# Cytotoxic T Lymphocyte-Associated Antigen 4 Gene Polymorphisms and Autoimmune Thyroid Diseases: An Updated Systematic Review and Cumulative Meta-Analysis

Authors: Hai-Feng Hou; Xu Jin; Tao Sun; Cheng Li; Bao-Fa Jiang; Qun-Wei Li
Journal: International Journal of Endocrinology
 (2015)

---

## Abstract

The association of the cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) gene and susceptibility to autoimmune thyroid diseases (AITDs) has been studied extensively. However, the results were not the same in different ethnic groups. We updated the meta-analysis of association of CTLA-4 gene polymorphisms with AITDs and summarized the results in specific ethnicity. The associations of A49G gene polymorphism with GD, A49G gene polymorphism with HT, CT60 gene polymorphism with GD, and CT60 gene polymorphism with HT were summarized based on the literatures published up to October 30, 2014, in English or Chinese languages. The participants involved in the studies of A49G with GD, A49G with HT, CT60 with GD, and CT60HT were 39004 subjects (in 51 studies), 13102 subjects (in 22 studies), 31446 subjects (in 22 studies), and 6948 subjects (in 8 studies), respectively. The pooled ORs of CTLA-4 gene polymorphisms with AITDs were larger than 1.00, and the 95% CIs of ORs were statistically significant among whole population analyses. However, the subgroup analysis demonstrated that pooled ORs of A49G polymorphisms with GD among Africans or Americans are less than 1.00. The accumulated evidence suggests that the G allele mutant of A49G and CT60 increased the risks of HT and GD.


---

## Body

## 1. Introduction

Autoimmune thyroid diseases (AITDs) are the most popular autoimmune thyroid diseases; hyperthyroid Graves’ disease (GD) and Hashimoto’s (goitrous) thyroiditis (HT) are two common types of AITDs. It is well known that AITDs are caused partly by specific genetic background [1]. The association of the cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) gene and susceptibility to AITDs has been studied extensively [2–4]. The CTLA-4 gene is located on the region of human chromosome 2q33 and encodes the immunoregulatory molecule. It is proved to be a key negative regulator of T-cell activity [5, 6]. Single nucleotide polymorphisms (SNPs) at position 49 in exon 1 (+49 A/G, A49G, rs231775) and +6230 G/A (CT60, rs3087243) showed an association with AITDs. A comprehensive meta-analysis including 43 studies and more than 13,000 subjects was published in 2007 [7]. Subsequently, about 30 studies that investigated the relationship between the CTLA-4 gene SNPs and AITDs have been published. We designed the current systematic review and cumulative meta-analysis to include the most recent data and summarized the results with more genetic models.


## 2. Methods

### 2.1. Identification of Eligible Studies

The literature published up to October 30, 2014, in English or Chinese was searched in the MEDLINE, EMBASE, and China Biology Medicine disc (CBMdisc) databases. The search strategy was based on the key terms of “CTLA4,” “CTLA-4,” “cytotoxic T-cell lymphocyte associated antigen 4,” “CD28,” “CD152,” “Graves’ disease,” “GD,” “Hashimoto’s thyroiditis,” and “HT.” Reference lists of relevant papers were reviewed to find additional studies. H.-F. Hou and X. Jin independently reviewed all studies and assessed the quality of each study according to the following inclusion criteria. (1) The publication was case-control study design, and the associations between A49G or CT60 genetic polymorphisms and AITDs were investigated. (2) Genotype distribution data were offered in both cases and controls. (3) For the overlapping data or the same papers, the largest population or the most recent study was included. (4) We limited the data to studies published in English and Chinese language. We compared our collection information with the data of Kavvoura et al. [7] on The Endocrine Society’s Journals Online website (available at http://press.endocrine.org/journal/jcem) and adopted the unpublished studies provided in Kavvoura’s meta-analysis.


### 2.2. Data Extraction

For published studies, two reviewers (H.-F. Hou and T. Sun) independently extracted data and resolved disagreements by discussion or with a third party (Li QW) when necessary. We collected the following information carefully: author name, journal source, publication year, ethnicity of study population (Asian, Caucasian, African, and American), the number of individuals in case and control groups, and genotype distribution of cases and controls.


