Open Access

Methylomic markers of persistent childhood asthma: a longitudinal study of asthma-discordant monozygotic twins

  • Therese M. Murphy1Email author,
  • Chloe C. Y. Wong2,
  • Louise Arseneault2,
  • Joe Burrage1,
  • Ruby Macdonald1,
  • Eilis Hannon1,
  • Helen L. Fisher2,
  • Antony Ambler2,
  • Terrie E. Moffitt2, 3, 4,
  • Avshalom Caspi2, 3, 4 and
  • Jonathan Mill1, 2
Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20157:130

DOI: 10.1186/s13148-015-0163-4

Received: 27 July 2015

Accepted: 11 December 2015

Published: 18 December 2015

Abstract

Background

Asthma is the most common chronic inflammatory disorder in children. The aetiology of asthma pathology is complex and highly heterogeneous, involving the interplay between genetic and environmental risk factors that is hypothesized to involve epigenetic processes. Our aim was to explore whether methylomic variation in early childhood is associated with discordance for asthma symptoms within monozygotic (MZ) twin pairs recruited from the Environmental Risk (E-Risk) longitudinal twin study. We also aimed to identify differences in DNA methylation that are associated with asthma that develops in childhood and persists into early adulthood as these may represent useful prognostic biomarkers.

Results

We examined genome-wide patterns of DNA methylation in buccal cell samples collected from 37 MZ twin pairs discordant for asthma at age 10. DNA methylation at individual CpG sites demonstrated significant variability within discordant MZ twin pairs with the top-ranked nominally significant differentially methylated position (DMP) located in the HGSNAT gene. We stratified our analysis by assessing DNA methylation differences in a sub-group of MZ twin pairs who remained persistently discordant for asthma at age 18. The top-ranked nominally significant DMP associated with persisting asthma is located in the vicinity of the HLX gene, which has been previously implicated in childhood asthma.

Conclusions

We identified DNA methylation differences associated with childhood asthma in peripheral DNA samples from discordant MZ twin pairs. Our data suggest that differences in DNA methylation associated with childhood asthma which persists into early adulthood are distinct from those associated with asthma which remits.

Keywords

Asthma DNA methylation Epigenetics Monozygotic twins

Background

Asthma is a common, complex and highly heterogeneous disorder characterized by recurring episodes of breathlessness and wheezing [1]. Asthma affects approximately 235 million people worldwide and is the most prevalent chronic inflammatory disorder in children [2]. The natural history of childhood-onset asthma is complex; it is commonly characterized by periods of remission and relapse [3, 4]. The heterogeneous features of asthma may indicate sub-types of the disease with distinct molecular pathology [5, 6], and recent analyses suggest that remitting and persistent childhood-onset asthma is aetiologically distinct [7]. Currently, the diagnosis of asthma relies heavily on clinical features [1]. Understanding the molecular mechanisms involved in the onset and stability of asthma will facilitate the identification of children at increased risk of developing severe and persistent asthma in the future.

Asthma is known to involve complex interplay between genetic and environmental risk factors. Twin studies have demonstrated that the aetiology of asthma has a significant genetic contribution, with heritability estimates ranging from 35 to 80 % [810]. Genome-wide association studies (GWAS) have identified a small number of common risk variants robustly associated with asthma, including a gene-rich locus on chromosome 17q21 specific to childhood-onset disease [11]. Identified variants, however, account for only a small proportion of the estimated heritability [1, 11, 12]. Epidemiological research also highlights associations between specific environmental exposures (including tobacco smoke [4, 13] and microbial and viral agents [14, 15]) and the onset of asthma, with exposures at certain critical periods during early childhood being particularly important [1]. Recent studies have started to examine the role of epigenetic processes—acting to developmentally regulate gene expression via modifications to DNA, histone proteins and chromatin—in complex disease phenotypes [16] including asthma. A number of studies have identified locus-specific variation in DNA methylation associated with asthma pathogenesis [1720]. One study linked differential DNA methylation in the vicinity of two candidate genes (FOXP3 and IFNγ) with asthma in a study of N = 21 asthma-discordant adult monozygotic (MZ) twin pairs [18]. A recent longitudinal study observed asthma-related changes in DNA methylation at specific genetic loci including IL4 and IL4R [17]. However, such published research examining epigenetic variation in asthma has primarily focused on candidate immune-related genes; few studies have taken a genome-wide approach [21]. Of note, a recent epigenome-wide methylation study (EWAS) identified significant DNA methylation changes at several loci associated with imunnunoglobulin E (IgE) levels, which are known to correlate positively with allergic diseases such as asthma [22]. However, most patient samples have been cross-sectional and thus it is unknown whether prior findings of methylomic differences are differentially associated with persistent or remitting asthma in early childhood.

The aim of the current study was to explore whether methylomic variation in early childhood is associated with discordance for asthma in 37 pairs of MZ twins. We were interested in identifying differences in DNA methylation that are associated with asthma that develops in childhood and persists into early adulthood as these may represent useful prognostic biomarkers. The use of disease-discordant MZ twins represents a powerful strategy in epigenetic epidemiology because identical twins are matched for genotype, age, sex, maternal environment, population cohort effects and exposure to many shared environmental factors during childhood [23]. We profiled DNA obtained from buccal swabs, which have previously been used as a surrogate for airway epithelial cells in DNA methylation studies [19].

Results

Brief overview of experimental approach

An overview of the methodological approach used in this study is given in Additional file 1: Figure S1. Briefly, we assessed genome-wide patterns of DNA methylation in asthma-discordant MZ twins and concordant unaffected MZ twins at age 10 using the Illumina 450K HumanMethylation microarray (450K array). Pre-processing, normalization and stringent quality control were performed as previously described [24] (see Methods for specific details). Our analyses focused on identifying differentially methylated positions (DMPs) associated with asthma in (i) all asthma-discordant MZ twins at age 10 (Additional file 1: Figure S1A) and (ii) a sub-group with persistent asthma who were discordant for asthma at age 10 and also at age 18 (Additional file 1: Figure S1B). Using DNA previously collected at age 5, we subsequently assessed longitudinal changes in DNA methylation (between ages 5 and 10) in persistent-asthma-discordant MZ twins (Additional file 1: Figure S1C). Finally, we examined epigenetic variation at top-ranked persistent-asthma-associated DMPs in (i) an asthma-remission group, comprising of MZ twin pairs discordant for asthma at age 10 but concordant for no asthma phenotype at 18, and (ii) concordant unaffected MZ twin pairs where neither twin had asthma at both ages 10 and 18 (Additional file 1: Figure S1D).

