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. 2015 Dec 8;10(11):1064–1073. doi: 10.1080/15592294.2015.1106672

The impact of genetic variation and cigarette smoke on DNA methylation in current and former smokers from the COPDGene study

Weiliang Qiu 1, Emily Wan 1,2, Jarrett Morrow 1, Michael H Cho 1,2, James D Crapo 3, Edwin K Silverman 1,2, Dawn L DeMeo 1,2,*
PMCID: PMC4844199  PMID: 26646902

Abstract

DNA methylation can be affected by systemic exposures, such as cigarette smoking and genetic sequence variation; however, the relative impact of each on the epigenome is unknown. We aimed to assess if cigarette smoking and genetic variation are associated with overlapping or distinct sets of DNA methylation marks and pathways. We selected 85 Caucasian current and former smokers with genome-wide single nucleotide polymorphism (SNP) genotyping available from the COPDGene study.  Genome-wide methylation was obtained on DNA from whole blood using the Illumina HumanMethylation27 platform. To determine the impact of local sequence variation on DNA methylation (mQTL), we examined the association between methylation and SNPs within 50 kb of each CpG site.  To examine the impact of cigarette smoking on DNA methylation, we examined the differences in methylation by current cigarette smoking status. We detected 770 CpG sites annotated to 708 genes associated at an FDR < 0.05 in the cis-mQTL analysis and 1,287 CpG sites annotated to 1,242 genes, which were nominally associated in the smoking-CpG association analysis (Punadjusted < 0.05). Forty-three CpG sites annotated to 40 genes were associated with both SNP variation and current smoking; this overlap was not greater than that expected by chance. Our results suggest that cigarette smoking and genetic variants impact distinct sets of DNA methylation marks, the further elucidation of which may partially explain the variable susceptibility to the health effects of cigarette smoking. Ascertaining how genetic variation and systemic exposures differentially impact the human epigenome has relevance for both biomarker identification and therapeutic target development for smoking-related diseases.

Keywords: cis-mQTL, CpG site, epigenetics, environmental factor, genetic variant

Introduction

Cigarette smoking is a major risk factor for cardiovascular, pulmonary, and neoplastic diseases and contributes to the leading causes of morbidity and mortality globally. While the prevalence of cigarette smoking is declining, due to population growth, the absolute number of smokers is increasing world-wide and the burden of smoking-related diseases is projected to grow.1 Genetic variation is known to play an important role in the risk for many smoking-related complex diseases, but the variable and prolonged susceptibility to the health effects of cigarette smoking are incompletely explained by genetic sequence variation alone. Several recent studies have suggested a role for epigenetic mediations, such as DNA methylation in smoking-related diseases.2-4

DNA methylation involves the addition of a methyl group to DNA, typically in CpG dinucleotide sites. There has been considerable interest in how environmental and personal exposures modulate the establishment and maintenance of the epigenome, including DNA methylation.5-12 In this context, many researchers have investigated the association of variable methylation of DNA from blood with various smoking metrics across the life course.13-26 In cohorts of smokers, differential methylation has been linked to current smoking status and time since smoking cessation, chronic obstructive pulmonary disease (COPD), asthma, and lung cancer.19 It has been reported that genetic sequence variation can also influence DNA methylation patterns.27-36 However, the relative impact of genetic variants and environmental exposures on DNA methylation are incompletely understood.

In this manuscript, we investigate both cigarette smoking (as an environmental factor) and common genetic sequence variations associated with site-specific methylation across the genome. Previous studies30,37-39 have defined methylation quantitative trait loci (mQTL), but comparisons of the genetic and exposure contexts of methylation in smokers have not been performed simultaneously. We hypothesized that genetic variation and current smoking would demonstrate a subset of overlapping associations with methylation marks. Identifying genetic and exposure factors which differentially impact the plasticity of the human epigenome represents a fertile landscape to investigate both DNA methylation as a biomarker and for future development of pharmacoepigenetic targets for neoplastic and non-neoplastic smoking-related disease.

