Identification of novel differentially methylated sites with potential as clinical predictors of impaired respiratory function and COPD

Abstract

Background

The causes of poor respiratory function and COPD are incompletely understood, but it is clear that genes and the environment play a role. As DNA methylation is under both genetic and environmental control, we hypothesised that investigation of differential methylation associated with these phenotypes would permit mechanistic insights, and improve prediction of COPD. We investigated genome-wide differential DNA methylation patterns using the recently released 850 K Illumina EPIC array. This is the largest single population, whole-genome epigenetic study to date.

Methods

Epigenome-wide association studies (EWASs) of respiratory function and COPD were performed in peripheral blood samples from the Generation Scotland: Scottish Family Health Study (GS:SFHS) cohort (n = 3781; 274 COPD cases and 2919 controls). In independent COPD incidence data (n = 149), significantly differentially methylated sites (DMSs; p < 3.6 × 10⁻⁸) were evaluated for their added predictive power when added to a model including clinical variables, age, sex, height and smoking history using receiver operating characteristic analysis. The Lothian Birth Cohort 1936 (LBC1936) was used to replicate association (n = 895) and prediction (n = 178) results.

Findings

We identified 28 respiratory function and/or COPD associated DMSs, which mapped to genes involved in alternative splicing, JAK-STAT signalling, and axon guidance. In prediction analyses, we observed significant improvement in discrimination between COPD cases and controls (p < .05) in independent GS:SFHS (p = .016) and LBC1936 (p = .010) datasets by adding DMSs to a clinical model.

Interpretation

Identification of novel DMSs has provided insight into the molecular mechanisms regulating respiratory function and aided prediction of COPD risk. Further studies are needed to assess the causality and clinical utility of identified associations.

Fund

Wellcome Trust Strategic Award 10436/Z/14/Z.

Research in context.

Evidence before this study

We searched for articles in PubMed published in English up to July 25, 2018, with the search terms “DNA methylation” and “respiratory function”, or “COPD”. We found some evidence for association between differential DNA methylation and both respiratory function and COPD. Of the twelve previous studies identified, eight used peripheral blood samples (sample size [N] range = 100–1,085) and four used lung tissue samples (N range = 24–160). The number of CpG loci analysed range from 27,578 to 485,512. These studies have not identified consistent changes in methylation, most likely due to a combination of factors including small sample sizes, technical issues, phenotypic definitions, and study design. In addition, no previous study has: analysed a sample from a large single cohort; used the recently released Illumina EPIC array (which assesses ~850,000 CpG loci); adjusted both methylation data and phenotype for smoking history, or used both prevalent and incident COPD electronic health record data.

Added value of this study

To our knowledge, this is the largest single cohort epigenome-wide association study (EWAS) of respiratory function and COPD to date (n = 3,781). After applying stringent genome-wide significance criteria (p < 3.6 × 10⁻⁸), we found that DNA methylation levels at 28 CpG sites in peripheral blood were associated with respiratory function or COPD. Of these 28, seven were testable in an independent population sample: all seven showed consistent direction of effect between the two samples and three showed replication (p < .007 [0.05/7 CpG sites tested]). Our results suggest that adjustment of both the phenotypic and the DNA methylation probe data for smoking history, which has not been carried out in previous studies, reduces the confounding effects of smoking, identifies larger numbers of associations, and reduces the heterogeneity of effects across smoking strata. We used gene set enrichment and pathway analyses, together with an approach that combines DNA methylation results with gene expression data to provide evidence for enrichment of differentially methylated sites in genes linked to alternative splicing, and JAK-STAT signalling and axon guidance. Finally, we demonstrated that the inclusion of DNA methylation data improves COPD risk prediction over established clinical variables alone in two independent datasets.

Implications of all the available evidence

There is now accumulating evidence that DNA methylation in peripheral blood is associated with respiratory function and COPD.

Our study has shown that DNA methylation levels at 28 CpG sites are robustly associated with respiratory function and COPD, provide mechanistic insights, and can improve prediction of COPD risk. Further studies are warranted to improve understanding of the aetiology of COPD, explore causality and to assess the utility of DNA methylation profiling in the clinical management of this condition.