Differentially methylated positions at age 10 associated with childhood asthma

Our primary focus was on within-pair DNA methylation differences detected at age 10, as an accurate diagnosis of asthma before age 5 is difficult [25, 26]. As expected, within-twin patterns of DNA methylation were highly correlated across all 37 MZ pairs (mean within-twin r for DNA methylation across all 450K array probes = 0.97) and no difference in overall mean DNA methylation (calculated by averaging across all probes on the 450K array) was observed between affected and unaffected twins (P = 0.81), indicating that childhood asthma is not associated with any global changes in DNA methylation. In contrast, DNA methylation at individual CpG sites demonstrated significant variability within discordant MZ twin pairs. Table 1 lists the top-ranked nominally significant DMPs associated with asthma at age 10 (listing all with a nominal P < 1 × 10−4). Although no DMP remained significant after Bonferroni multiple test correction, permutation analyses of the top-ranked loci revealed significant empirical P values. Consistent within-twin differences in DNA methylation were observed across the majority of discordant MZ twin pairs (n = 37) for these top-ranked DMPs (Fig. 1). The top-ranked DMP, cg03284554, located in the promoter region of the heparan-α-glucosaminide N-acetyltransferase (HGSNAT) gene on chromosome 8q21, was consistently hypermethylated in asthma-affected twins compared to their unaffected co-twin (mean Δβ = 0.041, P = 6.87E-06, permuted empirical P < 0.0001). Of note, HGSNAT gene expression is down-regulated in paediatric allergic asthma cases compared to controls [27]. Gene ontology (GO) term enrichment analysis of the age-10 DMPs (see Additional file 2: Table S1 for a list of included loci) identified 199 nominally significantly enriched terms (see Additional file 2: Table S2), including categories related to anion/ion transportation, regulation of ion transport (GO:0043269 (P = 0.0008), GO:0006811 (P = 0.003)) and regulation of anion transport (GO:0044070 (P = 0.0026)). Interestingly, changes in ion transport have previously been implicated in asthma pathogenesis [28].
Table 1

The top-ranked DMPs in asthma-discordant MZ twins

Probe ID

Co-twin mean

Asthma mean

Mean ∆β

P value

Empirical P value

Hg19

Relation to CpG island

Gene region feature category (UCSC)

Illumina gene annotation

Probe type

Gene annotation from GREAT (distance from TSS)

cg03284554

0.178

0.218

0.041

6.87E-06

<0.0001

Chr8:43113670

N_Shore

TSS1500

HGSNAT

II

HGSNAT (−1078)

cg01496463

0.761

0.795

0.034

1.80E-05

0.0002

Chr12:3262908

 

3′UTR

TSPAN9

II

PRMT8 (−207777), TSPAN9 (+206127)

cg04373937

0.836

0.859

0.023

4.26E-05

<0.0001

Chr14:29622576

 

Intergenic

 

II

G2E3 (−475503), PRKD1 (−155927)

cg14688104

0.035

0.038

0.003

4.29E-05

0.0001

Chr8:99508617

Island

1stExon; 5′UTR

KCNS2

I

KCNS2 (+192)

cg08158233

0.244

0.192

−0.052

5.36E-05

0.0001

Chr8:37470214

 

Intergenic

 

II

ZNF703 (−202244), KCNU1 (+709215)

cg17269596

0.581

0.637

0.056

5.49E-05

0.0004

Chr1:243061735

N_Shelf

Intergenic

 

II

FAM36A (−3526)

cg14299157

0.813

0.756

−0.058

5.59E-05

<0.0001

Chr9:137272770

N_Shelf

Intergenic

 

II

PPP1R26 (−238698), OLFM1 (+165861)

cg21201551

0.747

0.779

0.032

5.90E-05

0.0001

Chr1:31404820

S_Shelf

Intergenic

 

II

PUM1 (−93670), NKAIN1 (+80500)

cg16414472

0.503

0.414

−0.089

6.05E-05

0.0002

Chr11:67866681

 

Body

LRP5

I

PPP6R3 (−118080), LRP5 (+29998)

cg01431063

0.057

0.072

0.014

8.92E-05

<0.0001

Chr10:115850893

Island

Intergenic

 

II

TDRD1 (−78125), ADRB1 (+57098)

cg01048931

0.071

0.084

0.013

9.42E-05

<0.0001

Chr7:55639931

Island

Body

VOPP1

I

VOPP1 (+268)

Ranked by P value. Empirical P value = (number of permutations which are at least as significant as the true result (P < 0.0001) divided by the number of permutations performed (n =10000))

DMPs differentially methylated positions, MZ monozygotic, GREAT Genomic Regions Enrichment of Annotations Tool, TSS transcription start site, UCSC University of California Santa Cruz genome browser

Fig. 1

Graphs showing the difference in DNA methylation (∆β) at age 10 between each pair of monozygotic (MZ) twins discordant for asthma (affected twin − unaffected co-twin) for each of the ten top-ranked differentially methylated positions (DMPs). Mean within-twin pair ∆β across all 37 MZ twin pairs is highlighted in red. Error bars represent the ±standard deviation. Consistent within-twin pair differences in DNA methylation at age 10 are observed across asthma-discordant MZ twin pairs at the top ten ranked differentially methylated positions

Methylomic profiling of MZ twins in a persistent-asthma sub-group

Childhood asthma regularly remits in adolescence and early adulthood [3, 4], with remitting and persistent childhood asthma potentially having differing aetiology [7]. We therefore stratified our analysis by assessing age-10 DNA methylation differences in a sub-group of MZ twin pairs (n = 13 pairs) who remained persistently discordant for asthma at age 18. Table 2 and Fig. 2 show the top-ranked age-10 DMPs associated with asthma that persisted to age 18, which may represent useful childhood biomarkers of prognosis. Again, no DMP remained significant after Bonferroni correction, but empirical P values generated from permutation analyses suggest that these represent significant differences. The top-ranked DMP associated with persisting asthma (cg23603194, located downstream of the HLX gene) was consistently hypomethylated in asthma-affected twins compared with their unaffected co-twin (mean Δβ = −0.11, P = 6.87E-06, permuted empirical P = 0.0009). Interestingly, a number of additional probes (cg18788664, cg10975889, cg15730491) annotated to the HLX gene region were also differentially methylated in twins discordant for persistent asthma compared to their unaffected co-twin (see Fig. 3). Of note, genetic variation in this gene has been previously associated with the development of childhood asthma [29, 30] and cytokine secretion at birth [31]. We next tested the specificity of our persistent-asthma-associated DMPs, by comparing average within-twin DNA methylation differences at these loci in (i) an asthma-remission group, comprising of MZ twin pairs discordant for asthma at age 10 but concordant for no asthma phenotype at 18 (n = 20 twin pairs), and (ii) concordant unaffected MZ twin pairs where neither twin had asthma at both ages 10 and 18 (n = 19 twin pairs). At nine of the ten top-ranked DMPs, average within-twin differences in DNA methylation were significantly different between groups (see Fig. 2b), with post hoc pairwise comparisons indicating that average within-twin differences in DNA methylation are significantly larger at these top-ranked DMPs in the persistent-asthma-discordant twins compared to both the asthma-remission twin sub-group and twins concordant for no asthma. GO term enrichment analysis was undertaken on persistent age-10 asthma DMPs (nominal P < 0.001, see Additional file 2: Table S3 for a list of loci included in the analysis). We identified 142 enriched (nominal P < 0.05) terms (see Additional file 2: Table S4), including categories related to interleukin-2 (IL-2) processes (GO:0042094 (P = 0.0009), GO:0032623 (P = 0.0025)) and type I interferon production (GO:0032480 (P = 0.0039)). Of note, both IL-2 and interferon pathways play an important role in cell-mediated immunity [32, 33] and have previously been implicated in asthma pathogenesis [34, 35].
Table 2

The top-ranked DMPs in age-10 MZ twins persistently discordant for asthma

 

Age 10

Age 5

 

Probe ID

Co-twin mean

Asthma mean

Mean ∆β

P value

Empirical P value

Mean ∆β

P value

Relation to CpG island

Gene region feature category (UCSC)

Hg19

Illumina gene annotation

Probe type

Gene annotation from GREAT (distance from TSS)

cg23603194

0.289

0.180

−0.109

6.87E-06

0.0009

0.056

0.0519

N_Shore

Intergenic

Chr1:221060753

 

II

HLX (+8011), DUSP10 (+854762)

cg27385757

0.353

0.286

−0.067

2.16E-05

0.0012

0.029

0.1966

S_Shore

TSS1500; 3′UTR

Chr6:30656024

KIAA1949; NRM

II

PPP1R18 (−932)

cg06483698

0.805

0.848

0.043

2.64E-05

0.0007

−0.006

0.6847

N_Shore

Body

Chr7:6310290

CYTH3

II

CYTH3 (+1951), USP42 (+165741)

cg05895618

0.559

0.655

0.096

2.69E-05

0.0007

−0.026

0.5367

 