Results

Cohort description

Demographic and clinical characteristics of the 85 subjects by current smoking status are summarized in Table 1. Female subjects accounted for 61.2% of the total cohort; mean age was 65.1 y and mean pack-years smoked was 47.2. Current smokers were significantly younger than former smokers (p=0.003).

Table 1.

Cohort characteristics.

Variable All 85 subjects Current smokers Former smokers P
N 85 19 66  
Age at enrollment 65.1±8 .1 60.1±7 .9 66.6±7 .6 0.003
Pack-years 47.2±28 .2 55.6±42 .7 44.7±22 .3 0.73
Female (%) 52 (61.2%) 9 (47.4%) 43 (65.2%) 0.19
FEV1 % predicted 69.8±28 .4 67.4±23 .4 70.6±30 .0 0.51
FEV1/FVC ratio 0.60±0 .18 0.58±0 .15 0.61±0 .19 0.37
Batch 1 38 (44.7%) 10 (52.6%) 28 (42.4%) 0.45
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Data are presented as mean (SD) or number (%).

For age at enrollment and pack years, P-values were from Wilcoxon rank sum test; for gender and batch, P-values were from Fisher's exact test.

cis-mQTL analysis

There were 503,411 individual CpG-SNP pairs tested in our cis-mQTL analysis. Among these, we detected a significant excess of quantitative trait loci for DNA CpG methylation; 3,002 CpG-SNP pairs had an FDR-adjusted P < 0.05. The 3,002 significant tests were comprised of 2,757 unique SNPs associated with 770 unique CpG sites near or in 708 unique genes.

A volcano plot of the –log10 of the P-value (y-axis) relative to the regression coefficient is shown in Figure 1. A Manhattan plot of the –log10(P-value) vs. physical position of SNPs at each chromosome is shown in Figure 2; significant cis-mQTL SNPs are abundant throughout the genome. The proportion of significant mQTLs in this 27K survey is variably distributed across the 22 chromosomes. Chromosome 21 has the largest proportion of significant mQTLs (1.5%), while chromosome 15 has the smallest proportion (0.3%). Chromosome 21 also has the largest proportion (6.3%) of significant CpG sites, the largest proportion (8.4%) of genes corresponding to significant CpG sites, and the largest proportion (2.8%) of SNPs (Fig. 3).

Figure 1.

Figure 1.

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Volcano plot of the cis-mQTL analysis. Dashed red line represents an FDR<0 .05. Gene symbols for the top 20 cis-mQTL tests are shown in the volcano plot.

Figure 2.

Figure 2.

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Manhattan plot of cis-mQTL analysis.

Figure 3.

Figure 3.

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Top-left panel: Proportions of unique significant SNPs (i.e., SNPs in significant cis-mQTL tests) across the 22 chromosomes; Top-right panel: Proportions of unique significant CpG sites (i.e., CpG sites in significant cis-mQTL tests) across the 22 chromosomes; Bottom-left panel: Proportions of unique significant genes (i.e., genes corresponding to CpG sites in significant cis-mQTL tests) across the 22 chromosomes; Bottom-right panel: Proportions of significant cis-mQTL tests across the 22 chromosomes.

We investigated whether the physical distance between CpG sites and SNPs impacted the likelihood of being a mQTL. We binned the 100 kb region surrounding each CpG site (50 kb upstream, 50 kb downstream) into 20 5 kb regions. Within each region, we calculated the percent of association tests with P < 0.001. Figure 4a illustrates the distribution of the mQTLs with a P < 0.001 in the 100 kb region; proximity to the CpG site is associated with an increased likelihood of being a significant mQTL. Figure 4b illustrates the distribution of the mQTLs with a P < 0.001 within 5 kb of the CpG site, and shows a similar pattern to Figure 4a. Among the 3,002 CpG-SNP pairs tested with an FDR <0.05, the mean absolute distances between CpG and SNPs in the first, median, and third quartiles were 5.3 kb, 14.3 kb, and 27.6 kb respectively.

