University of Groningen
Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in
remission
Vermeulen, Cornelis J.; Xu, Cheng-Jian; Vonk, Judith M.; ten Hacken, Nick H. T.; Timens,
Wim; Heijink, Irene H.; Nawijn, Martijn C.; Boekhoudt, Jeunard; van Oosterhout, Antoon J.;
Affleck, Karen
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European Respiratory Journal DOI:
10.1183/13993003.01280-2019
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Vermeulen, C. J., Xu, C-J., Vonk, J. M., ten Hacken, N. H. T., Timens, W., Heijink, I. H., Nawijn, M. C., Boekhoudt, J., van Oosterhout, A. J., Affleck, K., Weckmann, M., Koppelman, G. H., & van den Berge, M. (2020). Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in remission. European Respiratory Journal, 55(2), [1901280]. https://doi.org/10.1183/13993003.01280-2019
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Early View
Original article
Differential DNA methylation in bronchial biopsies
between persistent asthma and asthma in
remission
Cornelis J. Vermeulen, Cheng-Jian Xu, Judith M. Vonk, Nick H. T. ten Hacken, Wim Timens, Irene H. Heijink, Martijn C. Nawijn, Jeunard Boekhoudt, Antoon J. van Oosterhout, Karen Affleck, Markus Weckmann, Gerard H. Koppelman, Maarten van den Berge
Please cite this article as: Vermeulen CJ, Xu C-J, Vonk JM, et al. Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in remission. Eur Respir J 2019; in press (https://doi.org/10.1183/13993003.01280-2019).
This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online.
Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in remission
Cornelis J. Vermeulen1,2, Cheng-Jian Xu2,3,4, Judith M. Vonk2,5, Nick H. T. ten Hacken1,2, Wim Timens2,6, Irene H. Heijink1,2,6, Martijn C. Nawijn2,6, Jeunard Boekhoudt6, Antoon J. van Oosterhout7, Karen Affleck7, Markus Weckmann8, Gerard H. Koppelman2,3, Maarten van den Berge1,2
Affiliations
1 University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, NL-9700 RB Groningen, Netherlands
2 Groningen Research Institute for Asthma and COPD (GRIAC)), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
3 University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children‟s Hospital, NL-9700 RB
Groningen, Netherlands
4 CiiM & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
5 University of Groningen, University Medical Center Groningen, Department of Epidemiology, NL-9700 RB Groningen, Netherlands
6 University of Groningen, University Medical Center Groningen, Department of Pathology and Medical Biology, NL-9700 RB Groningen, Netherlands
7 Allergic Inflammation Discovery Performance Unit, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK
Schlesswig-Holstein, Lübeck, Airway Research Centre North, Member of the German Centre of Lung Research, Germany
Correspondence and requests for reprints should be addressed to: Cornelis J. Vermeulen, University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, NL-9700 RB Groningen, Netherlands. E.mail: c.j.vermeulen@umcg.nl
Abstract
About 40% of asthmatics experience remission of asthma symptoms. A better understanding of biological pathways leading to asthma remission may provide insight into new therapeutic targets for asthma. As an important mechanism of gene regulation, investigation of DNA methylation provides a promising approach. Our objective was to identify differences in epigenome wide DNA methylation levels in bronchial biopsies between subjects with asthma remission and subjects with persistent asthma or healthy controls.
We analysed differential DNA methylation in bronchial biopsies from 26 subjects with persistent asthma, 39 remission subjects and 70 healthy controls, using the limma package. The comb-p tool was used to identify differentially methylated regions. DNA methylation of CpG-sites was associated to expression of nearby genes from the same biopsies to understand function.
Four CpG-sites and 42 regions were differentially methylated between persistent asthma and remission. DNA methylation at two sites was correlated in cis with gene expression at ACKR2 and DGKQ, respectively. Between remission subjects and healthy controls 1163 CpG-sites and 328 regions were differentially methylated. DNA methylation was associated with expression of a set of genes expressed in ciliated epithelium.
CpGs differentially methylated between remission and persistent asthma identify genetic loci associated with resolution of inflammation and airway responsiveness. Despite the absence of symptoms, remission subjects have a DNA methylation profile that is distinct from that of healthy controls, partly due to changes in cellular composition, with a higher gene expression signal related to ciliated epithelium in remission versus healthy controls.
Asthma is a chronic inflammatory disease of the airways, characterized by variable airflow obstruction associated with symptoms of wheezing, shortness of breath, chest tightness and coughing. Chronic inflammation in asthma is associated with remodeling of the airways, e.g. increased basal membrane thickness, airway epithelial shedding, increase of goblet cells, increased mucus production and increase of smooth muscle mass [1]. Interestingly, in some asthma patients, symptoms disappear over time, and the need for pulmonary medication ceases. The prevalence of asthma is highest between 10 and 25 years and gradually decreases at higher age, the latter indicating that asthma remission rates have become higher than incidence rates [2]. In adulthood, the average remission rate of asthma is approximately 2% per year, with a higher chance to go into remission with an earlier onset of asthma, less severe airway obstruction, and cessation of smoking [3, 4]. A subset of patients with asthma in remission still have airway obstruction and/or show airway hyperresponsiveness (AHR) in provocation tests [5]. Vonk et al. [6] therefore introduced the terms „clinical remission‟ and „complete remission‟. Clinical remission is defined as the absence of asthma symptoms and no use of asthma medication. Complete remission of asthma is defined as the absence of asthma symptoms, no use of asthma medication, normal lung function and no AHR.
We still have minimal understanding of the cellular and molecular mechanisms that determine whether or not asthma persists or undergoes apparent spontaneous resolution. As for asthma development, genetic and environmental factors are likely to be involved in asthma remission as well [7]. The effect of both genetic and environmental variation on health outcome is often mediated by differences in gene transcription [8]. The methylation of cytosine at CpG -sites is an important epigenetic modification that regulates transcription by affecting several
mechanisms [9]. Variation in DNA methylation at CpG-sites has been associated with asthma and other atopic diseases [10–15]. This warrants further investigation of this type of
epigenetic variation.
In order to identify molecular mechanisms related to asthma remission in the airways, we investigated whether variation in DNA methylation in bronchial biopsies is associated with remission of asthma. Our main aim was to detect CpG-sites that are differentially methylated between well characterized subjects in complete and clinical remission and subjects with persistent asthma or healthy controls. In addition, we investigated how differentially
methylated CpG-sites associate with the expression levels of nearby genes in these bronchial biopsies, and related these to candidate molecular and cellular pathways. This furthers our ultimate aim to identify cellular mechanisms related to asthma remission in the airways.
Methods
Subjects
From previous studies [16, 17] biopsies were available from clinically well characterized subjects with clinical remission (ClinR, n = 33), complete remission (ComR, n = 15), persistent asthma (PersA, n = 90). Biopsies from healthy controls (H, n = 94) were available from another study. The study design and methods have been published previously [16, 17]. The subjects with ClinR and ComR had a documented diagnosis of asthma, confirmed by AHR testing [6, 17]. Asthma was defined as documented reversibility and/or AHR to
histamine (PC20 =< 32 mg/mL). Subjects were considered to be ClinR if they had not had an
asthma attack or wheeze in the last 3 years, and did not use asthma medication (ß -agonists and ICS). Asthma patients were considered to be ComR if they, in addition to the previous criteria,
mg/mL in 2 min tidal breathing respectively) and had no signs of airflow obstruction (FEV1
%predicted > 80% pre bronchodilator or >90% post bronchodilator). The subjects with PersA were divided on the basis of use of inhaled corticosteroids (ICS), since corticosteroids are known to affect gene expression and may act as a confounder on DNA methylation as well [18]. The non-asthmatic healthy controls were recruited from the NORM study (clinical trials number NCT00848406) and were all current smokers and never smokers older than 18 years. Subjects were considered healthy if they had no respiratory symptoms, no history of
respiratory disease and normal pulmonary function defined as a post bronchodilator
FEV1/FVC higher than the lower limit of normal, absence of AHR to methacholine (PC20 >
16mg/mL) and absence of FEV1 reversibility (increase FEV1 after 400 μg salbutamol < 10%
of the predicted FEV1 value). Healthy subjects were excluded if they had used inhaled or oral
corticosteroids within the last 5 years, or during a total of 5 years of their lives. Biopsies from healthy subjects were collected between 2009-2012 and biopsies from all other subjects between 2005-2007. All subjects were included in the same center (UMCG) and
bronchoscopies and processing of samples were performed by the same team. For RNA and DNA extraction and further processing, an even distribution of case-control status, age, gender and smoking status among batches was carefully controlled during all steps in the experimental design. Details on the DNA/RNA extraction, sample preparation and QC are provided in the supplementary methods. The study protocol was approved by the local medical ethics committee. All subjects gave their written informed consent.
