The interaction of genetic variants and DNA methylation of the interleukin-4 receptor gene increase the risk of asthma at age 18 years

  • Nelís Soto-Ramírez1,

    Affiliated with

    • Syed Hasan Arshad2, 6,

      Affiliated with

      • John W Holloway2, 3,

        Affiliated with

        • Hongmei Zhang1,

          Affiliated with

          • Eric Schauberger4,

            Affiliated with

            • Susan Ewart5,

              Affiliated with

              • Veeresh Patil2, 6 and

                Affiliated with

                • Wilfried Karmaus1Email author

                  Affiliated with

                  Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20135:1

                  DOI: 10.1186/1868-7083-5-1

                  Received: 18 August 2012

                  Accepted: 5 December 2012

                  Published: 3 January 2013

                  Abstract

                  Background

                  The occurrence of asthma is weakly explained by known genetic variants. Epigenetic marks, DNA methylation (DNA-M) in particular, are considered to add to the explanation of asthma. However, no etiological model has yet been developed that integrates genetic variants and DNA-M. To explore a new model, we focused on one asthma candidate gene, the IL-4 receptor (IL4R). We hypothesized that genetic variants of IL4R in interaction with DNA-M at cytosine-phosphate-guanine (CpG) sites jointly alter the risk of asthma during adolescence. Blood samples were collected at age 18 years from 245 female cohort participants randomly selected for methylation analysis from a birth cohort (n = 1,456, Isle of Wight, UK). Genome-wide DNA-M was assessed using the Illumina Infinium HumanMethylation450 BeadChip.

                  Results

                  Thirteen single nucleotide polymorphisms (SNPs) and twelve CpG sites of IL4R gene were analyzed. Based on linkage disequilibrium and association with asthma, eight SNPs and one CpG site were selected for further analyses. Of the twelve CpG sites in the IL4R gene, only methylation levels of cg09791102 showed an association with asthma at age 18 years (Wilcoxon test: P = 0.01). Log-linear models were used to estimate risk ratios (RRs) for asthma adjusting for uncorrelated SNPs within the IL4R gene and covariates. Testing for interaction between the eight SNPs and the methylation levels of cg09791102 on the risk for asthma at age 18 years, we identified the statistically significant interaction term of SNP rs3024685 × methylation levels of cg09791102 (P = 0.002; after adjusting for false discovery rate). A total of 84 participants had methylation levels ≤0.88, 112 participants between 0.89 and 0.90, and 35 between 0.91 and 0.92. For the SNP rs3024685 (‘CC’ vs. ‘TT’) at methylation levels of ≤0.85, 0.86, 0.90, 0.91, and 0.92, the RRs were 0.01, 0.04, 4.65, 14.76, 14.90, respectively (interaction effect, P = 0.0003).

                  Conclusions

                  Adjusting for multiple testing, our results suggest that DNA-M modulates the risk of asthma related to genetic variants in the IL4R gene. The strong interaction of one SNP and DNA-M is encouraging and provides a novel model of how a joint effect of genetic variants and DNA-M can explain occurrence of asthma.

                  Keywords

                  Interleukin-4 receptor gene DNA methylation Genetic variants Asthma Epigenetics

                  Background

                  Asthma is a common chronic disease that affects around 235 million people around the world and 5.4 million in the United Kingdom (UK) [1]. The burden of disease affects 1.1 million children between ages 0 to 17 years in the UK. Asthma is characterized clinically by shortness of breath, wheezing episodes, chest tightness, and acute episodes of coughing [2]. The disease etiology is poorly understood and the postnatal development is not well established. Genetic susceptibility, environmental factors, and gene × environment interaction are believed to play a critical role in the development of asthma. Over 200 genes have been suggested to contribute to asthma occurrence [35]. The high heritability (35% to 95%) and the co-occurrence of asthma within families highlight the importance of a genetic component in disease pathogenesis [1]. In this work we focus on the interleukin receptor (IL4R) gene which has been clearly established as an asthma susceptibility gene in multiple candidate gene association studies [35].