Figure 4.

Figure 4.

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Distribution of mQTLs with p-value < 0.001 by distance from CpG site. For panel (a), each bin has a width of 5KB [range ≤ +45KB to ≥ −45KB from the CpG site]. For panel (b), each bin has a width of 0.5KB [range ≤ +5KB to ≥ −5KB from the CpG site].

The top 10 statistically significant mQTL tests are shown in Table 2, and include 7 CpG sites annotated to 7 genes. The CpG site-SNP pair with the most significant P =7.41×10−35 (cg18771300-rs4902214) is located near the gene ras homolog family member J (RHOJ) on chromosome 14. The parallel boxplots of DNA methylation levels vs. SNP genotype for the top 2 mQTL tests are shown (Fig. 5). Four SNPs were annotated to LOC126295; these 4 were all found to be in high linkage disequilibrium with each other (minimum R2≥ 0.94).

Table 2.

Top 10 CpG sites associated with common single nucleotide polymorphisms (cis-mQTLs).

CpG chr stat snp pvalue FDR Gene symbol island − dist(SNP, CpG) dist(CpG, TSS) Minor allele flag
cg18771300 14 −23.68 rs4902214 7.41E-35 3.73E-29 RHOJ TRUE −834 592 T 0
cg17829936 6 −22.39 rs3813355 2.31E-33 5.80E-28 TAAR5 FALSE −69 144 G 1
cg08634464 19 −21.05 rs4807358 9.60E-32 1.21E-26 LOC126295 TRUE −629 183 C 0
cg08634464 19 −21.05 rs8100809 9.60E-32 1.21E-26 LOC126295 TRUE −4329 183 C 0
cg10536916 6 18.96 rs7748520 4.52E-29 4.56E-24 HIST1H4L FALSE 23043 576 C 0
cg08634464 19 −17.82 rs10410539 1.59E-27 1.33E-22 LOC126295 TRUE 16465 183 T 0
cg05740244 11 −17.6 rs4757662 3.21E-27 2.31E-22 LDHC TRUE 5723 162 A 1
cg08634464 19 −17.38 rs11084971 1.02E-26 6.39E-22 LOC126295 TRUE 4766 183 G 0
cg16474696 19 16.43 rs371671 2.29E-25 1.26E-20 MGC3207 TRUE −4861 332 A 0
cg19766460 21 16.28 rs1571737 2.50E-25 1.26E-20 C21orf128 FALSE 2466 353 C 1
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Stat is the test statistic; island indicates if a CpG site is from a CpG island; dist(SNP, CpG) is the distance from the SNP to the CpG site; dist(CpG, TSS) is the distance from the CpG site to the gene's transcription starting site. Minor allele was determined based on the 85 subjects in this study; flag indicates if a CpG site has SNPs with MAF<0 .05 within 5 base pairs.

Figure 5.

Figure 5.

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Parallel boxplots of CpG site methylation level vs. SNP genotype for the top 2 significant cis-mQTL tests.

Among the 770 unique CpG sites identified in our mQTL analysis, 63.0% were annotated to CpG islands (as annotated by the R Bioconductor package IlluminaHumanMethylation27k.db). The median (minimum, maximum) distance in base pairs from CpG site to the transcription start site (TSS) was 339 (0, 1482). Two CpG sites (cg10660256 (BHMT, chr5); cg05521696 (SLC2A14, chr12)) were at the reputed TSS; both sites were in CpG islands. The information about these 2 CpG sites is shown in Table S1. At 1,311 (43.7%) of the 3,002 significant CpG-SNP pairs tested, the minor allele was associated with lower percent methylation.

