, and is critical for the control of M. tuberculosis infection [7]. Cases of active TB are characterized by decreased production of IFN-γ from the peripheral blood mononuclear cells compared with that observed in latent infection. In addition, local and systemic IFN-γ levels correlate with the severity of the disease [8].

Lio et al. first noted a significant association between the +874A/T polymorphism (rs2430561) in IFNG and protection against TB in Sicilia (P < 0.05) [9]. The SNP +874 (A/T) is located at the 5′-end of a CA repeat in the first intron of the human IFNG [10]. The +874 T-allele is linked to the 12 CA repeats, whereas the A-allele is linked to the non-twelve CA repeats [11]. The specific sequence of the T-allele provides a binding site for the transcription factor nuclear factor-κB [12, 13]. As nuclear factor-κB induces IFN-γ expression, the presence of the T-allele correlates with high IFN-γ expression, whereas that of the A-allele correlates with low expression.

Many epidemiologic studies, including meta-analyses [14, 15], suggested associations between IFNG +874 T/A (rs2430561) polymorphism and the risk of developing TB. However, the data were inconsistent, partly because of differences in study populations and case ascertainment criteria, or small sample sizes with a high rate of false-positives and limited power of detecting modest associations. Considering the important role of IFNG +874 T/A (rs2430561) polymorphism in TB development, we studied all eligible case-control studies that included characteristics such as ethnicity of the patients, types of tuberculosis, and control sources.

Results

Study characteristics

Using various combinations of key terms, 152 article titles were retrieved by a literature search using the PubMed and SinoMed databases. As shown in Figure 1, 66 articles were excluded after screening the abstracts of the manuscripts. The full texts were evaluated, and 47 additional articles were excluded because of the following reasons: duplication (two), meta-analysis (seven), review (twelve), discussion on polymorphism in other diseases (tegumentary leishmaniasis and leprosy) (two), other SNPs in IFNG (one), IFNG receptor gene polymorphisms (fifteen), and other genes (three), and lacked case-control study (five). Finally, 39 different articles [9, 16-53] concerning IFNG +874 T/A (rs2430561) polymorphism and TB susceptibility were included in our meta-analysis (Table 1). Overall, 42 case-control studies with 8,574 cases and 9,011 controls were retrieved based on the search criteria. The controls were mainly healthy populations. First, we checked the Minor Allele Frequency (MAF) reported for the five main worldwide populations in the 1000 Genomes Browser: East Asian, 0.8413; European, 0.5378; African, 0.8327; American, 0.7464; and South Asian, 0.6104. The MAF in our analysis was 0.324 and 0.382 in the case and control group, respectively, both lower than the results in the 1000 Genomes Browser database. Then, the frequency of the T-allele was found to be significantly higher in case individuals of Caucasian ethnicity than in those of African or mixed ethnicities (41.3% vs. 22.9%, P = 0.005, or 41.3% vs. 27.2%, P = 0.017, respectively). A similar trend was found in the control group (Figure 2). Except for seven studies [16, 21, 26, 30, 36, 38, 43], the distribution of genotypes in all the controls was in agreement with Hardy-Weinberg equilibrium (HWE).

Table 1: Basic information for included studies of the association between IFNG +874 T/A (rs2430561) polymorphism site and tuberculosis susceptibility.

First author

Year

Origin

Source of Control

Ethnicity

Type

Sample size

Case

Control

Case

Control

Control

Methods

NOS

Ref No

TT

TA

AA

MAF

HWE

TT

TA

AA

MAF

HWE

Amim*[34]

2008

Brazil

HB

Mixed

TB

<100

93

266

15

25

53

0.296

0.0006

67

88

111

0.417

0.001

PCR-ARMS

6

Anand[21]

2010

India

HB

Asian

PTB

<100

62

66

7

27

28

0.331

0.898

8

25

33

0.311

0.35

PCR-ASP

6

Ansari*[53]

2009

Pakistan

HB

Asian

PTB

>100

111

188

27

47

37

0.455

0.123

25

87

76

0.364

0.98

PCR-ARMS

7

Ansari*[53]

2009

Pakistan

HB

Asian

EPTB

<100

77

188

12

36

29

0.389

0.881

25

87

76

0.364

0.98

PCR-ARMS

7

Anuradha*[27]