5′UTR

Chr11:19222395

CSRP3

II

CSRP3 (+1193), ZDHHC13 (+83704)

cg14868530

0.081

0.098

0.017

3.36E-05

0.0005

−0.008

0.0351

Island

5′UTR; 1stExon

Chr15:73976679

CD276

I

CD276 (+58)

cg21304454

0.530

0.392

−0.138

3.59E-05

0.0009

0.070

0.1223

N_Shelf

Body

Chr1:17302859

MFAP2

II

MFAP2 (+4313), CROCC (+54415)

cg11998205

0.853

0.806

−0.047

4.28E-05

0.0017

−0.033

0.0361

S_Shelf

Body

Chr19:48840527

TMEM143

I

EMP3 (+11899), TMEM143 (+26658)

cg18513344

0.210

0.137

−0.072

4.39E-05

0.0007

−0.022

0.3459

 

Body

Chr3:195531298

MUC4

II

MUC4 (+7545), MUC20 (+83546)

cg08657206

0.256

0.221

−0.035

5.55E-05

0.0009

−0.005

0.7971

N_Shore

Intergenic

Chr1:180197901

 

I

LHX4 (−1531)

cg19865472

0.897

0.844

−0.052

5.90E-05

0.0004

−0.065

0.1373

Island

3′UTR

Chr19:617133

HCN2

I

POLRMT (+16434), HCN2 (+27241)

cg26280666

0.0976

0.0717

−0.0259

6.13E-05

0.0009

−0.0115

0.4006

N_Shore

Body

Chr8:72755568

MSC

II

EYA1 (−486590), MSC (+1162)

cg13512987

0.5252

0.6452

0.1200

6.77E-05

0.0005

−0.0438

0.1137

 

5′UTR; 1stExon

Chr12:6898785

CD4

II

CD4 (+148)

cg24664347

0.7364

0.8161

0.0797

7.51E-05

0.0006

−0.0624

0.0007

S_Shore

TSS1500

Chr8:86841010

REXO1L2P

II

REXO1L1 (−51705), PSKH2 (+240840)

cg25346117

0.6694

0.7450

0.0756

8.06E-05

0.0004

0.0030

0.8688

 

Body

Chr1:116923256

ATP1A1

II

ATP1A1 (+7462), CD58 (+190458)

cg09587549

0.2320

0.2647

0.0327

9.75E-05

0.0005

−0.0006

0.9742

Island

5′UTR; 1stExon

Chr16:31214126

PYCARD

I

PYCARD (−30)

cg03225548

0.7651

0.6807

−0.0844

9.90E-05

0.0004

−0.0014

0.9638

N_Shelf

Body; 5′UTR

Chr18:71811234

FBXO15

II

TIMM21 (−4511), FBXO15 (+3865)

cg17130754

0.7563

0.6967

−0.0597

9.99E-05

0.0004

0.0478

0.0739

 

Intergenic

Chr10:3536341

 

II

PITRM1 (−321309), KLF6 (+291131)

Ranked by P value. Empirical P value = (number of permutations which are at least as significant as the true result (P < 0.0001) divided by the number of permutations performed (n = 8192))

DMPs differentially methylated positions, MZ monozygotic, GREAT Genomic Regions Enrichment of Annotations Tool, TSS transcription start site, UCSC University of California Santa Cruz genome browser

Fig. 2

a Graphs showing the difference in DNA methylation (∆β) at age 10 between each pair of monozygotic (MZ) twins persistently discordant (at ages 10 and 18) for asthma (affected twin − unaffected co-twin) for each of the ten top-ranked differential methylated positions (DMPs). Mean within-twin pair ∆β across all 13 MZ twin pairs is highlighted in red. Error bars represent the ±standard deviation. Consistent within-twin pair differences in DNA methylation are observed across persistently discordant MZ twin pairs at the ten top-ranked DMPs. b Graph showing average within-twin beta difference of (i) persistent-asthma-discordant MZ twins at age 10, (ii) asthma-remission sub-group (twins discordant for asthma at age 10 but the affected twin had remitted at 18, n = 20 twin pairs) and the age-matched concordant unaffected MZ twins (19 twin pairs). Average within-twin differences in DNA methylation are significantly larger at nine of the ten top-ranked DMPs in asthma-discordant twins compared to both the asthma-remission sub-group and twins concordant for no asthma. Between group comparisons of average within-twin beta differences were examined using a one-way ANOVA. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05

Fig. 3

A number of probes annotated to the HLX gene were significantly differentially methylated in persistent-asthma twins compared to their unaffected co-twin. Asthma-associated differentially methylated positions are highlighted in red. Green bars denote the location of annotated CpG islands. Of note, asthma-associated DMPs overlap known CpG islands downstream of the HLX gene. ∆β = mean within-twin beta difference

Longitudinal DNA methylation changes in persistent-asthma MZ twins

DNA methylation at the top-ranked age-10 persistent-asthma DMPs (P < 0.0001) could be examined in matched buccal cell DNA collected at age 5 for 11 of the 13 persistently discordant MZ twin pairs (2 pairs lacked age-5 DNA methylation data). Although two of the top-ranked probes (cg14868530 (annotated to CD276) and cg11998205 (annotated to TMEM143)) were significantly differentially methylated in the same direction between affected twins and their unaffected co-twin at age 5 (P = 0.035 and P = 0.036, respectively), no significant differences at age 5 were observed for the other top-ranked loci, and there was no overall correlation in within-twin differences at ages 5 and 10 for the top-ranked persistent-asthma DMPs. These findings suggest that epigenetic variation associated with asthma persisting through adolescence is not detectable in very early childhood. Within our persistent-asthma sub-group, we therefore next examined persistent-asthma-associated intra-individual changes in DNA methylation between ages 5 and 10. For each probe, we calculated the change in DNA methylation from age 5 to age 10 (longitudinal ∆β) for each individual and examined the difference in longitudinal ∆β between affected twins and their unaffected co-twin. The top-ranked probe (cg26090469), located in AFF4, became significantly hypomethylated from age 5 to age 10 in twins who developed persistent asthma, compared to their unaffected co-twin (P = 1.41E-06, Table 3). GO term enrichment analysis was performed for the genes associated with the asthma-associated (nominal P < 0.001) intra-individual changes in DNA methylation identified between ages 5 and 10 (see Additional file 2: Table S5 for a list of genes included). This analysis identified 90 enriched (nominal P < 0.05) terms (see Additional file 2: Table S6), with the top-ranked categories being related to activin receptor signalling (GO:0032926 (P = 0.0006), GO:0032925 (P = 0.005)) and epithelium development (GO:0060429 (P = 0.0006)).
Table 3

The top-ranked CpG sites which show changes in DNA methylation levels overtime in the discordant MZ twins

Probe ID

Co-twin mean

Asthma mean

Mean ∆β

P value

Hg19

Relation to CpG island

Gene region feature category (UCSC)

Illumina gene annotation

Probe type

Gene annotation from GREAT (distance from TSS)

cg26090469

−0.0122

0.0341

0.0463

1.41E-06

Chr5:132271449

 

Body

AFF4

II

AFF4 (+27904), LEAP2 (+62092)

cg02181624

0.1204

−0.0649

−0.1854

1.68E-06

Chr6:168716070

N_Shelf

Body

DACT2

II

FRMD1 (−236232), DACT2 (+4331)

cg17199800

−0.0902

0.0620

0.1522

9.47E-06

Chr7:96632777

N_Shore

Body

DLX6AS

II

DLX6 (−2512)

cg21875946

0.0892

−0.0276

−0.1168

1.88E-05

Chr1:206663985

 