                  There is evidence that interleukin-4 (IL-4) and its receptor (IL-4R) are involved in the pathogenesis of asthma [68]. A recent meta-analysis indicated a modest risk associated with IL4R single nucleotide polymorphisms (SNPs) on occurrence of asthma, but other investigators found conflicting results [7]. Analysis of asthma candidate genes in a genome-wide association study population showed that SNPs in IL4R were significant related to asthma with significance level between P = 0.05 and P = 0.0035 [3] despite IL4R not being identified in genome-wide association study (GWAS) analysis suggesting that IL4R variation is not well captured in current GWAS platforms. Other genetic regulatory mechanisms beyond DNA sequence variation may aid in explaining the role of IL4R in asthma. It has been suggested that epigenetic mechanisms play a role in T-cell differentiation and regulation, a crucial event in the onset of atopic diseases such as asthma [9]. Epigenetic regulatory mechanisms, such as DNA-methylation (DNA-M), may alter gene expression and protein production without changing the DNA sequence. No etiological model has yet been developed that integrates genetic variants and DNA-M. We will explore the idea that an increase of DNA-M may silence or a decrease of DNA-M may activate the effect of specific SNPs. To test this new model, we focus on one asthma candidate gene, the IL4R gene. We hypothesized that SNPs in interaction with cytosine-phosphate-guanine (CpG) sites jointly predispose to asthma at age 18 years. To test vertical transmission of DNA-M to offspring in future steps, this work focuses on women.

                  Methods

                  Study design and population

                  A whole-population birth cohort was established on the Isle of Wight in 1989 to prospectively study the natural history of asthma and allergic conditions. After exclusion of adoptions, perinatal deaths and refusal, 1,456 children (95%) were enrolled. The local research ethics committee approved the study and informed written parental consent was obtained for all participants at recruitment and subsequently at follow-ups, which were conducted at ages 1, 2, 4, 10, and 18 years of age. The birth cohort has been described in detail elsewhere [10, 11]. In this study we focused on blood samples collected at 18 years of age from 245 female cohort participants who were randomly selected for genomic sequencing and DNA-M.

                  Clinical data collection and outcome

                  Maternal history of asthma and smoking during pregnancy was ascertained at birth. Birth weight was obtained from birth records. At ages 1, 2, 4, 10, and 18 years, the original questionnaire-based information was updated, and weight and height of the child were measured. Breastfeeding duration was assessed at follow-up visits at ages 1 and 2 years. At age 18 years, the questionnaire-based information was updated using the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire [12]. Asthma at age 18 years was defined as subjects with a physician diagnosis of asthma plus current symptoms and/or asthma medication.

                  SNP selection for the IL4R gene

                  An efficient genotype tagging scheme was developed that gave priority to variants that 1) showed strong association with asthma in the Isle of Wight birth cohort, and/or 2) have been reported by others to be associated with asthma/allergy, and/or 3) have functional importance. A literature search for IL4R gene plus asthma and allergy was used to identify associated variants (SNPs, indels). Functional variants included those that were non-synonymous, located in conserved DNA, and/or present in DNA regions with gene regulatory potential. Tagger implemented in Haploview 3.2 using Caucasian Hapmap data was used to develop a tagging scheme for the IL4R gene region, including 10 kb upstream and downstream of the gene [13]. An r2 value of 0.85 was the threshold for tagging and one, two and three SNP marker combination tests were used. The result was an efficient number of genotyped variants (n = 13) that would provide the needed information to statistically support or exclude the gene in its association with asthma outcomes.

                  DNA methylation protocol

                  DNA was extracted from whole blood using a standard salting out procedure [14]. DNA concentration was determined by PicoGreen quantitation. One microgram DNA was bisulfite-treated for cytosine to thymine conversion using the EZ 96-DNA methylation kit (Zymo Research, Irvine, CA, USA), following the manufacturer’s standard protocol. Genome-wide DNA methylation was assessed using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA, USA), which interrogates >484,000 CpG sites associated with approximately 24,000 genes. Arrays were processed using a standard protocol as described elsewhere [15], with multiple identical control samples assigned to each bisulphite conversion batch to assess assay variability and samples randomly distributed on microarrays to control against batch effects. The BeadChips were scanned using a BeadStation, and the methylation level (beta value) calculated for each queried CpG locus using the Methylation Module of BeadStudio software.