### 2.3. Meta-Analysis Methods

The analysis of data was performed with Review Manager 5.3 (The Cochrane Collaboration, Oxford, UK). Allele frequencies at the A49G or CT60 gene polymorphisms from the literatures were calculated by the allele counting method. Four genetic models, (1) allele contrast (G versus A), (2) additive genetic model (GG versus AA), (3) dominant model (GG + AG versus AA), and (4) recessive model (GG versus AG + AA), were measured in this meta-analysis, and association values of the CTLA-4 genetic polymorphisms with risk of AITDs were estimated by odds ratios (ORs) and 95% confidence intervals (CIs). We also assessed Hardy-Weinberg Equilibrium (HWE) of genotype frequencies in the control group with a chi-square test, andP value < 0.05 was considered to be significant. The heterogeneity across all studies was tested by the I
2 statistics and chi-square-based Q-test. The heterogeneity was considered to be significantly large when P
<
0.1
0 and I
2
>
50%. Then random effects model was used to combine eligible data. The statistical significance of pooled ORs was measured by the Z-test. Subgroup meta-analyses were conducted according to different ethnicities. In addition, sensitivity analysis was implemented to assess stability of the summary result by sequential removal of individual studies. Furthermore, publication bias was measured by funnel plots.


## 2.1. Identification of Eligible Studies

The literature published up to October 30, 2014, in English or Chinese was searched in the MEDLINE, EMBASE, and China Biology Medicine disc (CBMdisc) databases. The search strategy was based on the key terms of “CTLA4,” “CTLA-4,” “cytotoxic T-cell lymphocyte associated antigen 4,” “CD28,” “CD152,” “Graves’ disease,” “GD,” “Hashimoto’s thyroiditis,” and “HT.” Reference lists of relevant papers were reviewed to find additional studies. H.-F. Hou and X. Jin independently reviewed all studies and assessed the quality of each study according to the following inclusion criteria. (1) The publication was case-control study design, and the associations between A49G or CT60 genetic polymorphisms and AITDs were investigated. (2) Genotype distribution data were offered in both cases and controls. (3) For the overlapping data or the same papers, the largest population or the most recent study was included. (4) We limited the data to studies published in English and Chinese language. We compared our collection information with the data of Kavvoura et al. [7] on The Endocrine Society’s Journals Online website (available at http://press.endocrine.org/journal/jcem) and adopted the unpublished studies provided in Kavvoura’s meta-analysis.


## 2.2. Data Extraction

For published studies, two reviewers (H.-F. Hou and T. Sun) independently extracted data and resolved disagreements by discussion or with a third party (Li QW) when necessary. We collected the following information carefully: author name, journal source, publication year, ethnicity of study population (Asian, Caucasian, African, and American), the number of individuals in case and control groups, and genotype distribution of cases and controls.


## 2.3. Meta-Analysis Methods

The analysis of data was performed with Review Manager 5.3 (The Cochrane Collaboration, Oxford, UK). Allele frequencies at the A49G or CT60 gene polymorphisms from the literatures were calculated by the allele counting method. Four genetic models, (1) allele contrast (G versus A), (2) additive genetic model (GG versus AA), (3) dominant model (GG + AG versus AA), and (4) recessive model (GG versus AG + AA), were measured in this meta-analysis, and association values of the CTLA-4 genetic polymorphisms with risk of AITDs were estimated by odds ratios (ORs) and 95% confidence intervals (CIs). We also assessed Hardy-Weinberg Equilibrium (HWE) of genotype frequencies in the control group with a chi-square test, andP value < 0.05 was considered to be significant. The heterogeneity across all studies was tested by the I
2 statistics and chi-square-based Q-test. The heterogeneity was considered to be significantly large when P
<
0.1
0 and I
2
>
50%. Then random effects model was used to combine eligible data. The statistical significance of pooled ORs was measured by the Z-test. Subgroup meta-analyses were conducted according to different ethnicities. In addition, sensitivity analysis was implemented to assess stability of the summary result by sequential removal of individual studies. Furthermore, publication bias was measured by funnel plots.