2008

India

PB

Asian

Children TB

<100

25

90

5

13

7

0.46

0.815

29

42

19

0.555

0.6

PCR

6

Asgharzadeh[64]

2016

Iran

PB

Asian

TB

>100

124

200

10

42

72

0.25

0.281

28

81

91

0.342

0.15

PCR-ARMS

8

Bhanothu[47]

2015

India

HB

Asian

EPTB

>100

175

100

45

87

43

0.506

0.941

29

62

9

0.6

0.003

ARMS-MG/

MP-PCR

6

Cooke*[36]

2006

UK

PB

African

PTB

>100

667

597

20

159

488

0.149

0.115

13

166

415

0.162

0.45

PCR-ARMS

6

Ding*[26]

2008

China

PB

Asian

TB

>100

301

310

19

89

193

0.211

0.052

32

114

164

0.287

0.07

sequencing

8

Etokebe*[40]

2005

Croatia

HB

Caucasian

TB

>100

242

519

53

122

67

0.471

0.637

103

282

134

0.47

0.04

PCR-ASP

6

Fitness*[48]

2004

Malawi

PB

African

TB

>100

451

703

12

134

305

0.175

0.549

17

219

467

0.179

0.14

PCR-ARMS

8

Gutlapalli[32]

2016

India

HB

Asian

TB

>100

247

129

73

123

51

0.544

0.951

60

58

11

0.689

0.56

PCR-ARMS

6

Hashemi[20]

2011

Iran

PB

Asian

PTB

>100

142

166

18

84

40

0.422

0.011

33

111

22

0.533

0.001

PCR-ARMS

7

Hu[45]

2015

China

PB

Asian

TB

>100

360

480

36

124

200

0.272

0.013

67

201

212

0.349

0.08

PCR-ARMS

7

Hwang*[50]

2007

Korea

PB

Asian

PTB

<100

80

80

1

13

66

0.094

0.696

0

21

59

0.131

0.17

PCR-ARMS

7

Leandro[43]

2013

Brazil

PB

Mixed

TB

>100

172

179

22

78

72

0.354

0.902

26

91

62

0.399

0.42

PCR-ARMS

8

Lee[44]

2015

Taiwan

PB

Asian

TB

>100

200

202

131

56

13

0.795

0.046

144

54

4

0.846

0.68

TaqMan

8

Lio[9]

2002

Italy

PB

Caucasian

PTB

<100

45

97

4

30

11

0.422

0.013

25

47

25

0.5

0.06

PCR-ARMS

8

Lopez-

Maderuelo*[39]

2003

Spain

PB

Caucasian

PTB

>100

113

100

11

40

62

0.274

0.238

19

50

31

0.44

0.88

PCR

6

Ma*[49]

2007

China

PB

Asian

PTB

<100

60

60

2

10

48

0.117

0.138

8

20

32

0.3

0.11

PCR-SSP

6

Mabunda[29]

2015

Brazil

HB

Mixed

PTB

>100

101

360

3

32

66

0.188

0.708

21

136

203

0.247

0.77

TaqMan

7

Moran*[30]

2007

USA

PB

African

TB

>100

240

174

9

87

144

0.219

0.348

11

65

98

0.25

0.96

sequencing

7

Moran*[30]

2007

USA

PB

Caucasian

TB

<100

161

64

24

92

45

0.435

0.039

16

31

17

0.492

0.8

sequencing

7

Moran*[30]

2007

USA

PB

Caucasian

TB

<100

319

98

24

96

199

0.226

0.013

10

26

62

0.235

0.01

sequencing

7

Mosaad[42]

2010

Egypt

HB

Caucasian

TB

>100

110

118

32

60

18

0.563

0.253

52

60

6

0.695

0.03

PCR-ARMS

6

Onay*[22]

2010

Turkey

HB

Caucasian

Children TB

<100

40

67

9

22

9

0.5

0.527

16

35

16

0.5

0.71

PCR-ARMS

6

Oral*[35]

2006

Turkey

PB

Caucasian

TB

<100

81

50

21

29

31

0.438

0.014

8

21

21

0.37

0.48

PCR-SSP

8

Rossouw*[38]

2003

UK

PB

African

TB

>100

313

235

25

102

186

0.243

0.078

26

98

111

0.319

0.53

PCR

6

Sallakci*[33]

