Skip to main content

DNA methylation and smoking in Korean adults: epigenome-wide association study



Exposure to cigarette smoking can increase the risk of cancers and cardiovascular and pulmonary diseases. However, the underlying mechanisms of how smoking contributes to disease risks are not completely understood. Epigenome-wide association studies (EWASs), mostly in non-Asian populations, have been conducted to identify smoking-associated methylation alterations at individual probes. There are few data on regional methylation changes in relation to smoking. Few data link differential methylation in blood to differential gene expression in lung tissue.


We identified 108 significant (false discovery rate (FDR) < 0.05) differentially methylated probes (DMPs) and 87 significant differentially methylated regions (DMRs) (multiple-testing corrected p < 0.01) in current compared to never smokers from our EWAS of cotinine-validated smoking in blood DNA from a Korean chronic obstructive pulmonary disease cohort (n = 100 including 31 current, 30 former, and 39 never smokers) using Illumina HumanMethylation450 BeadChip. Of the 108 DMPs (FDR < 0.05), nine CpGs were statistically significant based on Bonferroni correction and 93 were novel including five that mapped to loci previously associated with smoking. Of the 87 DMRs, 66 were mapped to novel loci. Methylation correlated with urine cotinine levels in current smokers at six DMPs, with pack-years in current smokers at six DMPs, and with duration of smoking cessation in former smokers at eight DMPs. Of the 143 genes to which our significant DMPs or DMRs annotated, gene expression levels at 20 genes were associated with pack-years in lung tissue transcriptome data of smokers (Asan Biobank, n = 188).


Our study of differential methylation in Koreans confirmed previous findings from non-Asian populations and revealed novel loci in relation to smoking. Smoking-related differential methylation in blood is associated with gene expression in lung tissue, an important target of adverse health effects of smoking, supporting the potential functional importance of methylation in smoking-related disease.


Smoking is well-known for its adverse health effects [1]; however, between 10 and 35 % of people still smoke daily worldwide [2]. Despite established evidence of the causal relationships between smoking and elevated risk of diseases including cancers [3] and pulmonary [4] and cardiovascular diseases [5], the underlying mechanisms are not completely understood. One proposed mechanism is through DNA methylation.

DNA methylation, a type of epigenetic modification, plays a key role in regulating gene expression [6]. Unlike DNA sequence, methylation has cell-type and tissue-specific characteristics. DNA methylation can be impacted by age [7], gender [8], and exposures such as obesity [9] and smoking [10].

At least 16 epigenome-wide association studies (EWASs) of the association between smoking and blood DNA methylation in adults have been published [1126]. Only one study was conducted in an East Asian population [26]; most have been conducted in populations of European ancestry with others in African American, Arab, and South Asian populations. There is no study in Koreans. There are few data where reported smoking has been biochemically validated [11, 21, 25] or where methylation has been evaluated in relation to quantitative biomarkers of smoking [21, 27], pack-years, or duration of smoking cessation [2225]. Only one EWAS correlated differential methylation in blood with gene expression in lung tissue, and only one locus was examined in 10 individuals [19].

The published EWASs of smoking have identified individual differentially methylated probes (DMPs) rather than differentially methylated regions (DMRs). Identification of DMRs associated with an exposure can provide stronger evidence for causality than single DMPs [28]. In addition, DMR analysis is statistically more powerful for detection of association with disease traits or exposures [29].

To identify both DMPs and DMRs in relation to smoking, we conducted an EWAS in 100 adults from a Korean chronic obstructive pulmonary disease (COPD) cohort using the Infinium HumanMethylation450 BeadChip (450k). For the DMPs of genome-wide significance, we investigated their relationship with smoking intensity (urine cotinine) and cumulative smoking (pack-years) in current smokers and duration of smoking cessation in former smokers. As a replication look-up, we also evaluated association between methylation and smoking at previously published probes in our data. For the loci to which significant DMPs or DMRs mapped, we examined differential transcriptome profiles in relation to pack-years in lung tissue from a separate population—188 smokers from the Asan Biobank [30].


Study participants and exposure to cigarette smoking: the Korean COPD cohort

We aimed to compare methylation in current and former smokers separately to never smokers. For this purpose, we measured DNA methylation in 100 of 190 participants in a Korean COPD cohort [31]. Of the 100 participants, 60 had COPD and 40 were without COPD. The breakdown by smoking was 39 never, 30 former, and 31 current smokers. Subjects were recruited from a rural area in Korea. Having available clinical information, computed tomography (CT) data, survey questionnaire, and blood/urine samples were used for sample selections of methylation profiling. Additional approximate frequency matching on age and smoking status was applied. Details of the COPD cohort have been published [31]. All study participants completed a questionnaire and provided both blood and urine samples. Urine samples were collected at the time of participants’ baseline visits. Fresh morning urine samples were obtained from subjects at the time similar with blood sampling. Urine samples had been frozen at −70 °C. Height (cm) and weight (kg) were measured twice for each participant using a body composition analyzer IOI 353 (Aarna Systems., Udaipur, India); the average value of two measurements was used for further analyses. Body mass index (BMI, kg/m2) was calculated by dividing the weight (kg) by the square of the height (m2).

Self-reported smoking status—current, former, and never smoking—was obtained from the questionnaire, and the current status of non-smoking versus smoking was confirmed by urine cotinine levels (nmol/L) measured by immunoassay (Immulite 2000 Xpi; Siemens, NY, USA). One self-reported never smoker was re-assigned to current smoker based on a urine cotinine level of 16,909 nmol/L, higher than our cut-point for current smoking status of 283 nmol/L [32]. Smokers provided the duration (years) and amount (cigarette packs) of cigarette smoking. Pack-years were calculated by multiplying the number of smoked cigarette packs per day by the number of years smoked. Duration of smoking cessation (years) was reported by former smokers.

Genomic DNA preparation and DNA methylation profiling

We used blood DNA samples from participants’ baseline visits for methylation profiling. The DNA quality was checked with a spectrophotometer (NanoDrop® ND-1000 UV-vis), and genomic DNA was diluted to 50 ng/μl using Quant-iT PicoGreen (Invitrogen, Carlsbad, CA, USA). Bisulfite-conversion using EZ DNA methylation kit (Zymo Research, Irvine, CA, USA) was carried out according to the manufacturer’s protocols.

The Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA, USA) was used for our genome-wide methylation profiling. The methylation value (β)—a ratio between methylated probe intensity and total probe intensity—is interpreted as the proportion of methylation and ranges between 0 (unmethylated) and 1 (methylated). The signal extraction and normalization using Beta MIxture Quantile dilation (BMIQ) [33] were conducted in ChAMP [34]. The ComBat [35] method was applied to adjust for batch effects. Cell-type composition was estimated by Houseman’s algorithm [36] in minfi [37]. Cytosine-phosphate-guanine (CpG) probe filtering criteria [38] were applied to eliminate sources of possible false positive results, excluding probes that had a detection p value above 0.01 in any sample; had a bead-count less than 3 in 5 % or more of samples; were non-CpG probes; or were non-specific probes [39]. To minimize the effects of extreme outliers at each probe on association results, methylation values outside three times the interquartile range (IQR) from the first and third quartiles were removed from the analyses. Of all beta values across all participants, 75,549 (0.19 %) were removed. Probes mapping to the X or Y chromosomes were removed [40]. Therefore, a total of 402,508 CpG probes were used in our EWAS.

Statistical approach

We used methylation β values because they are more easily interpretable as methylation changes than M values [41]—the log2 ratio of methylated probe intensity and unmethylated probe intensity. To identify smoking-associated DMPs, we tested methylation levels (response) for association with smoking exposure status (predictor) using robust linear regression. We adjusted for COPD status because of the selection subjects and for age, sex, BMI, and estimated cell-type composition. Never smokers served as the reference group. The regression analysis and empirical Bayes approach was done using Linear Models for Microarray data (limma) [42]. For genome-wide significance, we set the threshold of false discovery rate (FDR) [43] adjusted p < 0.05. All results in this study are methylation differences in current smokers compared to never smokers unless otherwise noted.

In addition to association analyses at individual probes, we applied two different methods—DMRcate [44] and comb-p [45]—to detect regional methylation alterations. These methods can identify significant DMRs even when there is a lack of genome-wide significance at individual probe level. A DMR does not need to contain a DMP of genome-wide significance. DMRs were calculated based not on raw methylation data but the association results.

The DMR methods work in slightly different ways. DMRcate identifies DMRs using tunable kernel smoothing of association signals across the human genome. We used the “dmrcate” function in the DMRcate R package with an input file containing regression coefficients, standard deviations, and unadjusted p values for each probe from our EWAS of current smoking. In detail, DMRcate re-calculates p values at individual CpGs after modeling the Gaussian smoothing using Satterthwaite [46] method within a predefined bandwidth (the length of a distance), corrects p for multiple-testing, and combines information from nearby significant CpGs within the bandwidth. In contrast, comb-p identifies regional enrichments of low p values from unevenly spaced p values. It utilizes only unadjusted p values and chromosomal locations at each probe. It performs the Stouffer-Liptak-Kechris (slk) correction to adjust for adjacent p values after calculating auto-correlation, identifies regions of enrichment, generates Stouffer-Liptak region-corrected p values for each region, and performs Sidak [47] multiple-testing correction.

We defined significant DMRs (1) containing at least two probes, (2) combining information from probes residing within 1000 basepairs (bp), and (3) having multiple-testing corrected p < 0.01 (FDR for DMRcate and Sidak p for comb-p). These two values—the minimum number of CpGs in a region and the minimum length of a distance—were the defaults in DMRcate [48], so we used the same values for comb-p to compare results from two approaches. One DMR study using comb-p set the minimum number of probes to 2 and reported DMRs (Sidak p < 0.05) [49]. We used a more strict cutoff for multiple-testing correction (adjusted p < 0.01) for statistical significance because these methods have been updated and there is no consensus of the threshold. Relevant parameters for DMR calling can be found in Additional file 1: Table S2. We considered that the same region was identified as differentially methylated by the two methods if the start (bp) or end (bp) site was the same or a region identified by one of the two method resided inside a region identified by the other.

We evaluated whether the genome-wide significant (FDR < 0.05) differential methylation patterns seen in current smokers relative to never smokers were also seen in former compared to never smokers. Therefore, in the former smokers, we adjusted for 108 tests to determine look-up level replication (FDR < 0.05). In addition, we examined the dose-response relationships between methylation levels and quantitative indexes of smoking exposure: urine cotinine levels (nmol/L), pack-years in current smokers, and time since smoking cessation (years) in former smokers by using the Spearman correlation. For the dose-response analyses, we used nominal statistical significance (unadjusted p < 0.05) to report our findings.

We also examined the association with current smoking for the 192 CpGs reported more than once in the 16 published studies based on either Illumina Infinium HumanMethylation27 BeadChip or 450k array. Of these 192, 178 CpGs were checked for association after probe filtering in our data. The cutoff for statistical significance was set to FDR adjusted p < 0.05 after correcting for 178 tests.

All statistical analyses were performed in R (version 3.0.2) [50] except for comb-p [45]. The gene annotation for each probe was based on the manufacturer’s annotation file [51].

We used coMET [52] to visualize regional methylation patterns in the top four DMRs (adjusted p < 1.0E−10 at both analyses). In addition to gene names and regulatory elements of the region from ENSEMBLE, Digital DNaseI Hypersensitivity Clusters from ENCODE (DNase Cluster) and chromatin state segmentation by HMM from ENCODE/Broad (Broad ChromHMM) were added (Additional file 2: Figure S2).

Enrichment and functional network analysis

We performed an enrichment analysis to examine whether the significant DMPs (FDR < 0.05) were over- or under-represented, compared to all probes from the 450k array, in several biological features from the Illumina annotation file. The hypergeometric test (two-sided doubling mid-p) was used for the evaluation of enrichments or depletions.

For biological insights into differential methylation changes in relation to current smoking, we implemented a functional network analysis. Genes annotated from selected DMPs (FDR < 0.10) were included in the analysis. We used a core analysis of Ingenuity Pathway Analysis (Ingenuity Systems, Inc., Redwood City, CA, USA).

