Purpose: Accumulating evidence has shown that allergic diseases are caused by a complex interaction of genetic and environmental factors, some single nucleotide polymorphisms (SNPs) existing in high-affinity IgE receptor β chain (FcεRIβ) are potential risk factors for allergic diseases. However, the results have been inconsistent and inconclusive due to the limited statistical power in individual study. Thus, we conducted a meta-analysis to systematically evaluate the association between FcεRIβ SNPs and allergic diseases risk.

Methods: Eligible studies were collected from PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, and WanFang databases. Pooled odd ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were calculated to assess the strength of the relationships between five polymorphisms (E237G, -109 C/T, RsaI_in2, RsaI_ex7, and I181L) and the risk of allergic diseases by using five genetic models. In addition, the stability of our analysis was evaluated by publication bias, sensitivity, and heterogeneity analysis.

Results: Overall, a total of 29 case–control studies were included in this meta-analysis. We found that E237G (B vs. A: OR = 1.28, 95% CI = 1.06–1.53, P<0.001, I2 = 63.1%) and -109 C/T (BB vs. AA + AB: OR = 1.58, 95%CI = 1.26–1.98, P<0.001, I2 = 66.4%) were risk factors for allergic diseases.

Conclusion: Our meta-analysis suggests that polymorphisms in FcεRIβ may be associated with the development of allergic diseases.

Keywords:
allergic diseases, genetic model, FcεRIβ, Meta-analysis, polymorphism
Subjects:
Bioinformatics, DNA, Chromosomes & Chromosomal Structure, Genomics

Allergic rhinitis (AR) is a common nasal mucosal inflammation, approximately 10–20% of the global population suffers from AR, and the classic symptoms of AR are nasal congestion, nasal itching, sneezing, and rhinorrhea. Allergic conjunctivitis presents as itchy, watery eyes resulting from the same pathophysiology as AR and is not surprisingly a common comorbid condition.

As an allergen-mediated disorder of the nasal passage, AR shares several similarities with another allergic disease of the lower respiratory tract: asthma. Not surprisingly, the two conditions are often comorbid; 85% of patients with asthma have AR whereas 40% of patients suffering from AR have or will develop asthma [1]. As a type 1 immunoglobulin (Ig)E-mediated hypersensitivity process, symptoms of them are triggered by allergens. The reported prevalence of allergic diseases has been steadily increasing. The true incidence probably remains underestimated. Asthma, one of the most common chronic respiratory diseases of childhood, is characterized by recurrent respiratory symptoms, reversible variable airway obstruction, airway inflammation, and increased bronchial hyper-responsiveness [2–4]. Its incidence is on the rise among children, which brings heavy burden to the whole society and results in huge medical expenditure around the world. It is thought to be caused by a combination of genetic and environmental factors [5,6].

AR and asthma are complex multifactorial disorders, with both genetic and environmental components determining disease expression, show strong familial aggregation and heritability [7,8], thus suggesting that genetic risk factors may underlie the risk of developing, or the clinical presentation of, allergic diseases [9–11]. Allergic diseases are also associated with elevated serum IgE levels and increased mediator release from activated inflammatory cells. Allergens cross-link IgE bound to FcεRIα that causes FcεRI clustering and activates the receptor complexes (FcεRIα, FcεRIβ, and FcεRIγ-γ homodimer) on the surface of mast cells or basophils, releasing vasoactive mediators, such as histamine. Although the search for genetic susceptibility factors related to allergic diseases is a promising field, gene variations related to FcεRI as potential risk factors for allergic diseases have not been comprehensively analyzed, and the results available are in some cases contradictory, some studies showed the variant of Glu237Gly of FcεRIβ gene showed association with atopic diseases and the variant is also associated with very high total serum IgE levels [12–19], but others were showed no association with atopic asthma [20–22].

FcεRI has a tetrameric structure consisting of three distinct polypeptides including the IgE-binding α chain, 4-fold membrane-spanning β chain, and disulfide-linked γ–γ homodimer [23]. The β chain of the FcεRI is found on mast cells and basophils, and acts as a signal amplifier in mast cell activation [24–26]. Cross-linking of this receptor leads to increased IL-4 production by these cells. The aggregation of FcεRI by the bounding of IgE with multivalent antigens has been shown to induce the release of histamine, leukotrienes, and inflammatory cytokines, and plays an important role in allergic inflammation [27,28]. Furthermore, the β chain was previously reported to amplify early activation signals 5–7-fold through FcεRI in humans [25]. The β chain has also been suggested to function as a stabilizer of the FcεRI complex [29]. It contains an immunoreceptor tyrosine-based activation motif, a conserved feature of many antigen receptors that imparts signaling competence. The FcεRI β chain acts as a signal amplifier through the immunoreceptor tyrosine-based activation motif in its C-terminal intracellular region. Mutations in the FCERIB gene could alter IL-4 production and thus modify IgE levels.

Several studies on the genetic background of atopy likely to contribute to the pathogenesis of allergies [30–33], of these, a significant role for polymorphisms in the FcεRI β chain in the manifestation of the phenotype has been suggested. Genetic linkage studies demonstrated that a locus in chromosome 11q13 [34] encompassing the β chain gene was linked to various allergic disorders and high levels of serum IgE [35–37]. Polymorphisms in FcεRIβ have been linked to atopy, asthma, and allergies. This meta-analysis comprehensively discussed the association between the FcεRIβ polymorphisms and allergic diseases risk.

Strategy for literature search

The electronic databases of PubMed, Embase, Web of science, Chinese National Knowledge Infrastructure (CNKI), and WanFang database were comprehensively searched to retrieve relevant articles published between January 2000 and August 2017. Databases were searched using the search term: “bronchial asthma, asthma, allergic rhinitis, nasal allergy, allergic diseases”, “Fc epsilon RI beta, FcεRIβ, high-affinity IgE receptor beta chain, beta-subunit of the high-affinity receptor for IgE”, “single nucleotide polymorphism, SNP, polymorphism, polymorphisms” as well as their combinations were employed as the searching keywords. The corresponding Chinese version was used in the Chinese databases. To obtain more data, we manually searched the references of related articles. Our analysis only focused on the studies that were written in English and Chinese. When the same authors or laboratories reported this issue on the same population, only the latest published full-text article was included.

Inclusion and exclusion criteria

The following criteria were set to choose the studies included in the current meta-analysis: (1) case–control design; (2) the study must offer the sample size, distribution of alleles, genotypes, or other information that can help us infer the results; and (3) the publication on the association between polymorphisms of FcεRIβ and risk of asthma and/or allergic rhinitis. The exclusion criterions were as follows: (1) review articles, case reports, and meta-analysis; (2) the studies were conducted on animals; (3) genotype distribution data were unavailable; and (4) when multiple publications reported on the same or overlapping data, we used the most recent or largest population.

Data extraction

Data were carefully extracted independently by two authors (Huan-huan Guo and Ping Luo) according to the inclusion and exclusion criteria. Disagreements were resolved through discussion and arbitration by a third author if necessary. For each study, the following data were recorded: first author, year of publication, country, age, allergic status, number of cases and controls, and genotype distributions in cases and controls.

Quality assessment

The quality of studies was independently assessed by the two reviewers using the Newcastle–Ottawa scale (NOS) [38] based on three aspects: selection, comparability, and exposure of cases and controls. NOS scores ranged from 0 to 9, and articles with a score equal to or higher than six were regarded as high quality.

Statistical analysis

Hardy–Weinberg equilibrium (HWE) for the genotype distribution of FcεRIβ in controls was tested by χ2 analysis with exact probability. The pooled odd ratio (OR) with 95% confidence interval (CI) was used to assess the strength of the associations between the genetic variants and allergic diseases risk. For the FcεRIβ polymorphism, “A” stands for wild-type gene, and “B” for mutant gene, the allelic (B vs. A), heterozygous (AB vs. AA), homozygous (BB vs. AA), dominant (AB+BB vs. AA), and recessive (BB vs. AA+AB) genetic models were used to obtain pooled ORs. The evaluated genetic models for each study were based mostly on those used in primary studies. Heterogeneity assumption was evaluated by a X2 based Q test and I2 test [39]. A significant Q test (P<0.10) indicated heterogeneity across studies. I2 was used to measure the percentage of variability in point estimated that due to heterogeneity rather than sampling error. When there was no statistical heterogeneity, we used a fixed effects model (the Mantel–Haenszel method) [40], otherwise, a random effects model (DerSimonian and Laird method) was used [41]. The subgroup analysis was performed according to ethnicity, allergic status, and HWE status of controls. Begg rank correlation method and the Egger linear regression method were used to assess potential publication bias [42,43]. The meta-analysis was performed using STATA Version 12.0 (Stata Corp, College Station, TX, U.S.A.) software. P value less than 0.05 was considered statistically significant. All P values presented are two-tailed.

Main characteristics of the selected studies

Figure 1 outlined the study process of selection. Briefly, we first identified 234 articles. After applying the inclusion and exclusion criteria, a total of 29 articles including 6496 allergic diseases patients and 5828 controls were screened out. Of the 29 articles, 9 were written in Chinese [22,44–51] and 20 in English [13–21,52–62]. Among them, 22 were conducted in Asian populations and 7 in Caucasian populations. The FcεRIβ polymorphism was measured by seven different methods (ARMS-PCR, PCR-SSCP, PCR-RFLP, SNP-IT™, ABI, MALDI-TOF, and TaqMan). Within the genotype distribution in the controls, the value of HWE was either extracted in the articles directly or calculated using the data of controls. Only three studies deviated from HWE [52,60,49]. Table 1 listed the main characteristics of included studies. Table 2 exhibited the distribution information of alleles and genotypes of FcεRIβ polymorphism.