DNA methylation
DNA methylation levels were measured in bronchial biopsies using the Infinium HumanMethylation450 BeadChip array (450k array) (Illumina, San Diego, CA). Raw intensity data were processed using the minfi package [19]. Samples and probes failing QC were removed and raw beta values normalised using the dasen method as implemented in the wateRmelon package [20]. DNA methylation levels at each CpG-site were expressed as beta-values, ranging from zero (no methylation) to one (complete methylation). A detailed
description can be found in the supplementary methods. An overview of the sample dropout during QC is shown in Supplementary Figure 1a.
RNA sequencing
RNA samples from airway wall biopsies were processed using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina, San Diego, CA). The cDNA fragment libraries were loaded unto an Illumina HiSeq2500 sequencer for paired-end sequencing (2 × 100 bp). Trimmed fastQ files where aligned to build b37 of the human reference genome using HISAT (version 0.1.5) and gene level quantification was performed by HTSeq (version 0.6.1p1) using Ensembl version 75 as gene annotation database [21]. A detailed description can be found in the supplementary methods. An overview of the sample dropout during QC is shown in Supplementary Figure 1b.
Differential methylation analysis
Differential methylation between subject groups was assessed for each probe using robust linear modelling in the limma package [22, 23]. To remove heteroscedasticity, beta -values were logit-transformed to M-values [24]. M-values were used as the dependent variable in all
components explaining 95% of the variation in the control probes were used as covariates to correct for technical variation [25]. We also analysed a model that included ex-smoker status in addition to current smoking, and found consistent results with the majority of DMRs remaining above the significance threshold. In remission vs asthma, of the 46 CpGs plus DMRs, 42 (91%) remained significant. In remission vs. healthy from the 1491 CpGs plus DMRs, 1223 remained significant (82%), but as this did not appreciably affect the results, these data will not be presented. We analyzed five subject categories; persistent asthma using ICS (PersA_ICS), persistent asthma not using ICS (PersA_no_ICS), ClinR, ComR and H. We pooled ClinR and ComR to increase sample size. We specified two contrasts: [ComR + ClinR] vs. PersA_no_ICS and [ComR + ClinR] vs. H. We refer to those contrasts as
“remission vs. asthma” and “remission vs. healthy” respectively. To avoid the confounding of ICS use, we focused on the group of asthmatics without ICS. We applied correction for multiple testing by controlling the false discovery rate at 5% using the Benjamini-Hochberg method [26]. The most significant hits of individual CpG-sites were annotated to the nearest gene using information from the IlluminaHumanMethylation450kanno.ilmn12.hg19
annotation package, or when no annotation was provided we searched the UCSC genome browser [27, 28].
Differentially Methylated Regions (DMRs)
Regions of correlated CpG-sites with differential methylation were identified using the comb-p v0.48 command line tool and comb-python library [29]. We used the comb-picomb-peline with seed setting P = 0.05 and distance 750 bp. The region filter settings were n = 2 and a Sidak-corrected P = 0.05.
Association between DNA methylation and gene expression (eQTM mapping)
We used the MatrixEQTL package to determine whether DNA methylation levels at CpG-sites were associated with gene expression levels of nearby genes within 1 Mb of the CpG -site [30]. Raw read counts from RNA sequencing were normalized for library size using the
weighted trimmed mean of M-values (TMM) method as implemented in the edgeR package [31]. The normalised counts were transformed to log2(Counts Per Million). We used
methylation M-values as the explanatory variable and age, gender and current smoking status were set as covariables.
Cell type composition
Surrogate variable analysis (sva) provides estimates of latent variables, which is
recommended as substitute for cell type deconvolution [32]. A detailed description can be found in the supplementary methods. We performed this analysis for all FDR-significant differentially methylated CpGs from remission vs. asthma and the ten most differentially methylated CpGs from remission vs. healthy.
We also took a targeted approach to the analysis of cell type composition, by extending the method proposed by Xu et al. [13]. For the same set of CpG-sites as used in the surrogate variable analysis, we performed genome wide mapping of DNA methylation to gene
expression. For each CpG-site, the resulting set of test statistics was used as input for mean-rank gene set enrichment analysis as implemented in limma [33]. We tested enrichment of 20 gene sets expressed by the cell type clusters taken from single cell sequencing results of airway wall biopsies as reported in Vieira Braga et al. [34].
Patient characteristics
We obtained DNA methylation data for 179 samples, after excluding 53 samples that were lost during processing or excluded during the QC procedure (Supplementary Figure 1). Of the included samples, 12 subjects had ComR, 27 subjects ClinR, 70 subjects PersA and 70
subjects were H. Of the PersA subjects, 26 were not using ICS. The patient characteristics of the 5 subject groups are presented in Table 1.
Differential methylation in remission vs. persistent asthma
Methylation levels at four individual CpG-sites and 42 regions were significantly different in remission vs. asthma (Table 2A, 3A). The most significant site was cg08364654, which
showed 6% lower methylation in remission subjects than in asthmatics (Figure 1A). This CpG is also part of the most significant DMR on chromosome 3 (Table 3A). All CpGs in the DMR are located in the KRBOX1 gene, but we did not find these CpGs to be associated with
KRBOX1 expression. Instead, a higher methylation was associated (most significantly at
cg22714811 at P = 7.9·10-4) with increased expression of the atypical chemokine receptor 2 (ACKR2) gene (Figure 2A; Figure 3). The second CpG-site (cg23805470) also had 2% lower methylation in subjects in remission compared to persistent asthmatics. Methylation of cg23805470 was not significantly associated with expression of the gene it resides in
(tenascin XB, TNXB) or any other nearby gene. The third site cg13525448 lies in an exon of
ladybird homeobox 1 (LBX1), but was not associated with gene expression in cis. It had 2%
higher methylation in remission than in persistent asthma (Figure 1C). The fourth CpG -site (cg00741675) had 11% lower methylation in remission and is located in the 5'UTR of the diacylglycerol kinase, theta 110kDa (DGKQ) gene. It did not associate with gene expression
at the P = 0.001 cut-off, but is positively associated with gene expression of DGKQ at P < 0.004 (Figure 2B). The second significant DMR is located at chromosome 19 and associated with expression of RPL13A. The encoded product is a component of the
IFN-gamma-activated inhibitor of translation (GAIT) complex, which plays a role in the repression of inflammatory genes and contributes to the resolution of chronic inflammation [35]. Among the four significant individual CpG-sites from the contrast remission versus asthma (Table 2A), all remained significantly differentially methylated (at P < 0.001) after correction for the first five surrogate variables, indicating that the differences in methylation were not driven by cell type composition (Table 2A, Supplementary Figure 2A). Significance of cg00741675 dropped sharply, but for all four CpGs log fold changes remained similar after adjustment.
