Open Access

Asthma susceptible genes in Chinese population: A meta-analysis

Respiratory Research201011:129

https://doi.org/10.1186/1465-9921-11-129

Received: 25 January 2010

Accepted: 24 September 2010

Published: 24 September 2010

Abstract

Background

Published data regarding the associations between genetic variants and asthma risk in Chinese population were inconclusive. The aim of this study was to investigate asthma susceptible genes in Chinese population.

Methods

The authors conducted 18 meta-analyzes for 18 polymorphisms in 13 genes from eighty-two publications.

Results

Seven polymorphisms were found being associated with risk of asthma, namely: A Disintegrin and Metalloprotease 33 (ADAM33) T1-C/T (odds ratio [OR] = 6.07, 95% confidence interval [CI]: 2.69-13.73), Angiotensin-Converting Enzyme (ACE) D/I (OR = 3.85, 95%CI: 2.49-5.94), High-affinity IgE receptor β chain (FcεRIβ) -6843G/A (OR = 1.49, 95%CI: 1.01-2.22), Interleukin 13(IL-13) -1923C/T (OR = 2.99, 95%CI: 2.12-4.24), IL-13 -2044A/G (OR = 1.49, 95%CI: 1.07-2.08), Regulated upon Activation, Normal T cell Expressed and Secreted (RANTES) -28C/G (OR = 1.64, 95%CI: 1.09-2.46), Tumor Necrosis Factor-α (TNF-α) -308G/A(OR = 1.42, 95%CI: 1.09, 1.85). After subgroup analysis by age, the ACE D/I, β2-Adrenergic Receptor (β2-AR) -79G/C, TNF-α -308G/A, Interleukin 4 receptor(IL-4R) -1902G/A and IL-13 -1923C/T polymorphisms were found significantly associated with asthma risk in Chinese children. In addition, the ACE D/I, FcεRIβ -6843G/A, TNF-α -308G/A, IL-13 -1923C/T and IL-13 -2044A/G polymorphisms were associated with asthma risk in Chinese adults.

Conclusion

ADAM33, FcεRIβ, RANTES, TNF-α, ACE, β2-AR, IL-4R and IL-13 genes could be proposed as asthma susceptible genes in Chinese population. Given the limited number of studies, more data are required to validate these associations.

Introduction

Asthma is one of the most common chronic respiratory diseases, affecting about 300 millions of children and adults worldwide[1]. In China, more than 25 millions people are asthmatic patients, which includes almost 10 million children[2]. Compared with the western world, the preventive controls and treatments for asthma were not well established in China [3]. Only a few percent of asthma patients received proper treatment. Poverty and inadequate resources are the main hindrance to reduce the burden of disease in China especially in numerous of Chinese villagers. Therefore, the best approach to reduce asthma is primary prevention through modifying the risk factors of asthma.

It is well accepted that asthma is a complex disease and both genetic and environmental factors contribute to its inception and evolution[4, 5]. Many studies regarding associations between genetic variants and asthma risk have been published and many genes were proposed as asthma susceptible genes[69]. However, the conclusions obtained from different populations were often different or even controversial. Possible roles may be that different genetic backgrounds and environment exposures in different ethnic population that may affect the pathogenesis of asthma. Thus, asthma susceptible genes in different population may not be the same.

In recent years, host genetic susceptibility to asthma has been a research focus in scientific community in China. Many genes were suggested as asthma risk factors for Chinese population; however, many of the studies drew incompatible or even contradictory results. Considering a small number of sample size may be lack of power to reveal the reliable conclusion, we carried out a meta-analysis to assess the susceptible genes for asthma in Chinese population. To our knowledge, this is the first comprehensive and largest genetic meta-analysis conducted in people of Chinese descent for any respiratory diseases.

Materials and methods

Literature search

We conducted a literature search by using the electronic database Medline (Ovid), Pubmed, Embase, ScienceDirect, Springer, CNKI, Wanfang database, Weipu database and CBM database to identify articles that evaluated the association between genetic variants and the risk of asthma in Chinese population (Last search was updated on May 13, 2010). The search terms were used as follows: 'asthma or asthmatic', in combination with 'polymorphism or variant or mutation' and 'Chinese or China' for Medline (Ovid), Pubmed, Embase, ScienceDirect, Springer database; 'asthma or asthmatic', in combination with 'polymorphism or variant or mutation' for CNKI database, Wanfang database, Weipu database and CBM database. All languages were included. The following criteria were used for selecting literatures in the meta-analysis: (1) the study should evaluate the association between genetic variants and risk of asthma in Chinese population from either mainland, overseas or both, (2) the study should be a case-control design published in a journal (3) genotype distributions in both cases and controls were available for estimating an odds ratio with 95% confidence interval (CI) and P value, (4)genotype distributions of control population must be consistent with Hardy-Weinberg equilibrium(HWE), P > 0.05 (5) the polymorphism for data synthesis should be studied in at least three case-control studies, (6) polymorphisms for data synthesis should be characterized as -A/B, with the following genotypes: AA, AB and BB. Accordingly, the following exclusion criteria were used: (1) abstracts and reviews, (2)genotype frequency not reported, (3) repeated or overlapping publications (4) polymorphisms with data less than three case-control studies (5) genotype distributions of control population not consistent with HWE, (6)genetic variants not characterized as -A/B. For duplication or overlapping publications, the studies with larger number of cases and controls or been published latest were included.

Data extraction

Two independent authors (Xiaobo Li and Yonggang Zhang) checked all potentially relevant studies and reached a consensus on all items. In case of disagreement, a third author(Jie Zhang) would assess these articles. The following data were collected from each study: first author, year of publication, location of the people, ages, genotype frequencies in cases and controls.