## 3. Results

### 3.1. Identification of Eligible Studies

Besides the 43 studies mentioned in Kavvoura et al.’s meta-analysis [7], 25 additional studies were included in this review (Table 1). Sixteen studies were English language publications [8–23] and 9 studies were published in Chinese [24–32]. Thus, the present updated meta-analysis consisted of 68 studies.Table 1
Characteristics of new studies included in the meta-analysis.
Study
Year
Country
Ethnicity
Gene
Disease
Cases
Controls
AA
AG
GG
AA
AG
GG
Wang et al. [24]
2001
China
Asian
A49G
GD
37
47
3
27
26
7
Zhou et al. [25]
2003
China
Asian
A49G
GD
32
14
4
5
5
10
Zhang et al. [8]
2006
China
Asian
A49G
GD
37
18
1
26
25
29
Yao et al. [26]
2006
China
Asian
A49G
GD
58
53
9
55
57
11
Yu et al. [32]
2006
China
Asian
A49G
GD
51
36
13
26
46
28
Wang et al. [9]
2007
China
Asian
A49G
GD
124
69
15
46
60
20
Yu et al. [27]
2008
China
Asian
A49G
GD
67
45
13
13
27
29
Chong et al. [10]
2008
China
Asian
A49G
GD
97
73
7
16
67
103
Cury et al. [11]
2008
Brazil
American
A49G
GD
15
58
43
6
64
47
Bicek et al. [12]
2009
Slovenia
Caucasian
A49G
GD
17
73
33
14
52
24
Kimura et al. [13]
2009
Japan
Asian
A49G
GD
210
143
62
10
42
32
Wang et al. [28]
2010
China
Asian
A49G
GD
38
47
5
16
20
14
Guo et al. [29]
2010
China
Asian
A49G
GD
26
52
24
12
47
41
Zhao et al. [14]
2010
China
Asian
A49G
GD
1030
730
104
295
358
142
Pastuszak-Lewandoska et al. [15]
2012
Poland
Caucasian
A49G
GD
7
6
1
97
77
18
Veeramuthumari et al. [16]
2011
India
Caucasian
A49G
GD
32
37
11
71
56
24
Kimkong et al. [17]
2011
Thailand
Asian
A49G
GD
61
49
22
54
73
26
Farra et al. [18]
2012
Lebanon
Caucasian
A49G
GD
6
18
31
7
32
39
Pastuszak-Lewandoska et al. [19]
2013
Poland
Caucasian
A49G
GD
12
9
3
945
823
156
Pastuszak-Lewandoska et al. [15]
2012
Poland
Caucasian
A49G
HT
6
19
3
5
5
10
Zhou et al. [25]
2003
China
Asian
A49G
HT
24
14
8
46
60
20
Yu et al. [27]
2008
China
Asian
A49G
HT
41
34
5
15
64
22
Dallos et al. [20]
2008
Slovakia
Caucasian
A49G
HT
13
34
16
13
27
29
Kucharska et al. [21]
2009
Poland
Caucasian
A49G
HT
31
40
29
16
67
103
Bicek et al. [12]
2009
Slovenia
Caucasian
A49G
HT
15
46
51
6
64
47
Sahin et al. [22]
2009
Turk
Caucasian
A49G
HT
21
91
85
17
54
49
Farra et al. [18]
2012
Lebanon
Caucasian
A49G
HT
6
31
36
16
20
14
Ying et al. [30]
2012
China
Asian
A49G
HT
46
53
51
31
91
108
Pastuszak-Lewandoska et al. [19]
2013
Poland
Caucasian
A49G
HT
14
8
3
7
48
43
Wang et al. [9]
2007
China
Asian
CT60
GD
138
46
5
30
61
26
Chong et al. [10]
2008
China
Asian
CT60
GD
125
48
4
735
516
84
Tsai et al. [23]
2008
China
Asian
CT60
GD
136
48
5
125
58
9
Bicek et al. [12]
2009
Slovenia
Caucasian
CT60
GD
50
57
16
88
51
12
Kimura et al. [13]
2009
Japan
Asian
CT60
GD
267
127
21
82
59
12
Kimkong et al. [17]
2011
Thailand
Asian
CT60
GD
78
46
8
372
216
32
Qu et al. [31]
2014
China
Asian
CT60
GD
1989
487
114
1550
474
136
Dallos et al. [20]
2008
Slovakia
Caucasian
CT60
HT
31
28
4
20
50
25
Bicek et al. [12]
2009
Slovenia
Caucasian
CT60
HT
37
52
23
30
61
26