Differential methylation in remission vs. healthy
We found 1163 individual CpG-sites and 328 regions to be differentially methylated between remission subjects and healthy controls, with one-third of the individual sites and 20% of the DMRs being higher methylated in remission (Table 2B and 3B show the top10, complete list in Supplementary Table 1B and 2B). Of the 1163 significant CpG-sites, 167 were located in a DMR. Correction for surrogate variables resulted in similar log fold changes but did
appreciably affect the significance of the ten most significant CpG-sites, suggesting an impact of cell type composition in the contrast remission versus healthy (Supplementary Figure 2). To further investigate effects of cell type composition we identified cell type specific gene expression profiles by associating methylation levels of the ten most significant CpG-sites to genome-wide gene expression levels. Then we compared associated genes to gene sets characteristic for 20 major clusters of cells found in bronchial biopsies based on single cell sequencing data [34]. All ten tested CpG-sites from the contrast remission vs. healthy showed
(Supplementary Table 3). Highest enriched groups throughout were ciliated epithelial cells and basal cells. Several CpGs were most significantly enriched for other groups though: smooth muscle cells (cg06947286 and cg10986462), fibroblasts (cg23916878), neutrophils (cg18763536) and T-cells (cg08307963) (Figure 4a). Nine out of ten tested CpG-sites from the contrast remission vs. healthy showed strong enrichment (P << 0.001) for highly ranking genes from ciliated epithelium (Figure 4). We tested whether the enrichment was due to the underlying correlation structure of our data set, but this did not fully account for our findings. For comparison, only 49% of a randomly selected sample of CpG-sites has enrichment for this gene set at this significance level. In addition, five of ten tested CpG-sites belong to the top 1% enrichment as judged by comparison to P-values in the randomly selected sample. Figure 4B shows the ranking of correlated genes of a representative CpG-site from this set (cg07224931). Nearly all genes in the set related to ciliated epithelium have higher gene expression in remission versus healthy controls (99 %).
Discussion
We found four individual CpG-sites and 42 regions to be differentially methylated between subjects in remission and persistent asthmatics. Of the individual CpGs two associated with gene expression of ACKR2 and DGKQ, respectively. These four CpGs were not strongly related to cell type composition of the airway wall biopsy. There were 1163 CpG-sites and 328 regions differentially methylated between remission subjects and healthy controls. Many of the highly significant CpG-sites that distinguish remission subjects from healthy controls were associated with increases in the expression of genes associated with ciliated epithelial cells, consistent with cell type composition differences in remission versus healthy. We found
differences in absolute methylation between groups between 2% and 11%. It should be noted that even modest differences can be relevant when they reflect changes within a specific cell type.
The most significant CpG site, as well as DMR, with lower methylation in the contrast remission vs. asthma was associated with lower expression of ACKR2 in subjects with remission. This gene contributes to resolution of inflammation by post-inflammatory
clearance of chemokines in a mouse model [36]. Allergen-challenged mice that are deficient for ACKR2 have more infiltrating cells in the airways, primarily dendritic cells and
eosinophils. Interestingly, these ACKR2-deficient mice have less airway reactivity to methacholine [37]. We suggest that lower expression of ACKR2 may play a role in the remission of asthma, by reducing AHR. Of interest, the lower methylation and ACKR2 gene expression was paralleled by decreased airway reactivity in the ACKR2 deficient mice, but not by reduced inflammation as would be expected. The CpG-site in TNXB could not be related to expression of this gene, but tenascin genes are known to be important in airway remodeling [1]. Multiple CpG-sites in TNXB were less methylated in primary airway epithelium cells (AEC) after exposure to IL13, a type 2 cytokine and key mediator of airway inflammation and remodeling in asthma [15]. TNXB resides in the HLA class II region, which is important for immune function [38], but we were unable to link differential methylation at cg23805470 to expression of HLA genes. CpG-site cg00741675 in DGKQ has lower methylation in
remission subjects when compared to persistent asthmatics. DGKQ encodes a member of the family of diacylglycerol kinases (DGKs) that attenuate levels of the second messenger diacylglycerol in cells by converting it to phosphatidic acid [39]. The precise mechanism by which it may be involved in the remission of asthma is not clear, but members of the DGK
in inflammatory processes [40].
Subjects in remission have a methylation profile that is distinct from that of persistent asthmatics, which may reflect differential regulation of immune functions. However, their methylation profile does not resemble the healthy situation either. Individuals with complete asthma remission retain characteristics that differ from healthy subjects without a history of asthma, for example, persistent airway remodeling as shown by increased basal membrane thickness [17]. Analogous to this situation, we find methylation differences between remission and healthy that do not occur between remission and asthma. We found 21 out of 42 DMRs and 3 out of 4 differentially methylated CpGs in remission vs. asthma to overlap DMRs found in asthma vs. healthy. For remission vs. healthy this concerned 126 out of 327 DMRs and 310 out of 1163 differentially methylated CpGs. This clearly shows that differential methylation in both the remission vs. asthma and the remission vs. healthy contrasts capture aspects of
methylation differences between persistent asthmatics and healthy subjects, consistent with the intermediate position of remission subjects. A possible explanation is that subjects in asthma remission still display a epigenetic fingerprint of asthma. A similar lingering difference in methylation profiles was reported for ex-smokers [41]. Alternatively this may reflect an inherent predisposition to developing asthma, which puts remission subjects at an increased risk for relapse.
A strength of our design is the use of bronchial biopsies, which enabled us to study epigenetic profiles in the relevant tissue. Also, we included healthy subjects and well characterized subjects in clinical and complete remission. All remission subjects had previous objective
confirmation of their asthma diagnosis with detailed follow up. Finally, we added functional information to differentially methylated CpG-sites by their association with the expression levels of nearby genes, using the same biopsy as used for determination of methylation levels. One limitation is the absence of an independent replication cohort in which bronchial biopsies are available for remission subjects and healthy controls. To the best of our knowledge, no comparable replication cohort with a similar design is available. Another issue is that differential methylation among subject groups was partly driven by differences in cell type composition, which hampers identification of specific asthma or remission related CpG -sites. By annotating the associated CpGs with gene expression, we show that subjects in remission differ from healthy controls in methylation levels at CpG-sites associated with multiple cell types, and that there is strong enrichment of CpGs in genes associated with ciliated
epithelium. A possible interpretation is that changes in the cell type composition, in particular the proportion of ciliated epithelium in remission subjects explains part of the differential methylation between remission subjects and healthy controls. This is supported by the
inflation of the P-values in this particular contrast. The prominent signal from epithelium may reflect the abundance of this cell type in biopsies, and the importance of other cell types in asthma and remission should not be neglected, but it emphasises that the airways of subjects in remission still have not come to resemble the healthy profile.
Acknowledgements
We wish to thank Pieter van der Vlies, Bahram Sanjabi, Desiree Brandenburg-Weening and Harold de Bruin for their valuable work in the DNA extraction, sample preparation and hybridization.
Author‟s contributions
CJV performed differential methylation analysis and drafted and finalized the manuscript, C-JX and JMV were involved in data management, data analysis and editing the manuscript, NHTtH, WT, IHH, MCN, AJvO, KA and GHK provided guidance and technical knowledge and edited the manuscript, JB performed eQTM analysis, MW contributed expression and methylation data, MvdB, NHTtH, WT and JMV were involved in the design of the study and MvdB drafted parts of the manuscript. All authors approved the final manuscript.
Competing interests
KA and AJvO are employees of GlaxoSmithKline.
Funding statement
Data were generated as part of a Scientific Research Collaboration funded by GSK.