Alt-text: Unlabelled Box

1. Introduction

Respiratory function is influenced by both environmental factors and genetic factors, with heritability estimates ranging from 39 to 66% [1,2]. Epigenetic modifications are at the interface of genetics and the environment. DNA methylation, the covalent binding of a methyl group to the 5′ carbon of cytosine-phosphate-guanine (CpG) dinucleotide sequences in the genome, is an epigenetic modification of DNA that is associated with gene expression. Epigenome-wide association studies (EWASs) have the potential to provide mechanistic insights into impaired respiratory function and COPD pathogenesis. Previous EWASs of spirometric measures of respiratory function and respiratory disease have however produced inconsistent results, with some identifying significant associations [[3], [4], [5], [6]], and others not [[7], [8], [9]]. Moreover, there has been little consistency between the positive findings reported [9,10]. Studies of lung tissue [5,8] have been constrained by sample availability, with the largest study to date comprising 160 subjects [5]. Inconsistency amongst the results of the peripheral blood-based studies [3,6,7,9,11] is likely to be due to a number of factors, including small sample size (e.g., two studies had <200 samples) [6,11] and/or investigation of a relatively small number (~27,000) of CpG loci [3,11]. The study with the largest number of samples (n = 1085) analysed only 27,000 CpG loci, while the largest study using the 450 K array (the predecessor to the array used here) analysed 920 samples [12]. Differences in spirometric measures, definitions of COPD, study population characteristics and study design, in particular in the method used to adjust for smoking history, are also likely to be important sources of variation [9,10]. Smoking is established as a major risk factor for COPD [13], and previous genome-wide DNA methylation have focused on DNA methylation associated with smoking and COPD [5,14,15]. However, not all smokers develop COPD and >25% of COPD cases occur in never smokers [16]. Results from a growing number of studies suggested that impaired respiratory function and COPD are strongly associated with risk factors other than smoking [[17], [18], [19]], and have a strong genetic component [[20], [21], [22]] that generally acts independently of smoking [23]. To understand the pathological mechanisms of impaired respiratory function and COPD other than smoking we sought to identify robust associations by assessing methylation in a large single cohort sample, applying a more rigorous correction for smoking history and by performing sensitivity analyses. In contrast to prior studies, we used the recently released Illumina EPIC array, which interrogates over 850,000 methylation sites. All 3781 individuals in our sample were from a single cohort with extensive and consistent phenotyping comprising clinical investigation, questionnaire, and linkage to routine medical health records. The cross-sectional design of prior studies has limited their capacity to distinguish cause and effect [10]. To identity predictive biomarkers of COPD, and to provide insights into the causal nature of our findings we tested our findings for their predictive power. We used an independent subpopulation of 150 participants with incident COPD who were disease free at the time of blood sampling. Finally, where data were available, we attempted to replicate our EWAS and prediction findings in an independent cohort, LBC1936, drawn from the same population.

2. Material and methods

A flow chart showing the overall study design is outlined in Fig. 1, and full description of the methods is provided in the appendix.

Fig. 1.

Flow-chart showing the analysis pipeline. Direction of the arrows represents the workflow of the study design with performed analysis indicated. Lemon and blue boxes represent the in the discovery Generation Scotland: Scottish Family health study cohort and the replication Lothian Birth Cohort of 1936 (LBC1936) data sets respectively. The grey box indicates input data of COPD case-control differential expression in lung tissue. The green boxes indicate the analyses undertaken. The black arrows and gold boxes indicate output of significant results. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2.1. Epigenome-wide association study

2.1.1. Cohort information

The Generation Scotland Scottish Family Health Study (GS:SFHS; ≥18 years of age at recruitment) [24] and Lothian Birth Cohort of 1936 (LBC1936; ∼70 years of age at recruitment) [25] have extensive clinical, lifestyle, health and genetic data. Medical Research Ethics was obtained for all components of GS:SFHS and LBC1936. Written informed consent was obtained from all participants.

2.1.2. Genome-wide methylation profiling

In the GS:SFHS cohort, DNA methylation data was obtained from 5190 participants using peripheral blood collected at baseline [26]. DNA methylation was assessed using the Infinium MethylationEPIC BeadChip. Quality control procedures were implemented to identify and remove unreliable probes and samples, and probes on the X and Y chromosomes were excluded leaving data for 735,418 methylation loci in 5190 individuals. In the LBC1936, DNA methylation was assessed in whole blood samples from 1004 participants using the Illumina HumanMethylation450 BeadChip. Low-quality probes and samples, and probes on the X and Y chromosomes were removed, leaving 450,726 probes and 920 samples for inclusion in the analysis. M-values were calculated for both datasets. The M-values were then pre-corrected for relatedness (in GS:SFHS), array processing batch and estimated cell counts.

2.1.3. Trait data

Respiratory function was assessed at the time of blood sampling in 4193 GS:SFHS and 895 LBC1936 participants with methylation data. Forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) were measured in litres, using spirometry. Spirometry was performed three times, and the maximum values of FVC and FEV₁ were used in the analyses. Only pre-bronchodilator spirometry measures were available. Quality control of the phenotype data was undertaken to exclude participants with inaccurate spirometry or covariate data; 4171 and 895 individuals were retained in the GS:SFHS and LBC1936 samples respectively.

Following the Global Initiative for Obstructive Lung Disease (GOLD) criteria. Post-bronchodilator spirometry is used for GOLD COPD diagnosis. Pre-bronchodilator spirometry has been used in other studies [22,27], and has been shown to lead to minimal misclassification of moderate (GOLD 2) to severe (GOLD 4) COPD [27]. Therefore, Individuals with airflow limitation consistent with GOLD 2 or worse (FEV₁/FVC ≤0.7 and percent predicted FEV₁ ≤ 80%) were classified as cases in this study [28]. Individuals with FEV₁ >80% predicted and FEV₁/FVC >0.7 were classified as control subjects. Individuals meeting the criteria for GOLD stage 1 (FEV₁/FVC < 0.7, FEV₁ ≥ 80% predicted) were excluded from the comparison of COPD cases and controls to minimise potential misclassification of case and control subjects.

The GS:SFHS dataset (n = 4171) was then divided into an incident COPD dataset (i.e., where COPD developed after recruitment to the cohort) for prediction analysis and a discovery EWAS dataset. The prediction dataset (described below) comprised of incident COPD cases and matched controls. To ensure independence, individuals in the prediction data and their close relatives (identity by state [IBS] > 0.05) were not included in the discovery dataset; leaving 3781 for inclusion in the EWAS analysis.

2.1.4. Identification of differentially methylated sites (DMSs)

We first corrected the FEV₁, FVC, FEV₁/FVC, and COPD trait data for age, age2, sex, height, height [2], smoking status (current smoker, former smoker [quit <12 months], former smoker [quit ≥12 months], and never smoked), and pack-years using the R stats package. FVC data was additionally pre-corrected for weight. Linear regression models were then run in the limma package in R, fitting each CpG site (corrected M-values) as the dependent variable, and pre-corrected respiratory function traits or COPD, age, sex, smoking status, pack-years and the first 20 principal components from the corrected M-values. The genome-wide significance threshold was set at 3.6 × 10⁻⁸ [29].