Transcriptome analysis: Asan Biobank

Transcriptome profiles from the lung tissues of 188 male smokers from the Asan Biobank were used in this analysis. Details of transcriptome profiles using RNA-seq (HiSeq 2000 system, Illumina, Inc., San Diego, CA, USA) have been published [30]. Data was available at NCBI Gene Expression Omnibus (GEO) (accession number of GSE57148). To exclude potential impact of extreme values, we filtered gene expression values outside of three times the IQR from the first and third quartiles of each gene transcript. Of all gene expression values across all participants, 35,607 (1.1 %) were removed. We calculated pack-years from duration (years) and amount (cigarette packs) of cigarette smoking.

To identify differentially expressed genes in relation to smoking intensity (pack-years), we applied a robust linear regression model and empirical Bayes approach by using limma [42]. For robust linear regression, gene expression levels were the response and pack-years the predictor. We presented nominally significant results to provide a clue to understand relationships between methylation in blood and gene expression in lung tissue.


The descriptive characteristics of the study populations are shown in Table 1. The study participants were aged 53 to 84 years. There were 39 never, 30 former, and 31 current smokers. Among the never smokers, 6 were male and 33 were female. The former smokers were all male. There was one female current smoker. Individuals diagnosed with COPD were represented in each smoking group as follows: 19 in never, 20 in former, and 21 in current smoking group. The average BMI was 23.2 kg/m2 for never smokers, 23.5 kg/m2 for former smokers, and 22 kg/m2 for current smokers. The duration of smoking cessation in former smokers ranged 7 to 40 years. There were no significant differences in age, BMI, and proportion of COPD cases across smoking groups in our EWAS data.

Table 1 Descriptive characteristics of the study population

We identified 108 significant DMPs in current smokers compared to never smokers (FDR < 0.05) (Table 2, Additional file 3: Table S1, and Additional file 4: Table S3). Of these, nine were significant after Bonferroni correction (unadjusted p < 1.2E−07 correcting for 402,508 tests). Of the FDR-significant DMPs, 93 of these were novel and 15 were previously reported in EWASs of smoking. Decreased methylation in current smokers was observed at 85 % of the significant DMPs. The methylation differences between current and never smokers at significant CpGs ranged from −20.3 to 15.6 %. Among the top five probes, the most highly statistically significant was a CpG well-known for its association with smoking: cg05575921 (FDR = 2.6E−07) in aryl-hydrocarbon receptor repressor (AHRR). Among the remaining four probes in the top five, three were novel—cg10664184 (FDR = 1.80E−05) in DDA1; cg20723792 (FDR = 6.40E−05) in FAM53B; and cg24780263 (FDR = 0.001) in ALDOA—except for cg05951221 (FDR = 8.50E−04) located 12,850 base pair (bp) apart from ALPPL2. At five loci, more than one DMP at genome-wide significance was identified: AHRR (3 probes), 2q37.1 near ALPPL2 (2 probes), MYO1G (2 probes), NKX2-3 (2 probes), and FAM82A2 (2 probes). The genomic inflation factor (lambda) was 1.25. Manhattan plot and QQ plot are provided (Additional file 5: Figure S1).

Table 2 Top 30 CpGs differentially methylated in blood DNA in relation to current smoking compared to never smoking (FDR < 0.05, ordered by chromosomal location)

For our 108 significant DMPs, we found enrichment of probes mapping to CpG island shores (35 versus 23 % overall from the array, p = 0.002) and enhancer (29 versus 21 % overall from the array, p = 0.04). No significant over- or under-representation of probes in promoter-associated regions (19 versus 19 % overall, p > 0.05) or DNase hypersensitivity sites (18 versus 12 % overall, p > 0.05) were detected.

From the two different DMR analyses, we discovered 249 significant (FDR < 0.01) DMRs from DMRcate, 102 significant (Sidak p < 0.01) DMRs from comb-p, and 87 significant based on both approaches (Table 3). Of these 87 significant using both methods, 66 regions were novel, meaning never reported in previous EWASs of smoking in adults, including 7 that contained one of our genome-wide significant individual DMPs. Among those 87 DMRs, the most significant one (chromosome:start position-end position) from DMRcate was chr5:373378–374425 (FDR = 4.6E−17) in AHRR and this region contains five probes—cg05575921, cg22103736, cg08714121, cg04141806, and cg22356527—including our top-ranked DMP. AHRR differential methylation was also observed from comb-p with two probes—cg05575921 and cg22103736—in slightly shorter length (chr5:373378–373887; Sidak p = 4.8E−05) than that from DMRcate. The most significant DMR overall from comb-p was chr6:149805995–149806732 (Sidak p = 1.9E−14) in ZC3H12D and the exact same region, meaning the same start, end, and number of probes, was also observed from DMRcate (FDR = 2.3E−15) (Table 3). This region did not contain a genome-wide significant DMP. Among novel DMRs, the top two regions from both analyses were chr4:81117647–81119473 (FDR = 6.7E−13 from DMRcate; Sidak p = 2.9E−13 from comb-p) at PRDM8 including 11 probes and chr4:103940711–103941300 (FDR = 6.8E−14 from DMRcate; Sidak p = 2.7E−10 from comb-p) at SLC9B1 including 11 probes. Details of the top five DMRs from each software are in Additional file 6: Table S4. Those regions contain either one or two highly significant CpGs or tightly spaced CpGs of nominal statistical significance. The average (standard deviation, SD) of distances of nearby CpGs in those regions was 147 (153) bp for DMRcate and 158 (169) bp for comb-p.

Table 3 Differentially methylated regions in blood DNA in relation to current smoking compared to never smoking (multiple-testing corrected p < 0.01 at DMRcate and comb-p, ordered by chromosomal location)

Among the 108 significant DMPs from the comparison of current to never smokers, 104 were also significant in the former to never smoker comparison (FDR <0.05, look-up level replication) and had effects in the same direction (Additional file 7: Table S5). The attenuation in effect size in former compared with current smokers ranged from −12.3 to 4.3 %. The top-ranked DMP in former smokers compared to never smokers was cg20723792 (FDR = 1.3E−2) in FAM53B at which no relationship with smoking exposures in terms of DNA methylation has been previously reported.