Body

IKBKE

II

RASSF5 (−16893), IKBKE (+20400)

cg15691003

0.0082

−0.0205

−0.0287

3.26E-05

Chr1:22351864

N_Shore

Body; TSS200

HSPC157

II

CDC42 (−27255), CELA3B (+48447)

cg02836325

−0.1285

−0.0153

0.1132

3.37E-05

Chr17:76403955

 

Body

PGS1

II

PGS1 (+29221), DNAH17 (+169520)

cg16789707

−0.0958

0.0404

0.1362

4.33E-05

Chr16:88767723

S_Shelf

Body

RNF166

II

SNAI3 (−14842), RNF166 (+5105)

cg04313071

0.1063

−0.0249

−0.1312

4.33E-05

Chr14:102685619

 

Body

WDR20

II

HSP90AA1 (−79534), MOK (+85911)

cg23847069

0.1153

−0.0057

−0.1210

4.79E-05

Chr17:2702204

 

Body

RAP1GAP2

I

RAP1GAP2 (+2473), OR1D5 (+264696)

cg24293948

0.0646

−0.0729

−0.1375

5.04E-05

Chr18:61670413

S_Shore

Intergenic

 

II

SERPINB8 (+33151), LINC00305 (+95460)

cg06367321

0.1110

−0.0015

−0.1125

5.14E-05

Chr16:30125232

 

TSS200; TSS1500

LOC100271831; GDPD3

I

GDPD3 (−355)

cg06655665

−0.1603

−0.0359

0.1243

5.45E-05

Chr2:38607629

S_Shelf

Intergenic

 

II

ATL2 (−3198)

cg25406011

0.1312

−0.0175

−0.1487

6.04E-05

Chr11:1254421

Island

Body

MUC5B

II

MUC5B (+10127), TOLLIP (+76470)

cg25494605

0.0873

−0.0652

−0.1525

6.55E-05

Chr5:173472097

 

TSS1500

HMP19

II

HMP19 (−626)

cg24664347

−0.1381

0.0087

0.1468

6.88E-05

Chr8:86841010

S_Shore

TSS1500

REXO1L2P

II

REXO1L1 (−51705), PSKH2 (+240840)

cg18184910

0.2047

0.0402

−0.1644

7.10E-05

Chr6:47624247

 

TSS200

GPR111

II

GPR111 (−78)

cg00840341

0.1094

0.0131

−0.0963

7.60E-05

Chr16:17609453

 

Intergenic

 

II

XYLT1 (−44716), AK310228 (+825170)

cg14141399

0.2139

−0.0312

−0.2451

7.66E-05

Chr19:52228048

 

TSS1500

HAS1

II

HAS1 (−828)

cg14179581

−0.1193

0.0758

0.1951

7.74E-05

Chr1:50881502

Island

Intergenic

 

II

DMRTA2 (+7616), ELAVL4 (+306909)

cg21209859

−0.0892

0.0351

0.1242

7.78E-05

Chr1:167682516

N_Shore

Intergenic

 

II

CREG1 (−159461), MPZL1 (−8670)

cg21187770

0.1265

−0.0432

−0.1697

8.57E-05

Chr2:26205876

S_Shore

TSS1500

KIF3C

I

KIF3C (−434)

cg01382502

−0.1489

−0.0279

0.1210

8.96E-05

Chr16:22252504

 

Body

EEF2K

II

POLR3E (−56236), EEF2K (+34913)

cg09900436

−0.1813

0.0363

0.2175

8.96E-05

Chr20:58630954

 

TSS200

C20orf197

II

CDH26 (+97484)

cg16606561

0.2193

0.0631

−0.1562

9.72E-05

Chr20:824641

N_Shore

5′UTR; TSS1500

FAM110A

II

FAM110A (+10286), ANGPT4 (+72318)

cg15048437

0.0067

−0.0159

−0.0226

9.85E-05

Chr19:37019614

Island

TSS1500

ZNF260

I

ZNF260 (−367)

Ranked by P value

DMPs differentially methylated positions, MZ monozygotic, GREAT Genomic Regions Enrichment of Annotations Tool, TSS transcription start site, UCSC University of California Santa Cruz

Discussion

In this study, we first assessed genome-wide patterns of DNA methylation in buccal cell samples collected from 37 asthma-discordant MZ twin pairs at age 10. We identified a number of asthma-associated DMPs, characterized by consistent within-pair differences in DNA methylation. The top-ranked DMP (cg03284554) is located upstream of HGSNAT, which has been previously shown to be down-regulated in paediatric allergic asthma [27]. We next stratified our analysis by assessing DNA methylation differences in a sub-group of MZ twin pairs who remained persistently discordant for asthma 8 years later, at age 18. The top-ranked DMP associated with persisting asthma (cg23603194) is located 8 kb downstream of the transcriptional start site (TSS) of the HLX gene, which has been previously implicated in childhood asthma [29, 30]. Moreover, a number of additional probes (cg18788664, cg10975889, cg15730491) located in the proximity of the HLX gene were significantly differentially methylated in affected twins compared to their unaffected co-twin. Although the majority of these probes are located >8 kb downstream of the HLX TSS, their proximity to annotated CpG islands (see Fig. 3) suggests that they reside in potential regulatory regions. HLX has been identified as an important regulator of TH1 differentiation and a suppressor of TH2 commitment, mediating pathways that are known to be perturbed in asthma [29, 36]. The functional relevance of DNA methylation changes downstream of the HLX gene warrants further investigation.

Several other DMPs identified in the persistent-asthma sub-group analysis are located in the vicinity of genes that have previously been implicated in asthma. For example, cg13512987, located in the first exon of the cluster of differentiation 4 (CD4) gene, was significantly hypermethylated in asthma-affected twins compared to their unaffected co-twin. CD4 is a glycoprotein found on the surface of immune cells such as T helper cells, monocytes, macrophages and dendritic cells [37]. CD4+ T cells are increased in broncho–alveolar lavage fluid and bronchial biopsy specimens from asthmatic patients [38]. Similarly, cg14868530, which is hypermethylated in affected twins compared to their unaffected co-twin, is located in the first exon of CD276, a gene important for T cell proliferation [39]. Furthermore, cg18513344, which we found to be hypomethylated in affected twins, is associated with MUC4, a gene that encodes an airway mucin protein—a major constituent of mucus [40]. Interestingly, evaluation of airway biopsies from asthmatic patients have observed higher MUC4 mRNA levels compared to normal healthy controls [41]. DNA methylation studies in other complex phenotypes report [4244] similarly small absolute (<10 %) differences in DNA methylation to those observed in this study. The DNA methylation values reported in this study represent aggregate values across all cells from a given sample. A shift in DNA methylation of 5–10 % indicates that ~5–10 % of DNA molecules in the population assessed become fully methylated (or unmethylated) at that locus, with potentially large functional consequences in that specific cellular sub-population. Finally, GO analysis of differentially methylated loci associated with a persistent-asthma phenotype revealed an enrichment of immune-related pathways including IL-2 and interferon type I signalling pathways which have previously been implicated in asthma pathogenesis [34, 35]. Together, our data suggest that epigenetic variation is associated with childhood asthma, and that methylomic differences may potentially be useful markers for predicting the persistence of asthma at age 18.