2.1.5. Sensitivity analyses

To assess the impact of pre-correction of the traits for smoking status and pack-years, we undertook sensitivity analyses, in which FEV₁, FVC, FEV₁/FVC, and COPD were not pre-corrected for smoking status and pack-years. To assess the stability of the estimated effects in older adults, according to smoking status (ever smokers and non-smokers), smoking history, and non-restrictive spirometry pattern (individuals with FEV₁/FVC ≥ 0.7 and FVC or FEV₁ < 80% of predicted were excluded). The data was truncated by age (≥ 40 years; an age group with greater risk of COPD), smoking history (≥ 10 pack-years; smokers with a substantial smoking history), non-restrictive spirometry and stratified by smoking status. For each trait-specific genome-wide significant DMS, we conducted separate random-effects meta-analysis to combine regression coefficients and standard errors from analyses across the full dataset and data-subsets using the ‘rma’ function from the R-package metafor. Heterogeneity of effects across analyses was assessed descriptively with the I² index. We formally tested heterogeneity of effects via Cochran Q statistic.

2.2. Probe annotation and epigenetic regulation of gene expression

DNA methylation probes were mapped to genes based on the IlluminaHumanMethylationEPICanno.ilm10b2.hg19 library. For each trait, methylation probes were filtered at p < .001, and probes that mapped to genes extracted.

For biological processes and molecular function, and canonical pathway enrichment analyses, DMSs were analysed in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database and Ingenuity Pathway Analysis (IPA) software respectively. The Benjamini Hochberg, False Discovery Rate method, was used to correct for multiple-testing with p < .05 considered significant. We used the Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) package in R to combine summary statistics from publicly available COPD lung gene expression data [30] with methylation results from this study. Trait-specific gene modules (set of genes with shared regulation; p < .05 and 10–500 genes) were then identified and subjected to KEGG pathway enrichment analysis and terms with a p < .05 were held as significant.

2.3. Prediction

2.3.1. Case-control data

Incident cases in GS:SFHS were defined as any hospital admission where the primary diagnosis was assigned an ICD-10 J40 to J44 COPD exacerbation code [31]. During follow-up, 81 GS:SFHS participants (all 40 years or older) with DNA methylation data developed COPD. To obtain a balanced dataset for model training an equal number of controls ≥40 years of age were selected at random from those with no-self report, spirometry-defined, or ICD-10 diagnosis of COPD. Participants with missing records and closely related individuals (IBS > 0.05) were excluded; leaving 72 COPD cases and 78 controls. The data was then separated into a training set of 47 COPD cases and 48 controls, and a test set of 25 COPD cases and 30 controls.

As hospital admission data were not available for the LBC1936 cohort, spirometry data were used to define case-control status. In total, 89 participants with DNA methylation data had prevalent COPD [GOLD stage ≥2 cases]. Imbalanced data can negatively impact predictive performance. Controls were therefore selected at random from the participants with DNA methylation data in a 1:1 ratio to case participants. This dataset was used to replicate the prediction findings from GS:SFHS.

2.3.2. Model selection

For the training data, the reduced model, including clinical risk factors, age, age [2], sex, height, height [2], smoking status (current, former and never), and pack-years of smoking [6] was constructed using unpenalized logistic regression. The full model, including DMSs and clinical risk factors, was constructed using penalized logistic regression with an elastic net penalty. Selection of the full model was conducted based on 10-fold cross-validation (appendix p26) using the R package caret. The optimal model was selected based on the maximum mean area under the curve (AUC). Final models were constructed using the complete training set and evaluated on the independent test and replication datasets.

2.3.3. Model evaluation

Comparison of the predictive performance of the models was carried out using the AUC in the pROC R package. The incremental value of the DMS to predict COPD risk, when added to the model with established clinical predictors was assessed using the integrated discrimination improvement (IDI), and binary net reclassification improvement (NRI) measure. Finally, we performed decision curve analysis to estimate the potential clinical usefulness of the models in the ‘rmda’ R package.

3. Results

3.1. EWAS sample characteristics

The discovery sample for respiratory function traits comprised 3781 individuals from GS:SFHS. For the COPD analysis, there were 274 cases and 2919 controls (Table 1; Fig. 1).

Table 1.

Characteristics of Generation Scotland: Scottish Family Health Survey (GS:SFHS) participants (n = 3781) in the epigenome-wide association study discovery population.