Flow chart of selection process in this meta-analysis

Figure 1
Figure 1
View largeDownload slide
Flow chart of selection process in this meta-analysis
Figure 1
Figure 1
View largeDownload slide
Flow chart of selection process in this meta-analysis
Close modal
Table 1
Main characteristics of included studies in this meta-analysis
First authorYearCountryEthnicityAllergic statusSample sizeGenotype distributionGenotyping methodsP for HWEQuality score
Case/ControlCaseControl
WildHeterozygousHomozygousAllelesWildHeterozygousHomozygousAlleles
E237GEEEGGGEGEEEGGGEG
Laprise, C. [14] 2000 France and Canada Caucasian Asthma 100/100 80 19 1 179 21 98 2 0 198 2 ARMS-PCR 0.92 7 
Soriano, J.B. [52] 2000 Spain Caucasian Asthma 146/50 134 11 1 280 12 43 4 3 93 7 ARMS-PCR <0.05 6 
Takabayashi, A, [20] 2000 Japan Asian Asthma 100/100 69 27 4 166 34 65 33 2 162 38 PCR-SSCP 0.35 7 
Chen, H. [44] 2000 China Asian Asthma 101/60 59 39 3 157 45 30 16 1 76 18 PCR-RFLP 0.50 8 
Nagata, H. [15] 2001 Japan Asian Allergic rhinitis 233/100 155 76 7 373 93 77 18 5 172 28 PCR-RFLP 0.01 6 
Zeng, L.X. [45] 2001 China Asian Asthma 69/28 61 5 3 127 11 27 1 0 55 1 ARMS-PCR 0.92 8 
Cui, T.P. [17] 2003 China Asian Asthma 216/198 125 80 11 165 51 148 46 4 171 27 PCR-RFLP 0.85 8 
Tang, Y. [46] 2003 China Asian Asthma 60/65 49 11 0 109 11 61 4 0 126 4 ARMS-PCR 0.80 7 
Korzycka-Zaborowska, B. [21] 2004 Poland Caucasian Asthma and allergic rhinitis 98/87 92 6 0 190 6 83 4 0 170 4 ARMS-PCR 0.83 8 
Rigoli, L. [18] 2004 Italy Caucasian Asthma and allergic rhinitis 100/103 79 16 5 178 22 102 1 0 205 1 PCR-SSCP 0.96 7 
Zhang, X.Z. [53] 2004 China Asian Asthma 141/157 81 57 3 219 63 108 42 7 258 56 ARMS-PCR 0.27 8 
Zhang, X.Z. [53] 2004 Malaysia Asian Asthma 68/100 49 19 0 117 19 77 23 0 177 23 ARMS-PCR 0.19 8 
Zhang, X.Z. [53] 2004 India Asian Asthma 82/98 71 10 1 152 12 80 18 0 178 18 ARMS-PCR 0.32 8 
Cui, T.P. [47] 2004 China Asian Asthma 106/106 60 40 6 160 52 78 26 2 182 30 PCR-RFLP 0.92 8 
Zhao, K.S. [49] 2004 China Asian Asthma 151/105 126 23 2 275 27 92 13 0 197 13 ARMS-PCR 0.50 6 
Liu, T. [22] 2006 China Asian Asthma 60/50 45 14 1 48 11 39 10 1 88 12 PCR-RFLP 0.71 8 
Kim, E.S. [55] 2009 Korea Asian Asthma 347/127 244 99 4 582 112 99 28 0 224 30 SNP-IT™ 0.16 7 
Wang, J.Y. [19] 2009 China Asian Asthma 449/512 309 121 16 739 153 314 165 27 793 219 ABI 0.39 7 
Dmitrieva-Zdorova, E.V. [59] 2012 Russia Caucasian Asthma 224/172 221 3 0 441 7 170 2 0 342 2 MALDI-TOF 0.94 7 
Zheng, B.Q. [51] 2012 China Asian Asthma 198/110 126 61 11 313 83 76 29 5 181 39 PCR-RFLP 0.31 7 
Ramphul, K. [60] 2014 India Asian Asthma 192/188 170 21 1 361 23 163 24 1 350 26 TaqMan 0.91 8 
Ramphul, K. [60] 2014 China Asian Asthma 192/192 139 45 8 327 57 136 38 18 323 61 PCR-RFLP <0.05 7 
Amo, G. [61] 2016 Spain Caucasian Allergic rhinitis 149/526 146 3 0 295 3 144 277 105 1013 39 TaqMan 0.18 7 
Amo, G. [61] 2016 Spain Caucasian Asthma and allergic rhinitis 366/526 330 33 0 695 37 144 277 105 1013 39 TaqMan 0.18 7 
Hua, L. [62] 2016 China Asian Asthma 1000/1000 65 276 659 1594 406 23 289 688 1665 335 TaqMan 0.25 7 
                   
-109C/T      TT TC CC T C TT TC CC T C    
Hizawa, N. [13] 2000 Japan Asian Asthma 226/226 85 123 18 277 175 108 99 19 312 140 PCR-RFLP 0.58 8 
Cui, T.P. [17] 2003 China Asian Asthma 216/198 87 106 23 140 76 76 103 19 128 70 PCR-RFLP 0.06 8 
Cui, T.P. [47] 2004 China Asian Astnma 106/106 44 52 10 140 72 41 57 8 139 73 PCR-RFLP 0.05 7 
Gan, X. [48] 2004 China Asian Asthma 45/45 23 12 10 58 32 19 14 12 52 38 PCR-RFLP 0.02 7 
Zhao, K.S. [50] 2004 China Asian Asthma 126/87 46 69 11 161 91 40 38 9 118 56 PCR-RFLP 0.995 8 
Hizawa, N. [54] 2006 Japan Asian Asthma 374/374 157 178 39 485 263 156 169 49 483 265 TaqMan 0.76 8 
Kim, E.S. [55] 2009 Korea Asian Asthma 347/127 159 167 20 470 224 69 54 3 187 67 SNP-IT™ 0.04 6 
Li, H. [56] 2009 China Asian Asthma 192/192 110 58 24 291 93 78 90 24 245 139 PCR-RFLP 0.04 7 
Sharma, S. [57] 2009 India Asian Asthma 237/221 37 113 87 188 286 74 108 39 256 186 TaqMan 0.97 8 
Tikhonova, V. [58] 2010 Russia Caucasian Asthma 140/136 53 69 18 175 105 48 70 18 167 105 PCR-RFLP 0.34 7 
Ramphul, K. [60] 2014 India Asian Asthma 189/188 35 99 55 163 215 35 87 66 162 214 TaqMan 0.51 8 
Ramphul, K. [60] 2014 China Asian Asthma 192/192 78 90 24 245 139 110 58 24 291 93 PCR-RFLP <0.05 7 
Amo, G. [61] 2016 Spain Caucasian Allergic rhinitis 149/526 47 67 35 161 137 144 277 105 565 487 TaqMan 0.18 7 
Amo, G. [61] 2016 Spain Caucasian Asthma and allergic rhinitis 366/526 100 188 78 388 344 144 277 105 565 487 TaqMan 0.18 7 
Hua, L. [62] 2016 China Asian Asthma 1000/1000 148 436 416 1268 732 124 470 406 1282 718 TaqMan 0.50 7 
                   
RsaI_in2       AA  AB  BB A B AA AB BB A B    
Chen, H. [44] 2000 China Asian Asthma 101/60 9 38 54 56 146 1 17 42 19 101 PCR-RFLP 0.63 8 
Leung, T.F. [16] 2002 China Asian Asthma 75/70 3 22 50 27 123 3 23 44 30 110 ARMS-PCR 0.998 8 
Korzycka-Zaborowska, B. [21] 2004 Poland Caucasian Asthma and allergic rhinitis 98/87 83 15 0 180 16 82 5 0 169 5 ARMS-PCR 0.78 8 
                   
RsaI_ex7      AA AB BB A B AA AB BB A B    
Chen, H. [44] 2000 China Asian Asthma 101/60 93 8 0 194 8 53 6 0 112 6 PCR-RFLP 0.68 8 
Leung, T.F. [16] 2002 China Asian Asthma 76/70 70 6 0 146 6 65 5 0 135 5 ARMS-PCR 0.76 8 
                   
I181L      II IL LL I L II IL LL I L    
Soriano, J.B. [52] 2000 Spain Caucasian Asthma 146/50 146 0 0 292 0 50 0 0 100 0 ARMS-PCR 1 7 
Zhao, K.S. [49] 2004 China Asian Asthma 144/100 26 117 1 169 119 48 52 0 148 52 ARMS-PCR <0.05 6 
First authorYearCountryEthnicityAllergic statusSample sizeGenotype distributionGenotyping methodsP for HWEQuality score
Case/ControlCaseControl
WildHeterozygousHomozygousAllelesWildHeterozygousHomozygousAlleles
E237GEEEGGGEGEEEGGGEG
Laprise, C. [14] 2000 France and Canada Caucasian Asthma 100/100 80 19 1 179 21 98 2 0 198 2 ARMS-PCR 0.92 7 
Soriano, J.B. [52] 2000 Spain Caucasian Asthma 146/50 134 11 1 280 12 43 4 3 93 7 ARMS-PCR <0.05 6 
Takabayashi, A, [20] 2000 Japan Asian Asthma 100/100 69 27 4 166 34 65 33 2 162 38 PCR-SSCP 0.35 7 
Chen, H. [44] 2000 China Asian Asthma 101/60 59 39 3 157 45 30 16 1 76 18 PCR-RFLP 0.50 8 
Nagata, H. [15] 2001 Japan Asian Allergic rhinitis 233/100 155 76 7 373 93 77 18 5 172 28 PCR-RFLP 0.01 6 
Zeng, L.X. [45] 2001 China Asian Asthma 69/28 61 5 3 127 11 27 1 0 55 1 ARMS-PCR 0.92 8 
Cui, T.P. [17] 2003 China Asian Asthma 216/198 125 80 11 165 51 148 46 4 171 27 PCR-RFLP 0.85 8 
Tang, Y. [46] 2003 China Asian Asthma 60/65 49 11 0 109 11 61 4 0 126 4 ARMS-PCR 0.80 7 
Korzycka-Zaborowska, B. [21] 2004 Poland Caucasian Asthma and allergic rhinitis 98/87 92 6 0 190 6 83 4 0 170 4 ARMS-PCR 0.83 8 
Rigoli, L. [18] 2004 Italy Caucasian Asthma and allergic rhinitis 100/103 79 16 5 178 22 102 1 0 205 1 PCR-SSCP 0.96 7 
Zhang, X.Z. [53] 2004 China Asian Asthma 141/157 81 57 3 219 63 108 42 7 258 56 ARMS-PCR 0.27 8 
Zhang, X.Z. [53] 2004 Malaysia Asian Asthma 68/100 49 19 0 117 19 77 23 0 177 23 ARMS-PCR 0.19 8 
Zhang, X.Z. [53] 2004 India Asian Asthma 82/98 71 10 1 152 12 80 18 0 178 18 ARMS-PCR 0.32 8 
Cui, T.P. [47] 2004 China Asian Asthma 106/106 60 40 6 160 52 78 26 2 182 30 PCR-RFLP 0.92 8 
Zhao, K.S. [49] 2004 China Asian Asthma 151/105 126 23 2 275 27 92 13 0 197 13 ARMS-PCR 0.50 6 
Liu, T. [22] 2006 China Asian Asthma 60/50 45 14 1 48 11 39 10 1 88 12 PCR-RFLP 0.71 8 
Kim, E.S. [55] 2009 Korea Asian Asthma 347/127 244 99 4 582 112 99 28 0 224 30 SNP-IT™ 0.16 7 
Wang, J.Y. [19] 2009 China Asian Asthma 449/512 309 121 16 739 153 314 165 27 793 219 ABI 0.39 7 
Dmitrieva-Zdorova, E.V. [59] 2012 Russia Caucasian Asthma 224/172 221 3 0 441 7 170 2 0 342 2 MALDI-TOF 0.94 7 
Zheng, B.Q. [51] 2012 China Asian Asthma 198/110 126 61 11 313 83 76 29 5 181 39 PCR-RFLP 0.31 7 
Ramphul, K. [60] 2014 India Asian Asthma 192/188 170 21 1 361 23 163 24 1 350 26 TaqMan 0.91 8 
Ramphul, K. [60] 2014 China Asian Asthma 192/192 139 45 8 327 57 136 38 18 323 61 PCR-RFLP <0.05 7 
Amo, G. [61] 2016 Spain Caucasian Allergic rhinitis 149/526 146 3 0 295 3 144 277 105 1013 39 TaqMan 0.18 7 
Amo, G. [61] 2016 Spain Caucasian Asthma and allergic rhinitis 366/526 330 33 0 695 37 144 277 105 1013 39 TaqMan 0.18 7 
Hua, L. [62] 2016 China Asian Asthma 1000/1000 65 276 659 1594 406 23 289 688 1665 335 TaqMan 0.25 7 
                   