Association of CpG sites with current smoking status

Among the 22,375 CpG sites, 1,287 CpG sites (near or in 1,242 genes) were associated with current smoking status at a nominal P < 0.05. Results for the top 10 associations of current smoking status to DNA methylation are shown in Table 3. Although none of our associations met the FDR threshold of < 0.05, several CpG sites, including the top site cg03636183 [annotated to the coagulation factor II receptor-like 3 (F2RL3)] have been previously reported and validated in the literature.19,40 The parallel boxplots of DNA methylation levels by current smoking status for the top 2 CpG sites (in the F2RL3 and CSDE1 genes) are shown in Figure 6.

Table 3.

Top 10 associations between CpG sites and current smoking status.

Probe Chr Test statistic P value Symbol CpG ISLAND DISTANCE TO TSS (base pair) Flag
cg03636183 19 −4.82 6.52E-06 F2RL3 TRUE 759 1
cg08166982 1 4.64 1.33E-05 CSDE1 TRUE 184 0
cg24798047 1 −4.47 2.52E-05 GOLT1A TRUE 435 0
cg23323671 1 −4.25 5.66E-05 STMN1 TRUE 255 0
cg23959705 1 4.25 5.71E-05 TNFRSF9 TRUE 1415 1
cg17389295 19 4.17 7.68E-05 FBL TRUE 257 0
cg08229694 2 −4.11 9.35E-05 IMMT TRUE 400 0
cg17413703 6 3.97 1.57E-04 TAF11 TRUE 349 0
cg07389922 17 −3.96 1.59E-04 C17orf81 FALSE 1365 0
cg09655559 20 3.94 1.75E-04 TPD52L2 TRUE 334 0
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Flag = 1 indicates the CpG site had SNPs with MAF < 0.05 within 5 base pairs; Flag = 0 otherwise.

Figure 6.

Figure 6.

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Parallel boxplots of CpG site methylation level vs. smoking status for the top 2 significant smoking-CpG-association tests.

The proportion of associated tests was not uniformly distributed across the 22 autosomes.  Chromosome 9 had the largest proportion (6.8%) of significant CpGs to total CpGs, while chromosome 8 had the smallest proportion (4.3%). Chromosome 10 has the largest proportion (11.1%) of genes corresponding to significant CpG sites (Fig. S1). The majority (71.0%) of the 1,287 CpG sites associated with current smoking are located in CpG islands. The median (minimum, maximum) distance to the TSS was 276 (0, 1495). There was one CpG island site, cg16944093, annotated to the LIM and senescent cell antigen-like domains 2 (LIMS2) with 0 distance to transcription start site. Approximately 40.2% of the CpG sites associated with current smoking status demonstrated relative hypomethylation in current smokers (data not shown).

Overlap between CpG sites impacted by genetic variants vs. cigarette smoking

To investigate whether CpG sites associated with mQTLs were also impacted by current smoking, we examined the overlap between sites identified in each of the analyses above. For mQTL analysis, we included CpG sites with an FDR adjusted P < 0.05. For the CpG-smoking association analysis, we included sites with an unadjusted P < 0.05. The intersection of the associated CpG sites is summarized in Figure 7. There were 727 CpG sites near 675 genes identified only in the cis-mQTL analysis (Table S2). There were 1,244 CpG sites near 1,203 genes significant only in the general linear regression analysis for current smoking (Table S3). There were only 43 CpG sites near 40 genes significant in both analyses (Table S4). By using a binomial test and by assuming the probability of overlapping by chance is at most 5%, the number 43 was not significantly greater than the expected number of overlapping by chance (P = 0.25). Correlations of ranks, P-values, and effect sizes between mQTL results (if multiple SNPs were associated with a CpG, only the result for the SNP having the smallest P-value was used) and the results of smoke-CpG association are −0.018 (P = 0.91), −0.038 (P = 0.81), and −0.056 (P = 0.72), respectively. These findings suggest that the impact of smoking on CpG site methylation may be largely independent of the impact of genetic variation on methylation of CpG sites in current and former smokers.

Figure 7.