### 3.2. Quantitative Analysis

#### 3.2.1. A49G Gene Polymorphism and GD

The summary OR of included studies was increased 1.55-fold in susceptibility to GD in subjects with the G allele, and the associations of GD and A49G polymorphisms were statistically significant in an additive genetic model (GG versus AA: OR = 2.41, 95% CI: 2.01–2.89), a dominant genetic model (GG + AG versus AA: OR = 1.76, 95% CI: 1.52–2.03), and a recessive genetic model (GG versus AG + AA: OR = 1.79, 95% CI: 1.58–2.02). The detailed results were shown in Figures1 and 2 and Supplemental Figures 1 and 2 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/747816.Figure 1
Forest plot of the association between an allele model of A49G polymorphism and GD.Figure 2
Forest plot of the association between an additive model of A49G polymorphism and GD.The subgroup analysis was performed by ethnicity to decrease the heterogeneity. As shown in Figures1 and 2, significant associations between A49G SNP and GD risk were identified in Asians and Caucasians.


#### 3.2.2. A49G Gene Polymorphism and HT

The meta-analysis suggested (see Figure3 and Supplemental Figures 3–5) that A49G polymorphisms increased the risk of HT significantly in the allele frequencies (G versus A: OR = 1.36, 95% CI: 1.20–1.53), the additive genotype (GG versus AA: OR = 2.10, 95% CI: 1.75–2.51), the dominant genotype (GG + AG versus AA: OR = 1.57, 95% CI: 1.26–1.96), and the recessive genotype (GG versus AG + AA: OR = 1.46, 95% CI: 1.19–1.81). The subgroup analyses showed that A49G polymorphism was one of the risk factors for GD in Asians and Caucasians.Figure 3
Forest plot of the association between an allele model of A49G polymorphism and HT.


#### 3.2.3. CT60 Gene Polymorphism and GD

The summary analyses of CT60 gene polymorphism and GD are shown in Figure4 and Supplemental Figures 6–8. The pooled ORs of CT60 polymorphisms with GD in allele frequencies, the additive genetic model, the dominant genetic model, and the recessive genetic model were 1.48 (95% CI: 1.35–1.63), 1.98 (95% CI: 1.73–2.27), 1.72 (95% CI: 1.52–1.96), and 1.56 (95% CI: 1.39–1.76), respectively. The subgroup analyses suggested that CT60 polymorphism was a risk factor for GD in Asians and Caucasians.Figure 4
Forest plot of the association between an allele model of CT60 polymorphism and GD.


#### 3.2.4. CT60 Gene Polymorphism and HT

As shown in Figure5 and Supplemental Figures 9–11, CT60 genetic polymorphisms increased HT risk significantly in the allele frequencies contrast (G versus A: OR = 1.56, 95% CI: 1.15–2.13), the additive genetic contrast (GG versus AA: OR = 2.58, 95% CI: 1.33–5.01), the dominant genetic contrast (GG + AG versus AA: OR = 1.95, 95% CI: 1.20–3.15), and the recessive genetic contrast (GG versus AG + AA: OR = 1.79, 95% CI: 1.20–2.67). The subgroup analyses showed that CT60 genetic polymorphism was one of the risk factors for GD in Asians and Caucasians.Figure 5
Forest plot of the association between an allele model of CT60 polymorphism and HT.


### 3.3. Publication Bias

In order to evaluate publication bias in this updated systematic review, Begg’s Funnel plots were performed, and the results showed that no obvious asymmetry existed for the meta-analyses of A49G and CT60 genetic polymorphisms.


### 3.4. Sensitivity Analysis

In order to conduct sensitivity analyses, we calculated the pooled ORs through removing each study sequentially and leaving out certain studies, such as studies conducted among special population. The analyses showed that the results were not changed significantly. However, the summary results of the association between CT60 and HT among Caucasians were shifted in the sensitivity analyses.