                  Exposures

                  The main exposures are SNPs and the methylation levels at CpG sites in the IL4R gene (Table 1). The following SNPs were included in the analysis: rs3024622, rs3024685, rs6498012, rs12102586, rs16976728, rs4787423, rs3024676, and rs2057768.
                  Table 1

                  Location, position, and distance between the SNPs and the CpG sites in the IL4R gene

                    

                  CpG sites

                    

                  08932316

                  05729093

                  03980304

                  00090800

                  06641959

                  01706029

                  26937798

                  16649560

                  08317580

                  09791102

                  01165142

                  05903710

                    

                  TSS1500/ N_Shore

                  TSS1500/ Island

                  TSS1500/ Island

                  TSS200/ Island

                  5UTR/ Island

                  5UTR/ Island

                  5UTR/ S_Shore

                  5UTR

                  5UTR

                  Body

                  Body

                  3UTR

                    

                  Median, IQR (5%, 95%)

                    

                  0.89, 0.02 (0.86, 0.92)

                  0.06, 0.01 (0.04, 0.08)

                  0.07, 0.02 (0.04, 0.10)

                  0.03, 0.01 (0.01, 0.05)

                  0.08, 0.02 (0.05, 0.11)

                  0.07, 0.01 (0.05, 0.09)

                  0.09, 0.02 (0.06, 0.12)

                  0.21, 0.05 (0.15, 0.29)

                  0.90, 0.02 (0.87, 0.93)

                  0.88, 0.02 (0.85, 0.91)

                  0.58, 0.05 (0.51, 0.65)

                  0.87, 0.02 (0.84, 0.90)

                  SNPs

                  Location #

                  27324341

                  27324953

                  27325000

                  27325237

                  27325254

                  27325672

                  27326054

                  27338391

                  27345891

                  27353414

                  27367172

                  27375732

                  rs2057768

                  5UTR

                  27322095

                  −2246

                  −2858

                  −2905

                  −3142

                  −3159

                  −3577

                  −3959

                  −16296

                  −23796

                  −31319

                  −45077

                  −53637

                  rs6498012

                  Intron

                  27331974

                  7633

                  7021

                  6974

                  6737

                  6720

                  6302

                  5920

                  −6417

                  −13917

                  −21440

                  −35198

                  −43758

                  rs3024622

                  Intron

                  27365453

                  41112

                  40500

                  40453

                  40216

                  40199

                  39781

                  39399

                  27062

                  19562

                  12039

                  −1719

                  −10279

                  rs4787423

                  Intron

                  27367334

                  42993

                  42381

                  42334

                  42097

                  42080

                  41662

                  41280

                  28943

                  21443

                  13920

                  162

                  −8398

                  rs3024676

                  Coding

                  27373558

                  49217

                  48605

                  48558

                  48321

                  48304

                  47886

                  47504

                  35167

                  27667

                  20144

                  6386

                  −2174

                  rs3024685

                  3UTR

                  27376910

                  52569

                  51957

                  51910

                  51673

                  51656

                  51238

                  50856

                  38519

                  31019

                  23496

                  9738

                  1178

                  rs12102586

                  3UTR

                  27378053

                  53712

                  53100

                  53053

                  52816

                  52799

                  52381

                  51999

                  39662

                  32162

                  24639

                  10881

                  2321

                  rs16976728

                  3UTR

                  27381712

                  57371

                  56759

                  56712

                  56475

                  56458

                  56040

                  55658

                  43321

                  35821

                  28298

                  14540

                  5980

                  #The location is based on Build 37, also known as GRCh37. The distance was calculated by subtracting the location of the SNP to the CpG site in the IL4R gene. For instance, the distance between the SNP rs6498012 and the CpG site cg08932316 is 7633 (27331974–27324341).

                  Statistical analysis

                  To assess whether our analytic sample (245 DNA samples) was representative of the total cohort available at age 18 years, we compared the characteristics of these two subsets by using the chi-square test. After cleaning the DNA-M data, beta (β) values presented as the proportion of intensity of methylated (M) over the sum of methylated (M) and unmethylated (U) sites (β = M/[c + M + U] with c being a constant to prevent dividing by zero) were used to estimate the effect of DNA methylation [16]. The methylation levels of 12 CpG sites spanning the genomic region of the IL4R gene (Table 1) were tested for association with asthma at age 18 years using Wilcoxon tests. Of these CpG sites, only methylation levels of cg09791102 showed a statistically significant association with asthma at age 18 years (Wilcoxon test: P = 0.01).

                  The 13 SNPs shown in Figure 1 were tested for Hardy-Weinberg equilibrium using Haploview 3.2 software [13] and estimates of linkage disequilibrium (LD) between SNPs were calculated using D’ and r 2[17], to select one SNP that represents each LD block or an unlinked area.
                  http://static-content.springer.com/image/art%3A10.1186%2F1868-7083-5-1/MediaObjects/13148_2012_31_Fig1_HTML.jpg
                  Figure 1

                  IL4R LD plot; standard (D’/LOD) color scheme; D’ LD values displayed.