We examined dose-response relationships between methylation levels and quantitative measures of smoking exposure (urine cotinine levels and pack-years in current smokers and duration of smoking cessation in former smokers) for the 108 significant DMPs identified in our EWAS of current smoking (Table 4). There was no significant finding after FDR multiple-testing correction. Urine cotinine levels were positively correlated at nominal levels of significance (uncorrected p < 0.05) with methylation levels at a probe in MTNR1A and negatively correlated with methylation levels at five probes from five different loci: GNG12; GPR15; AHRR; FAM82A2; and F2RL3. Pack-years in current smokers showed positive correlation at five loci and negative correlation with methylation levels at one locus. Duration of smoking cessation in former smokers was positively correlated at nominal significance (p < 0.05) with methylation levels at seven loci and negatively correlated with methylation at one locus.

Table 4 CpGs differentially methylated in relation to smoking status also related to quantitative measures of smoking (p correlation <0.05, ordered by chromosomal location)

Our analysis of differential gene expression in lung tissue was conducted in 188 male smokers from a separate study, the Asan Biobank. The average age was 64.2 (SD = 8.7) years and average pack-years was 42.0 (SD = 20.6) (Table 1). Of the 174 genes to which the 108 DMPs or 87 DMRs that were significantly differentially methylated were annotated, we had gene transcript profiles for 143. Of these, 20 genes, annotated from 17 DMPs or eight DMRs, showed nominally significant differential gene expression profiles (p < 0.05) in relation to pack-years (Table 5). Fourteen of the 20 genes were novel loci for effects of smoking on methylation and six—GPR15, AHRR, ELMO1, SNED1, LPP, and GNA12—were previously reported in EWASs of smoking. No significant results were observed after FDR multiple-testing correction.

Table 5 Differential methylation in relation to current smoking for genes with transcripts differently expressed (p < 0.05) in relation to smoking pack-years (ordered by chromosomal location)

In current smokers compared to never smokers, there were lower methylation levels at 17 DMPs (Table 5). Of those, four CpGs were located in enhancer regions and their corresponding lung tissue gene expression values were positively associated with pack-years in smokers, regardless of whether or not they were located in a CpG island. Four of the 17 were at DNase I hypersensitivity sites (DHS). Three of these were outside of CpG islands and showed a positive association with pack-years in smokers. The remaining site, located on a shelf region of a CpG island, was negatively associated. At four promoter-associated CpGs, we did not find any relationships between methylation levels and gene expression values.

Our functional network mapping involving 221 genes annotated from probes in our EWAS (FDR < 0.10) identified four overrepresented pathways (Additional file 8: Table S6). Top three networks were “gene expression, cellular movement, and embryonic movement,” “cancer, cellular development, organismal injury, and abnormalities,” and “hematological, metabolic, and cardiovascular disease.”

From a replication look-up of 178 CpGs, selected based on significant findings in at least two published EWASs of smoking, we confirmed differential methylation at 70 CpGs (Table 6). Of these, all CpGs showed same direction of association compared to that in previous reports. Among these 178 probes from previous EWASs, 83 (47 %) showed nominal (p < 0.05) association in our analysis of current smokers which is much higher expected by chance (Kolmogorov p < 2.2E−16). There were also significant differential methylation changes in former smokers at 24 CpGs in 17 loci (Table 6).

Table 6 Look-up in the Korean COPD cohort of CpGs reported at least two epigenome-wide association studies (70 CpGs at FDRg < 0.05, ordered by chromosomal location)


This is the second EWAS for smoking exposure in an East Asian population and the first which links differential methylation changes in blood to large-scale differential transcriptome profiles in lung tissue at multiple loci. We discovered novel smoking-associated DMRs as well as DMPs and confirmed previous findings mostly from non-Asian populations. We identified nominally significant correlations in DNA methylation in relation to quantitative measures of smoking: urine cotinine levels, pack-years, and duration of smoking cessation. Differentially expressed genes in relation to smoking intensity in lung tissue support the potential utility of our findings as blood DNA methylation biomarkers for smoking exposure.

We discovered 108 significant DMPs and 87 significant DMRs in relation to current smoking. Fourteen loci were significant from both approaches; nine of which were novel: CALML4, CCND1, FOXK2, LINC01019, NKX2-3, NT5C1A, PRDM8, SPAG17, and SYNGR1. It has been reported that genetic variants in CCND1 and smoking exposure are associated with gastric carcinogenesis [53], nasopharyngeal carcinoma [54], and lung cancer [55] and useful for lung cancer prediction [56]. PRDM8 encodes a protein which belongs to a conserved family of histone methyltransferases regulating transcription negatively.

Of the 87 significant DMRs, in 32 all CpGs were of nominal (p < 0.05) statistical significance. On average, 78 % of CpGs in each identified DMR were nominally significant. Although a DMR does not need to include a genome-wide significant DMP in the region, 14 DMRs contained FDR-significant DMPs. In our analysis of differentially methylated regions, the most highly significant DMRs consist of either one or two highly significant DMPs or closely spaced neighboring CpGs of only nominal statistical significance in the region (Additional file 6: Table S4). Although it has been reported that two methods that we used to identify DMRs can correct for irregular spacing of probes across the genome [44, 45], we cannot conclude whether these are reflecting true differential methylation or false discovery driven by array-design.

Our EWAS identified 104 DMPs from the analysis of current smokers that were also seen in former smokers compared to never smokers; 93 of which were novel. The methylation differences in current and former smokers compared to never smokers were only slightly attenuated. The persistence of blood DNA methylation changes in former smokers, even after 7 to 40 years of smoking cessation, is notable. Our analysis of duration of smoking cessation in former smokers showed positive correlations at seven loci—IFI16, CLASP1, KTELC1, SPEF2, ACOT13, BSPRY, and FAM82A2—which has not been previously reported in EWASs. We also found a negative correlation at cg25799109 in ARHGEF3, a known smoking-associated CpG [12].

Although there are biomarkers of current smoking, including nicotine and its metabolite cotinine levels in urine, blood, or saliva, biomarkers reflecting past smoking have been lacking. Interestingly, we found that most of the signals for current smoking remained for past smoking. Recent studies suggest that methylation signals are promising biomarkers for both current and lifetime smoking [57] that are related to mortality [58]. Significant methylation alterations in former smokers compared to never smokers from our study can contribute to development of biomarkers for past smoking.