## 3.1. Identification of Eligible Studies

Besides the 43 studies mentioned in Kavvoura et al.’s meta-analysis [7], 25 additional studies were included in this review (Table 1). Sixteen studies were English language publications [8–23] and 9 studies were published in Chinese [24–32]. Thus, the present updated meta-analysis consisted of 68 studies.Table 1
Characteristics of new studies included in the meta-analysis.
Study
Year
Country
Ethnicity
Gene
Disease
Cases
Controls
AA
AG
GG
AA
AG
GG
Wang et al. [24]
2001
China
Asian
A49G
GD
37
47
3
27
26
7
Zhou et al. [25]
2003
China
Asian
A49G
GD
32
14
4
5
5
10
Zhang et al. [8]
2006
China
Asian
A49G
GD
37
18
1
26
25
29
Yao et al. [26]
2006
China
Asian
A49G
GD
58
53
9
55
57
11
Yu et al. [32]
2006
China
Asian
A49G
GD
51
36
13
26
46
28
Wang et al. [9]
2007
China
Asian
A49G
GD
124
69
15
46
60
20
Yu et al. [27]
2008
China
Asian
A49G
GD
67
45
13
13
27
29
Chong et al. [10]
2008
China
Asian
A49G
GD
97
73
7
16
67
103
Cury et al. [11]
2008
Brazil
American
A49G
GD
15
58
43
6
64
47
Bicek et al. [12]
2009
Slovenia
Caucasian
A49G
GD
17
73
33
14
52
24
Kimura et al. [13]
2009
Japan
Asian
A49G
GD
210
143
62
10
42
32
Wang et al. [28]
2010
China
Asian
A49G
GD
38
47
5
16
20
14
Guo et al. [29]
2010
China
Asian
A49G
GD
26
52
24
12
47
41
Zhao et al. [14]
2010
China
Asian
A49G
GD
1030
730
104
295
358
142
Pastuszak-Lewandoska et al. [15]
2012
Poland
Caucasian
A49G
GD
7
6
1
97
77
18
Veeramuthumari et al. [16]
2011
India
Caucasian
A49G
GD
32
37
11
71
56
24
Kimkong et al. [17]
2011
Thailand
Asian
A49G
GD
61
49
22
54
73
26
Farra et al. [18]
2012
Lebanon
Caucasian
A49G
GD
6
18
31
7
32
39
Pastuszak-Lewandoska et al. [19]
2013
Poland
Caucasian
A49G
GD
12
9
3
945
823
156
Pastuszak-Lewandoska et al. [15]
2012
Poland
Caucasian
A49G
HT
6
19
3
5
5
10
Zhou et al. [25]
2003
China
Asian
A49G
HT
24
14
8
46
60
20
Yu et al. [27]
2008
China
Asian
A49G
HT
41
34
5
15
64
22
Dallos et al. [20]
2008
Slovakia
Caucasian
A49G
HT
13
34
16
13
27
29
Kucharska et al. [21]
2009
Poland
Caucasian
A49G
HT
31
40
29
16
67
103
Bicek et al. [12]
2009
Slovenia
Caucasian
A49G
HT
15
46
51
6
64
47
Sahin et al. [22]
2009
Turk
Caucasian
A49G
HT
21
91
85
17
54
49
Farra et al. [18]
2012
Lebanon
Caucasian
A49G
HT
6
31
36
16
20
14
Ying et al. [30]
2012
China
Asian
A49G
HT
46
53
51
31
91
108
Pastuszak-Lewandoska et al. [19]
2013
Poland
Caucasian
A49G
HT
14
8
3
7
48
43
Wang et al. [9]
2007
China
Asian
CT60
GD
138
46
5
30
61
26
Chong et al. [10]
2008
China
Asian
CT60
GD
125
48
4
735
516
84
Tsai et al. [23]
2008
China
Asian
CT60
GD
136
48
5
125
58
9
Bicek et al. [12]
2009
Slovenia
Caucasian
CT60
GD
50
57
16
88
51
12
Kimura et al. [13]
2009
Japan
Asian
CT60
GD
267
127
21
82
59
12
Kimkong et al. [17]
2011
Thailand
Asian
CT60
GD
78
46
8
372
216
32
Qu et al. [31]
2014
China
Asian
CT60
GD
1989
487
114
1550
474
136
Dallos et al. [20]
2008
Slovakia
Caucasian
CT60
HT
31
28
4
20
50
25
Bicek et al. [12]
2009
Slovenia
Caucasian
CT60
HT
37
52
23
30
61
26


## 3.2. Quantitative Analysis

### 3.2.1. A49G Gene Polymorphism and GD

The summary OR of included studies was increased 1.55-fold in susceptibility to GD in subjects with the G allele, and the associations of GD and A49G polymorphisms were statistically significant in an additive genetic model (GG versus AA: OR = 2.41, 95% CI: 2.01–2.89), a dominant genetic model (GG + AG versus AA: OR = 1.76, 95% CI: 1.52–2.03), and a recessive genetic model (GG versus AG + AA: OR = 1.79, 95% CI: 1.58–2.02). The detailed results were shown in Figures1 and 2 and Supplemental Figures 1 and 2 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/747816.Figure 1
Forest plot of the association between an allele model of A49G polymorphism and GD.Figure 2
Forest plot of the association between an additive model of A49G polymorphism and GD.The subgroup analysis was performed by ethnicity to decrease the heterogeneity. As shown in Figures1 and 2, significant associations between A49G SNP and GD risk were identified in Asians and Caucasians.