-109C/T      TT TC CC T C TT TC CC T C    
Hizawa, N. [13] 2000 Japan Asian Asthma 226/226 85 123 18 277 175 108 99 19 312 140 PCR-RFLP 0.58 8 
Cui, T.P. [17] 2003 China Asian Asthma 216/198 87 106 23 140 76 76 103 19 128 70 PCR-RFLP 0.06 8 
Cui, T.P. [47] 2004 China Asian Astnma 106/106 44 52 10 140 72 41 57 8 139 73 PCR-RFLP 0.05 7 
Gan, X. [48] 2004 China Asian Asthma 45/45 23 12 10 58 32 19 14 12 52 38 PCR-RFLP 0.02 7 
Zhao, K.S. [50] 2004 China Asian Asthma 126/87 46 69 11 161 91 40 38 9 118 56 PCR-RFLP 0.995 8 
Hizawa, N. [54] 2006 Japan Asian Asthma 374/374 157 178 39 485 263 156 169 49 483 265 TaqMan 0.76 8 
Kim, E.S. [55] 2009 Korea Asian Asthma 347/127 159 167 20 470 224 69 54 3 187 67 SNP-IT™ 0.04 6 
Li, H. [56] 2009 China Asian Asthma 192/192 110 58 24 291 93 78 90 24 245 139 PCR-RFLP 0.04 7 
Sharma, S. [57] 2009 India Asian Asthma 237/221 37 113 87 188 286 74 108 39 256 186 TaqMan 0.97 8 
Tikhonova, V. [58] 2010 Russia Caucasian Asthma 140/136 53 69 18 175 105 48 70 18 167 105 PCR-RFLP 0.34 7 
Ramphul, K. [60] 2014 India Asian Asthma 189/188 35 99 55 163 215 35 87 66 162 214 TaqMan 0.51 8 
Ramphul, K. [60] 2014 China Asian Asthma 192/192 78 90 24 245 139 110 58 24 291 93 PCR-RFLP <0.05 7 
Amo, G. [61] 2016 Spain Caucasian Allergic rhinitis 149/526 47 67 35 161 137 144 277 105 565 487 TaqMan 0.18 7 
Amo, G. [61] 2016 Spain Caucasian Asthma and allergic rhinitis 366/526 100 188 78 388 344 144 277 105 565 487 TaqMan 0.18 7 
Hua, L. [62] 2016 China Asian Asthma 1000/1000 148 436 416 1268 732 124 470 406 1282 718 TaqMan 0.50 7 
                   
RsaI_in2       AA  AB  BB A B AA AB BB A B    
Chen, H. [44] 2000 China Asian Asthma 101/60 9 38 54 56 146 1 17 42 19 101 PCR-RFLP 0.63 8 
Leung, T.F. [16] 2002 China Asian Asthma 75/70 3 22 50 27 123 3 23 44 30 110 ARMS-PCR 0.998 8 
Korzycka-Zaborowska, B. [21] 2004 Poland Caucasian Asthma and allergic rhinitis 98/87 83 15 0 180 16 82 5 0 169 5 ARMS-PCR 0.78 8 
                   
RsaI_ex7      AA AB BB A B AA AB BB A B    
Chen, H. [44] 2000 China Asian Asthma 101/60 93 8 0 194 8 53 6 0 112 6 PCR-RFLP 0.68 8 
Leung, T.F. [16] 2002 China Asian Asthma 76/70 70 6 0 146 6 65 5 0 135 5 ARMS-PCR 0.76 8 
                   
I181L      II IL LL I L II IL LL I L    
Soriano, J.B. [52] 2000 Spain Caucasian Asthma 146/50 146 0 0 292 0 50 0 0 100 0 ARMS-PCR 1 7 
Zhao, K.S. [49] 2004 China Asian Asthma 144/100 26 117 1 169 119 48 52 0 148 52 ARMS-PCR <0.05 6 

Abbreviations: ARMS-PCR, primer amplification refractory mutation system polymerase chain reaction; HWE, Hardy–Weinberg equilibrium; MALDI-TOF, matrix-assisted laser desorption/ionization-time of flight mass spectrometry; NA, not available or applicable; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; PCR-SSCP, polymerase chain reaction-single strand conformation polymorphism; SNP, single nucleotide polymorphism.

Bold text indicates five different polymorphisms of FcεRIβ.