Statistical Analysis

For each case-control study, we first examined whether the genotype distribution in control group was according to Hardy-Weinberg equilibrium by Pearson's X 2 test http://ihg2.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl.

Any polymorphism that had been studied in at least three case-control studies was included in the meta-analysis. The strength of the associations between asthma risk and genetic variants were estimated by ORs and 95% CIs. The statistical significance of summary ORs were assessed by Z-test. The evaluated genetic models for each study were based mostly on those used in primary studies. Heterogeneity was evaluated by a X 2 -based Q statistic and was considered statistical significant at P value < 0.10. I 2 was used to measure the percentage of variability in point estimated that due to heterogeneity rather than sampling error. When the P-value is > 0.10, the pooled OR was calculated by the fixed-effects model, otherwise, a random-effects model was used. To evaluate the age-specific effects, subgroup analyses were performed by age for polymorphisms which were investigated in a sufficient number of studies(data were available from at least three case-control studies for at least one subgroup). Publication bias was examined by using the funnel plots, Begg's test and Egger's test[4]. The funnel plot is asymmetrical when there is evidence of publication bias. All statistical tests were performed by using REVMAN 4.2 software and STATA 10.0.

Results

Candidate asthma-genes in Chinese Population

The selection process is shown in Figure 1. Briefly, 2489 search results were identified from Medline (Ovid), Pubmed, Embase, ScienceDirect, Springer, CNKI database, Wanfang database, Weipu database and CBM database in the initial search. After reading the titles and abstracts, 2159 articles were excluded for abstracts, reviews, duplicated search results or not being relevant to genetic variants and asthma risk in Chinese population. By reading through the full texts of the remaining 330 articles, 7 articles were excluded for not being relevant to polymorphisms and asthma risk. The remaining 323 articles were used for data extraction. A total of 539 case-control studies were extracted from 248 articles, and 75 articles were excluded because of the absence of the usable data or not a case-control design. In meta-analysis, a small number of studies weaken the conclusions; therefore, only polymorphisms which had been investigated in at least three case-control studies were included for data synthesis. Thus, we excluded all these polymorphisms which were studied in less than three case-control studies(a total of 260 case-control studies were excluded). Hence, a total of 279 case-control studies were left. In addition, genotype frequencies for control population in 53 case-control studies were not consistent with HWE and these case-control studies were all excluded. In the remaining 226 case-control studies, data in 45 case-control studies were overlapped or duplicated with other studies and these case-control studies were all excluded. Thus, 181 case-control studies were left. Among the 181 case-control studies, some polymorphisms were studied in less than three case-control studies, and these polymorphisms were also excluded(a total of 62 case-control studies were excluded). Finally, a total of 18 polymorphisms in 13 genes in 119 case-control studies concerning genetic variants and asthma risk in Chinese population met the inclusion criteria, were identified for data synthesis (Table 1). The characteristics of each polymorphism are listed in Table 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 and 19. The genetic models for pooling data are also listed in Table 1.
Figure 1

Flow diagram of included/excluded studies.

Table 1

Genes identified from individual studies

Gene

Chromosome location of gene

Polymorphism

Aminoacid change

Genetic model

Genotypes Evaluated

Other genotypes

Cases

Controls

β2-AR

5q31-32

-46G/A

Arg16Gly

Recessive

GG

GA+AA

1796

1589

  

-79G/C

Gln27Glu

Recessive

GG

GC+CC

823

692

IL-4R

16p11.2-12.1

-1902G/A

Q576R

Dominant

GG+GA

AA

2308

1971

  

-223G/A

Ile/Val

Recessive

GG

GA+AA

1623

1304

IL-4

5q31

-589C/T

 

Dominant

CC+CT

TT

1724

1656

TNF-α

6p21.1-21.3

-308A/G

 

Dominant

AA+AG

GG

1428

1511

FcεRIβ

11q13

-6843G/A

Glu237Gly

Dominant

GG+GA

AA

1434

1276

  

-109C/T

 

Recessive

CC

CT+TT

428

371

ACE

17q23

D/I

 

Recessive

DD

DI+II

385

335

IL-13

5q31

-2044A/G

Gln130Arg

Dominant

AA+AG

GG

1512

1351

  

-1923C/T

 

Recessive

TT

CC+CT

645

588

IL-1β

2q12-21

-511C/T

 

Dominant

TT+TC

CC

333

255

LT-α

6q21.3

+252A/G

 

Dominant

GG+GA

AA

674

896

TGF-β1

19q13

-509C/T

 

Dominant

TT+TC

CC

406

390

CD14

5q31.1

-159C/T

 

Dominant

TT+TC

CC

1381

1219

ADAM33

20p13

T1

Met764Thr

Recessive

CC

TT+TC

569

512

RANTES

17q11.2-12

-28G/C

 

Dominant

GG+GC

CC

314

229

  

-403A/G

 

Dominant

AA+AG

GG

343

205

Table 2

Main data of all studies included in the meta-analysis for the -46G/A (Arg16Gly) polymorphism in β2-AR gene

    

Case

Control

  

Study

Population location

Year

Age

AA

AG

GG

AA

AG

GG

OR

95%CI

Chan, I H[16]

Hong Kong

2008

10.4 ± 3.7

101

135

59

51

89

33

1.06

0.66-1.70

Cui, LY(Han)[17]

Neimenggu

2007

21-62

6

34

2

6

20

4

0.33

0.06-1.90

Cui, LY(Meng)[17]

Neimenggu

2007

26-69

3

21

6

6

19

5

1.25

0.34-4.64

Gao, J M[18]

Beijing

2004

38.7 ± 13.8

38

59

28

35

53

8

3.18

1.37-7.33

Li, H[19]