### 3.2.2. A49G Gene Polymorphism and HT

The meta-analysis suggested (see Figure3 and Supplemental Figures 3–5) that A49G polymorphisms increased the risk of HT significantly in the allele frequencies (G versus A: OR = 1.36, 95% CI: 1.20–1.53), the additive genotype (GG versus AA: OR = 2.10, 95% CI: 1.75–2.51), the dominant genotype (GG + AG versus AA: OR = 1.57, 95% CI: 1.26–1.96), and the recessive genotype (GG versus AG + AA: OR = 1.46, 95% CI: 1.19–1.81). The subgroup analyses showed that A49G polymorphism was one of the risk factors for GD in Asians and Caucasians.Figure 3
Forest plot of the association between an allele model of A49G polymorphism and HT.


### 3.2.3. CT60 Gene Polymorphism and GD

The summary analyses of CT60 gene polymorphism and GD are shown in Figure4 and Supplemental Figures 6–8. The pooled ORs of CT60 polymorphisms with GD in allele frequencies, the additive genetic model, the dominant genetic model, and the recessive genetic model were 1.48 (95% CI: 1.35–1.63), 1.98 (95% CI: 1.73–2.27), 1.72 (95% CI: 1.52–1.96), and 1.56 (95% CI: 1.39–1.76), respectively. The subgroup analyses suggested that CT60 polymorphism was a risk factor for GD in Asians and Caucasians.Figure 4
Forest plot of the association between an allele model of CT60 polymorphism and GD.


### 3.2.4. CT60 Gene Polymorphism and HT

As shown in Figure5 and Supplemental Figures 9–11, CT60 genetic polymorphisms increased HT risk significantly in the allele frequencies contrast (G versus A: OR = 1.56, 95% CI: 1.15–2.13), the additive genetic contrast (GG versus AA: OR = 2.58, 95% CI: 1.33–5.01), the dominant genetic contrast (GG + AG versus AA: OR = 1.95, 95% CI: 1.20–3.15), and the recessive genetic contrast (GG versus AG + AA: OR = 1.79, 95% CI: 1.20–2.67). The subgroup analyses showed that CT60 genetic polymorphism was one of the risk factors for GD in Asians and Caucasians.Figure 5
Forest plot of the association between an allele model of CT60 polymorphism and HT.


## 3.2.1. A49G Gene Polymorphism and GD

The summary OR of included studies was increased 1.55-fold in susceptibility to GD in subjects with the G allele, and the associations of GD and A49G polymorphisms were statistically significant in an additive genetic model (GG versus AA: OR = 2.41, 95% CI: 2.01–2.89), a dominant genetic model (GG + AG versus AA: OR = 1.76, 95% CI: 1.52–2.03), and a recessive genetic model (GG versus AG + AA: OR = 1.79, 95% CI: 1.58–2.02). The detailed results were shown in Figures1 and 2 and Supplemental Figures 1 and 2 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/747816.Figure 1
Forest plot of the association between an allele model of A49G polymorphism and GD.Figure 2
Forest plot of the association between an additive model of A49G polymorphism and GD.The subgroup analysis was performed by ethnicity to decrease the heterogeneity. As shown in Figures1 and 2, significant associations between A49G SNP and GD risk were identified in Asians and Caucasians.


## 3.2.2. A49G Gene Polymorphism and HT

The meta-analysis suggested (see Figure3 and Supplemental Figures 3–5) that A49G polymorphisms increased the risk of HT significantly in the allele frequencies (G versus A: OR = 1.36, 95% CI: 1.20–1.53), the additive genotype (GG versus AA: OR = 2.10, 95% CI: 1.75–2.51), the dominant genotype (GG + AG versus AA: OR = 1.57, 95% CI: 1.26–1.96), and the recessive genotype (GG versus AG + AA: OR = 1.46, 95% CI: 1.19–1.81). The subgroup analyses showed that A49G polymorphism was one of the risk factors for GD in Asians and Caucasians.Figure 3
Forest plot of the association between an allele model of A49G polymorphism and HT.