Figure 7.

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Proportional Venn diagram of CpG sites associated with cigarette smoking (blue) at an unadjusted P < 0.05 and genetic variants (red) at a FDR < 0.05. A total of 43 sites were significant in both analyses.

Gene set enrichment analysis

We investigated the biologic processes annotated to the CpGs identified in the mQTL-only, current smoking-only, and overlap groups above. Official gene names annotated to each of the CpG sites were used as input for DAVID (The Database for Annotation, Visualization and Integrated Discovery). The 675 genes annotated to CpG sites significant in the cis-mQTL-only group were enriched for 116 biological process categories including membrane depolarization, response to oxidative stress, and immune response (Table S5). The 1,203 genes annotated to CpG sites in the current smoking-only group were enriched for 252 biological process categories including response to metal ion, response to hormone stimulus, and response to endogenous stimulus (Table S6). The 40 genes annotated to CpG sites associated with both cis-mQTLs and current smoking status were enriched in 2 biological process categories related to regulation of foam cell differentiation (Table S7). Of the 116 biological processes enriched in mQTL-only CpG sites and the 252 biological processes were enriched in the smoking-only CpG sites, 14 biological processes common to both sets (Table S8 and Fig. S2).

Discussion

Associations between environmental exposures such as smoking and genetic variation have been reported in various types of diseases. However, it is not clear yet if the exposure and genetic factors associate with the same or distinct sets of DNA methylation marks. This question is particularly timely with regards to smoking, as numerous studies have demonstrated associations between cigarette smoking and variable DNA methylation that persists long after smoking cessation. In the current study, we report that smoking and genetic factors associate with the methylation levels of largely distinct sets of CpG sites. Notably, while individual sites that were impacted by genetic variants and current smoking were distinct, functional annotation analysis suggests that these distinct marks may actually impact a subset of similar biological pathways and processes including response to wounding, cellular proliferation and phospholipid metabolic processes.

Variability of DNA methylation by genotype (mQTL) has been identified in previous studies.30,37-39,41,42 More recently, investigators have suggested that the spatial clustering of variably methylated regions is driven by underlying DNA sequence.43 Because DNA methylation patterns are tissue specific, we compared the overlap between mQTLs identified in our study with mQTLs reported from other tissue samples. Many of the CpG-SNP associations reported in our manuscript have been identified by other groups in other tissue samples (Table 4). In addition to independent replication of mQTL loci, the mQTLs observed in multiple tissue types support the importance of the integrative capacity of future studies across genome-wide SNP and epigenetics platforms.

Table 4.

cis-mQTL results compared to results in literature.

Paper N Subjects Radius #(CpG, SNP) #CpG #SNP n/% overlap(CpG, SNP) n/% overlap CpG n/% overlap SNP Tissue Type Race
our results 85 50KB 3002 770 2757         Caucasian subjects
Bell et al.33 77 50KB 180 180 176 16 (0.535) 51 (6.62%) 18 (0.65%) lymphoblastoid cell lines HapMap Yoruba
Gibbs et al.36 150 1MB 12102 1085 10606 254 (8.46%) 117 (15.19%) 309 (11.21%) cerebellum neurologically normal Caucasian subjects
Gibbs et al.36 150 1MB 12135 1153 10679 366 (12.19%) 147 (19.09%) 387 (14.04%) frontal cortex neurologically normal Caucasian subjects
Gibbs et al.36 150 1MB 11374 1123 9536 335 (11.16%) 136 (17.66%) 358 (12.99%) Pons neurologically normal Caucasian subjects
Gibbs et al.36 150 1MB 16734 1417 13761 427 (14.22%) 169 (21.95%) 469 (17.01%) Temporal Cortex neurologically normal Caucasian subjects
van Eijk et al.32 148 500KB 4021 70 551 95 (3.16%) 34 (4.42%) 94 (3.41%) blood sample healthy subjects (Dutch ancestry)
Zhang et al.30 153 1MB 3323 736 2878 67 (2.23%) 116 (15.06%) 83 (3.01%) Cerebellum European ancestry
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n/% overlap(cpg, snp) = number/percent of significant (CpG, SNP) pairs overlapping between our cis-mQTL results and those in literature; n/% overlap cpg = number/percent of unique CpG sites overlapping between the significant (CpG SNP) pairs in our cis-mQTL results and those in literature; n/% overlap SNP = number/percent of unique SNPs overlapping between the significant (CpG SNP) pairs in our cis-mQTL results and those in literature. IlluminaHumanMethylation27 Beadchip was used to measure DNA methylation levels in all 5 studies.