Shanghai

2009

3-12

86

76

30

46

100

46

0.59

0.35-0.98

Liao, W[20]

Chongqing

2001

5.8 ± 4.3

12

27

11

14

28

8

1.48

0.54-4.06

Qiu, Y Y(2008)[21]

Jiangsu

2008

63.2 ± 5.6

25

31

14

34

55

23

0.97

0.46-2.04

Shi, X H[22]

Jiangsu

2008

34(14-66)

22

19

7

10

25

13

0.46

0.17-1.28

Wang, Z[23]

Anhui

2001

30.6 ± 16.2

52

54

22

38

64

34

0.62

0.34-1.14

Xie, Y[24]

Shanghai

2008

4.98 ± 2.78

14

37

6

21

34

7

0.92

0.29-2.93

Xing, J[25]

Beijing

2001

20-66

9

62

29

29

55

16

2.14

1.08-4.26

Zhang, X Y[26]

Chongqing

2008

1.08-17

81

111

25

19

23

8

0.68

0.29-1.62

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

138

207

97

173

250

87

1.37

0.99-1.89

Table 3

Main data of all studies included in the meta-analysis for the -79G/C (Gln27Glu) polymorphism in β2-A R gene

    

Case

Control

  

Study

Population location

Year

Age

CC

CG

GG

CC

CG

GG

OR

95%CI

Cui, LY(Han)[17]

Neimenggu

2007

21-62

32

6

4

26

3

1

3.05

0.32-28.79

Gao, G K[28]

Beijing

2002

4-56

20

32

6

32

49

8

1.17

0.38-3.56

Liao, W[20]

Chongqing

2001

5.8 ± 4.3

26

20

4

20

27

3

1.36

0.29-6.43

Lin, Y C[29]

Taiwan

2003

13.9 ± 0.07

65

15

0

54

14

1

0.28

0.01-7.08

Pan, Y P[30]

Jiangxi

2005

-

15

24

6

17

23

5

1.23

0.35-4.37

Qiu, Y Y(2000)[31]

Jiangsu

2000

42 ± 5

23

30

6

29

36

7

1.05

0.33-3.32

Qiu, Y Y(2008)[21]

Jiangsu

2008

63.2 ± 5.6

56

13

1

90

20

2

0.80

0.07-8.96

Wang, Z[23]

Anhui

2001

30.6 ± 16.2

108

19

1

113

22

1

1.06

0.07-17.18

Ye, X W[32]

Guizhou

2003

42.68 ± 10.55

25

39

10

15

20

4

1.37

0.40-4.68

Zhang, X Y[26]

Chongqing

2008

1.08-17

54

119

44

8

24

18

0.45

0.23-0.88

Table 4

Main data of all studies included in the meta-analysis for the -1902G/A (Q576R) polymorphism in IL-4R gene

    

Case

Control

  

Study

Population location

Year

Age

AA

AG

GG

AA

AG

GG

OR

95%CI

Cui, T P[33]

Hubei

2003

3-68

129

89

23

130

41

4

2.51

1.64-3.83

Deng, R Q[34]

Guangdong

2006

8-75

26

42

32

15

38

47

0.50

0.25-1.02

Gui, Q[35]

Chongqing

2006

49(28-72)

33

15

2

34

14

2

1.09

0.48-2.52

Hu, S Y[36]

Guangdong

2005

2-16

90

66

19

130

41

4

2.73

1.74-4.28

Liu, L N[37]

Henan

2005

3-15

46

27

3

47

12

1

2.36

1.09-5.08

Mak, J C[38]

Hong Kong

2007

42.4 ± 16.1

200

81

4

191

91

9

0.81

0.57-1.15

Sun, J[39]

Heilongjiang

2010

3-14

67

24

0

33

9

0

1.31

0.55-3.14

Wu, X H[40]

Hubei

2010

8.8

183

61

8

168

55

4

1.07

0.72-1.61

Zhang, A M[41]

Hunan

2005

3-14

55

39

0

57

11

0

3.67

1.71-7.89

Zhang, H[42]

Shanghai

2007

-

257

87

8

87

27

0

1.19

0.73-1.95

Zhang, W[43]

Singapore

2007

-

115

30

0

115

38

4

0.71

0.42-1.22

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

326

112

9

360

140

12

0.88

0.66-1.17

Table 5

Main data of all studies included in the meta-analysis for the -223G/A (Ile/Val) polymorphism in IL-4R gene

    

Case

Control

  

Study

Population location

Year

Age

AA

AG

GG

AA

AG

GG

OR

95%CI

Chan, I H [16]

Hong Kong

2008

10.4 ± 3.7

79

159

57

49

80

38

0.81

0.51-1.29

Deng, R Q[44]

Guangdong

2006

8-75

24

47

29

9

33

58

0.30

0.16-0.53

Yang, Q[45]

Jiangxi

2004

18-71

6

21

7

8

16

5

1.24

0.35-4.44

Zhang, H[42]

Shanghai

2007

-

106

168

78

44

53

17

1.62

0.92-2.88

Zhang, W[43]

Singapore

2007

-

32

84

29

42

76

39

0.76

0.44-1.30

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

105

201

139

124

250

136

1.25

0.94-1.65

Wu, X H[40]

Hubei

2010

8.8

46

131

75

59

110

58

1.23

0.83-1.85

Table 6

Main data of all studies included in the meta-analysis for the -589 C/T polymorphism in IL-4 gene

    

Case

Control

  

Study

Population location

Year

Age

TT

CT

CC

TT

CT

CC

OR

95%CI

Cui, T P[33]