2007

Turkey

HB

Caucasian

TB

>100

361

115

50

182

129

0.391

0.262

26

58

31

0.478

0.91

sequencing

6

Selma*[25]

2011

Tunisia

PB

African

TB

>100

223

150

38

85

100

0.361

0.009

22

86

42

0.433

0.04

PCR-RFLP

7

Selvaraj*[52]

2008

India

HB

Asian

PTB

>100

160

178

20

72

68

0.35

0.889

23

76

79

0.342

0.48

PCR-RFLP

7

Shen[28]

2013

China

PB

Asian

Children TB

>100

189

164

136

53

0

0.859

0.024

133

29

2

0.899

0.77

sequencing

6

Tso*[37]

2005

Hong Kong

HB

Asian

TB

>100

385

451

17

101

267

0.175

0.068

55

190

206

0.332

0.28

sequencing

6

Vallinoto*[23]

2010

Brazil

PB

Mixed

TB

>100

162

156

11

58

93

0.247

0.635

24

86

46

0.429

0.12

PCR-ASO

7

Vidyarani*[31]

2006

India

HB

Asian

PTB

>100

129

127

14

54

61

0.318

0.694

20

52

55

0.362

0.2

PCR-ARMS

6

Wang*[54]

2011

China

HB

Asian

PTB

>100

273

297

3

78

192

0.154

0.107

3

84

210

0.151

0.08

PCR-RFLP

6

Wang*[24]

2010

China

PB

Asian

PTB

>100

521

526

14

80

427

0.104

<0.001

10

91

425

0.105

0.06

PCR-RFLP

6

Wit*[51]

2011

South Africa

PB

Mixed

TB

>100

500

315

40

194

266

0.274

0.579

33

124

158

0.301

0.24

PCR-RFLP

6

Wu*[41]

2008

China

PB

Asian

PTB

<100

61

122

1

10

50

0.098

0.554

2

24

96

0.114

0.72

PCR-RFLP

8

Yang*[57]

2010

China

HB

Asian

PTB

>100

189

191

6

41

142

0.14

0.168

3

49

139

0.143

0.57

PCR-SSP

6

Zhong[55]

2014

China

HB

Asian

PTB

>100

142

166

4

26

112

0.119

0.117

7

49

110

0.189

0.61

TaqMan

6

Zhuang[56]

2009

China

HB

Asian

PTB

>100

167

167

12

53

102

0.231

0.172

37

70

60

0.431

0.06

PCR-SSP

5

HWE: Hardy–Weinberg equilibrium; HB: hospital-based; PB: population-based; TB: tuberculosis; PTB: pulmonary TB, EPTB: extra-pulmonary TB; PCR-FLIP: polymerase chain reaction and restrictive fragment length polymorphism; PCR-ARMS: polymerase chain reaction-amplification refractory mutation specific; PCR-ASP: allele specific primers by polymerase chain reaction, PCR-SSP: polymerase chain reaction-sequence specific primer; PCR-ASO: polymerase chain reaction-allele-specific oligonucleotide; ARMS-MG/MP-PCR: Amplification refractory mutation system-multi gene/multi primer polymerase chain reaction; NOS: Newcastle-Ottawa Scale; *: studies reported in previous meta-analysis; bold: these nine studies including both PTB and EPTB.

Flowchart illustrating the search strategy used to identify association studies for

Figure 1: Flowchart illustrating the search strategy used to identify association studies for IFNG +874 T/A (rs2430561) polymorphism and tuberculosis risk.

T-allele frequencies for the

Figure 2: T-allele frequencies for the IFNG +874 T/A (rs2430561) polymorphism among cases/controls stratified by ethnicity. Vertical line, T-allele frequency; Horizontal line, ethnicity type.

Quantitative synthesis

In the overall analysis, a significant protective association could be observed between TB risk and the variant genotype of IFNG +874 T/A (rs2430561) polymorphism in different genetic models. For example, for the additive model (TA vs. AA), odds ratios (OR) = 0.71, 95% confidence intervals (CI) = 0.63-0.80, P-value of heterogeneity test (Ph) = 1×10-6, and in the dominant model, OR = 0.68, 95% CI = 0.60-0.77, Ph = 1×10-6 (Figure 3, Table 2). If some studies that do not conform to HWE were removed, these associations were not affected.