A recent EWAS analysis identified robust DNA methylation changes at several loci associated with imunnunoglobulin E (IgE) levels, which are known to correlate positively with allergic diseases such as asthma [22]. Two of the 69 IgE-associated DMPs (cg11398517 (P = 0.008), cg16522484 (P = 0.04)) were nominally significantly associated with asthma in our primary asthma-discordant cohort (37 twin pairs). Also, 5 of the 69 IgE-associated DMPs were nominally associated with asthma in our persistently discordant MZ twins (cg08404225 (P = 0.003), cg05215575 (P = 0.014), cg09676390 (P = 0.019), cg25494227 (P = 0.02), cg17749520 (P = 0.029)). These DMPs were not associated with asthma in twin pairs where the affected twin had remission of symptoms by age 18, although several IgE-associated DMPs were cg17890764 (P = 0.003), cg07374928 (P = 0.01) and cg11398517 (P = 0.028); these DMPs may be associated with IgE levels in childhood asthma which remits in early adulthood. Although our findings require replication in larger independent prospective asthma cohorts, the data suggest that prognostic DNA methylation markers of persistent asthma may be identified. It would be interesting to combine epigenetic data with polygenic risk scores, which have previously been associated with life-course-persistent asthma [7].

DNA methylation at the top-ranked age-10 persistent-asthma DMPs (P < 0.0001) was examined in matched buccal cell DNA collected at age 5 for 11 of the 13 persistently discordant MZ twin pairs. Although there was no overall correlation in within-twin differences at ages 5 and 10 for the top-ranked persistent-asthma DMPs, we identified a number of age-10 DMPs that were also nominally significantly differentially methylated in the twins at age 5. These data suggest that DNA methylation of these loci could potentially represent early biomarkers for persistent asthma. We next examined asthma-associated intra-individual changes in DNA methylation between ages 5 and 10. The top-ranked longitudinal DMP (cg26090469), which was significantly hypomethylated from age 5 to age 10 in twins with persistent asthma compared to their unaffected co-twin, is located in the AFF4 gene. Research suggests that AFF4, an important scaffold protein, may play a role in viral gene expression [45]. Similarly, cg25406011, located in exon 18 of the MUC5B gene, was significantly hypomethylated from age 5 to age 10 in twins with persistent asthma compared to their unaffected co-twin. MUC5B codes for the major gel-forming mucin in mucus. Recently, it has been shown that mouse Muc5b is required for mucociliary clearance, for controlling infections in the airways and for immune homeostasis in mouse lungs [46]. Our data suggests that site-specific longitudinal intra-individual DNA methylation changes in MUC5B may play a role in the pathogenesis and development of asthma. Genes annotated to the most DMPs between ages 5 and 10 in twins who develop persistent asthma highlighted significant GO term enrichment for activin receptor signalling and epithelium development. Notably, activin signalling has previously been implicated in airway remodelling in asthma [47].

Despite the power of the discordant MZ twin approach for epigenetic epidemiology, there are several limitations to this study. First, an event history calendar was used to record children’s history of chronic health conditions at ages 5 and 10 and a set of seven asthma symptoms were assessed to determine asthma status at age 18. However, the questions used in this study are well established and have been validated and used previously in studies of asthma and other childhood phenotypes [4851]. Second, our primary and secondary analyses utilized a small cohort of 74 twin children (37 MZ pairs) and 26 twin children (13 MZ twin pairs), respectively. Despite the modest sample size, the cases were exhaustive of asthma-discordant pairs in our birth cohort which has been shown to represent the UK population [52]. Nevertheless, our analyses were relatively underpowered to detect small changes in DNA methylation. Although no probe reaches Bonferroni-corrected levels of significance, DNA methylation studies in other complex phenotypes report similarly small absolute differences. Given the known non-independence of DNA methylation across the probes represented on the 450K array [53], it is likely that conventional methods of global statistical significance (which assume statistical independence of the multiple tests) are not appropriate for these analyses [54]. As an alternative approach, we therefore report empirical P values (generated by randomly re-assigning disease status and re-analysing the data) for each of the top-ranked probes using permutation testing [54]. After up to 10,000 permutations, each of our top-ranked probes was found to be significantly associated with asthma at an empirical P < 0.0009. Third, methylomic analysis was performed on DNA from buccal cells, which may not represent the primary target tissue involved in asthma. However, buccal cells are an easily accessible tissue that encompasses a portion of the upper airway and have previously been proposed as a surrogate for airway epithelial cells [19]. Due to the limited availability of buccal cell DNA, technical validation, using bisulfite pyrosequencing, of our top-ranked DMPs was not performed in this study. Of note, however, we and others have previously validated small changes in DNA methylation identified from the 450K array using pyrosequencing, highlighting the reliability of the 450K array [43, 5557]. Our analysis of asthma-concordant MZ twin pairs highlights the specificity of the differences we observed in disease-discordant twin pairs. Additionally, buccal cell RNA was not available for our MZ twin cohort and thus we were unable to directly examine the functional relevancy of asthma-associated DMPs on gene expression. One final caveat in this study is that chorionicity data were not available on these twins, a potential limitation given that whether or not MZ twins share a placenta may influence epigenomic and transcriptional differences mediated by subtle differences in the prenatal environment [58].

Conclusions

In summary, we have identified DNA methylation differences associated with childhood asthma in peripheral DNA samples from discordant MZ twin pairs. The top-ranked persistent-asthma-associated DMP (cg23603194) identified in this study is downstream of the HLX gene that has been previously implicated in the pathogenesis of asthma. Our data suggest that differences in DNA methylation associated with childhood asthma which persists into early adulthood are distinct from those associated with childhood asthma which remits before early adulthood.

Methods

Study cohort

Participants were recruited from the Environmental Risk (E-Risk) longitudinal twin study, which tracks the development of a birth cohort of 1116 British twin pairs (n = 2232 individuals). The sample was drawn from a larger birth register of twins born in England and Wales in 1994–1995 [59]. Full details about the sample are reported elsewhere [60]. The sample includes 55 % MZ twin pairs. Sex is evenly distributed within zygosity (49 % male). The sample was originally assessed when the twins were aged 5, and follow-up home visits took place when the children were aged 7 (98 % participation), 10 (96 % participation), 12 (96 % participation) and 18 years (93 % participation). The Joint South London and Maudsley and the Institute of Psychiatry Research Ethics Committee approved each phase of the study. Prior to age 18, parents gave informed consent and children gave assent; at age 18, the participants themselves gave informed consent.

Measures

At ages 5 and 10, information was collected from mothers about each child’s asthma using an event history calendar for recording children’s history of chronic health conditions. Methodological studies document that event history calendars improve accuracy of health information over conventional questionnaire instruments [48, 49]. A sub-sample of 56 Caucasian families (112 children, 56 twin pairs) was used for this study, comprising families who (a) had MZ twins, and (b) were discordant for asthma at age 10 (37 twin pairs) or (c) both twins did not have asthma (19 twin pairs). All MZ twins were Caucasian and born in the UK. Buccal cell samples were obtained from children during home visits at ages 5 and 10 (see Additional file 2: Table S7 for further details). Genomic DNA was extracted from buccal cells using a standard procedure [61].

When the children were 18 years old, asthmatic symptoms were further assessed in a private individual interview conducted by trained professionals. Three questions were taken from the European Community Respiratory Health Survey (ECRHS) II screening questionnaire (www.ecrhs.org) [51] related to asthma (“Have you been woken by an attack of shortness of breath at any time in the last 12 months?”, “Have you had an attack of asthma in the last 12 months?”, “Are you currently taking any medicine (including inhalers, aerosols or tablets) for asthma?”). A dichotomous variable representing children who reported no to all three questions and those who reported at least one definite asthma symptom was created. Follow-up phenotypic information was available for 36/37 discordant twin pairs at age 18. Thirteen MZ twin pairs discordant at age 10 remained discordant for asthma at age 18. Twenty MZ twin pairs discordant at age 10 became concordant unaffected at age 18, and three MZ twin pairs became concordant affected at age 18.