For urinary cotinine, we confirmed previous findings of differential methylation at GNG12, GPR15, F2RL3 [27], and AHRR [21, 27] at nominal statistical significance (p < 0.05) and negative directions of association were also consistent. We also identified novel positive and negative correlations with methylation levels at MTNR1A and FAM82A2, respectively. Gene-environment interactions of variants in MTNR1A and smoking have been reported in relation to oral cancer [59]. In studies without cotinine measured, differential methylation at loci correlated with cotinine could serve as objective biomarkers to confirm the self-reported current level of smoking. For pack-years, we found correlations with DNA methylation at NT5C1A, ZBTB9, HPX, CCND1, and RNF160 which were have not been reported in previous EWASs. Although cg19134728 in JAKMIP3 was previously shown to be differentially methylated in smokers compared to non-smokers [15], its relationship with pack-years in current smokers was never studied.

To gain some biological insight into the differential methylation from our EWAS, we linked our genome-wide significant results to large-scale transcriptome profiles in lung tissues. We discovered differential gene expressions in relation to pack-years at 20 genes which were mapped from 17 DMPs and 8 DMRs. Our findings include six genes—GPR15, AHRR, LPP, GNA12, CYB561, and SNED1—known for their association with smoking in previous EWASs, but none of these has been identified in transcriptome analyses of pack-years in lung tissue. Only one previous EWAS included smoking-associated differential gene expression at AHRR; that study included lung tissue samples from five smokers and five non-smokers [19].

Our finding of enrichment of significant DMPs in CpG island shore (regions within 2000 bp within a CpG island) is consistent with previous findings of variable DNA methylation in the regions [60], suggesting methylation in shore regions is more susceptible to environmental factors including smoking.

Our replication look-up confirmed 70 DMPs in the same direction of methylation changes from previous EWASs at strict look-up level significance. Of these, 51 were replicated in one EWAS [26] from a Chinese population. Nineteen were never replicated in an East Asian population. We could not replicate the novel findings identified from the EWAS in Chinese [26].

We had only one female current smoker and six male never smokers. Because of this imbalance, our adjustment for gender may not eliminate potential bias in the smoking results. We identified one EWAS of gender using Illumina’s 450k array [13] in blood DNA (n = 123). In their supplementary table, they presented 274 gender-associated CpGs of genome-wide significance (p < 1.07E−07) located in autosomes. None of our 108 smoking DMPs (FDR < 0.05) were among those suggesting that our top findings do not reflect the gender imbalance.

In our EWAS, we used COPD status as a covariate. The disease status could be a confounding factor. For 108 FDR-significant DMPs related to current smoking, we checked the association between COPD status and DNA methylation under two statistical models. Model 1 included covariates of age, sex, height, and estimated cell-type compositions; model 2 contained additional covariates of smoking status and pack-years. None of our DMPs were statistically significantly associated with COPD under either model (FDR ≤0.05 after correcting for 108 tests). Sixteen CpGs were nominally related to COPD at uncorrected p < 0.05 (Additional file 9: Table S9).

There are limitations and strengths in this study. First, these data were cross-sectional which limits causal inference regarding resolution of effects with cessation of smoking. Second, we do not have a replication dataset from an independent Korean, or similar, population. Therefore, there is a chance of false positives among our novel findings. Third, the study population was drawn from a COPD cohort. Although we adjusted for the disease status in the regression models, the possibility of some type of selection bias could be raised. Fourth, we used blood DNA methylation to examine effects of smoking. The use of blood DNA methylation changes can be limited due to cell- and tissue-specific characteristics of methylation. However, our findings of differential methylation were adjusted for estimated cell-type proportions. We also confirmed differential transcriptome patterns in relation to pack-years in lung tissue at multiple loci.

Our study also has strengths. This is one of the few studies in Asian populations and the first in Koreans. We verified self-reported non-smoking status with urine cotinine values. Underreporting of smoking status in surveys occurs [61] and the nondifferential misclassification could distort association results. We also implemented two DMR approaches to provide significant DMRs in our EWAS. The methodologies for the discovery of DMRs have been developed and revised over several years, and it has been reported that the performance of DMRcate and comb-p were superior to those of others [44]. We were also able to examine whether genes with differential methylation in relation to smoking also showed differential transcription in relation to smoking in lung tissue, an important target for smoking related pathology.


Our study in Koreans, we discovered novel smoking-associated DNA methylation changes in blood and also confirmed many previous findings mostly identified in Caucasians. Observed correlations between methylation levels and quantitative measures of smoking exposures support the utility of blood DNA methylation biomarkers for smoking intensity and history. Our evaluation of differential gene expression profiles of corresponding genes in lung tissues supports the potential functional importance of our methylation findings.


ACOT11 :

Acyl-CoA thioesterase 11

ACY3 :

Aminoacylase 3


ADAM metallopeptidase with thrombospondin type 1 motif 2


ADCYAP receptor type I


AT-hook DNA binding motif containing 1


Aryl-hydrocarbon receptor repressor


Aldolase, fructose-bisphosphate A


Arachidonate 15-lipoxygenase, type B


Alkaline phosphatase, placental-like 2


BCL tumor suppressor 7C


Body mass index


Beta MIxture Quantile dilation



C11orf21 :

Chromosome 11 open reading frame 21

C20orf27 :

Chromosome 20 open reading frame 27

C5orf63 (clone name: FLJ44606):

Chromosome 5 open reading frame 63


Calmodulin-like 4


Castor zinc finger 1

CCDC57 :

Coiled-coil domain containing 57


Cyclin D1

CD33 :

CD33 molecule

CD72 :

CD72 molecule


Cyclin-dependent kinase 2 associated protein 1

CFAP36 (alias CCDC104):

Cilia and flagella associated protein 36


Complement factor I

CFL2 :

Cofilin 2

CIZ1 :

CDKN1A interacting zinc finger protein 1


Cytoplasmic linker associated protein 1


Chronic obstructive pulmonary disease


Coronin 2B




Cysteine rich secretory protein 2

CYB561 :

Cytochrome b561

DDA1 :

DET1 and DDB1 associated 1


Defensin alpha 4


Deoxyguanosine kinase


DIX domain containing 1


Differentially methylated probe


Differentially methylated region

E2F8 :

E2F transcription factor 8

EPB49 :