Additional Files
Supplementary Tables 1-3 Supplementary Figures 1-5
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Figure Legends
Figure 1: Violin plots of highly differentially methylated CpG-sites in the “asthma vs.
remission” contrast. (A) cg08364654 in the KRBOX1 gene (B) cg23805470 in the TNXB gene (C) cg13525448 and (D) cg00741675 in the DGKQ gene. Subject groups: PersA_ICS
Persistent asthma with ICS use PersA_no_ICS Persistent asthma without ICS use, ClinR Clinical Remission, ComR Complete Remission, H Healthy Controls.
Figure 2: Correlation of DNA methylation at differentially methylated CpG-sites with gene
expression of nearby genes. The groups used in the contrasts (either “remission vs. asthma” or “remission vs. healthy” ) are marked with non-gray colors; remission in yellow, asthma in red, healthy in blue. Subject groups (PersA_ICS Persistent asthma with ICS use PersA_no_ICS Persistent asthma without ICS use, ClinR Clinical Remission, ComR Complete Remission, H Healthy Controls) are indicated with different symbols.
Figure 3: CoMET plot of the genomic region of cg08364654. The top plot shows the strength
of correlation with expression of the ACKR2 gene for all the CpG-sites in the interval. The plot below shows the strength of association in the remission versus asthma contrast. Red symbols indicates positive and blue symbols negative associations. The default coMET annotation tracks were plotted: ENSEMBL genes, CpG islands, chromatin-state (Broad institute), DNAse sensitive regions and SNPs. See coMET documentation for detailed description. The bottom plot shows a correlation matrix of DNA methylation among all plotted CpG-sites.
composition was gauged by genome wide correlations with gene expression. A) Heatmap and hierarchical clustering of -log10(P-values) of mean-rank gene set tests on the test statistics of DNA methylation - gene expression correlations . The CpG-sites are the four most significant sites from the contrast remission versus asthma (R vs A) and the ten most significant hits from the contrast remission versus healthy (R vs H). The gene sets distinguish the four main
clusters of epithelial cell types in single cell sequencing. See the MS for details. B) Ranks of test statistic for a selected CpG-site (cg07224931) demonstrates the enrichment of highly ranking genes of the ciliated epithelium.
Tables
Table 1: Patient characteristics.
Characteristic PersA_ICS PersA_no_ICS ClinR ComR H H0: No differences between
groups. test-statistic; df; P-value N 44 26 27 12 70 X2 = 55.2; df=4; P<<0.01 PC20AMP (mg/mL) median [min,max] 64.9 [0.02,4871.5] 47.4 [0.02,3811.4] 700.2 [0.02,5090.7] 639.0 [414.8,5635.8] 2674.9 [40,5612.4] F4,174 = 10.9; P(emp) = 0.001 FEV1 (%pred) mean [s.e.m.] 83.1 [3.02] 83.3 [2.39] 90.8 [2.70] 101.8 [4.05] 101.0 [1.34] F4,174 = 14.0, P<<0.001
ICS dose (ug/day) median[min,max] 800 [28,2000] NA NA NA NA NA Beta-agonist use (n(%)) 40 (91) 14 (54) 0 (0) 0 (0) 0 (0) X2 = 131.1; df=4; P<<0.01 Sex (m/f) 20/24 15/11 12/15 6/6 40/30 X2= 2.47, df = 4, P = 0.650 Age (years) mean [s.e.m.] 48.8 [1.85] 46.3 [2.44] 47.5 [2.33] 45.3 [4.78] 39.5 [2.03] F4,174 = 3.37; P = 0.011 FEV1/FVC (%) mean [s.e.m.] 69.6 [1.68] 70.1 [1.58] 75.6 [1.55] 78.2 [2.37] 79.7 [0.76] F4,174 = 12.2; P << 0.001 Reversibility FEV1 (%) mean [s.e.m.] 8.64 [0.97] 9.20 [1.24] 6.57 [0.85] 4.38 [1.30] 3.46 [0.34] F4,173 = 11.4, P << 0.001
Atopy (skin prick) (n(%)) NA NA NA NA 25 (36) NA
Atopy (phadiatop) (n(%)) 31 (70) 19 (73) 15 (56) 8 (67) NA X2 = 3.95, df = 3, P = 0.267 Smoking status (Ncurrent,Nex,Nnever) (6,21,17) (10,3,13) (4,11,12) (4,1,7) (33,3,34) X2 = 43.8; df = 8; P << 0.001 Blood eosinophils (*10^9/L) median [min,max] 0.195 [0.05,0.78] 0.260 [0.00,0.90] 0.170 [0.00,0.62] 0.115 [0.00,0.28] 0.155 [0.02,0.43] F4,174 = 3.373; P = 0.011
independence. Differences in continuous variables were tested with oneway ANOVA. Variables were transformed, if necessary, to satisfy the
assumption of normality. For FEV1 and FEV1/FVC ANOVA was performed on z-values (GLI2012_DataConversion software). When PC20AMP was
not reached at the highest dose, values were obtained by extrapolating from an exponential decay dose response curve. No appropriate transformation for PC20AMP was found, and these data were analysed by oneway ANOVA based on a permutated distribution of F with 1000 permutations [42]
Table 2: Table 2: Toptables of differentially methylated CpGs
(2A)
Probe ID Genomic position UCSC RefGene Name UCSC RefGene Group Correlated expression logFC Moderated t value P value Adjusted P value Sva corrected P value cg08364654 chr3:42,978,180 - - - 0.83 6.23 3.5e-9 0.0015 6.5e-08
cg23805470 chr6:32,056,820 TNXB Body - 0.60 6.07 7.9e-9 0.0017 1.1e-07
cg13525448 chr10:102,986,601 - - - -0.48 -5.26 4.4e-7 0.0466 3.2e-07
cg00741675 chr4:967,327 DGKQ 5'UTR; 1stExon
- 2.96 5.28 3.8e-7 0.0466 4.5e-04
(2B)
Probe ID Genomic position UCSC RefGene Name UCSC RefGene Group Correlated expression logFC Moderated t value P value Adjusted P value Sva corrected P value cg04886217 chr16:31,821,538 - - - 0.63 6.56 6.1e-10 0.0003 2.8e-08 cg06947286 chr5:131,596,602 PDLIM4 Body PDLIM4; P4HA2;
UQCRQ; HSPA4; KIF3A; ACSL6; ZCCHC10
-0.60 -6.20 4.2e-9 0.0006 6.8e-05
cg01716603 chr17:37,029,974 LASP1 Body SRCIN1; PLXDC1; ARHGAP23; HNF1B; LINC00672; PIP4K2B; MLLT6; NEUROD2; STARD3; PCGF2; ENSG00000214546; CACNB1 -0.83 -6.19 4.3e-9 0.0006 1.7e-05
Body; 5'UTR
FAM177B;
MARC2; DUSP10 cg18763536 chr12:11,812,062 ETV6 Body MANSC1;
CREBL2; ETV6 -0.44 -5.97 1.4e-8 0.0011 1.8e-05 cg23916878 chr6:44,011,187 DLK2; LRRC73; RSPH9; DNPH1; TCTE1; SPATS1; PTK7; ENSG00000272442 -0.68 -5.94 1.6e-8 0.0011 2.9e-05
cg20001791 chr6:16,239,799 GMPR Body GMPR; STMND1 -0.66 -5.88 2.1e-8 0.0013 4.2e-07
cg08307963 chr1:147,245,485 GJA5 TSS200 BCL9 0.54 5.78 3.4e-8 0.0017 2.0e-07
cg10986462 chr10:135,340,539 CYP2E1 TSS1500 FUOM -1.12 -5.77 3.6e-8 0.0017 1.2e-05 cg07224931 chr12:121,130,567 MLEC Body DYNLL1-AS1;
CIT; MORN3; UNC119B; PXN; DYNLL1
-0.53 -5.73 4.5e-8 0.0017 7.1e-05
Toptables of the contrasts (A) “remission versus asthma” and (B) “remission versus healthy”. The tables show the Probe ID, with its genomic position (hg19), the name of any nearby genes and its position relative to that gene, proximate genes with associat ed expression, the log2 fold change of the beta values, the moderated t-value and its associated probability of the null-hypothesis of no difference between the groups, as well as the adjusted P-value by Benjamini-Hochberg correction controlling the FDR at 5%. The rightmost column lists the P P-values of the analysis where surrogate variables were added as covariates (see text). Positive logFC indicate higher methylation in asthmatics than remission subjects in 2A a nd higher methylation in remission subjects than healthy controls in 2B.
Table 3: Toptables of differentially methylated regions (3A) Genomic position n_probe s z_sidak_ p t.su m
refGene_name refGene_feature Associated expression
3:42977776-42978248 8 9.6e-20 34.0 KRBOX1-AS1;KRBOX1 nc_exon;TSS+intron+exon+utr5 ACKR2 19:50666237-50666552
6 2.4e-11 22.5 IZUMO2 TSS+utr5+cds RPL13A
11:124746753 -124747263
6 7.7e-08 18.7 ROBO3 intron+cds ROBO3
17:1094028-1094555
7 8.6e-08 -20.7 ABR intron -
8:1049166-1049477
5 5.8e-06 -15.4 DLGAP2 nc_intron;intron -
15:45671000-45671347
10 3.1e-05 24.1 GATM intron+utr5 GATM;DUOX2;SPG11;B2M;SHF
3:142666107-142666476 4 1.3e-04 12.2 LOC10050738 9 intergenic GK5 15:75251490-75251733
3 2.3e-04 10.2 RPP25 intergenic CCDC33;ISLR
1:119532043-119532352 8 2.7e-04 19.0 TBX15 TSS+exon+utr5 - 11:1891931-1892307 10 3.3e-04 20.7 LSP1 TSS+intron+exon+utr5;intron+utr5;TSS+exon+utr5;int ron C11orf21;LSP1;TSPAN32;C11orf 89 (3B)
c position obes ak_p u m me 10:1353 40444-135341 025 9 3.4e-12 -32 .1
CYP2E1 TSS+utr5+cds FUOM;TUBGCP2;ECHS1;SPRN
16:4714 079-471481 5 7 3.9e-10 -24 .4 MGRN1 intron+cds;nc_intro n+nc_exon C16orf71;CORO7;SMIM22;GLYR1;CDIP1;C16orf89;ENSG00000266994;SEC14L5;G LIS2;TFAP4;ENSG00000262686 1:14724 5484-147245 626 3 4.1e-10 14 .8 NBPF19 intron BCL9 11:6991 9474-699200 54 5 1.8e-09 18 .6
LINC02584 nc_intron SHANK2;SHANK2-AS1;SHANK2-AS3;SHANK2-AS2;ENSG00000227726;CTTN
1:93805 65-938143 1 5 3.9e-09 -18 .1 SPSB1 intron+utr5 ENSG00000234546;SPSB1;TMEM201;SLC45A1;H6PD;PIK3CD;CLSTN1;KIF1B;RB P7;SLC2A5 8:14501 8815-145019 839 17 7.4e-09 -43 .5 PLEC;MIR6 61;PLEC intron;nc_gene;TSS +intron+utr5+cds FAM83H-AS1;EEF1D;RHPN1-AS1;ENSG00000255224;MIR4664;TOP1MT;ARHGAP39;ZNF34;TONSL;ENSG0000 0254973;MAPK15;GPIHBP1;FAM83H;GPT;EXOSC4;TSTA3;C8orf31 13:1148 12176-114813 015 6 9.5e-09 -20 .1
14:1059 32811-105933 283 4 1.1e-08 -16 .1 MTA1 intron+exon+utr3;in tron+cds C14orf79;ADSSL1;INF2;IGHV3-15;IGHV3-11;IGHV3-21;IGHV3-23;IGHM;IGHA1;KIAA0125;IGHV4-39;IGHJ3P;JAG2;IGHG2;IGHA2 12:1322 19327-132219 529 2 1.9e-08 -10 .6
SFSWAP intron POLE;STX2;GPR133;DDX51;ULK1;MMP17
14:9439 2717-943929 32 4 2.4e-08 -16 .4 FAM181A;F AM181A-AS1 intron;nc_exon;intro n+utr5 PPP4R4;UNC79;ASB2;C14orf142;ENSG00000258987;FAM181A-AS1;PRIMA1;SERPINA6;MOAP1;SERPINA10;SERPINA1;FAM181A;IFI27L2
Toptables of the contrasts (A) “remission versus asthma” and (B) “remission versus healthy”. The tables show the genomic position (hg19), the number of probes in the DMR, the one-step Sidak corrected P-value, the summed t-value, the name of any nearby genes and CpG positions relative to that gene, and proximate genes with associated expression. Positive t.sum values indicate higher methylation in asthmatics than remission subjects (Table 3A) and higher methylation in remission subjects than healthy controls (Table 3B).
DNA / RNA extraction
Bronchial biopsies were taken from segmental divisions of the main bronchi and immediately frozen in Tissue-Tek (VWR, Radnor, PA) at -80°C. After thawing at room temperature, biopsies were cut from the blocks when they were semi-solid. Samples were lysed in 600 μl RLT-plus using a IKA Ultra Turrax T10 Homogenizer. Total genomic DNA was extracted using AllPrep
DNA/RNA/miRNA Universal Kit, according to the manufacturer’s instructions (Qiagen, Venlo, the Netherlands). DNA samples were dissolved in 30 µl elution buffer. Concentrations of DNA were measured in 1 μl of fluid using a Nanodrop-1000 and in 2 μl of fluid run on a Labchip GX (Perkin Elmer, Waltham, MA).
DNA methylation: Sample preparation and Hybridization protocol
DNA samples were purified by a precipitation step, rinsed with ethanol (70%) and dissolved in elution buffer. Then, samples were treated with sodium bisulfite to convert unmethylated cytosine bases into uracil. This was followed by a PCR-free whole genome amplification, after which the treated DNA was hybridized to the Infinium HumanMethylation450 BeadChip array (450k array). After hybridization, allele-specific single-base extension incorporated a fluorescent label for detection of methylated and unmethylated sites. The conversion and hybridization protocol was performed according to the manufacturer’s instructions (Illumina, San Diego, CA).
Every beadchip was run with a control sample of blood DNA from a single female, which was used to assess the efficiency of the normalization procedure by verifying that the among sample variance was minimized. We used a randomized block design, where samples were assigned to blocks based on subject group, smoking status and gender, and randomized within blocks.
DNA methylation: Quality control, sample filtering, probe filtering and normalization
Raw intensity values were read from IDAT-files, and converted to beta values using the minfi-package [1]. We identified bad quality samples using the R-minfi-package MethylAid, based on five diagnostic filter variables with the following thresholds; MU = 10.5, OP = 11.75, BS = 12.5, HC = 13.25, and DP = 0.95 [2]. Genotypes from 65 SNP probes on the 450k array were compared to previously acquired genotypes from blood samples and discordant samples were discarded as swapped or contaminated samples and excluded from the analysis.
We excluded probes where >1% of samples had intensities indistinguishable from background levels at P = 0.05, probes which were crossreactive [following 3], probes which have SNPs with MAF > 10% at the interrogation or extension site, and type I probes which displayed high intensity
signals [following 4]. In addition, we excluded all sex linked probes. The probe filtering procedure resulted in the removal of 48,059 unreliable probes (10 %). After probe-and sample-filtering, raw beta-values were background-corrected and normalized using the dasen method from the
wateRmelon-package [5].