                  After identifying eight uncorrelated IL4R SNPs (Figure 1; Table 1) and identifying which CpG site was significantly associated with asthma, we ran eight independent models to estimate statistical interactions between these SNPs and the methylation level of cg09791102 on the risk for asthma at age 18 years. We assessed the interaction on a multiplicative scale in log-linear models using an overall chi-square test as a cutoff P value = 0.05 for each. Only one interaction (SNP rs3024685 × cg09791102) showed a significant effect on asthma at age 18. This interaction and those SNPs and four covariates that confounded the association between the SNP and CpG interaction with asthma at age 18 years were included in the final log-linear model. We then inspected which genotype (CC, CT, or TT) explains the overall effect. Confounders include child’s BMI at age 18 (kg/m2), maternal history of asthma, maternal smoking during pregnancy, and breastfeeding duration (weeks). All confounders were simultaneously entered as indicator variables into the log-linear model. A backward elimination process was used to identify confounders, those that changed the association of interest by 10% or more were retained in the final model. For the reduced model, we estimated risk ratios (RR) and their 95% confidence intervals (CI).

                  Since we tested a total of eight crude SNP × methylation interactions before selecting the full model, we adjusted for multiple testing by applying false discovery rate (P = 0.05) [18]. All statistical analyses were performed using the SAS statistical package, Version 9.2 (SAS Institute, Cary, NC, USA), except for cleaning the DNA methylation data, which was done using R statistical computing package [19].

                  Results

                  Blood samples from a subset of 245 of 750 female birth cohort participants were used to determine DNA-M at CpG sites. There were no substantial differences in prevalence of low birth weight, asthma at 18, BMI at 18, breastfeeding duration, maternal BMI, maternal history of asthma, nor maternal smoking between the female participants of the cohort and the subset included in this analysis (Table 2). For the subgroup with available methylation data 12% had maternal history of asthma, 19% had mothers that smoked during pregnancy, and 14.3% (35/245) had asthma at age 18 years.
                  Table 2

                  Subject characteristics with available methylation data compared to the female participants of the total cohort

                   

                  Total female participants n(%)

                  Female with DNA methylation data, n(%)

                  P value

                  Factors

                  n = 750

                  n = 245

                   

                  Maternal history of asthma

                  Yes

                  80 (10.8)

                  30 (12.3)

                  0.50

                  No

                  662 (89.2)

                  213 (87.7)

                   

                  Missing

                  8

                  2

                   

                  Maternal smoking during pregnancy

                  Yes

                  188 (25.3)

                  47 (19.3)

                  0.05

                  No

                  555 (74.7)

                  197 (80.7)

                   

                  Missing

                  7

                  1

                   

                  Maternal body mass index (kg/m 2 )

                  Underweight (<18.5)

                  10 (1.7)

                  4 (2.2)

                  0.82

                  Normal (18.5- <25)

                  355 (61.5)

                  109 (59.2)

                   

                  Overweight (≥25.00)

                  212 (36.7)

                  71 (38.6)

                   

                  Missing

                  173

                  61

                   

                  Low birth weight

                  Yes

                  35 (4.8)

                  9 (3.8)

                  0.53

                  No

                  699 (95.2)

                  228 (96.2)

                   

                  Missing

                  16

                  8

                   

                  Asthma at age 18 years

                  Yes

                  128 (19.4)

                  35 (14.3)

                  0.07

                  No

                  531 (80.6)

                  210 (85.7)

                   

                  Missing

                  91

                  0

                   

                  Median (5%, 95% value); n

                  Breastfeeding duration (weeks)

                  8.0 (0, 40); 664

                  10.5 (0, 40); 222

                  0.16

                  Missing

                  86

                  20

                   

                  Body mass index at age 18 (kg/m 2 )

                  22.2 (18, 32); 499

                  22.9 (19.05, 32.93); 240

                  0.56

                  Missing

                  251

                  5

                   
                  Of the thirteen SNPs genotyped in the IL4R gene, eight SNPs were analyzed since they were uncorrelated (D’ <0.95) (Figure 1, Table 1). A total of 12 CpG sites spanning the genomic region of the IL4R gene were analyzed for association with asthma at age 18 years. Only methylation levels of cg09791102 showed an association with asthma at age 18 years (Wilcoxon test: P = 0.01). Testing for interaction between the eight SNPs and the methylation levels of cg09791102 on the risk for asthma at age 18 years, we identified that the interaction term of SNP rs3024685 × methylation levels of cg09791102 was statistically significant (P = 0.0003; FDR adjusted P value = 0.002; Table 3). In other words, the genetic risk of asthma associated with rs3024685 increases as the methylation level of cg09791102 rises (Figure 2).
                  Table 3

                  Adjusted log-linear regression model of the interaction of genetic variants and DNA methylation of the IL4R gene on asthma at age 18 years

                  Parameter

                   

                  Estimate

                  95%CI

                  P value

                  Intercept

                   

                  19.59

                  −12.78

                  51.97

                  0.25

                  cg09791102

                   

                  −26.97

                  −63.62

                  9.67

                  0.14

                  rs3024685

                  CC

                  −102.45

                  −158.51

                  −46.40

                  0.0003

                   

                  CT

                  −38.48

                  −80.71

                  3.75

                  0.07

                   

                  TT

                  Reference

                     

                  cg09791102* rs3024685

                  CC

                  115.54

                  53.18

                  177.91

                  0.0003

                   

                  CT

                  43.90

                  −3.45

                  91.27

                  0.06

                   

                  TT

                  Reference

                     

                  rs3024622

                  CC

                  −1.24

                  −3.45

                  0.95

                  0.26

                   

                  CG

                  −0.14

                  −1.00

                  0.72

                  0.74

                   

                  GG

                  Reference

                     

                  rs12102586

                  TT

                  2.41

                  0.29

                  4.53

                  0.02

                   

                  CT

                  0.65

                  −0.24

                  1.55

                  0.15

                   

                  CC

                  Reference

                     

                  rs16976728

                  TT

                  −0.53

                  −2.03

                  0.95

                  0.48

                   

                  CT

                  0.16

                  −0.78

                  1.11

                  0.72

                   

                  CC

                  Reference

                     

                  Maternal smoking during pregnancy

                  0.43

                  −0.40

                  1.26

                  0.31

                  Maternal history of asthma

                  0.53

                  −0.41

                  1.49

                  0.26

                  Body mass index at age 18 years (kg/m2)

                  0.05

                  −0.009

                  0.12

                  0.09

                  Breastfeeding duration (weeks)

                  0.02

                  −0.004

                  0.04

                  0.11

                  http://static-content.springer.com/image/art%3A10.1186%2F1868-7083-5-1/MediaObjects/13148_2012_31_Fig2_HTML.jpg
                  Figure 2

                  Risk Ratio of asthma at age 18 years versus methylation score at different genotypes of IL4R rs3024685. The blue bars present the relative frequency of the DNA methylation levels. For instance, 87% methylation is found in 10% of the participants. The reference genotype is ‘TT’. The solid horizontal line that indicates a risk ratio value of ‘1’ shows the risk ratio of the reference ‘TT’ genotype. The black dot represents the ‘CC’ genotype, and the diamond is ‘CT’ genotype.

                  The DNA-M level range for cg09791102 was 0.48 to 0.92 (blue bars in Figure 2). Since the number of participants at methylation levels of 0.85 or less were low, we grouped these methylation levels into ≤0.85 (n = 9). For descriptive purposes, 84 participants had methylation levels of 0.88 and less, 112 participants of 0.89 to 0.90, and 35 of 0.91 to 0.92. Since the mode of inheritance is additive, we compared participants who had the ‘CC’ and ‘CT’ genotypes with those who were ‘TT’ genotype at rs3024685. For the genotype ‘CC’, compared to ‘TT’, we found that at methylation levels of 0.85, 0.86, 0.90, 0.91, and 0.92, the RRs of asthma were 0.01, 0.04, 4.65, 14.76, and 46.90 (Figure 2; FDR adjusted P value = 0.002), respectively. Similar results were found with ‘CT’ genotype, however the interaction term did not achieve statistical significance (P = 0.06).

                  Descriptively, 13.2% and 14.3% of the participants had asthma at a methylation level of 0.88 at the genotype ‘CT’ and ‘TT’, respectively; and none of the ‘CC’ genotype had asthma. Between 0.89 and 0.90 methylation levels, 15.0% of the ‘CC’, 16.7% of the ‘CT’, and 7.9% of the ‘TT’ genotype had asthma. At methylation levels larger than 0.90, 54.6% of the ‘CC’ and 16.7% of the ‘CT’ genotype had asthma, and none of the ‘TT’ genotype had asthma.