View Large
Table 2
Summary ORs and 95% CIs of FcεRIβ polymorphisms and allergic diseases risk
VariablesNB vs. AAB + BB vs. AABB vs. AA + ABBB vs. AAAB vs. AA
E237GOR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Overall 25 1.28 (1.06, 1.53) <0.001 63.1 1.00 (0.60, 1.67) <0.001 94.1 1.62 (0.85, 3.11) <0.001 80.2 0.79 (0.39, 1.60) <0.001 77.6 1.02 (0.63, 1.67) <0.001 93 
Ethnicity                 
Caucasian 7 1.80 (0.72, 4.53) <0.001 79 0.64 (0.08, 4.95) <0.001 96 0.40 (0.02, 6.79) <0.001 81 0.13 (0.00, 4.05) <0.001 86.8 0.72 (0.10, 5.10) <0.001 95.2 
Asian 18 1.23 (1.05, 1.45) 0.004 53.6 1.19 (0.92, 1.54) <0.001 73.2 2.10 (1.22, 3.62) <0.001 68.8 0.96 (0.60, 1.55) 0.021 46.6 1.19 (0.92, 1.55) <0.001 71 
Allergic status                 
Asthma 20 1.25 (1.04, 1.51) <0.001 60.5 1.19 (0.91, 1.57) <0.001 73.7 2.09 (1.19, 3.65) <0.001 67.7 0.97 (0.58, 1.62) 0.011 49.4 1.21 (0.92, 1.58) <0.001 70.2 
Allergic rhinitis 2 0.69 (0.12, 3.97) 0.006 86.9 0.12 (0.00, 55.88) <0.001 98.9 0.21 (0.00, 18.15) 0.003 88.6 0.06 (0.00, 57.66) <0.001 95.1 0.15 (0.00, 53.67) <0.001 98.8 
Allergic rhinitis and/or Asthma 3 2.74 (0.65, 11.43) 0.012 77.3 1.02 (0.02, 60.58) <0.001 97.3 0.55 (0.00, 1117.47) <0.001 93.1 0.17 (0.00, 1213.83) <0.001 94.9 1.02 (0.03, 37.61) <0.001 96.5 
HWE                 
≥0.05 22 1.33 (1.08, 1.63) <0.001 65 1.01 (0.56, 1.81) <0.001 94.7 1.99 (0.98, 4.08) <0.001 78.2 0.93 (0.40, 2.20) <0.001 80.8 0.99 (0.57, 1.72) <0.001 93.6 
<0.05 3 1.03 (0.64, 1.66) 0.103 56 1.06 (0.58, 1.92) 0.06 64.5 0.77 (0.40, 1.48) 0.477 0 0.45 (0.23, 0.88) 0.360 2.2 1.41 (0.88, 2.26) 0.223 33.3 
-109C/T                 
Overall 15 1.10 (0.95, 1.28) <0.001 76.8 1.08 (0.88, 1.33) 0.06 73.8 1.58 (1.26, 1.98) <0.001 66.4 1.12 (0.87, 1.44) 0.001 62 1.06 (0.86, 1.31) <0.001 71.8 
Ethnicity                 
Caucasian 3 1.00 (0.87, 1.15) 0.922 0 0.92 (0.75, 1.15) 0.716 0 1.50 (1.18, 1.92) 0.866 0 1.03 (0.77, 1.37) 0.929 0 0.89 (0.71, 1.11) 0.59 0 
Asian 12 1.13 (0.93, 1.36) <0.001 81.1 1.13 (0.88, 1.47) <0.001 78.4 1.60 (1.19, 2.14) <0.001 71.6 1.16 (0.83, 1.62) <0.001 69.9 1.12 (0.86, 1.46) <0.001 76.3 
Allergic status                 
Asthma 13 1.11 (0.93, 1.33) <0.001 79.7 1.11 (0.87, 1.42) <0.001 76.8% 1.58 (1.20, 2.08) <0.001 69.6 1.14 (0.83, 1.56) <0.001 67.4 1.10 (0.86, 1.41) <0.001 74.4 
Allergic rhinitis 1 0.99 (0.76, 1.28) – – 0.82 (0.55, 1.21) – – 1.65 (1.07, 2.55) – – 1.02 (0.62, 1.69) – – 0.74 (0.49, 1.13) – – 
Allergic rhinitis and/or Asthma 1 1.03 (0.85, 1.24) – – 1.00 (0.74, 1.35) – – 1.46 (1.05, 2.02) – – 1.07 (0.73, 1.58) – – 0.98 (0.71, 1.34) – – 
HWE                 
≥0.05 12 1.06 (0.91, 1.24) <0.001 76.8 1.02 (0.82, 1.27) <0.001 72.7 1.54 (1.19, 1.99) <0.001 72.2 1.08 (0.82, 1.44) 0.001 66.4 0.99 (0.80, 1.23) <0.001 68.8 
<0.05 3 1.30 (0.87, 1.94) 0.042 68.5 1.40 (0.88, 2.23) 0.084 59.7 1.80 (1.10, 2.92) 0.349 5.1 1.34 (0.69, 2.62) 0.212 35.4 1.45 (0.86, 2.43) 0.072 62 
RsaI_in2                 
Overall 3 1.14 (0.45, 2.88) 0.005 81.3 1.01 (0.21, 4.78) 0.049 66.7 0.84 (0.33, 2.17) 0.045 75 0.45 (0.06, 3.55) 0.119 58.8 1.13 (0.281, 4.54) 0.1 56.5 
RsaI_ex7                 
Overall 2 0.91 (0.41, 2.04) 0.659 0 0.90 (0.40, 2.07) 0.651 0 – – – – – – 0.90(0.40, 2.07) 0.651 0 
I181L                 
Overall 2 2.00 (1.35, 2.97) – – 4.19 (2.35, 7.47) – – 3.11 (0.13, 76.84) – – 5.49 (0.22, 139.56) – – 4.15 (2.33, 7.41) – – 
VariablesNB vs. AAB + BB vs. AABB vs. AA + ABBB vs. AAAB vs. AA
E237GOR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)OR (95% CI)PI2 (%)
Overall 25 1.28 (1.06, 1.53) <0.001 63.1 1.00 (0.60, 1.67) <0.001 94.1 1.62 (0.85, 3.11) <0.001 80.2 0.79 (0.39, 1.60) <0.001 77.6 1.02 (0.63, 1.67) <0.001 93 
Ethnicity                 
Caucasian 7 1.80 (0.72, 4.53) <0.001 79 0.64 (0.08, 4.95) <0.001 96 0.40 (0.02, 6.79) <0.001 81 0.13 (0.00, 4.05) <0.001 86.8 0.72 (0.10, 5.10) <0.001 95.2 
Asian 18 1.23 (1.05, 1.45) 0.004 53.6 1.19 (0.92, 1.54) <0.001 73.2 2.10 (1.22, 3.62) <0.001 68.8 0.96 (0.60, 1.55) 0.021 46.6 1.19 (0.92, 1.55) <0.001 71 
Allergic status                 
Asthma 20 1.25 (1.04, 1.51) <0.001 60.5 1.19 (0.91, 1.57) <0.001 73.7 2.09 (1.19, 3.65) <0.001 67.7 0.97 (0.58, 1.62) 0.011 49.4 1.21 (0.92, 1.58) <0.001 70.2 
Allergic rhinitis 2 0.69 (0.12, 3.97) 0.006 86.9 0.12 (0.00, 55.88) <0.001 98.9 0.21 (0.00, 18.15) 0.003 88.6 0.06 (0.00, 57.66) <0.001 95.1 0.15 (0.00, 53.67) <0.001 98.8 
Allergic rhinitis and/or Asthma 3 2.74 (0.65, 11.43) 0.012 77.3 1.02 (0.02, 60.58) <0.001 97.3 0.55 (0.00, 1117.47) <0.001 93.1 0.17 (0.00, 1213.83) <0.001 94.9 1.02 (0.03, 37.61) <0.001 96.5 
HWE                 
≥0.05 22 1.33 (1.08, 1.63) <0.001 65 1.01 (0.56, 1.81) <0.001 94.7 1.99 (0.98, 4.08) <0.001 78.2 0.93 (0.40, 2.20) <0.001 80.8 0.99 (0.57, 1.72) <0.001 93.6 
<0.05 3 1.03 (0.64, 1.66) 0.103 56 1.06 (0.58, 1.92) 0.06 64.5 0.77 (0.40, 1.48) 0.477 0 0.45 (0.23, 0.88) 0.360 2.2 1.41 (0.88, 2.26) 0.223 33.3 
-109C/T                 
Overall 15 1.10 (0.95, 1.28) <0.001 76.8 1.08 (0.88, 1.33) 0.06 73.8 1.58 (1.26, 1.98) <0.001 66.4 1.12 (0.87, 1.44) 0.001 62 1.06 (0.86, 1.31) <0.001 71.8 
Ethnicity                 
Caucasian 3 1.00 (0.87, 1.15) 0.922 0 0.92 (0.75, 1.15) 0.716 0 1.50 (1.18, 1.92) 0.866 0 1.03 (0.77, 1.37) 0.929 0 0.89 (0.71, 1.11) 0.59 0 
Asian 12 1.13 (0.93, 1.36) <0.001 81.1 1.13 (0.88, 1.47) <0.001 78.4 1.60 (1.19, 2.14) <0.001 71.6 1.16 (0.83, 1.62) <0.001 69.9 1.12 (0.86, 1.46) <0.001 76.3 
Allergic status                 
Asthma 13 1.11 (0.93, 1.33) <0.001 79.7 1.11 (0.87, 1.42) <0.001 76.8% 1.58 (1.20, 2.08) <0.001 69.6 1.14 (0.83, 1.56) <0.001 67.4 1.10 (0.86, 1.41) <0.001 74.4 
Allergic rhinitis 1 0.99 (0.76, 1.28) – – 0.82 (0.55, 1.21) – – 1.65 (1.07, 2.55) – – 1.02 (0.62, 1.69) – – 0.74 (0.49, 1.13) – – 
Allergic rhinitis and/or Asthma 1 1.03 (0.85, 1.24) – – 1.00 (0.74, 1.35) – – 1.46 (1.05, 2.02) – – 1.07 (0.73, 1.58) – – 0.98 (0.71, 1.34) – – 
HWE                 
≥0.05 12 1.06 (0.91, 1.24) <0.001 76.8 1.02 (0.82, 1.27) <0.001 72.7 1.54 (1.19, 1.99) <0.001 72.2 1.08 (0.82, 1.44) 0.001 66.4 0.99 (0.80, 1.23) <0.001 68.8 
<0.05 3 1.30 (0.87, 1.94) 0.042 68.5 1.40 (0.88, 2.23) 0.084 59.7 1.80 (1.10, 2.92) 0.349 5.1 1.34 (0.69, 2.62) 0.212 35.4 1.45 (0.86, 2.43) 0.072 62 
RsaI_in2                 
Overall 3 1.14 (0.45, 2.88) 0.005 81.3 1.01 (0.21, 4.78) 0.049 66.7 0.84 (0.33, 2.17) 0.045 75 0.45 (0.06, 3.55) 0.119 58.8 1.13 (0.281, 4.54) 0.1 56.5 
RsaI_ex7                 
Overall 2 0.91 (0.41, 2.04) 0.659 0 0.90 (0.40, 2.07) 0.651 0 – – – – – – 0.90(0.40, 2.07) 0.651 0 
I181L                 
Overall 2 2.00 (1.35, 2.97) – – 4.19 (2.35, 7.47) – – 3.11 (0.13, 76.84) – – 5.49 (0.22, 139.56) – – 4.15 (2.33, 7.41) – – 

Bold values indicate statistically significant results.

View Large

Association of E237G and -109C/T polymorphisms in asthma and/or allergic rhinitis risk

Twenty-five case–control studies involving the E237G polymorphism with 10,084 individuals (5081 cases and 5003 controls) were included in this meta-analysis. The overall results suggested that the allelic model of E237G polymorphism had an increased the risk of the allergic diseases (B vs. A: OR = 1.28, 95% CI = 1.06–1.53, P<0.001, I2 = 63.1%, Figure 2). No significant association was revealed in the pooled results under other genetic model statistically. For subgroup analysis based on the ethnicity, significantly increased risk were observed in Asian population for allelic model (B vs. A: OR = 1.23, 95% CI = 1.05–1.45, P=0.004, I2 = 53.6%, Figure 3) and recessive genetic model (BB vs. AA + AB: OR = 2.10, 95% CI = 1.22–3.62, P<0.001, I2 = 68.8%). In allergic status subgroup analysis, we also observed increased risk of asthma for allelic model (B vs. A: OR = 1.25, 95% CI = 1.04–1.51, P<0.001, I2 = 60.5%, Figure 4) and recessive genetic model (BB vs. AA + AB: OR = 2.09, 95% CI = 1.19–3.65, P<0.001, I2 = 67.7%). For subgroup analysis based on source of controls and HWE status of controls, a significant association was found (B vs. A: HWE ≥ 0.05, OR = 1.33, 95% CI = 1.08–1.63, P<0.001, I2 = 65%, Figure 5). Table 2 presented the detailed results of the meta-analysis.

ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model for overall populations

Figure 2
Figure 2
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model for overall populations
Figure 2
Figure 2
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model for overall populations
Close modal

ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by ethnicity

Figure 3
Figure 3
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by ethnicity
Figure 3
Figure 3
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by ethnicity
Close modal

ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by allergic status

Figure 4
Figure 4
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by allergic status
Figure 4
Figure 4
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by allergic status
Close modal

ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by HWE

Figure 5
Figure 5
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by HWE
Figure 5
Figure 5
View largeDownload slide
ORs and 95% CIs for the associations between E237G polymorphism and allergic diseases risk in allelic genetic model by HWE
Close modal

A total of 15 eligible studies, consisting of 3909 cases and 4145 controls focused on -109 C/T polymorphisms. The overall OR with its 95% CI revealed a significantly increased risk of allergic diseases in recessive genetic model (BB vs. AA + AB: OR = 1.58, 95% CI = 1.26–1.98, P<0.001, I2 = 66.4%). No significant association was revealed in the other genetic models. In recessive genetic model, significant increased risk was found in all the three subgroup analysis by ethnicity (BB vs. AA + AB: Caucasian, OR = 1.50, 95% CI = 1.18–1.92, P=0.866, I2 = 0%; Asian, OR = 1.60, 95% CI = 1.19–2.14, P<0.001, I2 = 71.6%), allergic status (BB vs. AA + AB: asthma, OR = 1.58, 95% CI = 1.20–2.08, P<0.001, I2 = 69.6%; allergic rhinitis, OR = 1.65, 95% CI = 1.07–2.55; OR = 1.46, 95% CI = 1.05–2.02), and HWE (BB vs. AA + AB: HWE ≥ 0.05, OR = 1.54, 95% CI = 1.19–1.99, P<0.001, I2 = 72.2%; HWE < 0.05, OR = 1.80, 95% CI = 1.10–2.92, P=0.349, I2 = 5.1%) respectively.

Table 3 summarized the association between the clinical characteristics and the polymorphisms of E237G and -109C/T, including the gender, age, positive RAST, and total serum IgE level.