In the ethnicity type subgroup, this pattern of positive association was maintained in the whole genetic models. For example, for the allelic effect, OR = 0.84, 95% CI = 0.75-0.95, Ph = 0.276 for Africans; OR = 0.73, 95% CI = 0.63-0.84, Ph = 1×10-6 for Asians; OR = 0.79, 95% CI = 0.65-0.97, Ph = 0.013 for Caucasians; OR = 0.67, 95% CI = 0.52-0.84, Ph = 0.009 for mixed ethnicity (Figure 4, Table 2). Similar results were detected in the source of control analysis. For example, in the dominant model, OR = 0.69, 95% CI = 0.54-0.86, Ph = 1×10-6 for HB, and OR = 0.68, 95% CI = 0.59-0.79, Ph = 1×10-6 for PB (Figure 5, Table 2).

In the stratified analysis by TB type, significant associations were found with both pulmonary tuberculosis (PTB) and extra-pulmonary tuberculosis (EPTB) risks and IFNG +874 T/A (rs2430561) polymorphism in the five genetic models. For example, for the additive model (TT vs. AA), OR = 0.60, 95% CI = 0.44-0.81, Ph = 1×10-6 for PTB, and OR = 0.45, 95% CI = 0.27-0.75, Ph = 0.007 for EPTB (Figure 6, Table 2).

To improve our study, finally we made stratified subgroup analyses according to genotyping methods and sample size. Some positive associations were also found in this section (Table 2).

Table 2: Total and stratified subgroup analysis for IFNG +874 T/A(rs2430561) polymorphism site and tuberculosis susceptibility.

Ph: value of Q-test for heterogeneity test; P: Z-test for the statistical significance of the OR. HWE: Except for seven studies[20,25,30,34,40,42,47], the distribution of genotypes in the controls in remaining 35 studies was in agreement with HWE, and it reports the meta-analysis results of studies showing no deviation from the HWE.

Forest plot of tuberculosis risk associated with

Figure 3: Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (additive model, TA vs. AA) in the whole. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Forest plot of tuberculosis risk associated with

Figure 4: Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (the allelic effect) by ethnicity. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Forest plot of tuberculosis risk associated with

Figure 5: Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (dominant model) by source of control. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Forest plot of tuberculosis risk associated with

Figure 6: Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (additive model, TT vs. AA) by type of tuberculosis. The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Bias diagnosis and sensitivity analysis

The Begg’s funnel plot and Egger’s test were performed to access the publication bias of the literature. The shape of the funnel plot did not reveal any obvious asymmetry, and the Egger’s test suggested the absence of publication bias. For example, in the additive model (TA vs. AA) analysis, z = 1.11, P = 0.269 for Begg’s test and t = −0.87, P = 0.389 for Egger’s test (Figures 7, 8, Table 3). We used a sensitivity analysis to determine whether modification of the meta-analysis inclusion criteria affected the results. No single study qualitatively influenced the summary ORs, as indicated by the sensitivity analysis (for example, the allelic effect, Figure 9).

Begg&#x2019;s funnel plot for publication bias test (additive model, TA vs.

Figure 7: Begg’s funnel plot for publication bias test (additive model, TA vs. AA). Each point represents a separate study for the indicated association. Log [OR], natural logarithm of OR. Horizontal line, mean effect size.

Egger&#x2019;s publication bias plot (additive model, TA vs.

Figure 8: Egger’s publication bias plot (additive model, TA vs. AA).

Sensitivity analysis between

Figure 9: Sensitivity analysis between IFNG +874 T/A (rs2430561) polymorphism and tuberculosis risk (the allelic effect).

Discussion

To understand the pathogenesis of TB and better predict individual risk, it is important to identify the SNPs that affect the function of genes contributing to TB susceptibility. Genome-wide association studies (GWAS) allow genotyping of frequent genetic polymorphisms in the genome without assuming the genomic location of the causal variants. As most of the genome has been surveyed, GWAS eliminates the disadvantages associated with the single polymorphism or candidate gene approach where only a few polymorphisms are investigated [54]. However, GWAS may identify false associations, and therefore, replication or validation of the results in independent populations is essential [55]. A Wellcome Trust Case-Control Consortium-based study initiated GWAS that considered up to 2,000 cases and controls each for tuberculosis and malaria. This study identified NRAMP1, IFNG, NOS2A, MBL, VDR, and certain TLR-associated genes as important for susceptibility to TB and malaria, and the list has been growing ever since [55-57].

