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. 2017 Apr 20;8(31):50415–50429. doi: 10.18632/oncotarget.17304

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

Zhang Wei 1,#, Shen Wenhao 2,#, Mi Yuanyuan 3, Li Yang 1, Zhou Daming 1, Xian Jiangchun 1, Jiang Jijun 1
PMCID: PMC5584145  PMID: 28881572

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.

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

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).

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

Figure 1

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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
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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.

Figure 2. T-allele frequencies for the IFNG +874 T/A (rs2430561) polymorphism among cases/controls stratified by ethnicity.

Figure 2

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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.

Figure 3. Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (additive model, TA vs. AA) in the whole.

Figure 3

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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.

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

Variables N Case/ Allelic effect Additive model (TA vs. AA)/(TT vs. AA) Dominant model Recessive model Overdominant model
Control OR(95%CI) Ph P OR(95%CI) Ph P OR(95%CI) Ph P OR(95%CI) Ph P OR(95%CI) Ph P
Total 42 8574/9011 0.75 (0.68-0.82) 1×10-6 1×10-6 0.71 (0.63-0.80) 1×10-6 1×10-6/0.60 (0.49-0.73) 1×10-6 1×10-6 0.68 (0.60-0.77) 1×10-6 1×10-6 0.72 (0.62-0.83) 0.002 1×10-6 0.83 (0.76-0.92) 1×10-6 1×10-6
HWE 35 7270/7594 0.75 (0.67-0.84) 1×10-6 1×10-6 0.74 (0.65-0.83) 1×10-6 1×10-6/0.62 (0.49-0.78) 1×10-6 1×10-6 0.71 (0.62-0.81) 1×10-6 1×10-6 0.70 (0.59-0.83) 0.004 1×10-6 0.85 (0.76-0.94) 0.001 0.002
Ethnicity
Asian 23 4180/4648 0.73 (0.63-0.84) 1×10-6 1×10-6 0.68 (0.57-0.81) 1×10-6 1×10-6/0.58 (0.42-0.81) 1×10-6 0.001 0.65 (0.54-0.79) 1×10-61×10-6 0.70 (0.57-0.87) 0.004 0.001 0.83 (0.72-0.95) 0.005 0.006
Caucasian 9 142/1228 0.79 (0.65-0.97) 0.013 0.025 0.83 (0.63-1.09) 0.095 0.181/0.60 (0.39-0.92) 0.014 0.019 0.77 (0.58-1.03) 0.036 0.078 0.70 (0.49-0.96) 0.003 0.028 0.99 (0.84-1.17) 0.135 0.926
African 5 1894/1859 0.84 (0.75-0.95) 0.276 0.004 0.74 (0.57-0.94) 0.026 0.016/0.79 (0.58-1.09) 0.276 0.152 0.75 (0.60-0.94) 0.048 0.013 0.97 (0.72-1.30) 0.392 0.833 0.76 (0.61-0.95) 0.047 0.017
Mixed 5 1028/1276 0.67 (0.52-0.84) 0.009 0.003 0.64 (0.45-0.92) 0.012 0.015/0.53 (0.42-0.72) 0.009 1×10-6 0.61 (0.42-0.87) 0.004 0.007 0.64 (0.48-0.85) 0.545 0.002 0.75 (0.57-0.97) 0.094 0.030
Source of Control
HB 18 3064/3693 0.76 (0.64-0.91) 1×10-6 0.003 0.72 (0.59-0.87) 1×10-6 0.001/0.59 (0.41-0.86) 1×10-6 0.006 0.69 (0.54-0.86) 1×10-6 0.001 0.72 (0.55-0.94) 1×10-6 0.016 0.85 (0.73-0.98) 0.030 0.023
PB 24 5510/5318 0.74 (0.68-0.82) 0.003 1×10-6 0.70 (0.61-0.81) 0.001 1×10-6/0.60 (0.49-0.75) 0.030 1×10-6 0.68 (0.59-0.79) 1×10-6 1×10-6 0.71 (0.63-0.82) 0.246 1×10-6 0.82 (0.72-0.94) 0.001 0.004
TB type
PTB 25 4244/5107 0.73 (0.64-0.83) 1×10-6 1×10-6 0.68 (0.58-0.80) 0.001 1×10-6/0.60 (0.44-0.81) 1×10-6 0.001 0.66 (0.55-0.78) 1×10-6 1×10-6 0.71 (0.57-0.90) 0.006 0.004 0.80 (0.70-0.91) 0.008 0.001
EPTB 10 984/1910 0.66 (0.55-0.79) 0.040 1×10-6 0.55 (0.39-0.77) 0.027 1×10-6/0.45 (0.27-0.75) 0.007 0.002 0.51 (0.36-0.73) 0.008 1×10-6 0.66 (0.49-0.89) 0.075 0.006 0.88 (0.66-1.17) 0.007 0.347
Sample size