## 3.2.3. CT60 Gene Polymorphism and GD

The summary analyses of CT60 gene polymorphism and GD are shown in Figure4 and Supplemental Figures 6–8. The pooled ORs of CT60 polymorphisms with GD in allele frequencies, the additive genetic model, the dominant genetic model, and the recessive genetic model were 1.48 (95% CI: 1.35–1.63), 1.98 (95% CI: 1.73–2.27), 1.72 (95% CI: 1.52–1.96), and 1.56 (95% CI: 1.39–1.76), respectively. The subgroup analyses suggested that CT60 polymorphism was a risk factor for GD in Asians and Caucasians.Figure 4
Forest plot of the association between an allele model of CT60 polymorphism and GD.


## 3.2.4. CT60 Gene Polymorphism and HT

As shown in Figure5 and Supplemental Figures 9–11, CT60 genetic polymorphisms increased HT risk significantly in the allele frequencies contrast (G versus A: OR = 1.56, 95% CI: 1.15–2.13), the additive genetic contrast (GG versus AA: OR = 2.58, 95% CI: 1.33–5.01), the dominant genetic contrast (GG + AG versus AA: OR = 1.95, 95% CI: 1.20–3.15), and the recessive genetic contrast (GG versus AG + AA: OR = 1.79, 95% CI: 1.20–2.67). The subgroup analyses showed that CT60 genetic polymorphism was one of the risk factors for GD in Asians and Caucasians.Figure 5
Forest plot of the association between an allele model of CT60 polymorphism and HT.


## 3.3. Publication Bias

In order to evaluate publication bias in this updated systematic review, Begg’s Funnel plots were performed, and the results showed that no obvious asymmetry existed for the meta-analyses of A49G and CT60 genetic polymorphisms.


## 3.4. Sensitivity Analysis

In order to conduct sensitivity analyses, we calculated the pooled ORs through removing each study sequentially and leaving out certain studies, such as studies conducted among special population. The analyses showed that the results were not changed significantly. However, the summary results of the association between CT60 and HT among Caucasians were shifted in the sensitivity analyses.


## 4. Discussion

GD and HT are the most prevalent autoimmune thyroid diseases (AITDs), which represent two opposite pathogenic paths: hyperthyroidism in GD and thyroid destruction in HT [15, 19]. Although the etiological mechanisms of GD and HT are not distinctly clarified, CTLA-4 gene polymorphisms (A49G and CT60) have been identified as the most important genetic factors in many genetic researches and genome-wide association study (GWAS) [2, 12]. A large-scale meta-analysis including 43 studies and more than 13,000 subjects was published in the present journal in 2007 [7]. The results identified the roles of A49G and CT60 gene polymorphism in AITDs. Subsequently, more than 30 studies repeatedly confirmed the associations of the CTLA-4 gene with GD and HT. The current updated meta-analysis included the most recent eligible studied and summarized the data in specific ethnicity.A49G gene polymorphism was widely investigated for the susceptibility to AITDs; the G allelic gene variation was considered as a risk factor of GD and HT. Our current meta-analysis showed that A49G polymorphisms significantly increased the risk of GD in total population. Nevertheless, the genetic variation had a protective effect in Africans according to the additive model analysis. Furthermore, a total of 22 studies were summarized for the A49G gene polymorphism with HT. The results suggested that the polymorphism distinctly increases the risk of HT among Caucasians and Asians.The G allele of CT60 gene is another focused genetic pathogenesis associated with HT and GD. A total of 22 studies were included in our meta-analysis for CT60 polymorphism and GD, and the pooled OR values indicated that G allele carriers might increase GD risk. Moreover, the summarized result involving 8 original studies suggested that CT60 polymorphisms were associated with susceptibility to HT among Caucasian and Asian population, except that no significant pooled OR was found in dominant genetic model of Caucasians.In this updated meta-analysis, we guaranteed the stability of results with sensitivity analysis. No obvious publication bias existed according to funnel plot test. We performed heterogeneity test to assess the reliability of the results and conducted subgroup analysis.There are some limitations in our study. The sample size in Africans or Americans was not large enough. More well-designed studies need to be conducted in Africans or Americans to clarify the associations of the CTLA-4 gene with AITDs.



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