Lio et al. [9] were the first to investigate the association between IFNG +874 T/A (rs2430561) polymorphism and the risk of developing TB. Since then, other researchers have reproduced their work on IFNG in different populations and for two disparate types of TB. However, the results were confounding, even within populations. Meta-analysis provides a means for effectively increasing the size of the sample by pooling data from individual correlation studies, thus enhancing the statistical power of the analysis to estimate genetic effects [58]. We used this method to demonstrate statistically significant genetic associations.

To the best of our knowledge, this is the first systematic meta-analysis exploring the associations between IFNG polymorphism and TB risk. Our analysis involved about 8,574 individuals with TB and 9,011 controls. The main discovery of our study was that the T-allele of the IFNG +874 T/A (rs2430561) polymorphism showed a protective effect for TB susceptibility, which was consistent with the observation that the presence of the T-allele correlates with high IFN-γ expression and increased resistance to M. tuberculosis infection, whereas the A-allele correlates with low expression. The highlight of our article is that we classified TB into PTB and EPTB, which assisted in the analysis of effective target sites for different types of TB, and thereby, we provided a novel concept compared to the previously published meta-analyses [14, 15].

Although considerable efforts and resources have been invested in examining the possible associations between IFN gene polymorphisms and TB risk, certain inherent limitations exist. First, although we collected all eligible studies, the sample sizes of these studies were not large enough to provide adequate statistical power to evaluate the association between this polymorphism and TB risk, particularly for certain ethnicities. Thus, not only was there an increased likelihood of type I/II error, but also a lack of sufficient statistical power to evaluate the association between these polymorphisms and tuberculosis risk. Second, because of the complex nature of the immune system and the polygenic nature of complex diseases, such as TB, it has become increasingly evident that gene-gene interactions play a far more important part in an individual’s susceptibility to tuberculosis than single polymorphisms would on their own [59]. Specific environmental and lifestyle factors, including age, sex, smoking habits, familial history, and disease stage, may possibly alter the associations between IFNG polymorphisms and TB. Therefore, it is necessary to evaluate the roles of specific environmental factors and lifestyles in disease prognosis.

In spite of these drawbacks, our meta-analysis provided several advantages. First, a substantial number of cases and controls were pooled from different studies, which significantly increased the statistical power of the analysis. Second, no publication bias was found and the Newcastle-Ottawa Scale (NOS) scores of the included studies indicated high quality. Third, our study contained the highest number of individuals so far, which conferred increased statistical power and credibility. The power of this meta-analysis of +874 T/A (rs2430561) polymorphism was greater than 0.95, which suggested that the data was trustworthy.

In conclusion, our present meta-analysis suggests that the +874 T/A (rs2430561) polymorphism in IFNG may be associated with the risk of developing TB. Further studies that consider additional gene-environment interactions and are based on larger sample sizes should be conducted to elucidate the role of IFNG polymorphisms in the etiology and clinical characteristics of TB development.

Table 3: Publication bias tests (Begg’s funnel plot and Egger’s test for publication bias test) for IFNG +874 T/A(rs2430561) polymorphism.

Egger's test

Begg's test

Genetic type

Coefficient

Standard error

t

P value

95%CI of intercept

z

P value

Allelic effect

-0.521

0.946

-0.55

0.585

(-2.432,1.390)

0.26

0.795

Additive model (TA vs. AA)

-0.443

0.509

-0.87

0.389

(-1.473, 0.587)

1.11

0.269

Additive model (TT vs. AA)

-0.08

0.345

-0.23

0.817

(-0.778, 0.617)

0.09

0.931

Dominant model

-0.414

0.545

-0.76

0.452

(-1.516, 0.688)

1

0.319

Recessive model

-0.108

0.381

0.28

0.779

(-0.663, 0.878)

0.76

0.448

Overdominant model

-0.559

0.687

-0.81

0.42

(-1.945, 0.829)

0.89

0.347

Materials and Methods

Search strategy

We searched the PubMed and SinoMed (http://www.sinomed.ac.cn/) databases through October 2016, using the keywords “tuberculosis,” “TB,” “polymorphism,” and “IFN-gamma.” One hundred and fifty-two articles were retrieved, 39 of which satisfied our inclusion criteria. We also screened references of the retrieved articles and reviewed all articles via a manual search.