<100 12 1104/1248 0.82 (0.67-0.99) 0.043 0.047 0.89 (0.72-1.09) 0.323 0.262/0.70 (0.53-0.94) 0.283 0.018 0.83 (0.69-1.01) 0.161 0.056 0.71 (0.54-0.92) 0.253 0.011 0.82 (0.67-0.99) 0.043 0.047
>100 30 7470/7763 0.73 (0.66-0.81) 1×10-6 1×10-6 0.67 (0.59-0.76) 0.000 0.000/0.56 (0.45-0.72) 0.000 0.000 0.64 (0.56-0.74) 1×10-6 1×10-6 0.71 (0.61-0.84) 0.001 1×10-6 0.81 (0.73-0.89) 0.001 1×10-6
Genotype methods
PCR-SSP 4 497/468 0.64 (0.33-1.22) 1×10-6 0.177 0.60 (0.45-0.80) 0.109 0.001/0.58 (0.14-2.30) 1×10-6 0.437 0.57 (0.31-1.08) 0.002 0.088 0.69 (0.21-2.33) 0.002 0.558 0.68 (0.51-0.90) 0.555 0.007
PCR 3 451/425 0.62 (0.50-0.77) 0.346 1×10-6 0.57 (0.42-0.77) 0.356 1×10-6/0.46 (0.29-0.72) 0.439 0.001 0.54 (0.41-0.72) 0.291 1×10-6 0.59 (0.38-0.90) 0.689 0.015 0.68 (0.51-0.90) 0.390 0.007
sequencing 7 1956/1376 0.69 (0.55-0.86) 0.003 0.001 0.78 (0.55-1.11) 0.001 0.173/0.45 (0.34-0.59) 0.153 1×10-6 0.71 (0.50-1.00) 0.001 0.050 0.52 (0.41-0.66) 0.729 1×10-6 0.97 (0.69-1.37) 1×10-6 0.858
PCR-ARMS 15 2845/3545 0.78 (0.68-0.90) 0.001 0.001 0.77 (0.69-0.86) 0.105 1×10-6/0.65 (0.46-0.92) 1×10-6 0.014 0.73 (0.61-0.87) 0.005 0.001 0.76 (0.59-0.98) 0.012 0.034 0.87 (0.79-0.97) 0.350 0.014
PCR-RFLP 6 1738/1588 0.91 (0.80-1.03) 0.640 0.131 0.84 (0.65-1.08) 0.042 0.177/0.87 (0.64-1.18) 0.793 0.360 0.87 (0.75-1.01) 0.114 0.072 0.98 (0.73-1.31) 0.771 0.902 0.85 (0.67-1.09) 0.045 0.204
TaqMan 3 443/728 0.67 (0.53-0.84) 0.771 0.001 0.59 (0.42-0.83) 0.377 0.002/0.39 (0.19-0.79) 0.714 0.009 0.57 (0.41-0.79) 0.357 0.001 0.72 (0.49-1.05) 0.795 0.084 0.79 (0.60-1.04) 0.149 0.095
PCR-ASP 2 284/585 1.02 (0.83-1.24) 0.761 0.871 0.93 (0.67-1.29) 0.359 0.675/1.03 (0.68-1.55) 0.997 0.891 0.96 (0.71-1.31) 0.464 0.809 1.11 (0.78-1.58) 0.725 0.570 0.91 (0.69-1.20) 0.318 0.505
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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.

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).

Figure 4. Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (the allelic effect) by ethnicity.

Figure 4

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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.

Figure 5. Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (dominant model) by source of control.

Figure 5

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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.

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).

Figure 6. Forest plot of tuberculosis risk associated with IFNG +874 T/A (rs2430561) polymorphism (additive model, TT vs. AA) by type of tuberculosis.

Figure 6

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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.

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).

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).

Figure 7. Begg's funnel plot for publication bias test (additive model, TA vs. AA).

Figure 7

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Each point represents a separate study for the indicated association. Log [OR], natural logarithm of OR. Horizontal line, mean effect size.

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

Figure 8

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

Figure 9

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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.

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

Footnotes

CONFLICTS OF INTEREST

The authors declare no conflict of interest.

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