Table 3
Clinical characteristics of E237G and -109 C/T polymorphisms
StudySex (F/M)Age (years)Positive RAST (≥0.35 UA/ml)Total IgECaseControl
CaseControlCaseControlCaseControl
E237G          
Laprise, C. [14] 41/59 NA 27 ± 2 (18–35) NA – – – – – 
Soriano, J.B. [52] 92/54 NA 58 ±16 (23–93) NA 68 NA Geometric mean total IgE in IU/l 72.4 NA 
Takabayashi, A. [20] 42/44 58/56 – – – – IgE level (IU/ml) 1080 ± 1381 34–10,430 
Nagata, H. [15] – – 7–71 15–79 – – IgE level (IU/ml) 641.5 ± 1234 56.1 ± 59.2 
Zeng, L.X. [45] 32/37 12/16 14–63 21–50 – – IgE level (μg/l) 611 ± 82.6 53 ± 7.1 
Cui, T.P. [17] 101/115 93/105 19.6 ± 21.9 (3–65) 22.3 ± 23.6 (3–60) – – IgE-log (IU/ml) GG 2.622 ± 0.937
EG 2.418 ± 0.894
2.306 ± 0.915 		NA 
Tang, Y. [46] – – – – – – – – – 
Korzycka-Zaborowska, B. [21] – – 18–45 18–45 – – – – – 
Rigoli, L. [18] 58/42 50/53 Children 5–13
Relatives 29–48 		Children 6–14
Relatives 33–49 		– – IgE-log (IU/ml) Children EE 2.63 ± 0.56/EG 2.37 ± 0.56/GG 2.44 ± 0.56
Relatives EE 2.98 ± 0.43/EG 2.76 ± 0.43/GG 2.54 ± 0.48 		Children EE 1.73 ± 0.57/EG 1.73 ± 0.58/GG 1.75 ± 0.57
Relatives EE 1.67 ± 0.50/EG 1.65 ± 0.58/GG 1.64 ± 0.45 
Zhang, X.Z., China [53] 77/64 53/104 52 ± 16 32 ± 9 70 NA IgE level (IU/ml) EE 247 ± 30
EG + GG 248 ± 30 		NA 
Zhang, X.Z., Malaysia [53] 43/25 45/55 45 ± 14 34 ± 9 56.7 NA IgE level (IU/ml) EE 375 ± 47
EG + GG 341 ± 60 		NA 
Zhang, X.Z., India [53] 50/32 39/59 50 ± 17 34 ± 10 63 NA IgE level (IU/ml) EE 367 ± 36
EG + GG 446 ± 65 		NA 
Cui, T.P. [47] 47/59 48/54 40.37 ± 15.09 (18–69) 37.12 ± 12.63 (20–60) – – IgE-log (IU/ml) EE 2.3060 ± 0.9152
EG 2.4180 ± 0.8936
GG 2.7220 ± 0.9374 		NA 
Zhao, K.S. [49] 60/91 42/63 1.5–14 2–14 EE 91
EG + GG 19 		EE 5 EG + GG 2 IgE-log (IU/ml) EE 2.33 ± 0.68
EG + GG 2.43 ± 0.59 		EE 1.49 ± 0.07
EG + GG 1.52 ± 0.09 
Liu, T. [22] – – 36.5 38.5 EE 19
EG 9
GG 1 		EE 39
EG 10
GG 1 		– – – 
Kim, E.S. [55] 107/240 NA 11.11 ± 4.05 NA – – IgE-log (IU/ml) 5.17 ± 1.76 NA 
Li, H. [56] 96/96 96/96 3–12 18–22 – – – – – 
Wang, J.Y. [19] 148/301 266/246 7.82 ± 3.81 8.37 ± 2.45 – – IgE-ln (IU/ml) 5.9848 ± 1.5276 4.5201 ± 1.6375 
Dmitrieva-Zdorova, E.V. [59] 119/105 74/98 Mild 32.7 ± 10.5
Moderate/severe 38.3 ± 12.6 		36.9 ± 10.1 – – IgE level (IU/ml) Mild 210 (53–535)
Moderate/severe 252 (128–645) 		45 (23–89) 
Zheng, B.Q. [51] 94/104 50/60 3.5 3.8 – – – – – 
Ramphul, K. [60] – – 3–12 18–22 – – – – – 
Amo, G. [61] 294/221 265/261 32.2 ± 15.1 (14–79) 28.4 ± 12.1 (18–84) – – IgE level (IU/ml) 254.1 ± 401.5 (0–4800) NA 
Hua, L. [62] 497/503 497/503 4.90 (3–12) 23.32 (18–25) 807 NA – – – 
-109C/T          
Hizawa, N. [13] 119/107 102/124 TT 45.8 ± 16.5
TC 44.3 ± 16.5
CC 42.8 ± 16.5 		TT 41.6 ± 11.5
TC 42.8 ± 11.5
CC 39.4 ± 11.5 		EE 68
EG 87
GG 17 		EE 30
EG 31
GG 4 		IgE-log (IU/ml) TT 2.63 ± 0.56
TC 2.37 ± 0.56
CC 2.44 ± 0.56 		TT 1.73 ± 0.57
TC 1.73 ± 0.58
CC 1.75 ± 0.57 
Cui, T.P. [17] 47/59 48/54 40.37 ± 15.09 (18–69) 37.12 ± 12.63 (20–60) – – IgE-log (IU/ml) TT 2.649 ± 0.9241
TC 2.296 ± 1.1040
CC 2.313 ± 0.8052 		NA 
Cui, T.P. [47] 101/115 93/105 19.6 ± 21.9 (3–65) 22.3 ± 23.6 (3–60) – – IgE-log (IU/ml) TT 2.441 ± 0.9438
TC 2.315 ± 0.8660
CC 2.287 ± 1.1150 		NA 
Gan, X. [48] 24/21 22/23 6–65 8–55 – – IgE level (IU/ml) ≥ 480 (N) 34 8 
Zhao, K.S, [50] 50/76 35/52 1.5–14 1–12 TT 10
TC 55
CC 39 		TT 1
TC 3
CC 3 		IgE-log (IU/ml) TT 2.26 ± 0.56
TC 2.32 ± 0.67
CC 2.66 ± 0.37 		TT 1.54 ± 0.09
TC 1.52 ± 0.08
CC 1.52 ± 0.09 
Hizawa, N. [54] 209/165 128/246 45 (16–81) 32 (18–72) 269 210 IgE-log (IU/ml) 2.40 ± 0.64 1.86 ± 0.64 
Kim, E.S. [55] 107/240 NA 11.11 ± 4.05 NA – – IgE-log (IU/ml) 5.17 ± 1.76 NA 
Li, H. [56] 96/96 96/96 3–12 18–22 – – – – – 
Sharma, S. [57] 123/114 117/104 34.4 ± 12.5 35.0 ± 10.6 – – IgE-log (IU/ml) 2.85 ± 0.47 2.32 ± 0.83 
Tikhonova, V. [58] 26/114 65/71 3–17 4–17 – – – – – 
Ramphul, K. [60] – – 3–12 18–22 – – – – – 
Amo, G. [61] 294/221 265/261 32.2 ± 15.1 (14–79) 28.4 ± 12.1 (18–84) – – IgE level (IU/ml) 254.1 ± 401.5 (0–4800) NA 
Hua, L. [62] 497/503 497/503 4.90 (3–12) 23.32 (18–25) 807 NA – – – 
StudySex (F/M)Age (years)Positive RAST (≥0.35 UA/ml)Total IgECaseControl
CaseControlCaseControlCaseControl
E237G          
Laprise, C. [14] 41/59 NA 27 ± 2 (18–35) NA – – – – – 
Soriano, J.B. [52] 92/54 NA 58 ±16 (23–93) NA 68 NA Geometric mean total IgE in IU/l 72.4 NA 
Takabayashi, A. [20] 42/44 58/56 – – – – IgE level (IU/ml) 1080 ± 1381 34–10,430 
Nagata, H. [15] – – 7–71 15–79 – – IgE level (IU/ml) 641.5 ± 1234 56.1 ± 59.2 
Zeng, L.X. [45] 32/37 12/16 14–63 21–50 – – IgE level (μg/l) 611 ± 82.6 53 ± 7.1 
Cui, T.P. [17] 101/115 93/105 19.6 ± 21.9 (3–65) 22.3 ± 23.6 (3–60) – – IgE-log (IU/ml) GG 2.622 ± 0.937
EG 2.418 ± 0.894
2.306 ± 0.915 		NA 
Tang, Y. [46] – – – – – – – – – 
Korzycka-Zaborowska, B. [21] – – 18–45 18–45 – – – – – 
Rigoli, L. [18] 58/42 50/53 Children 5–13
Relatives 29–48 		Children 6–14
Relatives 33–49 		– – IgE-log (IU/ml) Children EE 2.63 ± 0.56/EG 2.37 ± 0.56/GG 2.44 ± 0.56
Relatives EE 2.98 ± 0.43/EG 2.76 ± 0.43/GG 2.54 ± 0.48 		Children EE 1.73 ± 0.57/EG 1.73 ± 0.58/GG 1.75 ± 0.57
Relatives EE 1.67 ± 0.50/EG 1.65 ± 0.58/GG 1.64 ± 0.45 
Zhang, X.Z., China [53] 77/64 53/104 52 ± 16 32 ± 9 70 NA IgE level (IU/ml) EE 247 ± 30
EG + GG 248 ± 30 		NA 
Zhang, X.Z., Malaysia [53] 43/25 45/55 45 ± 14 34 ± 9 56.7 NA IgE level (IU/ml) EE 375 ± 47
EG + GG 341 ± 60 		NA 
Zhang, X.Z., India [53] 50/32 39/59 50 ± 17 34 ± 10 63 NA IgE level (IU/ml) EE 367 ± 36
EG + GG 446 ± 65 		NA 
Cui, T.P. [47] 47/59 48/54 40.37 ± 15.09 (18–69) 37.12 ± 12.63 (20–60) – – IgE-log (IU/ml) EE 2.3060 ± 0.9152
EG 2.4180 ± 0.8936
GG 2.7220 ± 0.9374 		NA 
Zhao, K.S. [49] 60/91 42/63 1.5–14 2–14 EE 91
EG + GG 19 		EE 5 EG + GG 2 IgE-log (IU/ml) EE 2.33 ± 0.68
EG + GG 2.43 ± 0.59 		EE 1.49 ± 0.07
EG + GG 1.52 ± 0.09 
Liu, T. [22] – – 36.5 38.5 EE 19
EG 9
GG 1 		EE 39
EG 10
GG 1 		– – – 
Kim, E.S. [55] 107/240 NA 11.11 ± 4.05 NA – – IgE-log (IU/ml) 5.17 ± 1.76 NA 
Li, H. [56] 96/96 96/96 3–12 18–22 – – – – – 
Wang, J.Y. [19] 148/301 266/246 7.82 ± 3.81 8.37 ± 2.45 – – IgE-ln (IU/ml) 5.9848 ± 1.5276 4.5201 ± 1.6375 
Dmitrieva-Zdorova, E.V. [59] 119/105 74/98 Mild 32.7 ± 10.5
Moderate/severe 38.3 ± 12.6 		36.9 ± 10.1 – – IgE level (IU/ml) Mild 210 (53–535)
Moderate/severe 252 (128–645) 		45 (23–89) 
Zheng, B.Q. [51] 94/104 50/60 3.5 3.8 – – – – – 
Ramphul, K. [60] – – 3–12 18–22 – – – – – 
Amo, G. [61] 294/221 265/261 32.2 ± 15.1 (14–79) 28.4 ± 12.1 (18–84) – – IgE level (IU/ml) 254.1 ± 401.5 (0–4800) NA 
Hua, L. [62] 497/503 497/503 4.90 (3–12) 23.32 (18–25) 807 NA – – – 
-109C/T          
Hizawa, N. [13] 119/107 102/124 TT 45.8 ± 16.5
TC 44.3 ± 16.5
CC 42.8 ± 16.5 		TT 41.6 ± 11.5
TC 42.8 ± 11.5
CC 39.4 ± 11.5 		EE 68
EG 87
GG 17 		EE 30
EG 31
GG 4 		IgE-log (IU/ml) TT 2.63 ± 0.56
TC 2.37 ± 0.56
CC 2.44 ± 0.56 		TT 1.73 ± 0.57
TC 1.73 ± 0.58
CC 1.75 ± 0.57 
Cui, T.P. [17] 47/59 48/54 40.37 ± 15.09 (18–69) 37.12 ± 12.63 (20–60) – – IgE-log (IU/ml) TT 2.649 ± 0.9241
TC 2.296 ± 1.1040
CC 2.313 ± 0.8052 		NA 
Cui, T.P. [47] 101/115 93/105 19.6 ± 21.9 (3–65) 22.3 ± 23.6 (3–60) – – IgE-log (IU/ml) TT 2.441 ± 0.9438
TC 2.315 ± 0.8660
CC 2.287 ± 1.1150 		NA 
Gan, X. [48] 24/21 22/23 6–65 8–55 – – IgE level (IU/ml) ≥ 480 (N) 34 8 
Zhao, K.S, [50] 50/76 35/52 1.5–14 1–12 TT 10
TC 55
CC 39 		TT 1
TC 3
CC 3 		IgE-log (IU/ml) TT 2.26 ± 0.56
TC 2.32 ± 0.67
CC 2.66 ± 0.37 		TT 1.54 ± 0.09
TC 1.52 ± 0.08
CC 1.52 ± 0.09 
Hizawa, N. [54] 209/165 128/246 45 (16–81) 32 (18–72) 269 210 IgE-log (IU/ml) 2.40 ± 0.64 1.86 ± 0.64 
Kim, E.S. [55] 107/240 NA 11.11 ± 4.05 NA – – IgE-log (IU/ml) 5.17 ± 1.76 NA 
Li, H. [56] 96/96 96/96 3–12 18–22 – – – – – 
Sharma, S. [57] 123/114 117/104 34.4 ± 12.5 35.0 ± 10.6 – – IgE-log (IU/ml) 2.85 ± 0.47 2.32 ± 0.83 
Tikhonova, V. [58] 26/114 65/71 3–17 4–17 – – – – – 
Ramphul, K. [60] – – 3–12 18–22 – – – – – 
Amo, G. [61] 294/221 265/261 32.2 ± 15.1 (14–79) 28.4 ± 12.1 (18–84) – – IgE level (IU/ml) 254.1 ± 401.5 (0–4800) NA 
Hua, L. [62] 497/503 497/503 4.90 (3–12) 23.32 (18–25) 807 NA – – – 