Inclusion and exclusion criteria

For inclusion, studies were required to (i) assess the association between TB risk and IFNG +874 T/A (rs2430561) polymorphism; (ii) be case-controlled, and (iii) contain all three kinds of genotypes (TT, TA, and AA) for cases and controls. Studies were excluded if they (i) included no control population; (ii) did not report genotype frequency data, and (iii) were duplicated publications.

Quality score assessment

The NOS [60] was selected to assess the quality of each study. This measure assesses aspects of the methodologies used in observational studies, which are related to the study quality, including selection of cases, comparability of populations, and ascertainment of exposure to risks. The NOS rating ranges from zero stars (worst) to nine stars (best). Studies with a score of seven stars or greater were considered to be of high quality.

Data extraction

Two authors independently extracted the data that complied with the selection criteria. These data included the first author’s last name, year of publication, country of origin, ethnicity of the study population, TB type, genotypes of both the case and control groups, source of control, HWE/ MAF in both case and control groups, and genotyping method. Ethnicity was categorized as Caucasian, Asian, African, and mixed population. The control subgroups were population-based (PB) and hospital-based (HB). The TB type included PTB and EPTB. All included studies were classified as larger (both number of case and control ≥ 100 samples) and medium-sized studies. The NOS score was also shown in this section.

Statistical analysis

Based on the genotype frequencies for cases and controls, OR with 95% CI were used to measure the strength of the associations between the +874 T/A (rs2430561) single nucleotide polymorphism (SNP) in IFNG and TB risk. The statistical significance of the OR was determined with the Z-test. The heterogeneity assumption among studies was evaluated using a χ2-based Q-test. A P-value of > 0.10 for the Q-test indicated a lack of heterogeneity among studies. If significant heterogeneity was detected, the DerSimonian-Laird random-effects model was used; otherwise, the Mantel-Haenszel fixed-effects model was chosen [61, 62]. We investigated the relationship between IFNG +874 T/A (rs2430561) polymorphism and tuberculosis risk by testing the allelic effect (T vs. A), additive model (TT vs. AA and TA vs. AA), dominant model (T carriers vs. AA), recessive model (TT vs. A carriers), and overdominant model (TA vs. AA + TT). A sensitivity analysis was performed by successively omitting studies to assess the stability of the results. The departure of frequencies of the IFNG +874 T/A (rs2430561) polymorphism from the expected under HWE was assessed by Pearson’s χ2 test, and P < 0.05 was considered significant [63]. Publication bias was assessed by both Egger’s test and Begg’s test, and a P-value < 0.05 was considered significant [64]. All statistical tests for this meta-analysis were performed using the Stata software version 11.0 (StataCorp LP, College Station, TX, USA). The power of our meta-analysis was calculated online using the website http://www.power-analysis.com/.

Genotyping methods

Genotyping of the IFNG +874 T/A (rs2430561) SNP was conducted using methods described in the retrieved literature, such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), polymerase chain reaction-amplification refractory mutation specific (PCR-ARMS), polymerase chain reaction-allele specific primers (PCR-ASP), polymerase chain reaction-sequence specific primer (PCR-SSP), polymerase chain reaction-allele-specific oligonucleotide (PCR-ASO), and amplification refractory mutation system-multi gene/multi primer polymerase chain reaction (ARMS-MG/MP-PCR).

Abbreviations

TB, tuberculosis; PB, population-based; HB, hospital-based; MTB, Mycobacterium tuberculosis; IFNG, interferon-γ; Th1, T helper 1; PTB, pulmonary tuberculosis; EPTB, extra-pulmonary tuberculosis; HWE, Hardy-Weinberg equilibrium; MAF, Minor Allele Frequency; GWAS, Genome-wide association studies; NOS, Newcastle-Ottawa Scale; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; PCR-ARMS, polymerase chain reaction-amplification refractory mutation specific; PCR-ASP, polymerase chain reaction-allele specific primers; PCR-SSP, polymerase chain reaction-sequence specific primer; PCR-ASO, polymerase chain reaction-allele-specific oligonucleotide; ARMS-MG/MP-PCR, amplification refractory mutation system-multi gene/multi primer polymerase chain reaction.

CONFLICTS OF INTEREST

The authors declare no conflict of interest.

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