Hubei

2003

3-68

141

89

11

114

52

9

1.33

0.89-1.98

Hu, S Y[36]

Guangdong

2005

2-16

108

59

8

114

52

9

1.16

0.75-1.79

Liu, L N[37]

Henan

2005

3-15

45

29

2

34

23

3

0.90

0.45-1.79

Mak, J C[38]

Hong Kong

2007

42.4 ± 16.1

179

95

15

186

87

19

1.08

0.77-1.51

Wang, W[46]

Xinjiang

2004

39 ± 8

22

42

29

15

26

21

1.03

0.49-2.19

Wu, X H[40]

Hubei

2010

8.8

163

83

6

132

84

11

0.76

0.52-1.10

Zhang, W D[47]

Singapore

2005

-

101

47

4

109

45

3

1.15

0.71-1.85

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

279

145

22

309

183

16

0.93

0.72-1.21

Table 7

Main data of all studies included in the meta-analysis for the -308A/G polymorphism in TNF-α gene

    

Case

Control

  

Study

Population location

Year

Age

GG

GA

AA

GG

GA

AA

OR

95%CI

Gao, J M[48]

Beijing

2003

38.7 ± 13.8

47

52

26

44

41

11

1.40

0.82-2.41

Guo, Y L[49]

Jiangxi

2004

-

4

28

16

7

11

3

5.50

1.40-21.60

Li, Z F[50]

Guangdong

2003

2-12

9

16

5

14

10

2

2.72

0.91-8.16

Liu, R M[51]

Hubei

2004

2-15

98

15

0

104

22

0

0.72

0.36-1.47

Mak, J C[38]

Hong Kong

2007

42.4 ± 16.1

244

47

1

250

40

2

1.17

0.75-1.84

Tan, E C[52]

Singapore

1999

-

49

18

0

115

36

0

1.17

0.61-2.26

Wang, T N[53]

Taiwan

2004

5-18

140

49

2

111

18

0

2.25

1.24-4.06

Zhai, F Z[54]

Shandong

2004

35.80 ± 10.18

44

14

6

67

12

1

2.34

1.06-5.19

Zhao, H J[55]

Jilin

2005

-

45

5

0

71

9

0

0.88

0.28-2.78

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

345

100

3

409

94

7

1.21

0.89-1.65

Table 8

Main data of all studies included in the meta-analysis for the -6843G/A polymorphism in FcεRI β gene

    

Case

Control

  

Study

Population location

Year

Age

AA

AG

GG

AA

AG

GG

OR

95%CI

Chan, I H[16]

Hong Kong

2008

10.4 ± 3.7

267

23

1

154

13

0

1.06

0.53-2.15

Cui, T P[56]

Hubei

2004

40.37 ± 15.09

60

40

6

78

26

2

2.14

1.20-3.81

Liu, T[57]

Shandong

2006

36.5

45

14

1

39

10

1

1.18

0.49-2.87

Tang, Y[58]

Guangdong

2003

39.5(12-67)

49

11

0

61

4

0

3.42

1.03-11.42

Wang, L[59]

Hubei

2003

2-16

65

40

5

70

20

2

2.20

1.20-4.06

Zeng, L X[60]

Jiangxi

2001

37(14-63)

61

5

3

27

1

0

3.54

0.42-29.73

Zhang, X Z[61]

Singapore

2004

52 ± 16

81

57

3

108

42

7

1.63

1.02-2.62

Zhao, K S[62]

Jilin

2004

1.5-14

126

23

2

92

13

0

1.40

0.68-2.89

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

309

121

16

314

165

27

0.73

0.55-0.95

Table 9

Main data of all studies included in the meta-analysis for the -109C/T polymorphism in FcεRI β gene

    

Case

Control

  

Study

Population location

Year

Age

TT

TC

CC

TT

CT

CC

OR

95%CI

Li, H[19]

Shanghai

2009

3-12

110

58

24

78

90

24

1.00

0.55-1.83

Wang, L[59]

Hubei

2003

2-16

43

54

13

35

46

11

0.99

0.42-2.32

Zhao, K S [63]

Jilin

2004

5.6 ± 3.1

46

69

11

40

38

9

0.83

0.33-2.09

Table 10

Main data of all studies included in the meta-analysis for the D/I polymorphism in ACE gene

    

Case

Control

  

Study

Population location

Year

Age(year)

II

DI

DD

II

DI

DD

OR

95%CI

Gao, J M[64]

Beijing

1999

39(16-69)

12

15

23

16

26

8

4.47

1.75-11.43

Guo, Y B[65]

Guangdong

2006

0.33-3

27

18

7

36

32

4

2.64

0.73-9.56

Lu, H M[66]

Tianjin

2004

37(18-52)

3

4

11

5

7

3

6.29

1.29-30.54

Lue, K H[67]

Taiwan

2006

9.91 ± 1.62

48

40

17

56

42

4

4.73

1.53-14.60

Qin, J H[68]

Liaoning

2000

6.9 ± 2.7

24

10

18

21

14

5

3.71

1.24-11.10

Song, L J[69]

Jilin

2001

1-14

22

45

41

18

29

9

3.20

1.42-7.20

Table 11

Main data of all studies included in the meta-analysis for the -2044A/G polymorphism in IL-13 gene

    

Case

Control

  

Study

Population location

Year

Age

GG

AG

AA

GG

AG

AA

OR

95%CI

Chan, I H[16]

Hong Kong

2008

10.4 ± 3.7

94

136

43

54

70

17

1.18

0.78-1.80

Feng, D[70]

Heilongjiang

2009

3-16

17

18

10

30

10

3

3.80

1.57-9.23

Liu, J L[71]