	Spirometry data
	COPD cases (n = 274)	Controls (n = 2919)	GOLD stage 1 (n = 588)	Missing (n = 905)
Characteristics
Age, years	53.96 ± 13.59	46.98 ± 13.37	52.15 ± 12.75	49.58 ± 15.56
Sex
– Male	93 (33.9)	1162 (39.8)	222 (37.8)	335 (37.0))
– Female	181 (66.1)	1757 (60.2)	366 (62.2)	570 (63.0)
Height, cm	166.39 ± 8.76	167.85 ± 9.08	166.98 ± 9.47	166.22 ± 9.75
Weight, kg	73.50 ± 15.63	75.82 ± 16.33	76.60 ± 16.87	75.24 ± 17.45
Smoking status
– Never	96 (35.0)	1603 (55.0)	249 (42.4)	388 (42.9)
– Former (quit >12 months)	74 (27.0)	715 (24.5)	164 (27.9)	242 (26.8)
– Former (quit <12 months)	3 (1.1)	88 (3.0)	20 (3.4)	31 (3.5)
– Current	89 (32.5)	443 (15.1)	133 (22.6)	206 (22.8)
– Missing records	12 (4.4)	70 (2.4)	22 (3.7)	25 (2.8)
Pack-year
– Former smokers (quit >12 months)	25.47 ± 31.84	16.67 ± 20.31	22.83 ± 22.85	22.83 ± 22.87
– Former smokers (quit <12 months)	30.00 ± 31.05	15.16 ± 15.76	18.35 ± 20.20	18.35 ± 20.20
– Current smokers	22.97 ± 19.15	15.71 ± 15.95	23.59 ± 16.85	23.59 ± 16.85
Lung function
– FEV1, litres/s	2.01 ± 0.60	3.24 ± 0.76	2.58 ± 0.66	–
– FVC, litres/s	3.45 ± 0.93	4.07 ± 0.94	3.72 ± 1.08	–
– FEV1/FVC	0.59 ± 0.10	0.80 ± 0.06	0.71 ± 0.08	–
– FEV1 percent predicted	66.96 ± 11.44	99.56 ± 11.17	83.97 ± 12.98	–
– FVC percent predicted	89.90 ± 13.09	99.49 ± 11.90	94.93 ± 20.81	–

Abbreviations: COPD, Chronic obstructive pulmonary disease; FEV₁, Forced expiratory volume in 1 s; FVC, Forced vital capacity. Figures shown are the mean ± standard deviation or n (%).

3.2. Differentially methylated sites

EWASs for the three respiratory function traits (FEV₁, FVC and FEV₁/FVC) and COPD on the discovery data identified 29 genome-wide significant associations (p < 3.6 × 10⁻⁸; Fig. 2; Table 2; appendix p6–7), representing 28 DMSs from 25 annotated genes. Fourteen of the DMS were not associated with smoking status (Table 2). We found only marginal evidence of genomic inflation (max = 1.12) across traits (appendix p6 & 27). Ten of the 25 genes that contain DMSs have previously been implicated by genetic, EWAS and functional analysis in respiratory function or disease (excluding cancer; appendix p8–9). Three FEV₁ related-DMSs mapped to the SOCS3 gene (Table 2); DNA methylation levels at these sites are highly correlated (appendix p28).

Fig. 2.

Manhattan plots of epigenome-wide association results for FEV₁ (forced expired volume in 1 s), FVC (forced vital capacity; bottom), FEV₁/FVC and COPD (chronic obstructive pulmonary disease) from the discovery Generation Scotland: Scottish Family health study cohort data. The red line correspond to the genome-wide (p = 3.6 × 10⁻⁸) and suggestive (p = 1.0 × 10⁻⁵) significance level. Labels are for the nearest gene to genome-wide significant CpG sites. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 2.

Genome-wide significant differentially methylated sites (DMSs) associated with the respiratory function traits or chronic obstructive pulmonary disease (COPD) in the Generation Scotland Scottish Family Health Study (GS:SFHS) discovery data. Results are ordered by chromosomal location.

Trait	Chr	Base pair	Gene name/annotation (region)	CpG site (location)	β (p-value)	DMSses
FEV1	3	3,010,002	CNTN4 (Body)	cg13993467 (Open sea)	−0.023 (5.19E-10)	6.31E-01
	3	150,479,084	SIAH2 (Body)	cg16963852 (North shore)	0.015 (2.88E-08)	1.36E-07
	7	8,201,134	ICA1 (Body)	cg26804423 (Open sea)	−0.011(4.88E-11)	3.44E-02
	7	72,775,853	[FKBP6, 3 kb, 3′ *]	cg26080684 (Open sea)	−0.010 (9.75E-09)	5.85E-09
	8	121,597,619	SNTB1 (Body)	cg01198738 (Open sea)	0.013 (5.12–09)	1.42E-03
	9	136,009,651	RALGDS (Body)	cg03770138 (Open sea)	0.010 (2.34E-08)	1.32E-15
	14	74,227,431	ELMSAN1 (TSS1500)	cg18871648 (South shore)	0.014 (4.30E-09)	1.10E-59
	14	74,227,441	C14orf43 (5’UTR)	cg10919522 (South shore)	0.014 (2.14E-10)	1.14E-47
	16	57,180,107	CPNE2 (Body)	cg09018739 (Open sea)	−0.008 (5.49E-10)	8.97E-14
	17	8,844,581	PIK3R5 (5’UTR)	cg07687574 (Open sea)	0.013 (6.68E-11)	3.15E-27
	17	76,274,856	LOC100996291 (TSS1500)	cg19748455 (Open sea)	0.015 (1.11E-9)	5.57E-04
	17	76,354,621	SOCS3 (Body)	cg18181703 (North shore)	0.011 (4.51E-09)	7.38E-14
	17	76,354,934	SOCS3 (Body)	cg11047325 (Island)	0.019 (3.91E-11)	7.54E-22
	17	76,355,061	SOCS3 (Body)	cg13343932 (Island)	0.014 (6.52E-10)	8.89E-25
	19	1,130,866	SBNO2 (Body)	cg18608055 (Open sea)	0.010 (5.52E-09)	1.15E-04
	19	17,955,786	JAK3 (5’UTR)	cg02370334 (North Shelf)	0.011 (3.48E-08)	1.76E-01
	22	46,884,476	CELSR1 (Body)	cg03187361 (Open sea)	0.012 (1.39E-08)	1.49E-05
FVC	3	3,010,002	CNTN4 (Body)	cg13993467 (Open sea)	−0.025 (1.71E-11)	6.31E-01
	4	129,715,236	[JADE1, 16 kb, 5′ *]	cg01620970 (Open sea)	−0.012 (4.11E-09)	1.71E-08
	6	10,210,316	[OFCC1, Body *]	cg00213822 (Open sea)	−0.020 (5.23E-11)	4.74E-02
	9	107,631,656	ABCA1 (Body)	cg15659943 (Open sea)	−0.009 (1.68E-08)	9.96E-09
	11	62,269,149	AHNAK (Body)	cg25465557 (Open sea)	−0.013 (2.11E-10)	2.78E-06
	15	71,041,066	UACA (Body)	cg18007249 (Open sea)	−0.013 (5.60E-10)	8.49E-01
	17	11,608,711	DNAH9 (Body)	cg13108341 (Open sea)	−0.032 (2.57E-08)	7.91E-02
FEV1/FVC	1	160,714,299	SLAMF7 (5’UTR)	cg00045592 (Open sea)	0.014 (1.22E-08)	7.91E-255
	5	373,378	AHRR (Body)	cg05575921 (North shore)	0.029 (3.48E-09)	0.00E+00
	19	17,000,585	F2RL3 (Body)	cg03636183 (North shore)	0.016 (5.10E-09)	0.00E+00
COPD	2	198,243,567	[SF3B1, 13 kb, 3′ *]	cg09455379 (Open sea)	−0.030 (3.28E-08)	4.16E-01
	6	10,466,788	[GCNT2, 27 kb, 5′ *]	cg20453862 (Open sea)	−0.039 (7.42E-09)	3.18E-01