                  Discussion

                  This is the first study to determine the role of both genetic and epigenetic factors within the genomic region of the IL4R gene on the risk for asthma. Although the CpG site cg09791102 is located 23,496 base pairs away from SNP rs3024685 in the intragenic region of the IL4R gene, we found that the risk of asthma is modulated by this CpG site even after adjusting for multiple testing. The distance between the SNP and the CpG sites is large. However, Bell et al. have demonstrated that for a regulation in cis even larger distances can show statistically significant effects [20]. Hence, these two factors (SNP and CpG site) may jointly contribute to gene expression or alternative splicing.

                  The SNP rs3024685 in the 3UTR region has no independent effect on asthma at age 18 years; however in interaction with the CpG site cg09791102 (gene body, Table 1) it is strongly associated with asthma in female participants. At 92% methylation level, rs3024685 (‘CC’ genotype compared to ‘TT’) showed a 46.9-fold increase risk for asthma. Our observation of a role of gene-body methylation is further supported by the emerging evidence, which shows that methylation in intragenic regions can be positively correlated with gene expression levels and phenotype variation [21, 22]. Intragenic DNA methylation has been linked to ‘exon definition’ through interaction with auxiliary proteins, by which DNA methylation in the body may result in alternative pre-mRNA splicing regulation (for example, inclusion or exclusion of exons) [2325]. We assume that a higher DNA-M may mask an otherwise protective effect of rs3024685 and thus increases the risk of asthma [26]. Our results indicate that considering both genetic variants and DNA methylation will significantly improve the explanation of asthma. Replication of these findings in an independent study population is needed to validate the interplay of DNA methylation with genetic polymorphism, which results in an increased asthma risk. However, currently there are only few studies that can provide both genetic and DNA methylation data.

                  A limitation of our study is that the RRs at methylation levels larger than 90% are high, which is due to the limited number of individuals (n = 36) with methylation levels larger than 90%. Evidence of selection bias is absent since prevalence of asthma and IL4R SNPs is comparable between those analyzed in this study and those from the original cohort. Multiple testing was a concern since we tested the joint effect of differential DNA methylation of cg09791102 and eight IL4R SNPs separately (a total of eight tests). Nevertheless, the observed increased risk remained statistically significant after penalizing its P value for false discovery rate. Regarding reliability and specificity of methylation status of CpG sites, a recent report demonstrated that the Infinium HumanMethylation450 array, which was used to obtained DNA methylation profiles in this study, had strong reproducibility and high validity [27]. The extent to which DNA methylation measured in blood relate to other tissues and whether can be used as a biomarker for phenotype variation is unclear and is an area of current scientific dispute [2830].

                  Conclusions

                  The strong interaction of one SNP and DNA-M is encouraging and provides a novel model how a joint effect of genetic variants and DNA-M can explain asthma. Although the sample size is limited and focused on female participants, our results should generally motivate other studies to replicate the interaction we found, while also searching for new interactions between genetic variants and DNA methylation, in particular for the IL4R gene and asthma.

                  Abbreviations

                  CI: 

                  Confidence interval

                  CpG: 

                  Cytosine-phosphate-guanine

                  DNA-M: 

                  DNA methylation

                  GWAS: 

                  Genome-wide association study

                  IL4R: 

                  Interleukin-4 receptor

                  ISAAC: 

                  International Study of Asthma and Allergies in Childhood

                  LD: 

                  Linkage disequilibrium

                  RR: 

                  Risk ratio

                  SNP: 

                  Single nucleotide polymorphisms

                  UTR: 

                  Untranslated region.

                  Declarations

                  Acknowledgements

                  Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases under Award Number R01 AI091905-01 (PI: Wilfried Karmaus) and R01 AI061471 (PI: Susan Ewart). The 10-year follow-up of this study was funded by National Asthma Campaign, UK (Grant No 364) and the 18-year follow-up by NIH/NHLBI R01 HL082925-01 (PI: S. Hasan Arshad). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

                  The authors gratefully acknowledge the cooperation of the children and parents who participated in this study, and appreciate the hard work of Mrs. Sharon Matthews and the Isle of Wight research team in collecting data and Nikki Graham for technical support. We thank the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics (funded by Wellcome Trust grant reference 090532/Z/09/Z and MRC Hub grant G0900747 91070) for the generation of the methylation data.

                  Authors’ Affiliations

                  (1)
                  Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina
                  (2)
                  Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton
                  (3)
                  Human Development and Health, Faculty of Medicine, University of Southampton
                  (4)
                  Department of Pediatrics, Medical College of Wisconsin
                  (5)
                  Department of Large Animal Clinical Sciences, Michigan State University
                  (6)
                  The David Hide Asthma and Allergy Research Centre, St Mary’s, Hospital

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