DNA methylation: Correction for cell type composition
We investigated how corrections for cell type composition affected the results for a subset of differentially methylated CpG sites. We estimated surrogate variables from our DNA methylation levels as implemented in the sva package and added those to our basic model as covariates [6]. This is a reasonable alternative to deconvolution using reference cell type profiles [7]. We reran the linear modelling analysis and monitored how this impacted the significance of the differential methylation in the contrasts.
RNA sequencing: Sample preparation and sequencing
RNA samples were further processed using the TruSeq Stranded Total RNA Sample Preparation Kit (Illumina, San Diego, CA), using an automated procedure in a Caliper Sciclone NGS Workstation (PerkinElmer, Waltham, MA). In this procedure, all cytoplasmic and mitochondria rRNA was removed (RiboZero Gold kit). The obtained cDNA fragment libraries were loaded in pools of multiple samples unto an Illumina HiSeq2500 sequencer using default parameters for paired-end sequencing (2 × 100 bp).
RNA sequencing: Gene expression quantification
The trimmed fastQ files where aligned to build b37 of the human reference genome using HISAT (version 0.1.5) allowing for 2 mismatches [8]. Before gene quantification SAMtools (version 1.2) was used to sort the aligned reads [9]. The gene level quantification was performed by HTSeq (version 0.6.1p1) using Ensembl version 75 as gene annotation database [10].
RNA sequencing: Quality Control
Quality control (QC) metrics were calculated for the raw sequencing data, using the FastQC tool (version 0.11.3) [11]. Alignments of 220 subjects were obtained. QC metrics were calculated for the aligned reads using Picard-tools (version 1.130) (URL http://picard.sourceforge.net)
CollectRnaSeqMetrics, MarkDuplicates, CollectInsertSize-Metrics and SAMtools flagstat. We discarded 36 samples due to poor alignment metrics. In addition, we checked for concordance between sexlinked (XIST and Y-chromosomal genes) gene expression and reported sex. All samples were concordant. This resulted in high quality RNAseq data from 184 subjects.
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Supplementary Figures
Figure 1: Sample drop out during QC for A) DNA methylation array and B) RNAsequencing
samples. The upper tier (dark blue) shows the total numbers of included subjects and the bottom tier (in green) shows the total numbers of samples analysed in this study. Samples are broken down by subject groups. : PersA_ICS Persistent asthma with ICS use PersA_no_ICS Persistent asthma without ICS use, ClinR Clinical Remission, ComR Complete Remission, H Healthy Controls. A) The middle tiers (light blue) show drop out of samples due to failed DNA extraction (no DNA), failure to meet the MethylAid QC criteria (MethylAid QC) and genotype mismatch between
methylation array and independent SNP array (genotype mismatch). B) The middle tiers (light blue) show drop out of samples due to failed RNA extraction (no RNA), failure during sample processing (sample processing) and failure to meet QC criteria after sequencing (QC).
A)
methylation of a subset of CpG-sites. Figure (A) shows the -log10 transformed P-values from the contrasts that were assessed in the manuscript. The leftmost four CpG-sites are the tophits from the contrast remission versus asthma and the remaining sites come from the contrast remission versus healthy. See the MS for details. Plotted are the uncorrected P-values (red, also reported in Table 2A and 2B) and the P-values from the analysis including surrogate variables (blue). Figure (B) shows the -log10 transformed P-values for the surrogate variables for the same CpG-sites as in (A).
A)
B)
Figure 3: Quantile-quantile plots of observed P-values plotted against the expected P-values from a
uniform distribution. Plots show data from the contrast (A) remission versus asthma and (B) remission versus healthy. The lambda values from chi-square statistics are reported in the figures. Lambda values differing from 1 indicate systematic bias, but may have a biological source (e.g. driven by differences cell composition).
A)
Figure 5: Overlap in genomic position of individual CpGs with DMRs. A) Asthma versus
Table 1A-B: limma output R-PersA_no_ICS and R-H for all CpG-sites nominally significant P.Value < 0.05. Toptables of CpG-sites are annotated with genes with significantly correlated transcript abundance (P < 0.001).
Table 2A-B: Final comb-p output table of regions passing the filter
Table 3: P-values from limma's Mean Rank Gene Set Test. Input were the t-statistics from
MatrixEQTL DNA methylation M-values versus gene expression for the 14 CpG-sites in the table rows. The colums show the twenty gene sets corresponding to genes expressed by four major cell type clusters.
Name chr pos sym logFC t P.Value adj.P.Val cg083646 54 chr3 42978180 0.831109 6.22982 3.52E-09 0.001505 cg238054 70 chr6 32056820 0.596536 6.07264 7.92E-09 0.001693 cg135254 48 chr10 1.03E+08 -0.47906 -5.25533 4.36E-07 0.046629 cg007416 75 chr4 967327 2.958508 5.283343 3.83E-07 0.046629 cg108095 60 chr10 79936959 -0.6872 -4.99415 1.45E-06 0.123965 cg227148 11 chr3 42977896 ACKR2 0.763206 4.910747 2.11E-06 0.15027 cg120013 57 chr2 2.33E+08 -0.50608 -4.81859 3.18E-06 0.151638 cg230258 93 chr3 1.83E+08 -0.46531 -4.86135 2.63E-06 0.151638 cg169549 28 chr19 56135916 -0.43614 -4.80308 3.40E-06 0.151638 cg147427 16 chr19 50666345 0.846393 4.783888 3.70E-06 0.151638 cg270847 12 chr3 42977845 0.934103 4.763484 4.05E-06 0.151638 cg139820 98 chr5 87955859 0.63036 4.751794 4.26E-06 0.151638 cg135580 87 chr9 37488517 -0.51009 -4.68447 5.70E-06 0.187549 cg257711 95 chr16 58163814 0.548798 4.623598 7.41E-06 0.192405 cg239406 14 chr15 84115479 0.579058 4.629434 7.23E-06 0.192405 cg058723 06 chr14 29236535 0.789351 4.621121 7.49E-06 0.192405 cg096977 95 chr19 50666379 RPL13A 0.903148 4.589269 8.58E-06 0.203846 cg114981 56 chr10 1.03E+08 -0.47079 -4.61617 7.65E-06 0.192405 cg128124 89 chr17 40896724 -0.39174 -4.55249 1.00E-05 0.224472 cg016471 11 chr8 22014425 -0.32094 -4.53023 1.10E-05 0.224472 cg154419 73 chr2 1.39E+08 0.601896 4.538659 1.06E-05 0.224472 cg088165 90 chr12 54954337 0.878218 4.47096 1.41E-05 0.25199 cg263519 16 chr17 78084228 -0.41761 -4.49071 1.30E-05 0.25199 cg138234 03 chr17 38296474 ENSG000002641 98 -0.30142 -4.47447 1.39E-05 0.25199