Key: Chr: chromosome; Gene name/annotation (region): human genome build 37/Hg19, region relative to the first listed transcript; including the gene body, transcription start site (TSS) 1500 (within 1500 base pairs of a TSS) and 5′-untranslated region (5′-UTR); CpG site location: location relative to CpG island; including shore (±2 kb) and shelf (2 to 4 kb) up (North)- and down (South)-stream from a CpG island, and open sea (>4 kb) from a CpG island; β, regression coefficient; DMSs_es: associated with having ever smoked in the GS:SFHS cohort, genome-wide significant differentially methylated CpG sites are highlighted in bold.; FEV₁, Forced expiratory volume in 1 s; FVC, Forced vital capacity. The CpG sites lacking gene information in the IlluminaHumanMethylationEPICanno.ilm10b2.hg19 library [*] were mapped to the closest gene (in kilobases [kb] from either the 5′ or 3′ end) using the UCSC Genome Browser (http://genome.ucsc.edu). Respiratory function was assessed by spirometry in the GS:SFHS cohort.

We next attempted to replicate these findings in 895 individuals from the LBC1936 (Table 3). No other Illumina EPIC dataset was available, but seven of the 28 DMSs identified in the discovery dataset had been profiled using the HumanMethylation450 BeadChip, which had been applied to the LBC1936. Of these seven, two FEV₁-associated DMSs (cg18181703 in SOCS3 and cg18608055 in SBNO2) and one FEV₁/FVC ratio-associated DMS (cg03636183 in F2RL3) replicated in LBC1936 (Bonferroni-corrected p ≤ .00714). For all seven probes, however, the direction of the effects were the same in the two datasets (Table 4).

Table 3.

The characteristics of Lothian Birth Cohort of 1936 (LBC1936) participants (n = 895) in the epigenome-wide association study replication population.

	COPD cases (n = 89)	Controls (n = 586)	GOLD stage 1 (n = 220)
Characteristics
Age, years	69.60 ± 0.79	69.54 ± 0.91	69.53 ± 0.88
Sex
– Male	44 (46.4)	305 (52.0)	102 (46.4)
– Female	45 (53.6)	281 (48.0)	118 (53.6)
Height, cm	165.75 ± 9.70	166.59 ± 8.84	166.15 ± 8.98
Weight, kg	73.54 ± 14.78	77.29 ± 13.77	77.56 ± 15.44
Smoking status
– Never	20 (22.5)	324 (55.3)	78 (35.5)
– Former (≥ 12 months)	36 (40.4)	223 (38.0)	109 (49.5)
– Former (< 12 months)	–	1 (0.2)	2 (0.9)
– Current	33 (37.1)	38 (6.5)	31(14.1)
Pack-year
– Former smokers (≥ 12 months)	45.91 ± 43.25	22.17 ± 23.73	32.37 ± 30.29
– Former smokers (< 12 months)	–	17.85 ± 0.00	26.63 ± 0.53
– Current smokers	45.64 ± 20.55	42.59 ± 18.94	47.15 ± 22.75
Lung function
– FEV1, litres/s	1.52 ± 0.51	2.62 ± 0.57	1.99 ± 0.59
– FVC, litres/s	2.59 ± 0.83	3.21 ± 0.77	2.72 ± 1.01
– FEV1/FVC	0.59 ± 0.09	0.82 ± 0.06	0.75 ± 0.09
– FEV1 percent predicted	58.34 ± 13.92	100.01 ± 11.83	76.58 ± 15.26
– FVC percent predicted	74.00 ± 15.19	90.90 ± 11.91	77.92 ± 23.10

Abbreviations: COPD, Chronic obstructive pulmonary disease; FEV₁, Forced expiratory volume in 1 s; FVC, Forced vital capacity. Figures shown are the mean ± standard deviation or n (%).

Table 4.