Dematin actin binding protein




Epigenome-wide association study


Exocyst complex component 3-like 4

F2RL3 :

F2R-like thrombin/trypsin receptor 3

FAM53B :

Family with sequence similarity 53 member B


False discovery rate


Fibroblast growth factor receptor-like 1


Forkhead box K2


Polypeptide N-acetylgalactosaminyltransferase 2


Gene Expression Omnibus

GFI1 :

Growth factor independent 1 transcriptional repressor

GLI4 :

GLI family zinc finger 4

GNA12 :

G protein subunit alpha 12

GNG12 :

G protein subunit gamma 12

GNG7 :

G protein subunit gamma 7

GPR15 :

G protein-coupled receptor 15

GRK5 :

G protein-coupled receptor kinase 5


Major histocompatibility complex, class II, DP beta 1

IER3 :

Immediate early response 3


Intermediate filament family orphan 1


Insulin-induced gene 1


Interquartile range

IRF7 :

Interferon regulatory factor 7

JAML (alias AMICA1):

Junction adhesion molecule-like

KIAA0182 :

Gse1 coiled-coil protein

KIAA1549L (alias C11orf41):



KRAB box domain containing 1

KRT7 :

Keratin 7


Leukocyte-associated immunoglobulin-like receptor 1

LDLRAD4 (alias C18orf1):

Low density lipoprotein receptor class A domain containing 4

LGI1 :

Leucine-rich glioma inactivated 1




Linear Models for Microarray data

LINC01019 :

Long intergenic non-protein coding RNA 1019


Lysophosphatidylcholine acyltransferase 1


LIM domain containing preferred translocation partner in lipoma

LY6G6E :

Lymphocyte antigen 6 complex, locus G6E

MAN2B1 :

Mannosidase alpha class 2B member 1


Matrix Gla protein

MIR155HG :

MIR155 host gene


Matrix remodeling associated 8


Myosin IG


Nuclear paraspeckle assembly transcript 1 (non-protein coding)


Solute carrier family 9 member B1

NKX2-3 :

NK2 homeobox 3

NT5C1A :

5′-nucleotidase, cytosolic IA

NTN1 :

Netrin 1


Outer dense fiber of sperm tails 3B

PAX8 :

Paired box 8


Polycomb group ring finger 3


Pleckstrin homology domain containing A8


PR/SET domain 8

PRR25 :

Proline-rich 25


Quantitative real-time reverse transcription polymerase chain reaction

RIN3 :

Ras and Rab interactor 3

RP11-474D1.3 :

Retinitis pigmentosa 11


SATB homeobox 2


Saccharopine dehydrogenase


Standard deviation


Shisa family member 8

SLC16A12 :

Solute carrier family 16 member 12


Schlafen family member 12-like

SMCO1 (alias C3orf43):

Single-pass membrane protein with coiled-coil domains 1


Synuclein gamma


Sushi-, nidogen-, and EGF-like domains 1

SOX30 :

SRY-box 30

SPAG17 :

Sperm-associated antigen 17

STX2 :

Syntaxin 2


Synaptic Ras GTPase activating protein 1


Synaptogyrin 1


Tubulin folding cofactor D


Thrombospondin 2


T cell lymphoma invasion and metastasis 2

TLE3 :

Transducin-like enhancer of split 3


Trafficking protein particle complex 9


T cell receptor gamma locus antisense RNA 1


Tetraspanin 13



ZBTB38 :

Zinc finger and BTB domain containing 38

ZC3H12D :

Zinc finger CCCH-type containing 12D

ZNF385A :

Zinc finger protein 385A

ZNF697 :

Zinc finger protein 697


  1. Ezzati M, Lopez AD. Estimates of global mortality attributable to smoking in 2000. Lancet. 2003;362:847–52.

    Article  PubMed  Google Scholar 

  2. OECD: Daily smokers (indicator). Accessed 18 Feb 2016.

  3. Vineis P, Alavanja M, Buffler P, Fontham E, Franceschi S, Gao YT, Gupta PC, Hackshaw A, Matos E, Samet J, et al. Tobacco and cancer: recent epidemiological evidence. J Natl Cancer Inst. 2004;96:99–106.

    Article  CAS  PubMed  Google Scholar 

  4. Cunningham TJ, Ford ES, Rolle IV, Wheaton AG, Croft JB. Associations of self-reported cigarette smoking with chronic obstructive pulmonary disease and co-morbid chronic conditions in the United States. COPD. 2015;12:276–86.

    Article  PubMed  Google Scholar 

  5. Conen D, Everett BM, Kurth T, Creager MA, Buring JE, Ridker PM, Pradhan AD. Smoking, smoking cessation, [corrected] and risk for symptomatic peripheral artery disease in women: a cohort study. Ann Intern Med. 2011;154:719–26.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Goldberg AD, Allis CD, Bernstein E. Epigenetics: a landscape takes shape. Cell. 2007;128:635–8.

    Article  CAS  PubMed  Google Scholar 

  7. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, Cigudosa JC, Urioste M, Benitez J, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A. 2005;102:10604–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. El-Maarri O, Becker T, Junen J, Manzoor SS, Diaz-Lacava A, Schwaab R, Wienker T, Oldenburg J. Gender specific differences in levels of DNA methylation at selected loci from human total blood: a tendency toward higher methylation levels in males. Hum Genet. 2007;122:505–14.

    Article  CAS  PubMed  Google Scholar 

  9. Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aissi D, Wahl S, Meduri E, Morange PE, Gagnon F, Grallert H, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383:1990–8.

    Article  CAS  PubMed  Google Scholar 

  10. Lee KW, Pausova Z. Cigarette smoking and DNA methylation. Front Genet. 2013;4:132.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Allione A, Marcon F, Fiorito G, Guarrera S, Siniscalchi E, Zijno A, Crebelli R, Matullo G. Novel epigenetic changes unveiled by monozygotic twins discordant for smoking habits. PLoS One. 2015;10:1–11.