Liu et al.43 identified smoking-related differentially methylated positions (DMPs) using publicly available methylation data generated on whole blood using the Illumina HumanMethylation450 array. A total of 93.8% of the CpG sites were within 5 Mb of the SNP, supporting the general finding that most mQTL are likely in cis. They identified 97,658 CpG-SNP pairs (6,211 unique CpG sites, 54,828 unique SNPs) that represented cis-mQTL at a P < 1×10−13. We compared our results with those of Liu et al. There are 495 CpG-SNP pairs (143 unique CpG sites and 480 unique SNPs) identified in both our results and Liu et al. In our analyses, there are 727 CpG sites significant in mQTL analysis, but not in the CpG-smoking association analysis. These 727 CpG sites correspond to 2,802 significant CpG-SNP pairs (727 unique CpG sites, 2,596 unique SNPs) in our cis-mQTL analysis. Among the 2,802 CpG-SNP pairs identified in our analysis, 446 (16%) CpG-SNP pairs (130 unique CpG sites and 431 unique SNPs) were identified in both our results and those of Liu et al. There are 1,244 CpG sites only significantly associated with smoking, but not with nearby SNPs in our data analyses. Only 4 (0.32%) of the 1,244 CpG sites appeared in the significant CpG-SNP pairs detected by Liu et al. In our data analyses, there are 43 CpG sites significantly associated with both smoking and nearby SNPs. These 43 CpG sites correspond to 200 significant CpG-SNP pairs (43 unique CpG sites and 190 unique SNPs) in our mQTL analysis. Among these 200 CpG-SNP pairs, 49 (25%) CpG-SNP pairs (13 unique CpG sites and 49 unique SNPs) were also detected by Liu et al.The top CpG-SNP association in our analysis was between cg18771300 and rs4902214, both of which are near the RHOJ gene on chromosome 14. The association between cg18771300 and rs4902214 was also detected by Liu et al.43 The gene RHOJ encodes a small GTP-binding protein associated with focal adhesion in endothelial cells. The encoded protein is activated by vascular endothelial growth factor and may regulate angiogenesis. RhoJ demonstrates endothelial-cell-restricted expression pattern across tissues, including in the lungs.44

Although none of the sites identified in our smoking analysis were significant following correction for multiple testing, the top association between current smoking status and DNA methylation was observed at CpG site cg03636183 near F2RL3 on chromosome 19, which is consistent with the findings in the literature.2,19,25,40,45,46 Differential methylation at this exact site cg03636183 was first reported by Breitling et al.25 and has also been reported by Wan et al.19 and Shenker et al.46 Although initial studies were conducted in largely Caucasian cohorts, Sun et al.47 (2013) have also reported variable methylation of this site in African Americans as well, supporting the generalizability of this finding across races.  The F2RL3 gene codes for a protein relevant for cardiovascular physiology and involved in various aspects of blood clotting.2 In addition to exploring the overlap between sites associated with genetic variants and environmental exposures, our work suggests the impact of cigarette smoking is relatively modest compared to the impact of genetic variants on site-specific methylation in blood.  This finding should not, however, be misinterpreted as a mitigation of the impact of cigarette smoking on the development of human disease or on the epigenome; future investigations should explore the mechanisms which contribute to these differences as well as the overlapping biological processes impacted by both processes.