Replication of the genome-wide significant differentially methylated sites (DMSs) associated with FEV₁ and FEV₁/FVC from the discovery Generation Scotland: Scottish Family Health Survey (GS:SFHS) cohort in the Lothian Birth Cohort of 1936 (LBC1936). Results are ordered by trait and chromosomal location.

Trait	Chr	Base pair	Gene name/annotation (region)	CpG site (location)	β (p-value)
FEV1	7	8,201,134	ICA1 (Body)	cg26804423 (Open sea)	−0.017 (6.32E-02)
	14	74,227,441	C14orf43 (5’UTR)	cg10919522 (South shore)	0.011 (2.95E-01)
	16	57,180,107	CPNE2 (Body)	cg09018739 (Open sea)	−0.015 (3.04E-02)
	17	76,354,621	SOCS3 (Body)	cg18181703 (North shore)	0.034 (1.04E-04)
	19	1,130,866	SBNO2 (Body)	cg18608055 (Open sea)	0.018 (6.54E-03)
FEV1/FVC	5	373,378	AHRR (Body)	cg05575921 (North shore)	0.041 (4.83E-02)
	19	17,000,585	F2RL3 (Body)	cg03636183 (North shore)	0.028 (8.75E-03)

Key: Chr: chromosome; Gene name/annotation (region): human genome build 37/Hg19, region relative to the first listed transcript; including the gene body, transcription start site (TSS) 1500 (within 1500 base pairs of a TSS) and 5′-untranslated region (5′-UTR); CpG site location: location relative to CpG island; including shore (±2 kb) and shelf (2 to 4 kb) up (North)- and down (South)-stream from a CpG island, and open sea (>4 kb) from a CpG island; β, regression coefficient; FEV₁, Forced expiratory volume in 1 s; FVC, Forced vital capacity.

3.3. Sensitivity analyses

As age and smoking affect both DNA methylation and lung function [13,32],and we do not know if pre-bronchodilator restrictive spirometry patterns were reversible in this study. We therefore undertook sensitivity analyses for each significant DMS for each trait. The associations between each significant DMS and its associated phenotype were there assessed in older adults (>40), smokers with a substantial smoking history and individuals with non-restrictive spirometry, and across smoking strata (ever smokers and non-smokers). Meta-analysis was used to compare regression coefficients from the discovery dataset and the sensitivity analyses. All but four of the associations identified in the discovery dataset were robust to differences in age, spirometry patterns, and smoking status and history (p < .05/30 DMSs; appendix p10–11). The associations between FVC and cg00213822, in OFCC1, and cg13108341, in DNAH9 were primarily driven by non-smokers. Whereas, the associations with FEV₁/FVC and the established smoking-associated DMSs, cg05575921, in AHHR, and cg03636183, in F2RL3, were primarily driven by smokers (Fig. 3).

Fig. 3.

Forest plot and meta-analysis across the discovery, older adult (> 40 years), smokers with a substantial smoking history, individuals with non-restrictive spirometry pattern, and stratified smoking status datasets from the Generation Scotland: Scottish Family Health Survey (GS:SFHS) cohort for differentially methylated sites associated with FVC and FEV₁/FVC that showed high heterogeneity in older adults and across the smoking strata. The sizes of the forest plot squares are proportional to the amount of information each dataset contains. Key: All, discovery data; >40, data from participants aged 40 or greater; >10py, data from participants with a smoking history of 10 pack years or greater.

We carried out sensitivity analyses in which the trait data were not pre-corrected for smoking history (appendix p12–15). Two of the identified associations were affected by pre-correction of the traits (appendix p15 & 29). In addition, an FVC-related DMS was identified at cg10919522 only when the trait data was not pre-corrected for smoking history. Pre-correction for smoking history reduced the heterogeneity of the effect size estimates of association across the age and smoking strata.

3.4. Gene ontology analysis

For each trait, to explore whether genes with DMSs share functional features, we filtered methylation probes at p < .01 and performed biological processes (appendix p30), molecular function (appendix p31) and canonical pathway enrichment analyses (appendix p16–23). In the study of molecular functions, we found that each of the four traits were significantly enriched for genes linked to the alternative splicing and phosphoprotein categories (appendix p31). Many of the canonical pathways identified were related to signalling, including apoptosis (appendix p16), cardiovascular signalling (appendix p17) and neurotransmission (appendix p22).

3.5. Integrative analysis of methylation and expression

To investigate the functional relevance of the methylation changes, we integrated transcriptional (COPD case-control differential expression in lung tissue) [30] and epigenetic (from this study) datasets to identify functional gene modules for the traits under study. This analysis identified two significant modules (p < .05) containing 27 and 35 genes with correlated differential methylation associated with FEV₁ and expression in COPD, respectively (appendix p24, p32–33). DMSs mapped to SIAH2 in module 1 (appendix p32) and SOCS3 in module 2 (appendix p33). Many of the genes in module 1 also had correlated differential methylation associated with FVC, FEV₁/FVC and COPD and expression in COPD (appendix p32). Gene enrichment analysis revealed that the top pathway for module 1 was axon guidance, while the top pathways for module 2 were cytokine-cytokine receptor interaction and JAK-STAT signalling (p < .05; appendix p24).