    Article  Google Scholar 

  12. Guida F, Sandanger TM, Castagne R, Campanella G, Polidoro S, Palli D, Krogh V, Tumino R, Sacerdote C, Panico S, et al. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum Mol Genet. 2015;24:2349–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zaghlool SB, Al-Shafai M, Al Muftah WA, Kumar P, Falchi M, Suhre K. Association of DNA methylation with age, gender, and smoking in an Arab population. Clin Epigenetics. 2015;7:6.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Besingi W, Johansson A. Smoke-related DNA methylation changes in the etiology of human disease. Hum Mol Genet. 2014;23:2290–7.

    Article  CAS  PubMed  Google Scholar 

  15. Dogan MV, Shields B, Cutrona C, Gao L, Gibbons FX, Simons R, Monick M, Brody GH, Tan K, Beach SR, Philibert RA. The effect of smoking on DNA methylation of peripheral blood mononuclear cells from African American women. BMC Genomics. 2014;15:151.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Elliott HR, Tillin T, McArdle WL, Ho K, Duggirala A, Frayling TM, Davey Smith G, Hughes AD, Chaturvedi N, Relton CL. Differences in smoking associated DNA methylation patterns in South Asians and Europeans. Clin Epigenetics. 2014;6:4.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Harlid S, Xu Z, Panduri V, Sandler DP, Taylor JA. CpG sites associated with cigarette smoking: analysis of epigenome-wide data from the sister study. Environ Health Perspect. 2014.

  18. Tsaprouni LG, Yang TP, Bell J, Dick KJ, Kanoni S, Nisbet J, Vinuela A, Grundberg E, Nelson CP, Meduri E, et al. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics. 2014;9:1382–96.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shenker NS, Polidoro S, van Veldhoven K, Sacerdote C, Ricceri F, Birrell MA, Belvisi MG, Brown R, Vineis P, Flanagan JM. Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking. Hum Mol Genet. 2013;22:843–51.

    Article  CAS  PubMed  Google Scholar 

  20. Sun YV, Smith AK, Conneely KN, Chang Q, Li W, Lazarus A, Smith JA, Almli LM, Binder EB, Klengel T, et al. Epigenomic association analysis identifies smoking-related DNA methylation sites in African Americans. Hum Genet. 2013.

  21. Philibert RA, Beach SR, Lei MK, Brody GH. Changes in DNA methylation at the aryl hydrocarbon receptor repressor may be a new biomarker for smoking. Clin Epigenetics. 2013;5:19.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zeilinger S, Kuhnel B, Klopp N, Baurecht H, Kleinschmidt A, Gieger C, Weidinger S, Lattka E, Adamski J, Peters A, et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS One. 2013;8:e63812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Philibert RA, Beach SR, Brody GH. Demethylation of the aryl hydrocarbon receptor repressor as a biomarker for nascent smokers. Epigenetics. 2012;7:1331–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wan ES, Qiu W, Baccarelli A, Carey VJ, Bacherman H, Rennard SI, Agusti A, Anderson W, Lomas DA, Demeo DL. Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome. Hum Mol Genet. 2012;21:3073–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Breitling LP, Yang R, Korn B, Burwinkel B, Brenner H. Tobacco-smoking-related differential DNA methylation: 27K discovery and replication. Am J Hum Genet. 2011;88:450–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhu X, Li J, Deng S, Yu K, Liu X, Deng Q, Sun H, Zhang X, He M, Guo H, et al. Genome-wide analysis of DNA methylation and cigarette smoking in Chinese. Environ Health Perspect. 2016.

  27. Zhang Y, Florath I, Saum KU, Brenner H. Self-reported smoking, serum cotinine, and blood DNA methylation. Environ Res. 2016;146:395–403.

    Article  CAS  PubMed  Google Scholar 

  28. Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462:315–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol. 2012;41:200–9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kim WJ, Lim JH, Lee JS, Lee SD, Kim JH, Oh YM. Comprehensive analysis of transcriptome sequencing data in the lung tissues of COPD subjects. Int J Genomics. 2015;2015:206937.

    PubMed  PubMed Central  Google Scholar 

  31. Yoonki Hong J-WK, Lee S-A, Young JH, Moon JY, Kim HY, Han S-S, Lee S-J, Kim WJ. Methdology of an observational cohort study for subjects with chronic obstructive pulmonary disease in dusty areas near cement plants. J Pulm Respir Med. 2014;04:169–74.

    Google Scholar 

  32. Gorber SC, Schofield-Hurwitz S, Hardt J, Levasseur G, Tremblay M. The accuracy of self-reported smoking: a systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tobacco Res. 2009;11:12–24.

    Article  Google Scholar 

  33. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450k DNA methylation data. Bioinformatics. 2013;29:189–96.

    Article  CAS  PubMed  Google Scholar 

  34. Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, Beck S. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics. 2014;30:428–30.

    Article  CAS  PubMed  Google Scholar 

  35. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.

    Article  PubMed  Google Scholar 

  36. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA. Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Morris TJ, Beck S. Analysis pipelines and packages for Infinium HumanMethylation450 Bead Chip (450k) data. Methods. 2015;72:3–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhang X, Mu W, Zhang W. On the analysis of the illumina 450k array data: probes ambiguously mapped to the human genome. Front Genet. 2012;3:73.

    PubMed  PubMed Central  Google Scholar 

  40. Marabita F, Almgren M, Lindholm ME, Ruhrmann S, Fagerstrom-Billai F, Jagodic M, Sundberg CJ, Ekstrom TJ, Teschendorff AE, Tegner J, Gomez-Cabrero D. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics. 2013;8:333–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, Lin SM. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11:587.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article 3.

    Google Scholar 

  43. Benjamini Y, Hochberg Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B. 1995;57:289–300.

    Google Scholar 

  44. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V Lord R, Clark SJ, Molloy PL. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin. 2015;8:6.

    PubMed  PubMed Central  Google Scholar 

  45. Pedersen BS, Schwartz DA, Yang IV, Kechris KJ. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28:2986–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Satterthwaite FE. An approximate distribution of estimates of variance components. Biometrics. 1946;2:110–4.

    Article  CAS  PubMed  Google Scholar 

  47. Sidak Z. Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc. 1967;62:8.

    Google Scholar 

  48. Package ‘DMRcate’ []. Accessed 10 Mar 2016.

  49. Yang IV, Pedersen BS, Liu A, O’Connor GT, Teach SJ, Kattan M, Misiak RT, Gruchalla R, Steinbach SF, Szefler SJ, et al. DNA methylation and childhood asthma in the inner city. J Allergy Clin Immunol. 2015;136:69–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Computing RFS. R: A language and environment for statistical computing. Vienna: R Core Team; 2013.