Abbreviations: NA, not available; RAST, allergy skin prick test result.

View Large

Association of RsaI_in2, RsaI_ex7, and I181L polymorphisms in asthma and/or allergic rhinitis risk

For these three polymorphisms, three studies that focused on the association of RsaI_in2 polymorphisms and allergic diseases risk involving 274 cases and 217 controls, two studies that focused on the association between RsaI_ex7 polymorphisms and allergic diseases risk involving 177 cases and 130 controls, and two studies that focused on the association of I181L polymorphisms and allergic diseases risk involving 290 cases and 150 controls were pooled into the meta-analysis. No significant association was found for RsaI_in2 and RsaI_ex7 polymorphisms in all genetic models. For I181L polymorphism, significant association with increased allergic diseases risk was also observed in B vs. A (OR = 2.00, 95%CI = 1.35-2.97), AB+BB vs. AA (OR = 4.19, 95%CI = 2.35-7.47) and AB vs. AA (OR = 4.15, 95%CI = 2.33-7.41) genetic models.

Sensitivity analysis and publication bias

We omitted each particular study to verify whether our results were influenced by each individual study or not. The pooled ORs were not materially altered, indicating the robustness and stable of the results in this meta-analysis (Figure 6). The Begg’s funnel plot and Egger’s test were used to evaluate the publication bias (Table 4). All the plots were found to be roughly symmetrical, indicating no publication bias presented as shown in Figure 7.

Sensitivity analysis through the deletion of each study to reflect the individual influence on the calculated ORs in allelic genetic model of E237G polymorphism

Figure 6
Figure 6
View largeDownload slide
Sensitivity analysis through the deletion of each study to reflect the individual influence on the calculated ORs in allelic genetic model of E237G polymorphism
Figure 6
Figure 6
View largeDownload slide
Sensitivity analysis through the deletion of each study to reflect the individual influence on the calculated ORs in allelic genetic model of E237G polymorphism
Close modal

Funnel plot analysis to detect publication bias for allelic genetic model of E237G polymorphism

Figure 7
The weight of studies is presented by the size of circles.
View largeDownload slide
Funnel plot analysis to detect publication bias for allelic genetic model of E237G polymorphism

The weight of studies is presented by the size of circles.

Figure 7
The weight of studies is presented by the size of circles.
View largeDownload slide
Funnel plot analysis to detect publication bias for allelic genetic model of E237G polymorphism

The weight of studies is presented by the size of circles.

Close modal
Table 4
Evaluation of the publication bias of E237G and -109 C/T polymorphisms of the included studies
GenotypeB vs. AAB + BB vs. AABB vs. AA + ABBB vs. AAAB vs. AA
E237G      
P(Begg’s) 0.168 0.797 0.085 0.487 0.907 
P(Egger) 0.102 0.358 0.012 0.307 0.333 
-109 C/T      
P(Begg’s) 0.656 0.656 0.921 0.235 0.882 
P(Egger) 0.894 0.555 0.128 0.411 0.589 
GenotypeB vs. AAB + BB vs. AABB vs. AA + ABBB vs. AAAB vs. AA
E237G      
P(Begg’s) 0.168 0.797 0.085 0.487 0.907 
P(Egger) 0.102 0.358 0.012 0.307 0.333 
-109 C/T      
P(Begg’s) 0.656 0.656 0.921 0.235 0.882 
P(Egger) 0.894 0.555 0.128 0.411 0.589 
View Large

In the last decade, analysis of SNPs has become the newest approach for detection and localization of the genetic determinants of asthma [63–66]. Genetic factors are important in defining total serum IgE levels. Linkage analyses have localized a gene or genes that influence atopic phenotype at chromosome 11q13 [34–36]. In this meta-analysis, we discussed five polymorphisms in FcεRIβ (E237G, -109 C/T, RsaI_in2, RsaI_ex7, and I181L) which were considered to have certain correlation to allergic diseases by pooled results from 29 eligible case–control studies. Only two extensively investigated SNPs (E237G and -109 C/T) were involving large sample of studies included this meta-analysis. Other SNPs (RsaI_in2, RsaI_ex7, and I181L) had limited number of studies, especially for V183L, we failed to collect enough studies and data to comprehensively analyze the risk for allergic diseases.

The results demonstrated that FcεRIβ E237G polymorphism in allelic model acts as significant increased risk for asthma, especially in Asians, which is consistent with previous results [66,67]. The stratification on allergic status and ethnicity did reveal a statistically significant association for E237G and the risk of allergic diseases. With respect to FcεRIβ -109 C/T polymorphism, a significantly association was observed in recessive genetic model, it has been demonstrated that -109 C/T polymorphism may play an important role in pathophysiologic mechanisms and the subgroup analysis by allergic status and ethnicity also showed the increased risk for allergic diseases, which validated the previous speculation [67]. For I181L polymorphism, significant association with increased allergic diseases risk was also observed in three genetic models, given the limited number of studies, more data are required to validate these associations.

Heterogeneity is one of the most important problems when performing the meta-analysis. The results should be interpreted with caution when heterogeneity exists. There was high heterogeneity in this meta-analysis. Considering that differences in allergic status, ethnicity and WHE may affect the results, we conducted subgroup analysis by allergic status, ethnicities and WHE, the heterogeneity was decreased or removed after subgroup analysis; however, there still existed or increased in some groups, perhaps, the source of heterogeneity may be from different ages or other clinical characteristics such as sex and environmental exposures, unfortunately, there were no enough data to extract to analyze.

Although this is not the first meta-analysis focused on the association between FcεRIβ polymorphisms and allergic diseases, there were some strengths of our study: first, most of the genotype distributions in controls were consistent with HWE. Second, the relationship was analyzed by using five kinds of genetic models, and the results were statistically significant. Third, the methodological issues for meta-analysis, such as Egger’s test, Begg’s funnel plots, and subgroup analysis were performed to ensure the stability of the results. On the other hand, the limitations could not be ignored: first, the interaction of gene–gene and gene–environment should be considered. Second, most of the included studies were conducted in Asian and Caucasian populations, although other ethnicities should be considered. Third, different genotyping methods were used in the respective studies, which might partly influence the result.

In conclusions, it is believed that subjects with FcεRIβ polymorphisms tend to develop allergic diseases, severity of symptoms caused by genetic variation could independently modify predisposition to allergic diseases. A greater understanding of the genetic basis of asthma and allergic rhinitis holds great promise for the identification of novel therapeutic targets. Further multicentric investigations still need to confirm the relationship of these polymorphisms of FcεRIβ and allergic diseases susceptibility.

H.H.G. and X.H.Z. conceived and designed the study. H.H.G. and P.L. searched the databases and extracted the data. T.P. and H.B.L. analyzed the data. H.H.G., S.H., and S.L. wrote the draft of the paper. X.H.Z. and W.D.Z. reviewed and revised the manuscript. All the authors approved the final manuscript.

The authors declare that there are no sources of funding to be acknowledged.

The authors declare that there are no competing interests associated with the manuscript.

AR

allergic rhinitis

CI

confidence interval

HWE

hardy–weinberg equilibrium

NOS

newcastle–ottawa scale

OR

odd ratio

SNP

single nucleotide polymorphism

1
Kakli
H.A.
and
Riley
T.D.
(
2016
)
Allergic rhinitis
.
Prim. Care
43
,
465
–
475
https://doi.org/10.1016/j.pop.2016.04.009
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
2
Accordini
S.
,
Corsico
A.
,
Cerveri
I.
,
Gislason
D.
,
Gulsvik
A.
,
Janson
C.
et al
(
2008
)
The socio-economic burden of asthma is substantial in Europe
.
Allergy
63
,
116
–
124
https://doi.org/10.1111/j.1398-9995.2007.01523.x
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
3
Tattersfield
A.E.
,
Knox
A.J.
,
Britton
J.R.
and
Hall
I.P.
(
2002
)
Asthma
.
Lancet
360
,
1313
–
1322
https://doi.org/10.1016/S0140-6736(02)11312-2
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
4
Bochner
B.S.
and
Busse
W.W.
(
2005
)
Allergy and asthma
.
J. Allergy Clin. Immunol.
115
,
953
–
959
https://doi.org/10.1016/j.jaci.2005.02.032
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
5
Sengler
C.
,
Lau
S.
,
Wahn
U.
and
Nickel
R.
(
2002
)
Interactions between genes and environmental factors in asthma and atopy: new developments
.
Respir. Res.
3
,
7
https://doi.org/10.1186/rr179
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
6
Kauffmann
F.
and
Demenais
F.
(
2012
)
Gene-environment interactions in asthma and allergic diseases: challenges and perspectives
.
J. Allergy Clin. Immunol.
130
,
1229
–
1240
,
quiz 1241-1222
https://doi.org/10.1016/j.jaci.2012.10.038
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
7
Los
H.
,
Koppelman
G.H.
and
Postma
D.S.
(
1999
)
The importance of genetic influences in asthma
.
Eur. Respir. J.
14
,
1210
–
1227
https://doi.org/10.1183/09031936.99.14512109
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
8
van Beijsterveldt
C.E.
and
Boomsma
D.I.
(
2007
)
Genetics of parentally reported asthma, eczema and rhinitis in 5-yr-old twins
.
Eur. Respir. J.
29
,
516
–
521
https://doi.org/10.1183/09031936.00065706
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
9
Andiappan
A.K.
,
Wang de
Y.
,
Anantharaman
R.
,
Suri
B.K.
,
Lee
B.T.
,
Rotzschke
O.
et al
(
2013
)
Replication of genome-wide association study loci for allergic rhinitis and house dust mite sensitization in an Asian population of ethnic Chinese in Singapore
.
J. Allergy Clin. Immunol.
131
,
1431.e1438
–
1433.e1438
https://doi.org/10.1016/j.jaci.2012.11.001
Google Scholar
Crossref
Search ADS
 
10
Nilsson
D.
,
Andiappan
A.K.
,
Hallden
C.
,
Tim
C.F.
,
Sall
T.
,
Wang de
Y.
et al
(
2013
)
Poor reproducibility of allergic rhinitis SNP associations
.
PLoS One
8
,
e53975
https://doi.org/10.1371/journal.pone.0053975
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
11
Andiappan
A.K.
,
Nilsson
D.
,
Hallden
C.
,
Yun
W.D.
,
Sall
T.
,
Cardell
L.O.
et al
(
2013
)
Investigating highly replicated asthma genes as candidate genes for allergic rhinitis
.
BMC Med. Genet.
14
,
51
https://doi.org/10.1186/1471-2350-14-51
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
12
Shirakawa
T.
,
Mao
X.Q.
,
Sasaki
S.
,
Enomoto
T.
,
Kawai
M.
,
Morimoto
K.
et al
(
1996
)
Association between atopic asthma and a coding variant of Fc epsilon RI beta in a Japanese population
.
Hum. Mol. Genet.
5
,
2068
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
13
Hizawa
N.
,
Yamaguchi
E.
,
Jinushi
E.
and
Kawakami
Y.
(
2000
)
A common FCER1B gene promoter polymorphism influences total serum IgE levels in a Japanese population
.
Am. J. Respir. Crit. Care Med.
161
,
906
–
909
https://doi.org/10.1164/ajrccm.161.3.9903128
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
14
Laprise
C.
,
Boulet
L.P.
,
Morissette
J.
,
Winstall
E.
and
Raymond
V.
(
2000
)
Evidence for association and linkage between atopy, airway hyper-responsiveness, and the beta subunit Glu237Gly variant of the high-affinity receptor for immunoglobulin E in the French-Canadian population
.
Immunogenetics
51
,
695
–
702
https://doi.org/10.1007/s002510000185
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
15
Nagata
H.
,
Mutoh
H.
,
Kumahara
K.
,
Arimoto
Y.
,
Tomemori
T.
,
Sakurai
D.
et al
(
2001
)
Association between nasal allergy and a coding variant of the Fc epsilon RI beta gene Glu237Gly in a Japanese population
.
Hum. Genet.
109
,
262
–
266
https://doi.org/10.1007/s004390100561
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
16
Leung
T.F.
,
Tang
N.L.
,
Chan
I.H.
,
Li
A.M.
,
Ha
G.
,
Lam
C.W.
et al
(
2002
)
Distribution in allele frequencies of predisposition-to-atopy genotypes in Chinese children
.
Pediatr. Pulmonol.
34
,
419
–
424
https://doi.org/10.1002/ppul.10210
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
17
Cui
T.
,
Wang
L.
,
Wu
J.
and
Xie
J.
(
2003
)
The association analysis of FcepsilonRIbeta with allergic asthma in a Chinese population
.
Chin. Med. J.
116
,
1875
–
1878
[PubMed]
Google Scholar
PubMed
 
18
Rigoli
L.
,
Di Bella
C.
,
Procopio
V.
,
Barberio
G.
,
Barberi
I.
,
Caminiti
L.
et al
(
2004
)
Molecular analysis of sequence variants in the Fc epsilon receptor I beta gene and IL-4 gene promoter in Italian atopic families
.
Allergy
59
,
213
–
218
https://doi.org/10.1046/j.1398-9995.2003.00385.x
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
19
Wang
J.Y.
,
Liou
Y.H.
,
Wu
Y.J.
,
Hsiao
Y.H.
and
Wu
L.S.
(
2009
)
An association study of 13 SNPs from seven candidate genes with pediatric asthma and a preliminary study for genetic testing by multiple variants in Taiwanese population
.
J. Clin. Immunol.
29
,
205
–
209
https://doi.org/10.1007/s10875-008-9256-6
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
20
Takabayashi
A.
,
Ihara
K.
,
Sasaki
Y.
,
Suzuki
Y.
,
Nishima
S.
,
Izuhara
K.
et al
(
2000
)
Childhood atopic asthma: positive association with a polymorphism of IL-4 receptor alpha gene but not with that of IL-4 promoter or Fc epsilon receptor I beta gene
.
Exp. Clin. Immunogenet.
17
,
63
–
70
https://doi.org/10.1159/000019125
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
21
Korzycka-Zaborowska
B.
,
Hopkin
J.M.
and
Gorski
P.
(
2004
)
Genetic variants of FcepsilonRIbeta and Il-4 and atopy in a Polish population
.
Allergol. Immunopathol.
32
,
53
–
58
[PubMed]
Google Scholar
 
22
Liu
T.
,
Teng
L.
,
Guan
L.X.
,
Wu
L.P.
and
Sun
K.Y.
(
2006
)
Study on the E237G polymorphism of the FcεRI β gene with asthma
.
Chin. J. Prac. Intern. Med.
26
,
1520
–
1522
Google Scholar
 
23
Blank
U.
,
Ra
C.
,
Miller
L.
,
White
K.
,
Metzger
H.
and
Kinet
J.P.
(
1989
)
Complete structure and expression in transfected cells of high affinity IgE receptor
.
Nature
337
,
187
–
189
https://doi.org/10.1038/337187a0
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
24
Dombrowicz
D.
,
Lin
S.
,
Flamand
V.
,
Brini
A.T.
,
Koller
B.H.
and
Kinet
J.P.
(
1998
)
Allergy-associated FcRbeta is a molecular amplifier of IgE- and IgG-mediated in vivo responses
.
Immunity
8
,
517
–
529
https://doi.org/10.1016/S1074-7613(00)80556-7
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
25
Lin
S.
,
Cicala
C.
,
Scharenberg
A.M.
and
Kinet
J.P.
(
1996
)
The Fc(epsilon)RIbeta subunit functions as an amplifier of Fc(epsilon)RIgamma-mediated cell activation signals
.
Cell
85
,
985
–
995
https://doi.org/10.1016/S0092-8674(00)81300-8
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
26
Hiraoka
S.
,
Furumoto
Y.
,
Koseki
H.
,
Takagaki
Y.
,
Taniguchi
M.
,
Okumura
K.
et al
(
1999
)
Fc receptor beta subunit is required for full activation of mast cells through Fc receptor engagement
.
Int. Immunol.
11
,
199
–
207
https://doi.org/10.1093/intimm/11.2.199
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
27
Galli
S.J.
and
Tsai
M.
(
2010
)
Mast cells in allergy and infection: versatile effector and regulatory cells in innate and adaptive immunity
.
Eur. J. Immunol.
40
,
1843
–
1851
https://doi.org/10.1002/eji.201040559
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
28
Kim
Y.K.
,
Oh
S.Y.
,
Oh
H.B.
,
Chun
S.Y.
,
Cho
S.H.
,
Koh
Y.Y.
et al
(
2002
)
Coding single nucleotide polymorphism in the high-affinity immunoglobulin E receptor b chain (FcepsilonRI-beta) gene is associated with immunoglobulin E receptor-mediated histamine release from basophils
.
Clin. Exp. Allergy: J. Br. Soc. Allergy Clin. Immunol.
32
,
751
–
755
https://doi.org/10.1046/j.1365-2222.2002.01295.x
Google Scholar
Crossref
Search ADS
 
29
Turner
H.
and
Kinet
J.P.
(
1999
)
Signalling through the high-affinity IgE receptor Fc epsilonRI
.
Nature
402
,
B24
–
B30
https://doi.org/10.1038/35037021
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
30
Holgate
S.T.
(
1999
)
Genetic and environmental interaction in allergy and asthma
.
J. Allergy Clin. Immunol.
104
,
1139
–
1146
https://doi.org/10.1016/S0091-6749(99)70005-9
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
31
Barnes
K.C.
(
1999
)
Gene-environment and gene-gene interaction studies in the molecular genetic analysis of asthma and atopy
.
Clin. Exp. Allergy: J. Br. Soc. Allergy Clin. Immunol.
29
,
47
–
51
Google Scholar
 
32
Howard
T.D.
,
Meyers
D.A.
and
Bleecker
E.R.
(
2000
)
Mapping susceptibility genes for asthma and allergy
.
J. Allergy Clin. Immunol.
105
,
S477
–
S481
https://doi.org/10.1016/S0091-6749(00)90046-0
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
33
Kraft
S.
,
Rana
S.
,
Jouvin
M.H.
and
Kinet
J.P.
(
2004
)
The role of the FcepsilonRI beta-chain in allergic diseases
.
Int. Arch. Allergy Immunol.
135
,
62
–
72
https://doi.org/10.1159/000080231
Google Scholar
Crossref
Search ADS
PubMed
 