3.6. Predictive value of the DMS

To determine the predictive value of DMSs in the prognosis (forecasting future risk) of COPD, we used an independent training and test set design to predict COPD risk in GS:SFHS and LBC1936. We calculated the improvement in prediction quality of a model where genome-wide significant DMSs with all traits were added to the reduced model, which included the clinical variables: age, age [2], sex, height, height [2], smoking status and pack-years of smoking [6]. For descriptive statistics of the prediction datasets see appendix p25 & 34. Discrimination of the full model in the GS:SFHS test data was good (AUC = 0.856 [95% CI: 0.757–0.956]; appendix p35) and calibration was fair (appendix p36). Addition of DMSs to the reduced model led to a significant improvement in accuracy (ΔAUC: 0.039 [95%CI: 0.025–0.055; p = .025]), discrimination (IDI: 0.048[95%CI: 0.018–0.079; p = .016]; appendix p37) and reclassification (NRI: 0.182 [95%CI: 0.030–0.334, p = .019]; appendix p38). There was no improvement in prediction accuracy observed when never smokers were removed from the prediction data (ΔAUC: −0.013 [95%CI: 0.007–0.019; p = .226]).

We examined glmnet's variable importance measures to determine which DMSs contributed most to the increased discriminatory power. Eight DMSs: cg03770138 (RALGDS), cg18181703 (SOCS3), cg26804423 (ICA1), cg18871648 (ELMSAN1), cg11047325 (SOCS3), cg01620970 (JADE1). cg15659943 (ABCA1) and cg18608055 (SBNO2); were retained for prediction (appendix p39).

We next assessed the full model in the LBC1936 replication sample, which comprised 89 cases and 89 controls. Due to differences in array coverage, only three of the DMSs retained in the full model built in the GS:SFHS training data could be tested in LBC1936 (cg18181703, cg26804423 and cg18608055). Addition of the three sites to the reduced model led to a significant improvement in accuracy (ΔAUC: 0.029(95%CI: 0.024–0.032; p = .006; appendix p35) and discriminative power (IDI: 0.019[95%CI: 0.005–0.033; p = .010]), but not reclassification (NRI: 0.045[−0.023–0.113, p = .196]).

Decision curve analysis showed that the model incorporating the DMSs had good clinical applicability and was superior to the reduced model over a wide range of threshold probabilities in the discovery and replication data (appendix p 40 & 41).

4. Discussion

We performed EWASs of three respiratory function traits and COPD in DNA extracted from peripheral blood using the high-density Illumina EPIC array, in 3781 individuals from a single cohort. These analyses identified 28 DMSs (27 novel, of which 14 were not associated with smoking status), of which 26 are associated with respiratory function and three with COPD in the discovery GS:SFHS data. Data were available to test seven of the 28 DMSs for replication in an independent dataset; three associations replicated. Incorporation of a subset of the identified DMSs into a model composed of established clinical variables improved discrimination of individuals at-risk of COPD in two independent samples. Finally, functional annotation provided insights into the biology of these phenotypes.

Smoking is a major risk for impaired respiratory function and COPD [28], and has been shown to impact DNA methylation [3,8]. We for the first time, adjusted both the phenotypic and DNA methylation data for smoking history. This approach appeared to reduce the confounding effects of smoking, identify more associations, and reduce the heterogeneity of effect estimates across smoking strata. It identified 14 DMS that did not associate with smoking status.

Ten of the 25 genes, harbouring the novel DMSs, have been previously linked to respiratory function or disease (appendix p8–9). In four cases, these links come from studies in lung tissue: DNA methylation changes in ABCA1 in lung tissue has been reported to be associated with pulmonary arterial hypertension; differential expression of ABCA1 and DNAH9 has been reported in lung tissue of patients with COPD and primary ciliary dyskinesia respectively and pathological changes in lung tissue have been reported following knockdown and knockout of SLAMF7, ABCA1, and SOCS3.

Three DMSs showed replication. The first, cg18181703, is one of three FEV₁-associated DMS in SOCS3, which has been associated with infection and autoimmunity [33], modulates the lung inflammatory response [34], and JAK-STAT signal transduction [35]. Transcriptional down-regulation of SOCS3 has been observed in COPD [36] and asthma [37] patients. Differentially methylated sites in SOCS3 within a FEV₁-related gene module in this study were correlated with differential gene expression in lung tissue of COPD patients [30]. DMSs in SOCS3 were the second and fifth most predictive DMSs in the prognostic model for COPD in the GS:SFHS cohort. Inclusion of this DMS in the prediction model also improved the prediction of prevalent COPD risk in the LBC1936. However, this was one of the three DMS that could be tested in the LBC1936.

The second replicated DMS, cg18608055, is a FEV₁-associated DMS in SBNO2, which encodes a transcriptional co-regulator of the pro-inflammatory cascade [38]. Inclusion of this DMS in the prediction model improved the prediction of COPD risk in the GS:SFHS cohort and LBC1936. The third, cg03636183, is a FEV₁/FVC ratio and smoking-associated DMS in F2RL3 [39,40], which has been previously reported to be associated with respiratory function [3].

As discussed above, SOCS3 and SBNO2 were found to provide discriminatory power in the prediction analysis. The inclusion of three other DMSs in RALGDS, ICA1, ELMSAN1, JADE1 and ABCA1 improved the prediction of incident COPD risk in the GS:SFHS. JADE1 is a negative regulator of Wnt signalling, which has been linked to the pathogenesis and progression of COPD [41]. RALGDS is a Ras effector and regulates cellular processes such as vesicular trafficking, endocytosis, and migration [42].

Functional annotation of differently methylated genes identified enrichment of the molecular function alternative splicing (Fig. 4). This finding is consistent with earlier reports that genes associated with COPD (unlike those associated with other complex traits examined) have greater transcriptional complexity due to a disproportionately high level of alternative splicing [43]. In addition, many such genes are spliced differently in COPD patients and controls [43].

Fig. 4.