Guangdong

2004

14-67

27

54

19

44

46

10

2.12

1.17-3.84

Wu, X H[40]

Hubei

2010

8.8

105

111

36

125

84

18

1.72

1.19-2.46

Yang, L F[72]

Gansu

2010

8 ± 4

71

60

47

73

66

19

1.29

0.84-2.00

Zhao, K S[73]

Jilin

2005

1.5-14

18

60

52

8

42

50

0.54

0.23-1.30

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

203

194

49

212

234

59

0.87

0.67-1.12

Xi, D[74]

Hubei

2004

≥20

15

24

6

23

20

3

2.08

1.28-3.38

Xi, D[74]

Hubei

2004

≥4

10

25

8

16

13

2

3.52

1.30-9.55

Table 12

Main data of all studies included in the meta-analysis for the -1923C/T polymorphism in IL-13 gene

    

Case

Control

  

Study

Population location

Year

Age

CC

CT

TT

CC

CT

TT

OR

95%CI

Song, Q Z[75]

Guangdong

2005

14-67

24

55

21

43

47

10

2.39

1.06-5.39

Shi, X H[22]

Jiangsu

2008

34(14-66)

12

26

10

30

16

2

6.05

1.25-29.32

Chen, J Q[76]

Jiangsu

2004

2.59 ± 1.45

41

43

12

39

14

0

15.83

0.92-272.92

Wang, X H[77]

Shandong

2009

39 ± 11

31

57

61

66

68

26

3.57

2.10-6.08

Wu, X H[40]

Hubei

2010

8.8

106

114

32

126

85

16

1.92

1.02-3.60

Table 13

Main data of all studies included in the meta-analysis for the -511C/T polymorphism in IL-1β gene

    

Case

Control

  

Study

Population location

Year

Age

GG

GA

AA

GG

GA

AA

OR

95%CI

Hsieh, C C[78]

Taiwan

2004

8.74 ± 4.09

69

93

40

48

70

26

0.96

0.61-1.52

Wu, Z F[79]

Jiangxi

2007

11-68

16

36

24

26

38

12

1.95

0.94-4.03

Zhao, X F[80]

Yunnan

2006

5.9(3-14)

51

4

0

30

5

0

0.47

0.12-1.89

Table 14

Main data of all studies included in the meta-analysis for the +252A/G polymorphism in LT-α gene

    

Case

Control

  

Study

Population location

Year

Age

AA

AG

GG

AA

AG

GG

OR

95%CI

Gao, J M[81]

Beijing

2003

38.7 ± 13.8

13

63

49

14

46

36

1.47

0.66-3.30

Ma, W C[82]

Guangdong

2005

1.8-9

8

14

10

26

46

28

1.05

0.42-2.64

Mak, J C[38]

Hong Kong

2007

42.4 ± 16.1

70

146

69

79

134

76

1.16

0.80-1.68

Tan, E C[52]

Singapore

1999

-

13

38

15

30

84

39

0.99

0.48-2.06

Xu, X[83]

Guangdong

2003

18-69

12

21

19

26

47

30

1.13

0.51-2.46

Huang, S C[84]

Taiwan

2008

9.9 ± 4.1

20

69

25

45

69

41

1.62

0.98-2.66

Table 15

Main data of all studies included in the meta-analysis for the -509C/T polymorphism in TGF-β1 gene

    

Case

Control

  

Study

Population location

Year

Age

CC

CT

TT

CC

CT

TT

OR

95%CI

Lu, J R[85]

Jilin

2004

1-13

45

38

15

30

19

3

1.61

0.81-3.17

Mak, J C[86]

Hong Kong

2006

41.0 ± 16.1

46

109

93

51

155

102

0.87

0.56-1.35

Xia, W[87]

Jiangxi

2006

15-60

22

26

12

17

11

2

2.26

0.92-5.52

Table 16

Main data of all studies included in the meta-analysis for the -159C/T polymorphism in CD14 gene

    

Case

Control

  

Study

Population location

Year

Age

CC

CT

TT

CC

CT

TT

OR

95%CI

Chan, I H[16]

Hong Kong

2008

10.4 ± 3.7

55

134

80

26

77

38

0.88

0.52-1.48

Chen, M[88]

Guangdong

2009

14-71

63

62

25

40

68

42

0.50

0.31-0.82

Cui, T P[89]

Hubei

2003

2-16

27

67

49

10

42

20

0.69

0.32-1.52

Tan, C Y[90]

Taiwan

2006

-

17

56

47

24

55

41

1.51

0.77-2.99

Wu, X H[40]

Hubei

2010

8.8

54

117

81

31

121

75

0.58

0.36-0.94

Wang, J Y[27]

Taiwan

2009

7.82 ± 3.81

160

230

57

177

236

96

0.96

0.73-1.25

Table 17

Main data of all studies included in the meta-analysis for the T1-C/T polymorphism in ADAM33 gene

    

Case

Control

  

Study

Population location

Year

Age

TT

TC

CC

TT

TC

CC

OR

95%CI

Su, D J[91]

Heilongjiang

2008

36.69 ± 11.53

63

78

40

117

29

5

8.28

3.18-21.59

Wang, P[92]

Shandong

2006

43.32

250

45

1

236

33

1

0.91

0.06-14.65

Xiong, J Y[93]

Guangdong

2009

6-13

71

19

2

80

10

1

2.00

0.18-22.45

Table 18

Main data of all studies included in the meta-analysis for the -28G/C polymorphism in RANTES gene

    

Case

Control

  

Study

Population location

Year

Age

CC

CG

GG

CC

CG

GG

OR

95%CI

Liu, M[94]

Yunnan

2005

7.2 ± 4.8

25

6

1

29

3

0

2.71

0.63-11.59

Wang, L J[95]