56 chr12 14135228 0.441805 4.458826 1.49E-05 0.254499 cg270766 69 chr12 69327457 ENSG000002400 87 0.538032 4.428326 1.69E-05 0.258021 cg171877 62 chr22 28070120 -0.59976 -4.43582 1.64E-05 0.258021 cg120642 76 chr13 79182205 0.355215 4.415871 1.78E-05 0.262346 cg028549 22 chr1 1.45E+08 LIX1L;RNF115;PI AS3;ANKRD35 0.58653 4.432367 1.66E-05 0.258021 cg246585 17 chr1 2.21E+08 0.433003 4.39036 1.98E-05 0.262941 cg211044 12 chr3 42978026 0.793248 4.376485 2.09E-05 0.262941 cg021862 98 chr11 65101602 0.376488 4.372202 2.13E-05 0.262941 cg018572 60 chr19 50666552 0.603942 4.380193 2.06E-05 0.262941 cg186124 61 chr15 75251733 ISLR 0.5395 4.366998 2.18E-05 0.262941 cg105013 05 chr16 71518232 0.35585 4.381505 2.05E-05 0.262941 cg140419 76 chr1 22978981 -0.46704 -4.3568 2.27E-05 0.262941 cg144201 08 chr9 34647483 0.499208 4.331087 2.52E-05 0.278409 cg077415 24 chr16 25026707 -0.30006 -4.32952 2.54E-05 0.278409 cg027997 12 chr20 48532227 -0.37473 -4.3563 2.28E-05 0.262941 cg109203 29 chr8 1.26E+08 -0.45759 -4.3078 2.78E-05 0.28802 cg124494 04 chr2 40437698 -0.88981 -4.29948 2.87E-05 0.28802 cg124898 96 chr6 364068 0.450503 4.294786 2.93E-05 0.28802 cg054017 64 chr3 1.58E+08 0.409231 4.288103 3.01E-05 0.28802 cg080859 17 chr12 1.33E+08 -0.36951 -4.28801 3.01E-05 0.28802 cg058477 55 chr22 50009422 -0.39272 -4.27662 3.15E-05 0.28802 cg022328 39 chr1 1.56E+08 0.612817 4.24191 3.63E-05 0.28802 cg135075 60 chr2 1.34E+08 -0.48896 -4.2492 3.52E-05 0.28802 cg256571 87 chr5 87969213 0.577382 4.244909 3.58E-05 0.28802
cg046183 33 chr17 46800674 0.557355 4.24354 3.60E-05 0.28802 cg055454 41 chr4 1.74E+08 0.476986 4.253315 3.46E-05 0.28802 cg128247 19 chr22 43116300 -0.44372 -4.24745 3.55E-05 0.28802 cg046000 77 chr5 1852839 -0.35772 -4.241 3.64E-05 0.28802 cg048413 58 chr8 10897293 -0.71889 -4.21251 4.08E-05 0.301247 cg087744 52 chr12 1.29E+08 0.671594 4.22594 3.87E-05 0.300447 cg226580 82 chr5 1.37E+08 -0.32823 -4.21205 4.09E-05 0.301247 cg004023 66 chr1 74663750 -0.56323 -4.24445 3.59E-05 0.28802 cg179035 90 chr1 28623081 DNAJC8 -0.72017 -4.20625 4.18E-05 0.303106 cg139166 33 chr11 35644854 -1.53416 -4.27563 3.16E-05 0.28802 cg024015 89 chr11 93584748 -0.60465 -4.21479 4.04E-05 0.301247 ch.19.732 135R chr19 17385198 -0.77619 -4.19057 4.45E-05 0.31378 cg170629 17 chr15 70054539 0.350957 4.18 4.65E-05 0.32034 cg242816 68 chr12 1.14E+08 CCDC42B;OAS1; ENSG000001998 99;RITA1;IQCD;D DX54 0.481058 4.189259 4.48E-05 0.31378 cg122333 63 chr6 1.01E+08 0.414921 4.123855 5.81E-05 0.336905 cg165128 95 chr19 13410117 0.449209 4.144353 5.35E-05 0.336905 cg091315 12 chr6 30713442 -0.40028 -4.14147 5.42E-05 0.336905 cg045309 78 chr10 44858679 -0.59063 -4.12327 5.82E-05 0.336905 cg110552 42 chr12 1.22E+08 0.396502 4.154417 5.14E-05 0.336905 cg222948 49 chr4 4292494 -0.37594 -4.13442 5.57E-05 0.336905 cg066407 18 chr6 91417950 0.72868 4.117439 5.96E-05 0.336905 cg164006 31 chr1 91300215 0.466468 4.130967 5.65E-05 0.336905
94 chr6 5996412 0.491776 4.139303 5.46E-05 0.336905 cg082226 18 chr4 941054 0.726098 4.127199 5.73E-05 0.336905 cg235706 94 chr10 50976348 0.372851 4.120426 5.89E-05 0.336905 cg179033 59 chr1 2.48E+08 -0.35518 -4.1084 6.17E-05 0.336905 cg068774 86 chr13 45694166 0.940722 4.10048 6.37E-05 0.336905 cg242020 21 chr6 31364902 -0.51178 -4.10846 6.17E-05 0.336905 cg233512 71 chr8 1.46E+08 0.39001 4.096762 6.46E-05 0.336905 cg088336
70 chr11 1.25E+08 ROBO3 0.776071 4.075525 7.02E-05 0.336905 cg047782 74 chr19 35615444 LIN37 0.473012 4.108437 6.17E-05 0.336905 cg183157 08 chr14 21673796 LINC00641 -0.62219 -4.06572 7.30E-05 0.338963 cg059577 36 chr3 1.12E+08 -0.35409 -4.07723 6.97E-05 0.336905 cg018120 45 chr2 2.2E+08 0.333636 4.080977 6.87E-05 0.336905 cg004710 00 chr20 31618468 E2F1 -0.33712 -4.08603 6.74E-05 0.336905 cg128618 09 chr5 1.26E+08 -0.53868 -4.07321 7.09E-05 0.336905 cg191989 19 chr12 1.1E+08 0.460316 4.078496 6.94E-05 0.336905 cg008464 83 chr8 1.25E+08 0.475822 4.075224 7.03E-05 0.336905 cg025042 29 chr1 9555380 0.425159 4.106376 6.22E-05 0.336905 cg180302 60 chr13 27884819 -0.36763 -4.08868 6.67E-05 0.336905 cg040958 26 chr1 2.3E+08 0.648028 4.072898 7.09E-05 0.336905 cg079404 28 chr4 1.85E+08 -0.48172 -4.0571 7.55E-05 0.343108 cg145462 58 chr9 95421919 FAM120AOS -0.38066 -4.07914 6.92E-05 0.336905 cg067487 92 chr17 56062305 0.430353 4.077853 6.96E-05 0.336905 cg125031 10 chr4 2760420 -0.36442 -4.06606 7.29E-05 0.338963 cg145499 51 chr9 1.38E+08 0.601559 4.050113 7.75E-05 0.345704 cg086235 48 chr1 1.54E+08 0.38578 4.057593 7.53E-05 0.343108
cg070225 54 chr16 1993481 0.38191 4.039002 8.10E-05 0.345704 cg113037 38 chr3 42977889 0.549872 4.045686 7.89E-05 0.345704 cg029173 21 chr22 38215204 KCTD17 0.441082 4.029656 8.40E-05 0.345704 cg159056 34 chr7 1.43E+08 -0.5246 -4.04304 7.97E-05 0.345704 cg252171 00 chr13 37249426 0.6474 4.033496 8.27E-05 0.345704 cg020168 38 chr11 1.1E+08 -0.42982 -4.04037 8.05E-05 0.345704 cg263778 75 chr5 5421933 -0.68174 -4.02798 8.45E-05 0.345704 cg200467 77 chr12 61961255 -0.51221 -4.03347 8.27E-05 0.345704 cg087248 91 chr2 2.2E+08 -0.6187 -4.01709 8.82E-05 0.345704 cg050434 61 chr5 1.41E+08 0.535876 4.019603 8.73E-05 0.345704 cg041472 72 chr15 79261496 0.630305 4.022331 8.64E-05 0.345704 cg215601 91 chr19 44408232 -0.58394 -4.02072 8.69E-05 0.345704 cg060815 18 chr16 3220915 0.480304 4.026042 8.51E-05 0.345704 cg085398 72 chr10 73004610 CDH23;PALD1;SL C29A3 -0.54481 -4.01442 8.91E-05 0.345704 cg007574 20 chr1 8398058 -0.40083 -4.01238 8.98E-05 0.345704 cg033436 31 chr13 1.15E+08 -0.53152 -4.00178 9.35E-05 0.350325 cg093687
04 chr12 1.23E+08 CLIP1 -0.67186 -3.99624 9.56E-05 0.352105 cg114030 27 chr5 42971411 0.600347 4.005978 9.20E-05 0.350325 cg008762 73 chr10 94452311 0.635794 3.999793 9.43E-05 0.350325 cg215870 66 chr13 37004940 0.540958 3.993336 9.66E-05 0.353025 cg053899 58 chr16 614986 -0.41598 -4.0153 8.88E-05 0.345704 cg008040 97 chr1 3349982 -0.56142 -4.00203 9.34E-05 0.350325 ch.9.9195 37F chr9 76690975 0.558432 3.971762 0.000105 0.367688 cg207601 16 chr3 13324924 0.528536 3.984707 9.99E-05 0.358828
13 chr14 1.03E+08 -0.5169 -3.97338 0.000104 0.367688 cg032363 28 chr15 75101344 -0.33477 -3.96977 0.000106 0.367688 cg162452 65 chr11 14995233 0.620567 3.987382 9.89E-05 0.35816 cg148847 93 chr13 1.09E+08 0.684991 3.946064 0.000116 0.381055 cg086572 47 chr17 27279763 0.331556 3.978493 0.000102 0.364451 cg253802 07 chr10 49365197 0.415744 3.965814 0.000107 0.370301 cg022947 64 chr13 1.13E+08 0.529251 3.95531 0.000112 0.37721 cg214669 63 chr19 22817774 0.429915 3.954756 0.000112 0.37721 cg071401 94 chr2 73150236 -0.36605 -3.9379 0.00012 0.38116 cg187710 34 chr12 69327947 -0.54284 -3.944 0.000117 0.381055 cg175845 12 chr3 73225164 -0.46545 -3.94972 0.000114 0.381055 cg253686 51 chr11 6518287 OLFML1 0.605275 3.936053 0.00012 0.38116 cg149892 52 chr5 57786449 0.596397 3.927864 0.000124 0.387509 cg169889 89 chr15 25925801 -0.51207 -3.92356 0.000126 0.389161 cg166467 43 chr19 9608947 0.710113 3.939913 0.000119 0.38116 cg226979 62 chr2 71192118 0.446734 3.961455 0.000109 0.373533 cg173649 13 chr7 1.57E+08 -0.58821 -3.91995 0.000128 0.389161 cg128536 33 chr14 57264919 0.488271 3.928667 0.000124 0.387509 cg132525 83 chr19 36909831 0.600661 3.913467 0.000131 0.390828 cg246921 70 chr1 2.05E+08 SLC41A1 -0.51811 -3.92073 0.000128 0.389161 cg139128 58 chr1 1.1E+08 C1orf194;GSTM 4;STRIP1;GSTM3 ;GPSM2;CELSR2; CSF1;WDR47 0.465411 3.945727 0.000116 0.381055 cg162823 39 chr11 47927019 0.476459 3.919189 0.000128 0.389161
cg077955 48 chr16 69457652 0.706891 3.912275 0.000132 0.390828 cg035179 19 chr1 10398268 0.377681 3.938172 0.000119 0.38116 cg107188 94 chr17 1094057 -1.06097 -3.91432 0.000131 0.390828 cg227186 36 chr4 961658 DGKQ 1.034075 3.883278 0.000147 0.392971 cg054415 96 chr4 905441 -0.46226 -3.88346 0.000147 0.392971 cg134723 41 chr11 85522005 -0.27238 -3.9071 0.000134 0.390828 cg253422 07 chr10 1.32E+08 0.506398 3.899483 0.000138 0.392971 cg070915 48 chr1 18432960 -0.57654 -3.88824 0.000144 0.392971 cg273284 69 chr4 79367640 -0.46162 -3.90104 0.000138 0.392971 cg164181 83 chr16 51360402 -0.5585 -3.90827 0.000134 0.390828 cg120063 99 chr15 84954185 -0.27247 -3.90883 0.000134 0.390828 cg253432 27 chr22 42353589 0.356785 3.879478 0.000149 0.395357 cg006201 90 chr6 26240307 BTN2A1 0.725659 3.887298 0.000145 0.392971 cg061667 67 chr8 41167848 0.488723 3.88646 0.000145 0.392971 cg100798 67 chr2 2.42E+08 -0.46841 -3.88855 0.000144 0.392971 cg046384 68 chr4 1.23E+08 0.436483 3.863502 0.000158 0.405563 cg274132 90 chr8 1.45E+08 TSTA3 0.693918 3.889624 0.000144 0.392971 cg211489 23 chr5 45553518 -0.59201 -3.88883 0.000144 0.392971 cg015755 19 chr16 89151852 -0.46585 -3.87573 0.000151 0.396896 cg059349 97 chr16 81214503 -0.24763 -3.89366 0.000141 0.392971 cg270151 74 chr15 43622946 -0.46712 -3.89029 0.000143 0.392971 cg081339 37 chr11 46868042 -0.39397 -3.88915 0.000144 0.392971 cg189980 02 chr17 38570480 0.439196 3.878386 0.00015 0.395357 cg103792 09 chr19 45569281 -0.47426 -3.86995 0.000155 0.400727 cg055738 44 chr12 54354590 0.496037 3.872593 0.000153 0.399166
75 chr15 45671028 0.558943 3.841991 0.000172 0.432036 cg268299 90 chr12 1.1E+08 -0.44156 -3.83755 0.000175 0.434171 cg008965 78 chr14 38083827 -0.54358 -3.86437 0.000158 0.405563 cg254210 69 chr16 75563114 0.456196 3.854544 0.000164 0.414591 cg194085 72 chr4 967324 1.947552 3.861976 0.000159 0.405563 cg141614 26 chr5 96143592 -0.20911 -3.82652 0.000182 0.441799 cg107545 05 chr20 60303881 -0.73348 -3.82263 0.000185 0.441799 cg075151 96 chr19 11354240 ICAM1 0.486886 3.805982 0.000197 0.456645 cg066477 51 chr10 88149324 0.508603 3.828687 0.000181 0.441799 cg182817 44 chr6 29455512 -0.49724 -3.82599 0.000182 0.441799 cg095960 25 chr16 84090260 0.433453 3.839866 0.000173 0.432945 cg052165 42 chr16 48109635 NETO2;PHKB -0.63231 -3.81016 0.000194 0.452042 cg246992 71 chr3 4638584 0.490888 3.826569 0.000182 0.441799 cg044613 11 chr17 47926783 -0.37072 -3.81926 0.000187 0.444257 cg162906 89 chr13 31506685 0.41732 3.822233 0.000185 0.441799 cg047323 24 chr11 68564292 0.792877 3.792733 0.000207 0.464555 cg265546 74 chr1 1.01E+08 EXTL2 0.464668 3.82258 0.000185 0.441799 cg003575 03 chr15 44119060 0.328482 3.788678 0.00021 0.465615 cg227555 34 chr11 1.29E+08 -0.39701 -3.78758 0.00021 0.465615 cg133850 16 chr4 1.12E+08 0.73475 3.811004 0.000193 0.452042 cg089226 03 chr4 4763690 -0.37949 -3.79794 0.000203 0.460503 cg023114 56 chr4 6710661 0.585017 3.802062 0.000199 0.457548 cg218156 67 chr2 1.77E+08 0.478672 3.801105 0.0002 0.457548 cg271913 12 chr19 12750958 0.985929 3.795528 0.000204 0.462189 cg156988 51 chr8 29940391 -0.51117 -3.81227 0.000192 0.452042