Venn diagram showing additional sources of evidence for the functional annotation categories showing enrichment in the epigenome-wide association study (EWAS) data: JAK-STAT signalling, axon guidance and alternative splicing. Key: the black, blue and red text represent genes enriched for FEV₁, FVC and FEV₁/FVC ratio-related differentially methylated positions (DMPs) respectively. A single FEV₁ and FVC-related DMP mapped to the CNTN4 gene. The genes with the gold asterisk, represent the FEV₁-related DMPs that replicated in the Lothian Birth Cohort of 1936 (LBC1936). The genes underlined with magenta represent the DMPs that improved the prediction of incident COPD risk in an independent Generation Scotland Scottish Family Health Study (GS:SFHS) dataset when included in the full model. The inclusion of a DMPs in SOCS3 and SBNO2 in the full model additionally improved the prediction of COPD risk in the LBC1936 replication data. The genes in yellow rectangles represent those with correlated FEV₁-related differential methylation in whole blood with the altered gene expression in COPD in lung tissue. Gene lists were extracted from Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov/tools.jsp) Bioinformatics resources database and Ingenuity Pathway Analysis (IPA; http://www.ingenuity.com) functional enrichment and Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) result tables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Functional analysis also identified two cellular pathways (Fig. 4). JAK-STAT signalling (appendix p42) was highlighted in the EWAS data at both the DMS level and in the gene ontology analysis. A module comprising genes (including SOCS3) that showed both differential methylation with FEV₁ in this study, and COPD-based gene expression in lung tissue [30] was enriched for JAK-STAT signalling genes. Thus our data add further support to previous evidence [44] for the importance of this pathway in respiratory function and COPD. Modules comprising genes (including SIAH2) that showed both differential methylation with all three respiratory function traits and COPD in this study, and COPD-based gene expression in lung tissue [30] were enriched for axon guidance signalling genes. SIAH2 upregulation mediates the ubiquitination of NRF2 which has been previously associated with respiratory function [45] and COPD [46]. Axon guidance signalling (appendix p43) was highlighted by two of our analyses, adding support to the neuropathology hypothesis of COPD [47].

To investigate the potential clinical implications of our findings we assessed the predictive properties of DMSs in the prognosis of COPD. The inclusion of DMSs provided added clinical value to established clinical variables in both the discovery and replication datasets. Clinical studies are needed to provide formal proof that changes in DNA methylation at these sites contribute causally to the pathogenesis, and can impact prognosis of COPD.

There are three main limitations to this study. Firstly, DNA methylation was quantified in peripheral blood. There is ongoing debate about whether DNA methylation from peripheral blood can serve as a surrogate marker for DNA methylation in lung tissue [48]. The overlap between our findings and previous studies performed in lung tissue (appendix p8–9) suggest that, for at least some loci, the study of DNA methylation in blood may yield mechanistic insights. Moreover, our data demonstrate that DMSs from peripheral blood have both predictive and clinical value. As such, blood may be an appropriate tissue for the development of biomarkers, as it is easily and repeatedly accessible.

The second limitation is that DNA methylation was measured in blood samples collected at the same time that spirometry tests were performed. As such, our reported associations are subject to reverse causality. However, the integrated alterations in DNA methylation in this study and gene expression profiles in COPD [30] and prospective predictive value of the selected DMSs provide indications that the DNA methylation alterations observed in blood may play a causal role in respiratory function. Nevertheless, longitudinal studies, with serial measurements of DNA methylation will be required to address causality formally.

The third limitation is that only seven of the 29 could be tested for replication due the Illumina HumanMethylation450 BeadChip array been used to profile participants in the LBC1936.

Another limitation of this study is that COPD cases in the EWAS were classified based on pre-bronchodilator spirometry data. It is therefore not possible to determine if their airflow limitation was reversible, and so a proportion of these cases may have been suffering from other respiratory diseases, such as asthma. Nevertheless, sensitivity analysis demonstrated that restrictive spirometry had little impact on our results. Also, GOLD COPD diagnosis is based on post-bronchodilator spirometry. Hence, COPD defined cases in this study might not have met the GOLD stage 2–4 criteria if given bronchodilators. Nonetheless, pre-bronchodilator spirometry classification of COPD has been used previously [22,27], and has been shown to lead to minimal misclassification of moderate to severe (GOLD stage ≥2) COPD [27].

A further limitation is that incident COPD was defined based on ICD-10 COPD exacerbation codes. We were not able to remove never smokers in prediction analyses, as the small sample size leads to overfitting of the training data and no improvement in accuracy. We were therefore not able to rule out confounding by other respiratory conditions in never smokers.

In conclusion, using a large dataset and a robust methodological approach, we have identified DMSs associated with respiratory function and COPD, provided new mechanistic insights and supported previous hypotheses into impaired respiratory function and the pathogenesis of COPD. We also demonstrated that DMSs can be incorporated into existing models for predicting COPD risk, yielding better prediction than established clinical variables alone.

Author contributions

MLB and KLE designed the study. MLB, RMW, SWM, KR, AC, PR, YZ, HCW and MJA prepared the data. MLB carried out the data analysis. MLB and KLE wrote the manuscript with editorial input from RMW, DJP, REM, AMM, CH and IJD. All authors read and approved the final manuscript. Funding for the study was obtained by AMM, KLE, DJP and IJD.

Declarations of interests

Dr. McIntosh reports grants from The Sackler Trust and Dr. Deary reports grants from Wellcome and Medical Research Council outside the submitted work. The remaining authors have nothing to disclose.

Appendix A. Supplementary data

Supplementary material