Methylomic profiling

Genomic DNA (500 ng) was treated with sodium bisulfite using the EZ-96 DNA Methylation Kit (Zymo Research, CA, USA) following the manufacturers’ protocol. DNA methylation was quantified using the Infinium HumanMethylation450 BeadChip array (Illumina, Inc., San Diego, CA) as previously described [24]. Each twin pair was processed together on the same array to avoid batch effects. Genome Studio software (Illumina, Inc.) was used to extract signal intensities for each probe and perform initial quality control. Further quality control checks, quantile normalization and separate background adjustment of methylated and unmethylated intensities of type I and II probes were employed using the wateRmelon package in R (available from the bioconductor repository www.bioconductor.org) [24]. Samples with 5 % of sites with a detection P value >.05 or a bead count <3 in 5 % of samples were removed from the analysis. Non-specific probes and probes on the X and Y chromosomes were removed [62, 63]. The final analyses included 391,554 probes and included 31 asthma-discordant MZ pairs (62 individual samples) at age 5, 37 MZ pairs (74 individual samples) at age 10, and 19 MZ (38 individual samples) age-10 concordant unaffected pairs. Polymorphic single nucleotide polymorphism control probes (n = 65) located on the array were used to confirm monozygosity for all twin pairs included in the final analysis.

Data analyses

All statistical analyses were performed using R statistical package (version 3.1.1). Our primary analyses employed a paired t test to identify DMPs between affected twins (children who were reported as having a history of asthma) and unaffected (children who had no reported history of asthma) twin children using DNA collected at age 10. Next, we identified a persistent-asthma sub-group (13 MZ twin pairs) of asthma-discordant MZ twins at age 10 who remained discordant for asthma at age 18. A paired t test was employed to identify DMPs between twins discordant (at both ages 10 and 18) for asthma using DNA collected at age 10. Probes were ranked according to P value. Analyses were repeated for buccal samples collected at age 5. The Beta value (β) is a ratio between methylated probe intensity and total probe intensities (sum of methylated and unmethylated probe intensities) and ranges from 0 to 1. Within the persistent-asthma sub-group, intra-individual changes in DNA methylation from ages 5 to 10 were calculated (longitudinal ∆β) and the difference in longitudinal ∆β between persistent-asthma-affected twins and their unaffected co-twin was examined using a paired t test. An empirical P value was calculated for top-ranked DMPs (P < 0.0001) in both the asthma-discordant (n = 37 twin pairs) and the persistent-asthma sub-group (n = 13 twin pairs) by first randomly assigning twin status (affected/unaffected) and performing a paired t test. This was repeated for up to 10,000 permutations. An empirical P value was calculated by dividing the number of permutations which are at least as significant as the true result (P < 0.0001) by the number of permutations performed.

The specificity of our persistent-asthma-associated DMPs was determined by examining within-twin DNA methylation differences at these loci in (i) an asthma-remission sub-group (twins discordant for asthma at age 10 but the affected twin had remitted at 18, n = 20 twin pairs) and (ii) concordant unaffected control MZ twins, with no history of asthma in either twin (n = 19 twin pairs). For the asthma-remission sub-group, the average within-twin beta difference (∆β) was calculated, at these loci, by taking the average within-twin beta difference (beta value of asthma-affected twin (age 10) − beta value of unaffected co-twin (age 10)). For the concordant unaffected control MZ twins, the average within-twin beta difference, at these loci, was calculated using permutation testing (n = 1000). Briefly, concordant unaffected twin pairs were randomly assigned a dichotomous variable (0 (twin 1) or 1 (twin 2)) and the within-twin beta difference (beta value of twin 1 − beta value of twin 2) was calculated for each twin pair at each probe. Next, the average within-twin beta difference (within-twin ∆β) was calculated across all twin pairs at each probe, and this was repeated 1000 times. The average within-twin ∆β was calculated by taking the average of the permutated within-twin ∆β values obtained at each DMP of interest. The average within-twin ∆β was then compared to (i) the average within-twin ∆β calculated from the persistent-asthma-discordant MZ twin pairs and (ii) the average within-twin ∆β calculated from the asthma-remission group at each probe of interest using a one-way analysis of variance (ANOVA). Post hoc pairwise comparisons (pairwise t test) were performed to identify which groups were significantly different from each other.

Gene ontology term enrichment analysis

Gene ontology (GO) term enrichment analysis was performed on genes annotated (Illumina UCSC gene annotation) to age-10 DMPs, age-10 persistent-asthma DMPs and longitudinal DMPs (nominal P < 0.001), respectively, using the R package GOseqv1.18.1 (downloaded from Bioconductor [64]). GOseq can be used to correct for the number of Illumina 450K probes in each gene during GO term enrichment analysis. The number of probes per gene was calculated in our final dataset to create a probability weighting function, which was then used in the GO term enrichment analysis.

Declarations

Acknowledgements

We are grateful to the study mothers and fathers, the twins, and the twins’ teachers for their participation. We thank the members of the E-Risk team for their contribution to this study. The E-Risk Study was funded by a Medical Research Council (MRC) grant (G1002190) and a National Institute of Child Health and Development (NICHD) grant (HD077482), and this work was funded by the American Asthma Foundation and an MRC Centenary Award to HLF (G9817803-E02/1).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
University of Exeter Medical School, University of Exeter
(2)
MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London
(3)
Department of Psychology and Neuroscience, Duke University
(4)
Department of Psychiatry and Behavioral Sciences, Duke University Medical School