Hubei

2004

9 ± 3

65

31

4

72

17

1

2.15

1.11-4.17

Yao, T C[96]

Taiwan

2003

-

134

39

9

83

23

1

1.24

0.71-2.17

Table 19

Main data of all studies included in the meta-analysis for the -403A/G polymorphism in RANTES gene

    

Case

Control

  

Study

Population location

Year

Age

GG

GA

AA

GG

GA

AA

OR

95%CI

Leung, T F[97]

Hongkong

2005

9.9 ± 3.4

60

53

16

37

21

8

1.47

0.81-2.66

Liu, M[94]

Yunnan

2005

7.2 ± 4.8

17

13

2

16

14

2

0.88

0.33-2.35

Yao, T C[96]

Taiwan

2003

-

98

65

19

60

41

6

1.09

0.68-1.77

Summary results of Meta-analyzes

For each polymorphism, heterogeneity was analyzed by a X 2 -based Q statistic and was considered statistical significant at P-value <0.10. When the P-value is less than 0.10, the pooled OR of each meta-analysis was calculated by the fixed-effects model; otherwise, a random-effects model was used. The chosen models to synthesize the data for each polymorphism can be seen in Table 20.
Table 20

Summary results of the meta-analysis and publications bias

      

Pubilication bias (Begg's test)

Gene

Polymorphism

Genotype investigated

Studies Number

Effect Model

OR(95%CI)

t

P

β2-AR

-46G/A

GG

13

Random

1.02(0.75, 1.38)

-0.66

0.525

 

-79G/C

GG

10

Fixed

0.86(0.58, 1.29)

1.60

0.148

IL-4R

-1902G/A

GG+GA

12

Random

1.30(0.94, 1.80)

0.92

0.377

 

-223G/A

GG

7

Random

0.92(0.63, 1.35)

-0.81

0.453

IL-4

-589C/T

CC+CT

8

Fixed

1.01(0.88, 1.16)

0.53

0.615

TNF-α

-308A/G

AA+AG

10

Random

1.42(1.09, 1.85)

1.38

0.205

FcεRIβ

-6843G/A

GG+GA

9

Random

1.49(1.01, 2.22)

2.82

0.026

 

-109C/T

CC

3

Fixed

0.96(0.62, 1.48)

-1.10

0.471

ACE

D/I

DD

6

Fixed

3.85(2.49, 5.94)

0.88

0.429

IL-13

-2044A/G

AA+AG

9

Random

1.49(1.07, 2.08)

1.93

0.095

 

-1923C/T

TT

5

Fixed

2.99(2.12, 4.24)

1.19

0.320

IL-1β

-511C/T

TT+TC

3

Fixed

1.10(0.76, 1.59_

-0.16

0.896

LT-α

+252A/G

GG+GA

6

Fixed

1.26(0.98, 1.62)

-0.02

0.985

TGF-β1

-509C/T

TT+TC

3

Fixed

1.17(0.83, 1.64)

8.57

0.074

CD14

-159C/T

TT+TC

6

Random

0.79(0.59, 1.06)

-0.41

0.700

ADAM33

T1-C/T

CC

3

Fixed

6.07(2.69, 13.73)

-8.22

0.077

RANTES

-28G/C

GG+GC

3

Fixed

1.64(1.09, 2.46)

0.87

0.544

 

-403A/G

AA+AG

3

Fixed

1.18(0.83, 1.67)

-0.37

0.777

Forest plots of each polymorphism can be seen in Figure 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 and 19. In summary, we abstained significant results for seven polymorphisms: ADAM33 T1-C/T (OR = 6.07, 95%CI: 2.69-13.73, Z = 4.33, P < 0.0001), ACE D/I(OR = 3.85, 95%CI: 2.49-5.94, Z = 6.07, P < 0.00001), FcεRIβ -6843G/A (OR = 1.49, 95%CI: 1.01-2.22, Z = 1.99, P = 0.05), IL-13 -1923C/T(OR = 2.99, 95%CI: 2.12-4.24, Z = 6.19, P< 0.00001), IL-13 -2044A/G(OR = 1.49, 95%CI: 1.07-2.08, Z = 2.34, P = 0.02), RANTES -28C/G (OR = 1.64, 95%CI: 1.09-2.46, Z = 2.36, P = 0.02), TNF-α -308G/A (OR = 1.42, 95%CI: 1.09-1.85, Z = 2.63, P = 0.009). These results indicated that these polymorphisms were significant associated with asthma risk in Chinese population. All results for all 18 meta-analyzes are summarized in table 20.
Figure 2

Forest plot of asthma risk associated withβ2-AR-46G/A in Chinese population. Subgroup analysis by age.

Figure 3

Forest plot of asthma risk associated withβ2-AR-79G/C in Chinese population. Subgroup analysis by age.

Figure 4

Forest plot of asthma risk associated withIL-4R-1902G/A in Chinese population. Subgroup analysis by age.

Figure 5

Forest plot of asthma risk associated withIL-4R-223G/A in Chinese population.

Figure 6

Forest plot of asthma risk associated withIL-4-589C/T in Chinese population. Subgroup analysis by age.

Figure 7

Forest plot of asthma risk associated withTNF-α-308A/G in Chinese population. Subgroup analysis by age.

Figure 8

Forest plot of asthma risk associated withFcεRIβ-6843G/A in Chinese population. Subgroup analysis by age.

Figure 9

Forest plot of asthma risk associated withFcεRIβ-109C/T in Chinese population.

Figure 10

Forest plot of asthma risk associated withACED/I in Chinese population. Subgroup analysis by age.