    Google Scholar 

  51. Illumina Infinium HumanMethylation450 BeadChip Annotation. Accessed 18 Sept 2015.

  52. Martin TC, Yet I, Tsai PC, Bell JT. coMET: visualisation of regional epigenome-wide association scan results and DNA co-methylation patterns. BMC Bioinformatics. 2015;16:131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kuo HW, Huang CY, Fu CK, Liao CH, Hsieh YH, Hsu CM, Tsai CW, Chang WS, Bau DT. The significant association of CCND1 genotypes with gastric cancer in Taiwan. Anticancer Res. 2014;34:4963–8.

    CAS  PubMed  Google Scholar 

  54. Shih LC, Tsai CW, Tsai MH, Tsou YA, Chang WS, Li FJ, Lee MH, Bau DT. Association of cyclin D1 genotypes with nasopharyngeal carcinoma risk. Anticancer Res. 2012;32:1093–8.

    CAS  PubMed  Google Scholar 

  55. Sobti RC, Kaur P, Kaur S, Singh J, Janmeja AK, Jindal SK, Kishan J, Raimondi S. Effects of cyclin D1 (CCND1) polymorphism on susceptibility to lung cancer in a North Indian population. Cancer Genet Cytogenet. 2006;170:108–14.

    Article  CAS  PubMed  Google Scholar 

  56. Hsia TC, Liu CJ, Lin CH, Chang WS, Chu CC, Hang LW, Lee HZ, Lo WC, Bau DT. Interaction of CCND1 genotype and smoking habit in Taiwan lung cancer patients. Anticancer Res. 2011;31:3601–5.

    CAS  PubMed  Google Scholar 

  57. Zhang Y, Yang RX, Burwinkel B, Breitling LP, Brenner H. F2RL3 methylation as a biomarker of current and lifetime smoking exposures. Environ Health Perspect. 2014;122:131–7.

    PubMed  Google Scholar 

  58. Zhang Y, Schottker B, Florath I, Stock C, Butterbach K, Holleczek B, Mons U, Brenner H. Smoking-Associated DNA Methylation Biomarkers and Their Predictive Value for All-Cause and Cardiovascular Mortality. Environ Health Perspect. 2016;124:67-74.

  59. Lin FY, Lin CW, Yang SF, Lee WJ, Lin YW, Lee LM, Chang JL, Weng WC, Lin CH, Chien MH. Interactions between environmental factors and melatonin receptor type 1A polymorphism in relation to oral cancer susceptibility and clinicopathologic development. PLoS One. 2015;10.

  60. Ziller MJ, Gu H, Muller F, Donaghey J, Tsai LT, Kohlbacher O, De Jager PL, Rosen ED, Bennett DA, Bernstein BE, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013;500:477–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Rebagliato M. Validation of self reported smoking. J Epidemiol Community Health. 2002;56:163–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


We appreciate all of the study participants for their contribution to this research. We thank Drs. Shuangshuang Dai, Tianyuan Wang, and Sarah Reese of NIEHS and Jianping Jin of Westat, Inc. for expert computational assistance.


This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A1A1057961), the Ministry of Education, Science and Technology (NRF-355-2011-1-E00060, NRF-2012R1A6A3A01039450), the Ministry of Education (2013R1A6A3A04059017), and grants from the Environmental Health Center funded by the Ministry of Environment, Republic of Korea. This study was also supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (NIEHS).

Availability of data and materials

The results of epigenome-wide association study of current versus never smoking using Infinium HumanMethylation450 BeadChip are provided in Additional file 3: Table S1 of this manuscript.

Authors’ contributions

WJK and YH have designed the cohort study. SJL and SYK advised analytic approach. MKL analyzed the data and wrote the manuscript draft. All authors have read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The Institute Review Board of the Kangwon National University Hospital approved analyses of the clinical and imaging data (Institutional Review Board of Kangwon National University Hospital 2012-06-007-001 and KNUH-2016-05-003-001). Individual informed written consent was obtained from all participants. The study adhered to the tenets of the Helsinki Declaration of 1975, as revised in 2008.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Stephanie J. London or Woo Jin Kim.

Additional files

Additional file 1: Table S2.

Relevant parameters for differential methylated region calling. (DOC 32 kb)

Additional file 2: Figure S2.

Regional visualization of the association between current smoking and DNA methylation in blood. (DOC 555 kb)

Additional file 3: Table S1.

Epigenome-wide association results of current smoking (compared to never smoking). (XLSX 30455 kb)

Additional file 4: Table S3.

CpGs differentially methylated in blood DNA in relation to current smoking compared to never smoking: 108 probes (FDR <0.05, ordered by chromosomal location). (DOC 166 kb)

Additional file 5: Figure S1.

Manhattan plot and quantile-quantile plot. (DOC 468 kb)

Additional file 6: Table S4.

Table S4 CpGs included in the top five differentially methylated regions from each analysis: DMRcate and comb-p (ordered by software and chromosomal location). (DOC 146 kb)

Additional file 7: Table S5.

CpGs differentially methylated in blood DNA in relation to current and former smoking compared to never smoking, 104 probes (FDR* <0.05, ordered by chromosomal location). (DOC 177 kb)

Additional file 8: Table S6.

Enriched networks in genes related to current smoking. (DOC 35 kb)

Additional file 9: Table S9.

Association results of COPD status and DNA methylation at the 16 CpGs with unadjusted p < 0.05 in Model 1 or 2. (DOC 47 kb)

Additional file 10: Table S7.

Top 30 CpGs differentially methylated in blood DNA in relation to current smoking compared to never smoking (FDR ≤ 0.05, ordered by p values). (DOC 69 kb)

Additional file 11: Table S8.

Differentially methylated regions in blood DNA in relation to current smoking compared to never smoking (multiple-testing corrected p < 0.01 at DMRcate and comb-p, ordered by p values). (DOC 173 kb)

Rights and permissions

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, M.K., Hong, Y., Kim, SY. et al. DNA methylation and smoking in Korean adults: epigenome-wide association study. Clin Epigenet 8, 103 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: