Front. Pediatr. Frontiers in Pediatrics Front. Pediatr. 2296-2360 Frontiers Media S.A. 10.3389/fped.2022.843691 Pediatrics Original Research Meta-Analysis of Vitamin D Receptor Gene Polymorphisms in Childhood Asthma Zhou Yong Li Sheng * Department of Pediatrics, Yancheng Maternal and Child Health Care Hospital, Yancheng, China

Edited by: Viviana Moschese, University of Rome Tor Vergata, Italy

Reviewed by: Nesrine Aly, Ain Shams University, Egypt; Peng Chen, Nanjing Medical University, China

 *Correspondence: Sheng Li lishengvip2084@163.com

This article was submitted to Pediatric Immunology, a section of the journal Frontiers in Pediatrics

 01 04 2022 2022 10 843691 26 12 2021 23 02 2022 Copyright © 2022 Zhou and Li. 2022 Zhou and Li

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

We conducted the systematic review to investigate the potential relationship between the vitamin polymorphisms of D receptor (VDR) gene and childhood asthma. Relevant studies researching on VDR polymorphisms and asthma susceptibility were searched throughout Embase, PubMed, China Science and technology journal database (CQVIP), etc. till 12 April, 2021. We calculated the pooled odds ratios (OR) and its 95% confidence interval (CI) using RevMan 5.3 software and Stata 11.0. FokI (rs2228570) could significantly affect childhood asthma risk across co dominant model (Ff vs. FF: OR (95%CI) = 0.82 (0.65, 1.02), P = 0.071) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.77 (0.63, 0.95), P = 0.016), especially among Caucasians in additive model (f vs. F: OR (95%CI) = 0.63 (0.43, 0.92), P = 0.015) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.67 (0.51, 0.88), P = 0.004). TaqI (rs731236) was significantly related with childhood asthma in additive model (t vs. T: OR (95%CI) = 0.45 (0.23, 0.89), P = 0.022), co dominant model (Tt vs. TT: OR (95%CI) = 0.36 (0.17, 0.77), P = 0.009), and dominant model (tt+Tt vs. TT: OR (95%CI) = 0.36 (0.15, 0.87), P = 0.024) among Asian, as well as population-based subgroup in co dominant model (Tt vs. TT: OR (95%CI) = 0.53 (0.31, 0.94), P = 0.029). However, no evidence supported the role of ApaI (rs7975232) and BsmI (rs1544410) polymorphisms in childhood asthma. FokI and TaqI polymorphisms were found to be related with the susceptibility of childhood asthma. However, it seems that ApaI and BsmI polymorphisms are not related with childhood asthma susceptibility.

 vitamin D receptor polymorphisms childhood asthma systematic review susceptibility Introduction

Asthma is recognized as a chronic heterogeneous respiratory disease, which has characterized by airway inflammation and hyper-responsiveness, and the disease affects more than 300 million people worldwide, especially among children (1). The incidence, morbidity, and mortality related with asthma was influenced by several potential risk factors such as environmental factors (2), infancy microbial, biome influences, and genetic background, including vitamin D receptor (VDR) gene (3).

Vitamin D has been shown to have potent immunomodulatory properties, and Vitamin D correlated with the regulation of adaptive and innate immune function through VDR (4). Recently, increasing evidence researched on the effect of vitamin D in asthma and demonstrated that the severity of symptoms was related with vitamin D deficiency (5, 6). Among VDR polymorphisms, four SNPs, including BsmI (rs1544410), ApaI (rs7975232), FokI (rs2228570), and TaqI (rs731236) have been widely researched (7), but the relationship remains inconsistent. For example, the meta-analysis by Makoui et al. showed a statistical significant association between asthma risk and TaqI SNP (7). However, the systematically review by Zhen et al. showed no association between TaqI SNP and asthma risk (8). Additionally, thereafter, some new studies have been published (9–13).

Thus, it is necessary to update the report based on the previous results of researches to further explore the potential role of VDR genes polymorphism in childhood asthma susceptibility. Then, we designed the meta-analysis and explored this relationship in different races and source of controls. Finally, our data demonstrated that FokI and TaqI polymorphisms might be associated with childhood asthma susceptibility. However, ApaI and BsmI polymorphisms are not related with childhood asthma susceptibility.

 Materials and Methods Selection Strategy

The published studies were searched from numerous databases including Embase, PubMed, WANFANG data, China National Knowledge Infrastructure (CNKI), China Science and technology journal database (CQVIP), etc. The comprehensive systematic search process was exploited till 12 April, 2021 using the following key words: (“Vitamin D receptor” OR “VDR”) AND (“polymorphisms” OR “polymorphism” OR “variant” OR “mutant”) AND (“children” OR “child” OR “teenager” OR “pediatric”). The selection strategies in Pubmed and Embase were shown in Supplementary Tables 1, 2. Moreover, in order to enroll more researches, print-out literatures, reviews, and the references of included articles were also retrieved.

 Study Selection

The following inclusion criteria were designed: (1) the study was designed as a case-control study or cohort study; (2) the subjects in the experiment group were children and/or adolescents with asthma, and subjects in the control group were healthy children and/or adolescents; (3) The study explored the association of VDR ApaI (rs7975232), TaqI (rs731236), BsmI (rs1544410), FokI (rs2228570) gene polymorphisms and asthma susceptibility; (4) genotype data were reported or could be calculated based on information provided in the study.

When the control group and the case group were family members or close family members, the study would be excluded. The non-research articles, such as reviews, comments, and conference summaries, would be excluded. When duplicated studies were found or same data were showed in more than one study, the study with the most specific information would be included in the present study, and other duplicated articles would be excluded.

 Data Extraction and Quality Assessment

Based on the designed criteria, studies were screened by two investigators independently. According to the standardized form, the information including year of publication, the name of the first author, research regions, the demographic information (age, sample size, source of the control group), polymorphism detection methods, the ethnicity of the included population, and genotype data, etc.

Newcastle-Ottawa Scale (NOS) criteria was used to assess the methodological quality of included studies, and the scale was assessed according to three aspects including subjects selection, comparability, and exposure (14). The study with a score of five or more would be considered as moderate quality, and the study with a score of four or less would be considered as poor quality.

When data extraction was finished, the extraction form would be exchanged, and the disagreements were solved by discussing.

 Statistic Analysis

Firstly, the Hardy-Weinberg equilibrium test (HWE) of the frequency distribution of genotypes among controls was performed. We defined the population were not in the HWE if P < 0.05. For each single nucleotide polymorphisms (SNP), we examined four models, including computational additive model [m (mutation) vs. W (Wild)], co dominant model (mm vs. WW, Wm vs. WW), dominant model (mm+ Wm vs. WW), and recessive model (mm vs. WW + Wm). The effect of VDR polymorphisms in the childhood asthma susceptibility was assessed based on the pooled odds ratio (OR) and its 95% confidence interval (95%CI). Heterogeneity among individual studies was assessed using Cochran's Q test and I2 test (15). If P < 0.05, and/or I2>50%, suggesting obvious heterogeneity between the studies, the random effects model would be selected to calculate the pooled data; If P ≥ 0.05 and/or I2 ≤ 50%, the fixed effect model would be used. RevMan 5.3 software and Stata 11.0 were enrolled to perform all statistical analyses.

 Results Studies Inclusion

The detailed information associated with search process was shown in Figure 1. In this study, a total of 197 studies were firstly searched, including 54 articles in PubMed, 104 articles in Embase, 16 articles in Wanfang data, 18 articles in CNKI, and 5 articles in CQVIP. After removing 55 duplicated documents, there were 142 articles remaining. After that, we excluded 117 articles after browsing the titles and reading the abstract. Then, total 25 articles were fully reviewed, and seven articles were excluded, including five articles with adults as study subjects and two reviews. Finally, 18 articles were included in this meta-analysis (8–13, 16–27).

The detailed flow chart for study selection.

 The Baseline Characteristics and Quality Assessment of Included Studies

As shown in Table 1, total 3,495 subjects including 1,392 cases in asthma group and 2,103 cases in control group were enrolled in the present study. The studies included in the meta-analysis were all published from 2010 to 2020. Among these articles, the subjects in eight studies were Asians, eight articles were Caucasians, one study were Americans, and one study were African-Americans. For subjects in the control group, there were two studies with population-based controls and 16 studies with hospital-based controls.

Characteristics of the included studies.

Study Area Ethnicity Source of control Diagnostic of asthma Polymorphisms Group n, M/F Age, years
Ahmed et al. (9) Egypt European HB GINA guidelines ApaI, TaqI, BsmI Asthma 50, 28/22 12.66 ± 3.34
Control 50, 29/21 12.08 ± 3.53
Batmaz et al. (16) Turkey European HB GINA guidelines ApaI, TaqI, FokI, BsmI Asthma 30, 20/10 11.74 ± 2.4
Control 30, 13/17 11.31 ± 2.27
Einisman et al. (17) Chile American HB GINA guidelines ApaI, TaqI, FokI Asthma 75, 43/32 9.1 (3.5) $
Control 227, 114/113 10.3 (7.9)
Hou et al. (10) China Asia HB DPGPBA (2008) ApaI, BsmI Asthma 70, 43/27 8.84 ± 3.21
Control 70, 37/33 8.04 ± 3.01
Hutchinson et al. (11) Ireland European HB GINA guidelines ApaI, TaqI Asthma 44, 23/21 8.7 (6–13, 15–17)
Control 57, NR NR
Iordanidou et al. (18) Greece European HB GINA guidelines ApaI, TaqI, FokI, BsmI Asthma 127, 82/45 8.4 ± 2.3
Control 91, 41/50 9.5 ± 3.8
Ismail et al. (12) Egypt European HB GINA guidelines FokI Asthma 51, 28/23 8.6 ± 2.7
Control 33, 18/15 7.8 ± 2.6
Kilic et al. (12) Turkey European HB GINA guidelines ApaI, TaqI, FokI Asthma 100, 52/48 9.5 ± 2.8
Control 80, 42/38 9.5 ± 2.5
Liu et al. (20) China Asia HB DPGPBA (2008) ApaI, TaqI, FokI, BsmI Asthma 41, 24/17 3.9 ± 1.2
Control 41, 23/18 3.7 ± 1.3
Ma et al. (21) China Asia PB DPGPBA (2008) ApaI, TaqI, FokI, BsmI Asthma 60, 32/28 10.2
Control 60, 30/30 10.6
Maalmi et al. (22) Tunisia European HB DPGPBA (2008) ApaI, TaqI, FokI, BsmI Asthma 155, 59/96 9.1 (4–13, 15–17)
Control 225, 99/126 9.5 (2–13, 15–17)
Mo et al. (23) China Asia HB DPGPBA (2008) ApaI, BsmI Asthma 71, NR NR
Control 71, NR NR
Papadopoulou et al. (24) Cyprus European PB NR ApaI, TaqI, BsmI Asthma 69, 30/39 16.9 (15.9-18.1)
Control 671, 282/389 17.0 (15.9-18.0)
Pillai et al. (25) USA African- American HB NAEPP (2007) ApaI, TaqI, FokI Asthma 139, 81/58 11.2 ± 3.5
Control 74, 26/48 11.8 ± 4.3
Zhang et al. (26) China Asia HB NR ApaI, FokI, BsmI Asthma 143, 86/57 7.56 ± 2.39
Control 143, 87/56 7.28 ± 2.54
Zhao et al. (27) China Asia HB DPGPBA (2008) ApaI, TaqI, FokI, BsmI Asthma 40, 22/18 3.41 ± 1.07
Control 40, 21/19 3.37 ± 1.04
Zhen and Wang (8) China Asia HB NR ApaI Asthma 30, 17/13 5.70 ± 2.84
Control 40, 22/18 5.53 ± 2.93
Zhu et al. (13) China Asia HB DPGPBA (2008) FokI, BsmI Asthma 97, 50/47 8.76 ± 1.22
Control 100, 55/45 8.60 ± 1.16

$, median (IQR); HB, hospital-based; PB, population-based; PCR, Polymerase chain reaction; RT-PCR, Realtime polymerase chain reaction; RFLP, restriction fragment lengths polymorphism; NR, not reported; BMI, body mass index; GINA, the Global Initiative for Asthma; NAEPP, National Asthma Education and Prevention Program (2007) (28).

The genotype data and HWE test results of the case group and the control group were shown in Table 2. NOS scores of all included studies ranged from 5 to 8, suggesting an overall moderate methodological quality (Table 3).

Frequency distribution of gene polymorphisms in the experimental group and the control group.

References Country Case group Control group Pvalue for HWE
N WW WM MM N WW WM MM
ApaI (rs7975232)
Ahmed et al. (9) Egypt 50 25 15 10 50 20 20 10 0.2386
Batmaz et al. (16) Turkey 30 5 25 0 30 7 23 0 0.0070
Einisman et al. (17) Chile 70 21 35 14 50 10 28 12 0.3891
Hou et al. (10) China 70 4 30 36 70 0 5 65 0.7567
Hutchinson et al. (11) Ireland 44 11 23 10 57 5 20 32 0.4721
Iordanidou et al. (18) Greece 127 41 63 23 91 35 41 15 0.6120
Kilic et al. (12) Turkey 100 18 60 22 80 26 42 12 0.4569
Liu et al. (20) China 41 7 15 19 41 1 4 36 0.1599
Ma et al. (21) China 60 46 7 7 60 48 4 8 <0.0001
Maalmi et al. (22) Tunisia 155 92 57 6 225 142 70 13 0.2729
Mo et al. (23) China 71 4 31 36 71 14 28 29 0.1416
Papadopoulou et al. (24) Cyprus 61 19 34 8 633 232 312 89 0.3290
Pillai et al. (25) USA 125 55 59 11 72 35 33 4 0.2762
Zhang et al. (26) China 143 54 75 14 143 8 69 66 0.0637
Zhao et al. (27) China 40 0 15 25 40 0 27 13 0.0013
Zhen and Wang (8) China 30 3 2 25 40 6 14 20 0.2061
TaqI (rs731236)
Ahmed et al. (9) Egypt 50 5 30 15 50 10 40 0 <0.0001
Batmaz et al. (16) Turkey 30 18 9 3 30 15 10 5 0.1709
Einisman et al. (17) Chile 72 35 34 3 50 24 24 2 0.1780
Hutchinson et al. (11) Ireland 44 17 21 6 57 34 23 0 0.0564
Iordanidou et al. (18) Greece 127 43 68 16 91 35 38 18 0.1990
Kilic et al. (12) Turkey 100 31 61 8 80 28 32 20 0.0861
Liu et al. (20) China 41 6 11 24 41 1 5 35 0.1607
Ma et al. (21) China 60 6 7 47 60 5 6 49 <0.0001
Maalmi et al. (22) Tunisia 155 59 81 15 225 79 101 45 0.2230
Papadopoulou et al. (24) Cyprus 61 28 20 13 630 224 325 81 0.0276
Pillai et al. (25) USA 118 52 55 11 74 40 31 3 0.3137
Zhao et al. (27) China 40 26 14 0 40 13 27 0 0.0013
BsmI (rs1544410)
Ahmed et al. (9) Egypt 50 10 25 15 50 20 20 10 0.2386
Batmaz et al. (16) Turkey 30 2 12 16 30 5 13 12 0.6477
Hou et al. (10) China 70 0 4 66 70 0 5 65 0.7567
Iordanidou et al. (18) Greece 127 20 67 40 91 19 39 33 0.2442
Liu et al. (20) China 41 9 11 21 41 1 4 36 0.0723
Ma et al. (21) China 60 5 10 45 60 6 6 48 <0.0001
Maalmi et al. (22) Tunisia 155 34 72 49 225 26 119 80 0.0663
Mo et al. (23) China 71 0 5 66 71 0 4 67 0.8070
Papadopoulou et al. (24) Cyprus 63 11 32 20 631 127 327 177 0.2801
Zhang et al. (26) China 143 56 74 13 143 72 65 6 0.0635
Zhao et al. (27) China 40 0 23 17 40 0 11 29 0.3133
Zhu et al. (13) China 97 0 10 87 100 0 2 98 0.9195
FokI (rs2228570)
Batmaz et al. (16) Turkey 30 19 11 0 30 12 12 6 0.3613
Einisman et al. (17) Chile 73 11 62 0 50 5 45 0 <0.0001
Iordanidou et al. (18) Greece 127 67 54 6 91 38 45 8 0.2958
Ismail et al. (19) Egypt 51 29 22 0 33 12 14 7 0.4497
Kilic et al. (12) Turkey 100 58 33 9 80 48 28 4 0.9744
Liu et al. (20) China 41 8 13 20 41 1 5 35 0.1607
Ma et al. (21) China 60 12 24 24 60 18 31 11 0.7124
Maalmi et al. (22) Tunisia 155 88 56 11 152 70 59 23 0.0808
Pillai et al. (25) USA 122 76 41 5 74 42 29 3 0.4636
Zhang et al. (26) China 143 2 49 92 143 6 63 74 0.0975
Zhao et al. (27) China 40 19 14 7 40 15 16 9 0.2508
Zhu et al. (13) China 97 27 48 22 100 30 45 25 0.3283

Quality assessment of the included studies.

References Representativeness of the cases Case definition adequate Ascertainment of exposure Same method of ascertainment for cases and controls Control for important factor or additional factor Selection of controls Definition of controls Non-response rate Total quality scores
Ahmed et al. (9) – 7
Batmaz et al. (16) – 7
Einisman et al. (17) – – 6
Hou et al. (10) – – 6
Hutchinson et al. (11) – – – 5
Iordanidou et al. (18) – 7
Ismail et al. (19) – – 6
Kilic et al. (12) – 7
Liu et al. (20) – 7
Ma et al. (21) – 6
Maalmi et al. (22) – – 6
Mo et al. (23) – – – 5
Papadopoulou et al. (24) – 7
Pillai et al. (25) – 7
Zhang et al. (26) – 7
Zhao et al. (27) – 7
Zhen and Wang (8) – – 6
Zhu et al. (13) – 7
Meta-Analysis of VDR Polymorphism and Asthma ApaI (rs7975232)

As shown in Figure 2, total 16 articles reported the association between ApaI (rs7975232) and asthma risk (8–12, 16–18, 20–27). Obvious heterogeneity across studies was observed in additive model (a vs. A: I2 = 89%, P < 0.00001), co dominant model (aa vs. AA: I2 = 84%, P < 0.00001; Aa vs. AA: I2 = 63%, P < 0.0006), dominant model (aa+Aa vs. AA: I2 = 77%, P < 0.00001), and recessive model (aa vs. AA+Aa: I2 = 86%, P < 0.00001). No significant association between ApaI (rs7975232) and asthma risk was calculated across additive model (a vs. A: OR (95%CI) = 0.82 (0.56, 1.21), P = 0.317), co dominant model (aa vs. AA: OR (95%CI) = 0.65 (0.31, 1.38), P = 0.263; Aa vs. AA: OR (95%CI) = 0.97 (0.66, 1.42), P = 0.866), dominant model (aa+Aa vs. AA: OR (95%CI) = 0.86 (0.55, 1.35), P = 0.520), and recessive model (aa vs. AA+Aa: OR (95%CI) = 0.73 (0.40, 1.32), P = 0.295).

Forest plot for meta-analyzing the association between Vitamin D receptor ApaI (rs7975232) polymorphisms and childhood asthma. (A) additive model: a vs. A; (B) co dominant model: aa vs. AA; (C) co dominant model: Aa vs. AA; (D) dominant model: aa+Aa vs. AA; (E) recessive model: aa vs. AA+Aa.

The subgroup analysis was performed stratified by ethnicity, HWE, and source of controls (Table 4). No significant association was observed in all subgroup analysis (P>0.05). Meanwhile, the results of heterogeneity analysis showed that ethnicity, HWE, and source of subjects were not the source of heterogeneity.

Outcomes of the subgroup analysis.

Model No. of studies Heterogeneity test Effect size
I2 (%) PH OR (95% CI) P value
ApaI (rs7975232)
a vs. A 16 88.7 <0.001 0.82 (0.56, 1.21) 0.317
Ethnicity
   American 1 NA NA 0.76 (0.45, 1.26) 0.285
   Asian 7 93.6 <0.001 0.65 (0.25, 1.70) 0.378
   Caucasians 7 67.3 0.005 0.98 (0.72, 1.33) 0.876
   African-American 1 NA NA 1.20 (0.77, 1.89) 0.417
HWE
   Yes 13 90.3 <0.001 0.73 (0.47, 1.13) 0.163
   No 3 17.7 0.297 1.37 (0.88, 2.16) 0.167
Source
   HB 14 90.0 <0.001 0.79 (0.50, 1.22) 0.287
   PB 2 0 0.927 1.09 (0.78, 1.52) 0.607
aa vs. AA 16 83.7 <0.001 0.65 (0.31, 1.38) 0.263
Ethnicity
   American 1 NA NA 0.56 (0.19, 1.63) 0.285
   Asian 7 90.8 <0.001 0.37 (0.06, 2.48) 0.305
   Caucasians 7 65.2 0.013 0.89 (0.46, 1.74) 0.737
   African-American 1 NA NA 1.75 (0.52, 5.93) 0.369
HWE
   Yes 13 85.0 <0.001 0.63 (0.28, 1.42) 0.264
   No 3 NA NA 0.91 (0.31, 2.72) 0.280
Source
   HB 14 86.0 <0.001 0.59 (0.24, 1.46) 0.254
   PB 2 0 0.795 1.02 (0.52, 2.01) 0.948
Aa vs. AA 16 62.7 <0.001 0.97 (0.66, 1.42) 0.866
Ethnicity
   American 1 NA NA 0.60 (0.24, 1.47) 0.260
   Asian 7 72.9 <0.001 0.71 (0.19, 2.67) 0.615
   Caucasians 7 9.2 0.358 1.24 (0.94, 1.63) 0.128
   African-American 1 NA NA 1.14 (0.62, 2.08) 0.674
HWE
   Yes 13 67.0 <0.001 0.90 (0.59, 1.38) 0.637
   No 3 0 0.844 1.67 (0.67, 4.14) 0.272
Source
   HB 14 66.6 <0.001 0.89 (0.57, 1.39) 0.612
   PB 2 0.0 0.662 1.40 (0.82, 2.40) 0.213
aa+Aa vs. AA 16 76.9 <0.001 0.86 (0.55, 1.35) 0.520
Ethnicity
   American 1 NA NA 0.58 (0.25, 1.38) 0.220
   Asian 7 86.9 <0.001 0.53 (0.12, 2.25) 0.391
   Caucasians 7 46.8 0.080 1.14 (0.79, 1.62) 0.488
   African-American 1 NA NA 1.20 (0.67, 2.15) 0.532
HWE
   Yes 13 79.9 <0.001 0.80 (0.48, 1.33) 0.393
   No 3 0 0.778 1.31 (0.64, 2.68) 0.467
Source
   HB 14 79.8 <0.001 0.79 (0.47, 1.36) 0.400
   PB 2 0 0.926 1.26 (0.78, 2.03) 0.339
aa vs. AA+Aa 16 85.6 <0.001 0.73 (0.40, 1.32) 0.295
Ethnicity
   American 1 NA NA 0.79 (0.33, 1.90) 0.600
   Asian 7 92.5 <0.001 0.60 (0.17, 2.04) 0.409
   Caucasians 7 58.5 0.034 0.81 (0.48, 1.39) 0.446
   African-American 1 NA NA 1.64 (0.50, 5.36) 0.412
HWE
   Yes 13 85.8 <0.001 0.64 (0.34, 1.20) 0.165
   No 3 72.9 0.055 1.78 (0.45, 6.96) 0.409
Source
   HB 14 87.6 <0.001 0.70 (0.35, 1.40) 0.319
   PB 2 0.0 0.916 0.90 (0.48, 1.69) 0.745
TaqI (rs731236)
t vs. T 12 71.1 <0.001 0.93 (0.70, 1.23) 0.608
Ethnicity
   American 1 NA NA 0.99 (0.56, 1.75) 0.970
   Asian 3 55.4 0.106 0.45 (0.23, 0.89) 0.022
   Caucasians 7 71.5 0.002 1.06 (0.77, 1.47) 0.711
   African-American 1 NA NA 1.45 (0.92, 2.30) 0.112
HWE
   Yes 8 70.8 0.001 0.91 (0.66, 1.27) 0.582
   No 4 77.9 0.004 0.96 (0.52, 1.78) 0.888
Source
   HB 10 76.3 <0.001 0.93 (0.66, 1.30) 0.670
   PB 2 0 0.699 0.93 (0.66, 1.30) 0.661
tt vs. TT 12 63.5 0.002 0.92 (0.49, 1.70) 0.783
Ethnicity
   American 1 NA NA 1.03 (0.16, 6.63) 0.976
   Asian 3 56.5 0.129 0.37 (0.06, 2.40) 0.300
   Caucasians 7 71.0 0.002 0.96 (0.44, 2.09) 0.912
   African-American 1 NA NA 2.82 (0.74, 10.79) 0.130
HWE
   Yes 8 56.2 0.025 0.71 (0.36, 1.40) 0.320
   No 4 70.6 0.033 2.01 (0.46, 8.77) 0.351
Source
   HB 10 67.7 0.002 0.93 (0.42, 2.07) 0.857
   PB 2 3.5 0.407 1.15 (0.62, 2.12) 0.665
Tt vs. TT 12 50.2 0.024 1.00 (0.72, 1.38) 0.996
Ethnicity
   American 1 NA NA 0.97 (0.46, 2.03) 0.939
   Asian 3 0 0.379 0.36 (0.17, 0.77) 0.009
   Caucasians 7 48.6 0.070 1.13 (0.78, 1.65) 0.513
   African-American 1 NA NA 1.36 (0.75, 2.49) 0.312
HWE
   Yes 8 0 0.693 1.26 (0.99, 1.60) 0.064
   No 4 49.2 0.116 0.57 (0.29, 1.15) 0.120
Source
   HB 10 41.4 0.082 1.11 (0.81, 1.53) 0.521
   PB 2 0.0 0.438 0.53 (0.31, 0.94) 0.029
tt+Tt vs. TT 12 53.3 0.015 0.96 (0.70, 1.32) 0.817
Ethnicity
   American 1 NA NA 0.98 (0.47, 2.01) 0.947
   Asian 3 29.0 0.244 0.36 (0.15, 0.87) 0.024
   Caucasians 7 42.7 0.106 1.08 (0.77, 1.50) 0.663
   African-American 1 NA NA 1.49 (0.83, 2.68) 0.178
HWE
   Yes 8 30.6 0.184 1.12 (0.84, 1.50) 0.437
   No 4 64.1 0.039 0.70 (0.32, 1.51) 0.362
Source
   HB 10 56.6 0.014 1.02 (0.71, 1.47) 0.901
   PB 2 0 0.739 0.67 (0.41, 1.10) 0.112
tt vs. TT+Tt 12 72.6 <0.001 0.87 (0.47, 1.62) 0.658
Ethnicity
   American 1 NA NA 1.04 (0.17, 6.48) 0.964
   Asian 3 65.5 0.089 0.46 (0.14, 1.50) 0.198
   Caucasians 7 79.3 <0.001 0.97 (0.41, 2.28) 0.941
   African-American 1 NA NA 2.43 (0.66, 9.03) 0.184
HWE
   Yes 8 57.8 0.020 0.60 (0.32, 1.09) 0.095
   No 4 73.5 0.023 2.06 (0.59, 7.27) 0.259
Source
   HB 10 69.2 0.001 0.78 (0.38, 1.61) 0.503
   PB 2 51.7 0.150 1.30 (0.59, 2.86) 0.521
BsmI (rs1544410)
b vs. B 12 73.2 <0.001 0.87 (0.62, 1.21) 0.408
Ethnicity
   Asian 7 79.9 <0.001 0.57 (0.28, 1.17) 0.128
   Caucasians 5 62.5 0.031 1.12 (0.82, 1.54) 0.481
HWE
   Yes 11 75.6 <0.001 0.86 (0.60, 1.23) 0.419
   No 1 NA NA 0.88 (0.44, 1.77) 0.724
Source
   HB 10 77.6 <0.001 0.81 (0.54, 1.24) 0.336
   PB 2 0 0.527 1.08 (0.78, 1.49) 0.662
bb vs. BB 12 68.0 0.003 1.16 (0.61, 2.21) 0.665
Ethnicity
   Asian 7 79.5 0.008 0.74 (0.12, 4.40) 0.741
   Caucasians 5 66.2 0.019 1.24 (0.62, 2.50) 0.539
HWE
   Yes 11 72.6 0.001 1.16 (0.56, 2.40) 0.690
   No 1 NA NA 1.13 (0.32, 3.94) 0.854
Source
   HB 10 76.7 0.001 1.12 (0.45, 2.76) 0.805
   PB 2 0 0.844 1.25 (0.65, 2.42) 0.501
Bb vs. BB 12 55.6 0.027 1.22 (0.76, 1.96) 0.409
Ethnicity
   Asian 7 0 0.401 1.42 (0.90, 2.23) 0.130
   Caucasians 5 68.4 0.013 1.22 (0.62, 2.40) 0.561
HWE
   Yes 11 60.8 0.018 1.18 (0.71, 1.96) 0.524
   No 1 NA NA 2.00 (0.42, 9.52) 0.384
Source
   HB 10 67.3 0.009 1.20 (0.64, 2.24) 0.576
   PB 2 0.0 0.514 1.25 (0.65, 2.39) 0.505
bb+Bb vs. BB 12 68.5 0.002 1.14 (0.67, 1.93) 0.627
Ethnicity
   Asian 7 70.5 0.034 0.80 (0.23, 2.81) 0.732
   Caucasians 5 72.2 0.006 1.25 (0.64, 2.47) 0.515
HWE
   Yes 11 73.0 0.001 1.13 (0.63, 2.01) 0.686
   No 1 NA NA 1.22 (0.35, 4.24) 0.752
Source
   HB 10 77.4 <0.001 1.10 (0.54, 2.25) 0.788
   PB 2 0 0.972 1.20 (0.66, 2.17) 0.552
bb vs. BB+Bb 12 61.5 0.003 0.80 (0.54, 1.20) 0.278
Ethnicity
   Asian 7 69.2 0.003 0.55 (0.25, 1.18) 0.124
   Caucasians 5 0 0.414 1.01 (0.77, 1.32) 0.948
HWE
   Yes 11 64.9 0.001 0.80 (0.52, 1.24) 0.326
   No 1 NA NA 0.75 (0.32, 1.77) 0.513
Source
   HB 10 66.5 0.001 0.75 (0.46, 1.24) 0.267
   PB 2 0.0 0.375 1.04 (0.65, 1.66) 0.870
FokI (rs2228570)
f vs. F 12 76.7 <0.001 0.78 (0.57, 1.05) 0.102
Ethnicity
   American 1 NA NA 0.90 (0.54, 1.51) 0.694
   Asian 5 84.4 <0.001 0.90 (0.50, 1.63) 0.721
   Caucasians 5 62.7 0.030 0.63 (0.43, 0.92) 0.015
   African-American 1 NA NA 0.85 (0.52, 1.39) 0.524
HWE
   Yes 11 78.8 <0.001 0.76 (0.55, 1.06) 0.107
   No 1 NA NA 0.90 (0.54, 1.51) 0.694
Source
   HB 11 73.2 <0.001 0.72 (0.54, 0.97) 0.030
   PB 1 NA NA 1.90 (1.14, 3.17) 0.015
ff vs. FF 12 67.6 0.001 0.67 (0.34, 1.34) 0.260
Ethnicity
   American 1 NA NA NA NA
   Asian 5 70.7 0.008 1.05 (0.38, 2.91) 0.925
   Caucasians 5 62.9 0.029 0.37 (0.13, 1.07) 0.067
   African-American 1 NA NA 0.92 (0.21, 4.05) 0.913
HWE
   Yes 11 67.6 0.001 0.67 (0.34, 1.34) 0.260
   No 1 NA NA NA NA
Source
   HB 11 59.3 0.008 0.57 (0.29, 1.11) 0.095
   PB 1 NA NA 3.27 (1.18, 9.09) 0.023
Ff vs. FF 12 0 0.890 0.82 (0.65, 1.02) 0.071
Ethnicity
   American 1 NA NA 0.63 (0.20, 1.93) 0.415
   Asian 5 72.9 <0.001 1.06 (0.68, 1.65) 0.796
   Caucasians 5 32.1 0.172 0.75 (0.56, 1.00) 0.052
   African-American 1 NA NA 0.78 (0.43, 1.43) 0.425
HWE
   Yes 11 0 0.854 0.82 (0.66, 1.03) 0.093
   No 1 NA NA 0.63 (0.20, 1.93) 0.415
Source
   HB 11 0 0.883 0.80 (0.64, 1.00) 0.052
   PB 1 NA NA 1.16 (0.47, 2.87) 0.746
ff+Ff vs. FF 12 34.7 0.112 0.77 (0.63, 0.95) 0.016
Ethnicity
   American 1 NA NA 0.63 (0.20, 1.93) 0.415
   Asian 5 53.6 0.071 1.08 (0.72, 1.63) 0.714
   Caucasians 5 11.6 0.340 0.67 (0.51, 0.88) 0.004
   African-American 1 NA NA 0.79 (0.44, 1.43) 0.443
HWE
   Yes 11 40.2 0.081 0.78 (0.63, 0.96) 0.021
   No 1 NA NA 0.63 (0.20, 1.93) 0.415
Source
   HB 11 24.1 0.214 0.73 (0.59, 0.91) 0.005
   PB 1 NA NA 1.71 (0.74, 3.97) 0.208
ff vs. FF+Ff 12 74.2 <0.001 0.71 (0.39, 1.29) 0.266
Ethnicity
   American 1 NA NA NA NA
   Asian 5 81.6 <0.001 0.94 (0.42, 2.10) 0.880
   Caucasians 5 59.8 0.041 0.43 (0.16, 1.17) 0.099
   African-American 1 NA NA 1.01 (0.23, 4.36) 0.988
HWE
   Yes 11 74.2 <0.001 0.71 (0.39, 1.29) 0.266
   No 1 NA NA NA NA
Source
   HB 11 70.7 <0.001 0.60 (0.33, 1.11) 0.103
   PB 1 NA NA 2.97 (1.29, 6.83) 0.010
TaqI (rs731236)

As shown in Figure 3, total 12 articles researched on the role of TaqI (rs731236) in asthma risk (9, 11, 12, 16–18, 20–25, 27). Obvious heterogeneity across studies was observed in additive model (t vs. T: I2 = 71%, P < 0.0001), co dominant model (tt vs. TT: I2 = 63%, P = 0.002; Tt vs. TT: I2 = 50%, P = 0.02), dominant model (tt+Tt vs. TT: I2 = 53%, P = 0.82), and recessive model (tt vs. TT+Tt: I2 = 73%, P < 0.0001). Thus, the randomed effects model was used to calculated the pooled data, and the results showed that no significant association between TaqI (rs731236) and asthma risk was observed across additive model (t vs. T: OR (95%CI) = 0.93 (0.70, 1.23), P = 0.608), co dominant model [tt vs. TT: OR (95%CI) = 0.92 (0.49, 1.70), P = 0.783; Tt vs. TT: OR (95%CI) = 1.00 (0.72, 1.38), P = 0.996], dominant model [tt+Tt vs. TT: OR (95%CI) = 0.96 (0.70, 1.32), P = 0.817], and recessive model [tt vs. TT+Tt: OR (95%CI) = 0.87 (0.47, 1.62), P = 0.658].

Forest plot for meta-analyzing the association between Vitamin D receptor TaqI (rs731236) polymorphisms and childhood asthma. (A) additive model: t vs. T; (B) co dominant model: tt vs. TT; (C) co dominant model: Tt vs. TT; (D) dominant model: tt+Tt vs. TT; (E) recessive model: tt vs. TT+Tt.

Further subgroup analysis showed that HWE and source of control were two sources for the obvious heterogeneity across co dominant model (Tt vs. TT). Notably, significant association was found in additive model [t vs. T: OR (95%CI) = 0.45 (0.23, 0.89), P = 0.022], co dominant model [Tt vs. TT: OR (95%CI) = 0.36 (0.17, 0.77), P = 0.009], and dominant model [tt+Tt vs. TT: OR (95%CI) = 0.36 (0.15, 0.87), P = 0.024] among Asians. Moreover, significant association was also found in the population-based subgroup in co dominant model [Tt vs. TT: OR (95%CI) = 0.53 (0.31, 0.94), P = 0.029].

 BsmI (rs1544410)

As shown in Figure 4, total 12 articles researched on the role of BsmI (rs1544410) in asthma risk (9, 10, 13, 16, 18, 20–24, 26, 27). Obvious heterogeneity across studies was observed in additive model (b vs. B: I2 = 73%, P < 0.0001), co dominant model (bb vs. BB: I2 = 68%, P = 0.003; Bb vs. BB: I2 = 56%, P = 0.03), dominant model (bb+Bb vs. BB: I2 = 68%, P = 0.002), and recessive model (bb vs. BB+Bb: I2 = 62%, P = 0.003). Thus, the randomed effects model was used to calculated the pooled data, and the results showed that no significant association between BsmI (rs1544410) and asthma risk was observed across additive model (b vs. B: OR (95%CI) = 0.87 (0.62, 1.21), P = 0.408), co dominant model (bb vs. BB: OR (95%CI) = 1.16 (0.61, 2.21), P = 0.665; Bb vs. BB: OR (95%CI) = 1.22 (0.76, 1.96), P = 0.409), dominant model (bb+Bb vs. BB: OR (95%CI) = 1.14 (0.67, 1.93), P = 0.627), and recessive model (bb vs. BB+Bb: OR (95%CI) = 0.80 (0.54, 1.20), P = 0.278).

Forest plot for meta-analyzing the association between Vitamin D receptor BsmI (rs1544410) polymorphisms and childhood asthma. (A) additive model: b vs. B; (B) co dominant model: bb vs. BB; (C) co dominant model: Bb vs. BB; (D) dominant model: bb+Bb vs. BB; (E): recessive model: bb vs. BB+Bb.

Further subgroup analysis showed that no significant association was observed in all subgroup analysis (P > 0.05). Meanwhile, the results of heterogeneity analysis showed that ethnicity, HWE, and source of subjects were not the source of heterogeneity.

 FokI (rs2228570)

As shown in Figure 5, total 12 articles researched on the role of FokI (rs2228570) in asthma risk (12, 13, 16–22, 25–27). Obvious heterogeneity across studies was observed in additive model (f vs. F: I2 = 77%, P < 0.00001), co dominant model (ff vs. FF: I2 = 68%, P = 0.0006), and recessive model (ff vs. FF+Ff: I2 = 74%, P < 0.0001). Thus, the randomed effects model was used to calculated the pooled data, and the results showed that no significant association between FokI (rs2228570) and asthma risk was observed across additive model (f vs. F: OR (95%CI) = 0.78 (0.57, 1.05), P = 0.102), co dominant model (ff vs. FF: OR (95%CI) = 0.67 (0.34, 1.34), P = 0.260), and recessive model (ff vs. FF+Ff: OR (95%CI) = 0.71 (0.39, 1.29), P = 0.266).

Forest plot for meta-analyzing the association between Vitamin D receptor FokI (rs2228570) polymorphisms and childhood asthma. (A) additive model: f vs. F; (B) co dominant model: ff vs. FF; (C) co dominant model: Ff vs. FF; (D) dominant model: ff+Ff vs. FF; (E): recessive model: ff vs. FF+Ff.

No significant obvious heterogeneity across studies was observed in co dominant model (Ff vs. FF: I2=0%, P = 0.89) and dominant model (ff+Ff vs. FF: I2=35%, P = 0.02), thus, the fixed effect model was used to calculate the pooled data, and the results showed that FokI (rs2228570) could significantly affect the risk of asthma across co dominant model (Ff vs. FF: OR (95%CI) = 0.82 (0.65, 1.02), P = 0.071) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.77 (0.63, 0.95), P = 0.016).

As for FokI (rs2228570), race, source of controls, HWE were not the source for the obvious heterogeneity. Subgroup analysis showed that FokI (rs2228570) SNP was significantly related with the risk of asthma in additive model (f vs. F: OR (95%CI) = 0.63 (0.43, 0.92), P = 0.015) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.67 (0.51, 0.88), P = 0.004) among Caucasians. Meanwhile, significant association was found in additive model (f vs. F: OR (95%CI) = 0.72 (0.54, 0.97), P = 0.03) and dominant model (ff+Ff vs. FF: OR (95%CI) = 0.67 (0.51, 0.88), P = 0.004) in the hospital-based subgroup. Significant association was found in additive model (f vs. F: OR (95%CI) = 1.90 (1.14, 3.17), P = 0.015), co dominant model (ff vs. FF: OR (95%CI) = 3.27 (1.18, 9.09), P = 0.023), and recessive model (ff vs. FF+Ff: OR (95%CI) = 2.97 (1.29, 6.83), P = 0.01) in population-based subgroup.

 Publication Bias

No significant publication bias was observed for ApaI (rs7975232), TaqI (rs731236), BsmI (rs1544410), FokI (rs2228570) across the genotype models (P>0.05).

 Discussion

Among childhood, asthma is accepted as the most common chronic disease. Recently, accumulating evidence researched the function role of VDR gene polymorphism in childhood asthma, and four SNPs, including BsmI (rs1544410), ApaI (rs7975232), FokI (rs2228570), and TaqI (rs731236), were the main gene locuses (3, 29). Based on the meta-analysis, our data showed that FokI (rs2228570) could significantly affect the risk of childhood asthma across co dominant model and dominant model, especially among Caucasians. Notably, among Asians, significant correction between TaqI (rs731236) and childhood asthma was also found in additive model (t vs. T), co dominant model (Tt vs. TT), and dominant model (tt+Tt vs. TT), as well as population-based subgroup in co dominant model (Tt vs. TT). No relationship was found between childhood asthma and the polymorphisms of ApaI (rs7975232) and BsmI (rs1544410).

Previous evidence showed that the level of Vitamin D was closely related with airway remodeling, the number of T regulatory cells, and expression level of pro-inflammatory cytokines and NF-κB (30). The connection between the deficiency of Vitamin D and poor asthma outcomes has been previously reported, such as worse symptomatology and poor lung function, and these defects could be reversed for offspring if Vitamin D was supplemented in deficient pregnant rodents (31). Zhen et al. demonstrated that, two out of four VDR polymorphisms could significantly affect the susceptibility of childhood asthma, including FokI and TaqI (8). Similarly, our study supported FokI and TaqI polymorphisms were associated with childhood asthma. Interestingly, it was different from the finding of a previous study (32), which gave support for that VDR gene ApaI (rs7975232) could contribute to asthma susceptibility.

The conflicting results might be explained by the following aspects. Firstly, it is well known that asthma is a clinical syndrome, and no gold standard test have been reported for making the diagnosis. Thus, physicians used multiple algorithms to make the final diagnosis, such as breath shortness, cough history, or wheezing history (33). Meanwhile, other baseline characteristics, such as smoking status, stress, gender, and age, were all related with the diagnosis of asthma (1). Secondly, based on genome-wide analysis studies, the researchers found that over 100 candidate genes were associated with the risk and development of asthma (34). Thirdly, the study designs and different genotyping methods might also account for the conflicting results. The obvious heterogeneity across included studies might also be attributed to these reasons.

There are some limitations should be noted. Firstly, the number of studies included in some subgroups was small, and more high-quality studies would be needed to verify the stability of the results. Secondly, since most of the included studies did not report the family history, living habits and other information of the study subjects, the quantitatively analyze based on these factors could not be performed to determine whether they affect the relationship between VDR gene polymorphisms and the childhood asthma susceptibility. Thirdly, the obvious heterogeneity across included studies could not be ignored. However, the moderate quality suggested that the analysis results had good credibility.

 Conclusion

In summary, we concluded that FokI and TaqI polymorphisms were associated with childhood asthma susceptibility. However, it seems that ApaI and BsmI polymorphisms are not related with childhood asthma susceptibility. Due to these limitations, further multi-center study with high quality should be designed to verify the present conclusion.

 Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

 Author Contributions

YZ: conception and design of the research, acquisition of data, analysis and interpretation of data, and drafting the manuscript. SL: statistical analysis and revision of manuscript for important intellectual content. All authors read and approved the final manuscript.

 Funding

This study was supported by the Medical Science and Technology Development project of Yancheng (Nos. YK2019043 and YK2021051).

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2022.843691/full#supplementary-material

 References Stern J Pier J Litonjua AA. Asthma epidemiology and risk factors. Semin Immunopathol. (2020) 42:5–15. 10.1007/s00281-020-00785-132020334 Deng Q Lu C Norback D Bornehag CG Zhang Y Liu W . Early life exposure to ambient air pollution and childhood asthma in China. Environ Res. (2015) 143:83–92. 10.1016/j.envres.2015.09.03226453943 Makoui MH Imani D Motallebnezhad M Azimi M Razi B. Vitamin D receptor gene polymorphism and susceptibility to asthma, Meta-analysis based on 17 case-control studies. Ann Allergy Asthma Immunol. (2020) 124:57–69. 10.1016/j.anai.2019.10.01431654764 Wang TT Tavera-Mendoza LE Laperriere D Libby E MacLeod NB Nagai Y . Large-scale in silico and microarray-based identification of direct 1,25-dihydroxyvitamin D3 target genes. Mol. Endocrinol. (2005) 19:2685–95. 10.1210/me.2005-010616002434 Amorim C Oliveira JM Rodrigues A Furlanetto KC Pitta F. Vitamin D, association with eosinophil counts and IgE levels in children with asthma. J Bras Pneumol. (2020) 47:e20200279. 10.36416/1806-3756/e2020027933174974 Emami Ardestani M Movahedi A. Effect of vitamin D supplementation on improvement of symptoms in mild-to-moderate asthma patients with vitamin d insufficiency and deficiency. Tanaffos. (2020) 19:322–9.33959169 Kostner K Denzer N Muller CS Klein R Tilgen W Reichrath J. The relevance of vitamin D receptor (VDR) gene polymorphisms for cancer, a review of the literature. Anticancer Res. (2009) 29:3511–36.19667145 Zhen YF Wang L. Relationship of vitamin D receptor gene polymorphism with children asthma and wheezy bronchitis (In Chineses). Chin J Prev Vet Med. (2010) 11:1055−8. Ahmed AE Hassan MH Toghan R Rashwan NI. Analysis of 25-hydroxy cholecalciferol, immunoglobulin E, and vitamin D receptor single nucleotide polymorphisms (Apa1, Taq1, and Bsm1), among sample of Egyptian children with bronchial asthma, A case-control study. Ann Allergy Asthma Immunol. (2020) 55:1349–58. 10.1002/ppul.2478532311846 Hou C Zhu X Chang X. Correlation of vitamin D receptor with bronchial asthma in children. Exp Ther Med. (2018) 15:2773–6. 10.3892/etm.2018.573929456680 Hutchinson K Kerley CP Faul J Greally P Coghlan D Louw M . Vitamin D receptor variants and uncontrolled asthma. J Clin Lab Anal. (2018) 50:108–16. 10.23822/EurAnnACI.1764-1489.4629384117 Kilic M Ecin S Taskin E Sen A Kara M. The vitamin D receptor gene polymorphisms in asthmatic children, a case-control study. Pediatr Allergy Immunol. (2019) 32:63–9. 10.1089/ped.2018.094831508258 Zhu L Li JH Ma XB. Study on the relationship between 25-hydroxyvitamin D concentration and its receptor gene polymorphisms and childhood asthma (In Chinese). Experimental and Laboratory Medicine. (2019) 37:897–900. Wells G Shea B O'Connell D Peterson J Welch V Losos M. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. (2014). Available online at: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed 20 June, 2021). Higgins JP Thompson SG Deeks JJ Altman DG. Measuring inconsistency in meta-analyses. BMJ. (2003) 327:557–60. 10.1136/bmj.327.7414.55712958120 Batmaz SB Arikoglu T Uyar N Barlas I Kuyucu S. The effect of vitamin D pathway genes on asthma susceptibility, asthma control and vitamin D levels in Turkish Asthmatic children. Int J Hum Genet. (2017) 17:76–85. 10.1080/09723757.2017.1351128 Einisman H Reyes ML Angulo J Cerda J López-Lastra M Castro-Rodriguez JA. Vitamin D levels and vitamin D receptor gene polymorphisms in asthmatic children, a case-control study. Pediatr Allergy Immunol. (2015) 26:545–50. 10.1111/pai.1240926011658 Iordanidou M Paraskakis E Giannakopoulou E Tavridou A Gentile G Borro M . Vitamin D receptor ApaI a allele is associated with better childhood asthma control and improvement in ability for daily activities. Omics. (2014) 18:673–81. 10.1089/omi.2014.002325353337 Ismail MF Elnady HG Fouda EM. Genetic variants in vitamin D pathway in Egyptian asthmatic children, a pilot study. Hum Immunol. (2013) 74:1659–64. 10.1016/j.humimm.2013.08.28424007655 Liu Y Zhang H Qiao Y. Relationship of vitamin D receptor gene polymorphism and children asthma and wheezy bronchitis (In Chineses). China J Modern Med. (2016) 26:36–9. Ma JH Tang CC Zhang XH Gao Y Bai H. Correlation between Vitamin D receptor gene polymorphisms and asthma in Chinldren of Hui nationality in Ningxia (In Chinese). Ningxia Medical J. (2014) 36:870–3. 10.13621/j.1001-5949.2014.100870 Maalmi H Sassi FH Berraies A Ammar J Hamzaoui K Hamzaoui A. Association of vitamin D receptor gene polymorphisms with susceptibility to asthma in Tunisian children, a case control study. Hum Immunol. (2013) 74:234–40. 10.1016/j.humimm.2012.11.00523200756 Mo LY Deng YC Huang CZ Liu JL. Association between polymorphism of vitamin D receptor gene and asthma in Children (In Chinese). Chin J Contemp Pediatr. (2015) 23:742–4. 10.11852/zgetbjzz2015-23-07-21 Papadopoulou A Kouis P Middleton N Kolokotroni O Karpathios T Nicolaidou P . Association of vitamin D receptor gene polymorphisms and vitamin D levels with asthma and atopy in Cypriot adolescents, a case-control study. Multidiscip Respir Med. (2015) 10:26. 10.4081/mrm.2015.30426346690 Pillai DK Iqbal SF Benton AS Lerner J Wiles A Foerster M . Associations between genetic variants in vitamin D metabolism and asthma characteristics in young African Americans, a pilot study. J Investigat Med. (2011) 59:938–46. 10.2310/JIM.0b013e318220df4121613960 Zhang Y Wang Z Ma T. Associations of Genetic Polymorphisms Relevant to Metabolic Pathway of Vitamin D3 with Development and Prognosis of Childhood Bronchial Asthma. DNA Cell Biol. (2017) 36:682–92. 10.1089/dna.2017.373028590769 Zhao HX Chen XR Wu CY Zhuang HN. Study on the correlation of 25-(OH)-VD and Vitamin D receptor gene polymorphism of Children and asthma (In Chinese). Chin Lab Diagno. (2015). 1894–7. Horner CC Bacharier LB. Diagnosis and management of asthma in preschool and school-age children: focus on the 2007 NAEPP Guidelines. Curr Opin Pulm Med. (2009) 15:52–6. 10.1097/MCP.0b013e32831da8ea19077706 Ruan Z Shi Z Zhang G Kou J Ding H. Asthma susceptible genes in children, A meta-analysis. Medicine (Baltimore). (2020) 99:e23051. 10.1097/MD.000000000002305133157959 Hall SC Agrawal DK. Vitamin D and Bronchial Asthma, An Overview of Data From the Past 5 Years. Clin Ther. (2017) 39:917–29. 10.1016/j.clinthera.2017.04.00228449868 Yurt M Liu J Sakurai R Gong M Husain SM Siddiqui MA . Vitamin D supplementation blocks pulmonary structural and functional changes in a rat model of perinatal vitamin D deficiency. Am J Physiol Cell Physiol. (2014) 307:L859–867. 10.1152/ajplung.00032.201425305247 Zhao DD Yu DD Ren QQ Dong B Zhao F Sun YH. Association of vitamin D receptor gene polymorphisms with susceptibility to childhood asthma, A meta-analysis. Pediatr Pulmonol. (2017) 52:423–9. 10.1002/ppul.2354827551963 de Jong C. C. M. Pedersen E. S. L. Mozun R. Muller-Suter D. Jochmann A. Singer F. . (2020). Diagnosis of asthma in children, findings from the Swiss Paediatric Airway Cohort. Eur Respir J. 56 10.1183/13993003.congress-2020.402032499334 MacArthur J Bowler E Cerezo M Gil L Hall P Hastings E . The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. (2017) 45:D896–901. 10.1093/nar/gkw113327899670