34
Simon Thomas
N.
,
Wilkinson
J.
,
Lonjou
C.
,
Morton
N.E.
and
Holgate
S.T.
(
2000
)
Linkage analysis of markers on chromosome 11q13 with asthma and atopy in a United Kingdom population
.
Am. J. Respir. Crit. Care Med.
162
,
1268
–
1272
https://doi.org/10.1164/ajrccm.162.4.9909078
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
35
Cookson
W.O.
,
Sharp
P.A.
,
Faux
J.A.
and
Hopkin
J.M.
(
1989
)
Linkage between immunoglobulin E responses underlying asthma and rhinitis and chromosome 11q
.
Lancet
1
,
1292
–
1295
https://doi.org/10.1016/S0140-6736(89)92687-1
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
36
Sandford
A.J.
,
Shirakawa
T.
,
Moffatt
M.F.
,
Daniels
S.E.
,
Ra
C.
,
Faux
J.A.
et al
(
1993
)
Localisation of atopy and beta subunit of high-affinity IgE receptor (Fc epsilon RI) on chromosome 11q
.
Lancet
341
,
332
–
334
https://doi.org/10.1016/0140-6736(93)90136-5
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
37
Collee
J.M.
,
ten Kate
L.P.
,
de Vries
H.G.
,
Kliphuis
J.W.
,
Bouman
K.
,
Scheffer
H.
et al
(
1993
)
Allele sharing on chromosome 11q13 in sibs with asthma and atopy
.
Lancet
342
,
936
https://doi.org/10.1016/0140-6736(93)91988-X
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
38
Stang
A.
(
2010
)
Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses
.
Eur. J. Epidemiol.
25
,
603
–
605
https://doi.org/10.1007/s10654-010-9491-z
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
39
Higgins
J.P.
and
Thompson
S.G.
(
2002
)
Quantifying heterogeneity in a meta-analysis
.
Stat. Med.
21
,
1539
–
1558
https://doi.org/10.1002/sim.1186
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
40
Mantel
N.
and
Haenszel
W.
(
1959
)
Statistical aspects of the analysis of data from retrospective studies of disease
.
J. Natl. Cancer Inst.
22
,
719
–
748
[PubMed]
Google Scholar
PubMed
 
41
Higgins
J.P.
,
Thompson
S.G.
and
Spiegelhalter
D.J.
(
2009
)
A re-evaluation of random-effects meta-analysis
.
J. R. Stat. Soc. Series A
172
,
137
–
159
https://doi.org/10.1111/j.1467-985X.2008.00552.x
Google Scholar
Crossref
Search ADS
 
42
Begg
C.B.
and
Mazumdar
M.
(
1994
)
Operating characteristics of a rank correlation test for publication bias
.
Biometrics
50
,
1088
–
1101
https://doi.org/10.2307/2533446
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
43
Song
F.
and
Gilbody
S.
(
1998
)
Bias in meta-analysis detected by a simple, graphical test. Increase in studies of publication bias coincided with increasing use of meta-analysis
.
BMJ
316
,
471
[PubMed]
Google Scholar
PubMed
 
44
Chen
H.
,
Chen
Y.Z.
,
Hu
L.P.
,
Fu
J.
,
Zhang
H.Q.
and
Ma
Y.
(
2000
)
Study on the Fcε RI-β polymorphism and susceptibility of asthma in a Chinese population
.
Natl. Med. J. China.
80
,
664
–
667
,
[PubMed]
Google Scholar
 
45
Zeng
L.X.
,
Zhou
S.L.
,
Kuang
J.L.
and
Rao
W.H.
(
2001
)
Study of Mutation of B Chain Gene E237G༌A High Affinity Receptor of lgE in Asthmatics
.
Acta Academiae Medicinae Jiangxi
41
,
43
–
45
Google Scholar
 
46
Tang
Y.
,
Wu
X.Q.
,
Liu
X.Y.
,
Zeng
Y.
,
Li
Y.Q.
,
Wu
Q.
et al
(
2003
)
Study on mutations of β-chain of high affinity IgE receptor gene in people of Han nationality in the south of China
.
Chin. J. Mod. Med.
13
,
6
–
10
Google Scholar
 
47
Cui
T.P.
,
Jiang
W.C.
,
Wang
L.
,
Xie
J.G.
and
Wu
J.M.
(
2004
)
Association analysis of FcεRIβ gene with allergic asthma in Chinese
.
Chin. J. Pathophys.
20
,
2049
–
2052
Google Scholar
 
48
Gan
X.
,
Kuang
J.L.
,
Zou
Y.Q.
and
Rao
W.H.
(
2004
)
Study on the relationship between IgE high affinity receptor β-chain gene polymorphism and serum total IgE in patients with bronchial asthma
.
Chin. J. Tuberc. Respir. Dis.
27
,
704
–
705
Google Scholar
 
49
Zhao
K.S.
,
Cheng
H.J.
,
Qiao
H.M.
,
Zhao
F.X.
,
Sun
M.Y.
and
Fu
W.Y.
(
2004
)
Analysis of gene mutation for high affinity immunoglobulin E receptor chain in asthmatic children
.
J. Clin. Pediatr.
22
,
794
–
797
Google Scholar
 
50
Zhao
K.S.
,
Lu
J.R.
,
Wang
Z.H.
,
Guo
Y.
,
Yu
L.Y.
and
Fu
W.Y.
(
2004
)
Association between FcεRI-β gene promoter polymorphism and total serum IgE levels of asthma in children
.
Chin. J. Prac. Pediatr.
19
744
–
746
Google Scholar
 
51
Zheng
B.Q.
,
Wang
G.L.
,
Yang
S.
,
Lu
Y.Q.
,
Liu
R.J.
and
Li
Y.
(
2012
)
Study of genetic susceptibility in 198 children with asthma
.
Chin. J. Contemp. Pediatr.
14
,
811
–
814
Google Scholar
 
52
Soriano
J.B.
,
De Cid R
Estivill X
,
Antó
J.M.
,
Sunyer
J.
,
Otero
D.
,
Roca
J.
et al
(
2000
)
Association study of proposed candidate genes/regions in a population of Spanish asthmatics
.
Eur. J. Epidemiol.
16
,
745
–
750
https://doi.org/10.1023/A:1026758319621
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
53
Zhang
X.
,
Zhang
W.
,
Qiu
D.
,
Sandford
A.
and
Tan
W.C.
(
2004
)
The E237G polymorphism of the high-affinity IgE receptor beta chain and asthma
.
Annals Allergy Asthma Immunol.: Off. Publication Am. College Allergy, Asthma, & Immunol.
93
,
499
–
503
https://doi.org/10.1016/S1081-1206(10)61419-6
Google Scholar
 
54
Hizawa
N.
,
Maeda
Y.
,
Konno
S.
,
Fukui
Y.
,
Takahashi
D.
and
Nishimura
M.
(
2006
)
Genetic polymorphisms at FCER1B and PAI-1 and asthma susceptibility
.
Clin. Exp. Allergy: J. Br. Soc. Allergy Clin. Immunol.
36
,
872
–
876
https://doi.org/10.1111/j.1365-2222.2006.02413.x
Google Scholar
Crossref
Search ADS
 
55
Kim
E.S.
,
Kim
S.H.
,
Kim
K.W.
,
Park
H.S.
,
Shin
E.S.
,
Lee
J.E.
et al
(
2009
)
Involvement of FcE >R1 beta gene polymorphisms in susceptibility to atopy in Korean children with asthma
.
Eur. J. Pediatr.
168
,
1483
–
1490
https://doi.org/10.1007/s00431-009-0960-x
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
56
Li
H.
,
Xiaoyan
D.
,
Quanhua
L.
,
Jie
L.
and
Yixiao
B.
(
2009
)
Single-nucleotide polymorphisms in genes predisposing to asthma in children of Chinese Han nationality
.
J. Investig. Allergol. Clin. Immunol.
19
,
391
–
395
Google Scholar
PubMed
 
57
Sharma
S.
and
Ghosh
B.
(
2009
)
Promoter polymorphism in the MS4A2 gene and asthma in the Indian population
.
Int. Arch. Allergy Immunol.
149
,
208
–
218
https://doi.org/10.1159/000199716
Google Scholar
Crossref
Search ADS
PubMed
 
58
Tikhonova
V.
,
Voitovich
A.
,
Korostovsev
D.
and
Larionova
V.
(
2010
)
The -109C>T Polymorphism of the Fcer1B Gene in Children with Asthma
.
Pediatr. Res.
68
,
413
–
413
https://doi.org/10.1203/00006450-201011001-00821
Google Scholar
Crossref
Search ADS
 
59
Dmitrieva-Zdorova
E.V.
,
Voronko
O.E.
,
Latysheva
E.A.
,
Storozhakov
G.I.
and
Archakov
A.I.
(
2012
)
Analysis of polymorphisms in T(H)2-associated genes in Russian patients with atopic bronchial asthma
.
J. Investig. Allergol. Clin. Immunol.
22
,
126
–
132
Google Scholar
PubMed
 
60
Ramphul
K.
,
Lv
J.
,
Hua
L.
,
Liu
Q.H.
,
Fang
D.Z.
,
Ji
R.X.
et al
(
2014
)
Single nucleotide polymorphisms predisposing to asthma in children of Mauritian Indian and Chinese Han ethnicity
.
Brazilian J. Med. Biological Res. = Revista brasileira de pesquisas medicas e biologicas
47
,
394
–
397
Google Scholar
 
61
Amo
G.
,
Garcia-Menaya
J.
,
Campo
P.
,
Cordobes
C.
,
Seron
M.C.P.
,
Ayuso
P.
et al
(
2016
)
A Nonsynonymous FCER1B SNP is associated with risk of developing allergic rhinitis and with IgE levels
.
Sci. Rep.
6
,
https://doi.org/10.1038/srep19724
Google Scholar
 
62
Hua
L.
,
Zuo
X.B.
,
Bao
Y.X.
,
Liu
Q.H.
,
Li
J.Y.
,
Lv
J.
et al
(
2016
)
Four-locus gene interaction between IL13, IL4, FCER1B, and ADRB2 for asthma in Chinese Han children
.
Pediatr. Pulmonol.
51
,
364
–
371
https://doi.org/10.1002/ppul.23322
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
63
Denham
S.
,
Koppelman
G.H.
,
Blakey
J.
,
Wjst
M.
,
Ferreira
M.A.
,
Hall
I.P.
et al
(
2008
)
Meta-analysis of genome-wide linkage studies of asthma and related traits
.
Respir. Res.
9
,
38
https://doi.org/10.1186/1465-9921-9-38
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
64
Nanavaty
U.
,
Goldstein
A.D.
and
Levine
S.J.
(
2001
)
Polymorphisms in candidate asthma genes
.
Am. J. Med. Sci.
321
,
11
–
16
https://doi.org/10.1097/00000441-200101000-00003
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
65
Palmer
L.J.
and
Cookson
W.
(
2001
)
Using single nucleotide polymorphisms as a means to understanding the pathophysiology of asthma
.
Respir. Res.
2
,
102
–
112
https://doi.org/10.1186/rr45
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
66
Li
X.
,
Zhang
Y.
,
Zhang
J.
,
Xiao
Y.
,
Huang
J.
,
Tian
C.
et al
(
2010
)
Asthma susceptible genes in Chinese population: a meta-analysis
.
Respir. Res.
11
,
129
https://doi.org/10.1186/1465-9921-11-129
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
67
Yang
H.J.
,
Zheng
L.
,
Zhang
X.F.
,
Yang
M.
and
Huang
X.
(
2014
)
Association of the MS4A2 gene promoter C-109T or the 7th exon E237G polymorphisms with asthma risk: a meta-analysis
.
Clin. Biochem.
47
,
605
–
611
https://doi.org/10.1016/j.clinbiochem.2014.01.022
[PubMed]
Google Scholar
Crossref
Search ADS
PubMed
 
© 2018 The Author(s).
2018
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).