References

  1. Ober C, Yao TC. The genetics of asthma and allergic disease: a 21st century perspective. Immunol Rev. 2011;242(1):10–30. doi:10.1111/j.1600-065X.2011.01029.x.PubMed CentralPubMedView ArticleGoogle Scholar
  2. WHO Media Center. Asthma fact sheet N°307. Updated November 2014;http://www.who.int/mediacentre/factsheets/fs307/en/.
  3. Sears MR, Greene JM, Willan AR, Wiecek EM, Taylor DR, Flannery EM, et al. A longitudinal, population-based, cohort study of childhood asthma followed to adulthood. N Engl J Med. 2003;349(15):1414–22. doi:10.1056/NEJMoa022363.PubMedView ArticleGoogle Scholar
  4. Strachan DP, Butland BK, Anderson HR. Incidence and prognosis of asthma and wheezing illness from early childhood to age 33 in a national British cohort. BMJ. 1996;312(7040):1195–9.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Agache I, Akdis C, Jutel M, Virchow JC. Untangling asthma phenotypes and endotypes. Allergy. 2012;67(7):835–46. doi:10.1111/j.1398-9995.2012.02832.x.PubMedView ArticleGoogle Scholar
  6. Bonfield TL, Ross KR. Asthma heterogeneity and therapeutic options from the clinic to the bench. Curr Opin Allergy Clin Immunol. 2012;12(1):60–7. doi:10.1097/ACI.0b013e32834edb5b.PubMedView ArticleGoogle Scholar
  7. Belsky DW, Sears MR, Hancox RJ, Harrington H, Houts R, Moffitt TE, et al. Polygenic risk and the development and course of asthma: an analysis of data from a four-decade longitudinal study. Lancet Respir Med. 2013;1(6):453–61. doi:10.1016/S2213-2600(13)70101-2.PubMed CentralPubMedView ArticleGoogle Scholar
  8. Thomsen SF, van der Sluis S, Kyvik KO, Skytthe A, Backer V. Estimates of asthma heritability in a large twin sample. Clin Exp Allergy. 2010;40(7):1054–61. doi:10.1111/j.1365-2222.2010.03525.x.PubMedView ArticleGoogle Scholar
  9. Duffy DL, Martin NG, Battistutta D, Hopper JL, Mathews JD. Genetics of asthma and hay fever in Australian twins. Am Rev Respir Dis. 1990;142(6 Pt 1):1351–8. doi:10.1164/ajrccm/142.6_Pt_1.1351.PubMedView ArticleGoogle Scholar
  10. Nieminen MM, Kaprio J, Koskenvuo M. A population-based study of bronchial asthma in adult twin pairs. Chest. 1991;100(1):70–5.PubMedView ArticleGoogle Scholar
  11. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010;363(13):1211–21. doi:10.1056/NEJMoa0906312.PubMed CentralPubMedView ArticleGoogle Scholar
  12. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43(9):887–92. doi:10.1038/ng.888.PubMed CentralPubMedView ArticleGoogle Scholar
  13. Tariq SM, Hakim EA, Matthews SM, Arshad SH. Influence of smoking on asthmatic symptoms and allergen sensitisation in early childhood. Postgrad Med J. 2000;76(901):694–9.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Stein RT, Sherrill D, Morgan WJ, Holberg CJ, Halonen M, Taussig LM, et al. Respiratory syncytial virus in early life and risk of wheeze and allergy by age 13 years. Lancet. 1999;354(9178):541–5. doi:10.1016/S0140-6736(98)10321-5.PubMedView ArticleGoogle Scholar
  15. Bisgaard H, Hermansen MN, Buchvald F, Loland L, Halkjaer LB, Bonnelykke K, et al. Childhood asthma after bacterial colonization of the airway in neonates. N Engl J Med. 2007;357(15):1487–95. doi:10.1056/NEJMoa052632.PubMedView ArticleGoogle Scholar
  16. Murphy TM, Mill J. Epigenetics in health and disease: heralding the EWAS era. Lancet. 2014;383(9933):1952–4. doi:10.1016/S0140-6736(14)60269-5.PubMedView ArticleGoogle Scholar
  17. Zhang H, Tong X, Holloway JW, Rezwan FI, Lockett GA, Patil V, et al. The interplay of DNA methylation over time with Th2 pathway genetic variants on asthma risk and temporal asthma transition. Clin Epigenetics. 2014;6(1):8. doi:10.1186/1868-7083-6-8.PubMed CentralPubMedView ArticleGoogle Scholar
  18. Runyon RS, Cachola LM, Rajeshuni N, Hunter T, Garcia M, Ahn R, et al. Asthma discordance in twins is linked to epigenetic modifications of T cells. PLoS One. 2012;7(11):e48796. doi:10.1371/journal.pone.0048796.PubMed CentralPubMedView ArticleGoogle Scholar
  19. Lovinsky-Desir S, Ridder R, Torrone D, Maher C, Narula S, Scheuerman M, et al. DNA methylation of the allergy regulatory gene interferon gamma varies by age, sex, and tissue type in asthmatics. Clin Epigenetics. 2014;6(1):9. doi:10.1186/1868-7083-6-9.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Mikhaylova L, Zhang Y, Kobzik L, Fedulov AV. Link between epigenomic alterations and genome-wide aberrant transcriptional response to allergen in dendritic cells conveying maternal asthma risk. PLoS One. 2013;8(8):e70387. doi:10.1371/journal.pone.0070387.PubMed CentralPubMedView ArticleGoogle Scholar
  21. Yang IV, Pedersen BS, Liu A, O'Connor GT, Teach SJ, Kattan M, et al. DNA methylation and childhood asthma in the inner city. J Allergy Clin Immunol. 2015;136(1):69–80. doi:10.1016/j.jaci.2015.01.025.PubMedView ArticleGoogle Scholar
  22. Liang L, Willis-Owen SA, Laprise C, Wong KC, Davies GA, Hudson TJ, et al. An epigenome-wide association study of total serum immunoglobulin E concentration. Nature. 2015. doi:10.1038/nature14125.Google Scholar
  23. Mill J, Heijmans BT. From promises to practical strategies in epigenetic epidemiology. Nat Rev Genet. 2013;14(8):585–94. doi:10.1038/nrg3405.PubMedView ArticleGoogle Scholar
  24. Pidsley R, CC YW, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013;14:293. doi:10.1186/1471-2164-14-293.PubMed CentralPubMedView ArticleGoogle Scholar
  25. Pedersen S. Preschool asthma—not so easy to diagnose. Prim Care Respir J. 2007;16(1):4–6. doi:10.3132/pcrj.2007.00011.PubMedView ArticleGoogle Scholar
  26. Doherty G, Bush A. Diagnosing respiratory problems in young children. Practitioner. 2007;251(1697):20. 2–5.PubMedGoogle Scholar
  27. Lu LQ, Liao W. Screening and functional pathway analysis of genes associated with pediatric allergic asthma using a DNA microarray. Mol Med Rep. 2015. doi:10.3892/mmr.2015.3277.Google Scholar
  28. Danahay H, Atherton H, Jones G, Bridges RJ, Poll CT. Interleukin-13 induces a hypersecretory ion transport phenotype in human bronchial epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2002;282(2):L226–36. doi:10.1152/ajplung.00311.2001.PubMedView ArticleGoogle Scholar
  29. Suttner K, Ruoss I, Rosenstiel P, Depner M, Pinto LA, Schedel M, et al. HLX1 gene variants influence the development of childhood asthma. J Allergy Clin Immunol. 2009;123(1):82–8. doi:10.1016/j.jaci.2008.09.047. e6.PubMedView ArticleGoogle Scholar
  30. Suttner K, Rosenstiel P, Depner M, Schedel M, Pinto LA, Ruether A, et al. TBX21 gene variants increase childhood asthma risk in combination with HLX1 variants. J Allergy Clin Immunol. 2009;123(5):1062–8. doi:10.1016/j.jaci.2009.02.025. 8 e1-8.PubMedView ArticleGoogle Scholar
  31. Casaca VI, Illi S, Suttner K, Schleich I, Ballenberger N, Klucker E, et al. TBX21 and HLX1 polymorphisms influence cytokine secretion at birth. PLoS One. 2012;7(1):e31069. doi:10.1371/journal.pone.0031069.PubMed CentralPubMedView ArticleGoogle Scholar
  32. Malek TR, Castro I. Interleukin-2 receptor signaling: at the interface between tolerance and immunity. Immunity. 2010;33(2):153–65. doi:10.1016/j.immuni.2010.08.004.PubMed CentralPubMedView ArticleGoogle Scholar
  33. Gonzalez-Navajas JM, Lee J, David M, Raz E. Immunomodulatory functions of type I interferons. Nat Rev Immunol. 2012;12(2):125–35. doi:10.1038/nri3133.PubMed CentralPubMedGoogle Scholar