Figure 11

Forest plot of asthma risk associated withIL-13-2044A/G in Chinese population. Subgroup analysis by age.

Figure 12

Forest plot of asthma risk associated withIL-13-1923C/T in Chinese population.

Figure 13

Forest plot of asthma risk associated withIL-1β-511C/T in Chinese population.

Figure 14

Forest plot of asthma risk associated withLT-α+252A/G in Chinese population.

Figure 15

Forest plot of asthma risk associated withTGF-β1-509C/T in Chinese population.

Figure 16

Forest plot of asthma risk associated withCD14-159C/T in Chinese population.

Figure 17

Forest plot of asthma risk associated withADAM33T1-C/T in Chinese population.

Figure 18

Forest plot of asthma risk associated withRANTES-28G/C in Chinese population.

Figure 19

Forest plot of asthma risk associated withRANTES-403A/G in Chinese population.

To evaluate the age-specific effects, subgroup analyses were performed by age for polymorphisms which were investigated in a sufficient number of studies(data were available from at least three case-control studies for at least one subgroup). Three subgroups were used: adults, children, others(ages in these case-control studies were not mentioned or mixed with adults and children). Briefly, we obtained significant results from five polymorphisms(ACE D/I, β2-AR -79G/C, TNF-α -308G/A, IL-4R -1902G/A and IL-13 -1923C/T) in children and five polymorphisms(ACE D/I, FcεRIβ -6843G/A, TNF-α -308G/A, IL-13 -1923C/T, IL-13 -2044A/G) in adults.

Publication bias

The Begg's funnel plots and Egger's tests were performed to assess the potential publication bias (Begg's funnel plots can be seen in Additional File 1). The results did not suggest evidence of publication bias except for the FcεRIβ -6843G/A polymorphism. Statistical results of Begg's test are summarized in Table 20.

Discussion

The aim of meta-analysis is to combine results from studies on the same topic and to produce more precise results. The current study is to reveal the roles of genetic variants and their associations with risk of asthma in Chinese population. In summary, we finally identified 18 polymorphisms in 13 genes. Among them, seven polymorphisms (ADAM33 T1-C/T, ACE D/I, FcεRIβ -6843G/A, IL-13 -1923C/T, IL-13 -2044A/G, RANTES -28C/G and TNF-α -308G/A) were statistically associated with increased risk of asthma. In order to analysis the age-specific associations, subgroup analysis were performed by age. The ACE D/I, β2-AR -79G/C, TNF-α -308G/A, IL-4R -1902G/A and IL-13 -1923C/T polymorphisms were found being associated with asthma risk in Chinese children, while the ACE D/I, FcεRIβ -6843G/A, TNF-α -308G/A, IL-13 -1923C/T, IL-13 -2044A/G polymorphisms were associated with asthma risk in Chinese adults. Given that the data for each polymorphism were from at least three case-control studies, the obtained results could be more precise than results obtained form any individual study.

The β2-AR gene is a critical gene in the pathogenesis of asthma. β2-ARs are present on many airway cells, especially in smooth muscle cells which are hyperreactive in asthmatic patients. At present, β2-AR agonists were major methods for treating asthmatic patients. In this meta-analysis, ten case-control studies for β2-AR -79G/C and eleven for -46G/A polymorphism were identified. The results indicated the two polymorphisms were not associated with asthma risk in Chinese population. After subgroup analysis by age, the -79G/C polymorphism was associated with decreased risk of asthma in Chinese children. Up to now, three meta-analyses had been performed to investigate the association between polymorphism of β2-AR gene and risk of asthma [1012]. Thakkinstian A[12] found that the heterozygote in -79G/C was associated with decreased risk of asthma in both adults and children. However, we didn't find these associations in Chinese adults, which suggested different roles of this polymorphism may exist in the pathogenesis of asthma in difference age groups. Previous study indicated that the -46G allele enhanced agonist-induced down regulation of the receptor, and the -79G allele might enhance resistance to down regulation. In combination with our results, personalized therapy of asthma patients in different age population with different genetic backgrounds in Chinese population should also be carried out in clinical practices.

The TNF-α gene, encodes a key proinflammatory cytokine in airway, is located on an asthma susceptible region-chromosome 6p. The TNF-α protein plays a central role in inflammation and involves in pathogenesis of asthma. Several polymorphisms have been identified in this gene, such as -308A/G, -238A/G. The -308A/G polymorphism in the promoter may affect the expression of this cytokine, which may affect the occurrence of asthma. In the meta-analysis performed by Gao and colleagues[13], they found the A allele was significant with increased risk of asthma (OR = 1.37, 95%CI = 1.02-1.84 for A vs. G). Consistently, we found the TNF-α-308A/G polymorphism was significantly associated with increased risk of asthma (OR = 1.36, 95%CT = 1.13-1.63 for AA+AG vs. GG) in Chinese population. For A vs G, the pooled OR is 1.26 with 95%CI: 1.08-1.47 in this study, which suggested a weaker association between this polymorphism and asthma risk in Chinese population.

IL-4 gene is located on chromosome 5q31, it was suggested to be associated with asthma risk, including elevated serum IgE levels and airway hypersensitiveness. A few studies indicated the -589C/T polymorphism in the promoter as a risk factor for asthma, but with inconclusive results. Li and colleagues performed a meta-analysis and found the T allele was associated with decrease risk of asthma(T vs C: OR = 0.86, 95%CI = 0.78-0.94)[14]. However, our results didn't reveal a positive association between this polymorphism and risk of asthma in Chinese. Compared with Li's study, the total number of studies concerning the Chinese population was smaller, which suggested more studies should be carried out to reveal these associations.

IL-4 and IL-13 signal through binding to a receptor complex comprised of the IL-13Rα1 and IL-4Rα with subsequent phosphorylation of JAKs and STAT6[15]. IL-4 receptor plays its role in inflammation through IL-4 and IL-13. The IL-4 receptor gene is located on chromosome 16 p12.1-p11.2. Some polymorphisms had been identified as risk factors for asthma, such as -1902G/A and -223G/A. Our results indicated the -1902G/A polymorphism was associated with increased risk of asthma in Chinese children, but not in Chinese adults. The results also indicated the -223G/A polymorphism was not associated with risk of asthma in Chinese population.

The FcεRIβ gene is a major candidate gene, involving in the pathogenesis of asthma. It is located on the chromosome 11q13. The -6843G/A polymorphism, leading change in an amino acid sequence at residue 237 from glutamic acid to glycine, is associated with increased IgE levels in atopic asthmatic children. In Chinese population, the -6843G/A polymorphism is the most extensively studied polymorphism in FcεRIβ gene. Our study revealed this polymorphism as a risk factor of asthma in Chinese population. Chinese who carry the GG or GA genotype have an 49% increased risk of asthma than AA carriers. Our results also demonstrated the -109C/T polymorphism in this gene was not associated with increased risk of asthma in Chinese population.

Up to date, we first found that ADAM33 T1-C/T, ACE D/I, IL-13 -1923C/T, RANTES -28C/G and IL-13 -2044A/G polymorphisms were associated with risk of asthma in Chinese population by using meta-analyzes. Some results are similar to other studies performed in other ethnic- groups and some are not. In future, more published results should be included to update and validate these associations in Chinese population.

In this study, the rigorous inclusive criteria made the results more precise. Any study in which genotype distribution of control group divorced from HWE was excluded. In this meta-analysis, 11 polymorphisms were synthesized by using the fixed-effect model, 7 used random-effects model. Because the fixed-effect model is more precise than random effect model, the strength of evidence of ADAM33 T1-C/T, ACE D/I, IL-13 -1923C/T, RANTES -28C/G, as risk factors for asthma was greater than that of FcεRIβ -6843G/A, IL-13 -2044A/G and TNF-α -308G/A.

The heterogeneity of clinical information among studies should also be mentioned. Heterogeneity is an important issue when interpreting the results of meta-analysis. Significant heterogeneity existed in overall comparisons in a few meta-analyses, such as FcεRIβ -6843G/A. After subgroup analyses by age, the heterogeneity was effectively decreased or removed in adults. Possible explanation may be that differences in etiology may exist in difference age groups. Another important factor contributing to heterogeneity was that homogeneity in either the case and control groups was uncertain. Ideally, all cases and controls in this meta-analysis should be matched for age, sex, atopic status and environmental exposures. However, these issues could not all be explained precisely because of insufficient clinical information for individual person. In addition, because this study is based on population of Chinese descent with the same genetic background, so the similarity of these studies might be very good, despite most studies were conducted in different areas of China.

Some limitations of this meta-analysis should be acknowledged when explaining our results. First, only published articles in the selected electronic databases were included in this study, it may be possible that some studies were not included in those databases or some unpublished studies which had null results, which might bias the results. Second, due to lack of sufficient data, the homogeneity in either the case and control groups was uncertain and data were not stratified by other factors such as atopic status or sex. The tests for gene-environment interactions were not carried out either. Third, publication bias may affect the results. Although P values of Begg's test were more than 0.05 in 18 meta-analyses, we could not rule out this possibility, because for some polymorphisms, the included number of studies were relatively small. Third, this study didn't included some polymorphisms with lack of number of studies, or polymorphisms which were not characterized as -A/B for lack of quality analysis for HWE, some polymorphism, such as GSTM1-P/N, or HLA DR1 alleles and MHC alleles were not included, future studies should performed to analysis the effect of these polymorphism in Chinese population.

To our knowledge, this is the first and most comprehensive genetic meta-analysis to date conducted in Chinese descent for any respiratory diseases. In conclusion, this meta-analysis indicated the T1-C/T polymorphism in ADAM33 gene, the D/I polymorphism in ACE gene, the -6843G/A polymorphism in FcεRIβ gene, the -1923C/T polymorphism in IL-13 gene, the -2044A/G polymorphism in IL-13 gene, the -28C/G polymorphism in RANTES gene and the -308G/A polymorphism in TNF-α gene are associated with asthma risk in Chinese population. And these results may also implicate in personalized therapy for asthma in Chinese population. In future, more studies should be conducted to investigate the gene-gene and gene-environment interactions between these polymorphisms in Chinese population.

Abbreviations

ADAM33: 

A Disintegrin and Metalloprotease 33

FcεRIβ: 

High-affinity IgE receptor β chain

ACE: 

Angiotensin-Converting Enzyme

β2-AR: 

β2-Adrenergic Receptor

IL-4: 

Interleukin 4

IL-13: 

Interleukin 13

IL-1β: 

Interleukin 1β

LT-α: 

Lymphotoxin-α

RANTES: 

Regulated upon Activation, Normal T cell Expressed and Secreted

TNF-α: 

Tumor Necrosis Factor-α

TGF-β1: 

Transforming Growth Factor β1.

Declarations

Acknowledgements

This work was supported by the National Natural Science Foundation of China (30470761 and 30871117).

Authors’ Affiliations

(1)
Department of Respiratory Medicine, The 452nd Military Hospital of China
(2)
Department of Respiratory Medicine, West China Hospital of Sichuan University
(3)
Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College
(4)
Department of Laboratory Medicine, West China Hospital of Sichuan University
(5)
West China Medical School/West China Hospital, Sichuan University
(6)
Chinese Evidence-Based Medicine/Cochrane Center

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