  34. Boonpiyathad S, Pornsuriyasak P, Buranapraditkun S, Klaewsongkram J. Interleukin-2 levels in exhaled breath condensates, asthma severity, and asthma control in nonallergic asthma. Allergy Asthma Proc. 2013;34(5):e35–41. doi:10.2500/aap.2013.34.3680.PubMedView ArticleGoogle Scholar
  35. Patel DA, You Y, Huang G, Byers DE, Kim HJ, Agapov E, et al. Interferon response and respiratory virus control are preserved in bronchial epithelial cells in asthma. J Allergy Clin Immunol. 2014;134(6):1402–12. doi:10.1016/j.jaci.2014.07.013. e7.PubMed CentralPubMedView ArticleGoogle Scholar
  36. Mullen AC, Hutchins AS, High FA, Lee HW, Sykes KJ, Chodosh LA, et al. Hlx is induced by and genetically interacts with T-bet to promote heritable T(H)1 gene induction. Nat Immunol. 2002;3(7):652–8. doi:10.1038/ni807.PubMedGoogle Scholar
  37. Isobe M, Huebner K, Maddon PJ, Littman DR, Axel R, Croce CM. The gene encoding the T-cell surface protein T4 is located on human chromosome 12. Proc Natl Acad Sci U S A. 1986;83(12):4399–402.PubMed CentralPubMedView ArticleGoogle Scholar
  38. Kon OM, Kay AB. T cells and chronic asthma. Int Arch Allergy Immunol. 1999;118(2–4):133–5. doi:24049.PubMedView ArticleGoogle Scholar
  39. Chapoval AI, Ni J, Lau JS, Wilcox RA, Flies DB, Liu D, et al. B7-H3: a costimulatory molecule for T cell activation and IFN-gamma production. Nat Immunol. 2001;2(3):269–74. doi:10.1038/85339.PubMedView ArticleGoogle Scholar
  40. Damera G, Xia B, Sachdev GP. IL-4 induced MUC4 enhancement in respiratory epithelial cells in vitro is mediated through JAK-3 selective signaling. Respir Res. 2006;7:39. doi:10.1186/1465-9921-7-39.PubMed CentralPubMedView ArticleGoogle Scholar
  41. Ordonez CL, Khashayar R, Wong HH, Ferrando R, Wu R, Hyde DM, et al. Mild and moderate asthma is associated with airway goblet cell hyperplasia and abnormalities in mucin gene expression. Am J Respir Crit Care Med. 2001;163(2):517–23. doi:10.1164/ajrccm.163.2.2004039.PubMedView ArticleGoogle Scholar
  42. Dempster EL, Pidsley R, Schalkwyk LC, Owens S, Georgiades A, Kane F, et al. Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder. Hum Mol Genet. 2011;20(24):4786–96. doi:10.1093/hmg/ddr416.PubMed CentralPubMedView ArticleGoogle Scholar
  43. Wong CC, Meaburn EL, Ronald A, Price TS, Jeffries AR, Schalkwyk LC, et al. Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits. Mol Psychiatry. 2014;19(4):495–503. doi:10.1038/mp.2013.41.PubMed CentralPubMedView ArticleGoogle Scholar
  44. McDermott E, Ryan EJ, Tosetto M, Gibson D, Burrage J, Keegan D, et al. DNA methylation profiling in inflammatory bowel disease provides new insights into disease pathogenesis. J Crohns Colitis. 2015. doi:10.1093/ecco-jcc/jjv176.PubMedGoogle Scholar
  45. He N, Liu M, Hsu J, Xue Y, Chou S, Burlingame A, et al. HIV-1 Tat and host AFF4 recruit two transcription elongation factors into a bifunctional complex for coordinated activation of HIV-1 transcription. Mol Cell. 2010;38(3):428–38. doi:10.1016/j.molcel.2010.04.013.PubMed CentralPubMedView ArticleGoogle Scholar
  46. Roy MG, Livraghi-Butrico A, Fletcher AA, McElwee MM, Evans SE, Boerner RM, et al. Muc5b is required for airway defence. Nature. 2014;505(7483):412–6. doi:10.1038/nature12807.PubMed CentralPubMedView ArticleGoogle Scholar
  47. Kariyawasam HH, Pegorier S, Barkans J, Xanthou G, Aizen M, Ying S, et al. Activin and transforming growth factor-beta signaling pathways are activated after allergen challenge in mild asthma. J Allergy Clin Immunol. 2009;124(3):454–62. doi:10.1016/j.jaci.2009.06.022.PubMed CentralPubMedView ArticleGoogle Scholar
  48. Belli RSL, Andreski P, Agawal S. Methodological comparisons between CATI event history calendar and standardized conventional questionnaire instruments. Public Opin Q. 2007;71(4):603–22.View ArticleGoogle Scholar
  49. Caspi AMT, Thornton A, Freedman D, Amell J, Harrington H, Smeijers J, et al. The life history calendar: a research and clinical assessment method for collecting retrospective event-history data. Int J Method Psych. 1996;6(2):101–14.View ArticleGoogle Scholar
  50. Kelleher I, Harley M, Murtagh A, Cannon M. Are screening instruments valid for psychotic-like experiences? A validation study of screening questions for psychotic-like experiences using in-depth clinical interview. Schizophr Bull. 2011;37(2):362–9. doi:10.1093/schbul/sbp057.PubMed CentralPubMedView ArticleGoogle Scholar
  51. European Community Respiratory Health Survey IISC. The European Community Respiratory Health Survey II. Eur Respir J. 2002;20(5):1071–9.View ArticleGoogle Scholar
  52. Odgers CL, Donley S, Caspi A, Bates CJ, Moffitt TE. Living alongside more affluent neighbors predicts greater involvement in antisocial behavior among low-income boys. J Child Psychol Psychiatry. 2015. doi:10.1111/jcpp.12380.PubMedGoogle Scholar
  53. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–41. doi:10.1038/nrg3000.PubMed CentralPubMedView ArticleGoogle Scholar
  54. Wilhelm-Benartzi CS, Koestler DC, Karagas MR, Flanagan JM, Christensen BC, Kelsey KT, et al. Review of processing and analysis methods for DNA methylation array data. Br J Cancer. 2013;109(6):1394–402. doi:10.1038/bjc.2013.496.PubMed CentralPubMedView ArticleGoogle Scholar
  55. Dempster EL, Wong CC, Lester KJ, Burrage J, Gregory AM, Mill J, et al. Genome-wide methylomic analysis of monozygotic twins discordant for adolescent depression. Biol Psychiatry. 2014;76(12):977–83. doi:10.1016/j.biopsych.2014.04.013.PubMed CentralPubMedView ArticleGoogle Scholar
  56. Lunnon K, Smith R, Hannon E, De Jager PL, Srivastava G, Volta M, et al. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nat Neurosci. 2014;17(9):1164–70. doi:10.1038/nn.3782.PubMed CentralPubMedView ArticleGoogle Scholar
  57. Roessler J, Ammerpohl O, Gutwein J, Hasemeier B, Anwar SL, Kreipe H, et al. Quantitative cross-validation and content analysis of the 450 k DNA methylation array from Illumina, Inc. BMC Res Notes. 2012;5:210. doi:10.1186/1756-0500-5-210.PubMed CentralPubMedView ArticleGoogle Scholar
  58. Gordon L, Joo JE, Powell JE, Ollikainen M, Novakovic B, Li X, et al. Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence. Genome Res. 2012;22(8):1395–406. doi:10.1101/gr.136598.111.PubMed CentralPubMedView ArticleGoogle Scholar
  59. Trouton A, Spinath FM, Plomin R. Twins early development study (TEDS): a multivariate, longitudinal genetic investigation of language, cognition and behavior problems in childhood. Twin Res. 2002;5(5):444–8. doi:10.1375/136905202320906255.PubMedView ArticleGoogle Scholar
  60. Moffitt TE, Team ERS. Teen-aged mothers in contemporary Britain. J Child Psychol Psychiatry. 2002;43(6):727–42.PubMedView ArticleGoogle Scholar
  61. Freeman B, Smith N, Curtis C, Huckett L, Mill J, Craig IW. DNA from buccal swabs recruited by mail: evaluation of storage effects on long-term stability and suitability for multiplex polymerase chain reaction genotyping. Behav Genet. 2003;33(1):67–72.PubMedView ArticleGoogle Scholar
  62. Price ME, Cotton AM, Lam LL, Farre P, Emberly E, Brown CJ, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin. 2013;6(1):4. doi:10.1186/1756-8935-6-4.PubMed CentralPubMedView ArticleGoogle Scholar
  63. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9. doi:10.4161/epi.23470.PubMed CentralPubMedView ArticleGoogle Scholar
  64. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14. doi:10.1186/gb-2010-11-2-r14.PubMed CentralPubMedView ArticleGoogle Scholar

Copyright

© Murphy et al. 2015