The goal of this study was to explore whether differential methylation associated with genetic variation and cigarette smoking impacted overlapping or distinct CpG associations. The strengths of our study include the use of a well-characterized cohort and the systematic, unbiased interrogation of both genetic sequence variations and site-specific methylation throughout the genome. We acknowledge the following limitations to our study. While peripheral blood profiling is a clinically useful endeavor, the generalizability of our findings to organ-specific methylation profiles may be limited.   Additionally, we acknowledge that peripheral blood is comprised of a mixture of cell populations and that cell type heterogeneity may impact our findings.  In our analysis, we adjusted for this and additional confounders through the inclusion of principal components as covariates in our analyses; reassuringly, our results were similar when we applied established algorithms such as those published by Houseman et al.48 Second, our study was limited to Caucasian subjects—future studies examining mQTLs in other ancestries are needed.   We further acknowledge that our modest sample size limited our power to detect both cigarette smoking-associated CpG sites as well as our ability to include imputed SNPs in our analysis.  We plan to include imputed SNPs in future analyses conducted in larger cohorts.  Despite these limitations, we provide evidence supportive of the utility of integrative genomics profiling including in the development of molecular signatures that may be relevant for the diagnosis, staging, and treatment of smoking-related diseases.

Patients and Methods

Cohort and samples

Ninety four subjects from the COPDGene study (clinicaltrials.gov identifier: NCT 000608764) were profiled for the current analysis.37 COPDGene is a study population initially enrolled from 21 clinical centers throughout the United States between January 2008 and June 2011.37 Subjects were current and former smokers between the ages of 45 to 80 years, with at least 10 pack-years of smoking. All subjects completed questionnaire data and post-bronchodilator spirometry. Current smoking was defined as an affirmative answer to the question “Do you currently smoke cigarettes?”  A blood sample for DNA extraction was obtained at the time of enrollment. This study was approved by the Institutional Review Boards of the participating centers, and informed consent was obtained from all subjects.

Methylation profiling with the Illumina HumanMethylation27 BeadChip array

One microgram of DNA from whole blood was bisulfite treated.3 Genome-wide DNA methylation data was generated using the Illumina Infinium HumanMethylation27  BeadChip (Illumina Inc., San Diego, CA). For each locus an intensity value for methylated (Methylated) and unmethylated (Unmethylated) alleles is generated. Percent methylation was expressed as the Illumina β value, which represents a ratio of the Methylated to Unmethylated fluorescence signals, such that β = Max (Methylated, 0)/[Max(Methylated,0)+Max(Unmethylated, 0)+100]. Using this metric, DNA methylation is represented by a variable between 0 (no methylation) and 1 (complete methylation). Percent methylation values were calculated using BeadStudio software (Illumina), then exported for analysis into the statistical software R for further processing.

Data pre-processing and annotation

The annotations for each CpG site were extrapolated using the R Bioconductor packages IlluminaHumanMethylation27k.db and FDb.Infinium Methylation.hg19. Using the method outlined by Du et al, we performed color balance adjustment and quantile normalization.  M-values [log2(Methylated/Unmethylated)] were generated for analysis to reduce the effects of severe heteroscedasticity.  Scatter plots of the first 2 principal components identified 2 outliers who were removed from further analysis.  Four subjects were excluded due to non-Caucasian race, 1 subject was excluded due to missing spirometry data, and 2 subjects were excluded due to unclassified spirometry status.38 Eighty-five subjects were included in the final cohort for analysis. Based on criteria outlined by Christensen et al.,49 we excluded one CpG site having median detection P > 0.05. We did not detect any arrays having detection P-values > 10−5 at more than 25% of CpG loci. Three CpG sites were removed due to lack of adequate annotations. CpG sites annotated to the X- or Y-chromosome were also excluded (1,092 sites).  Based on R BioConductor packages SNPlocs.Hsapiens.dbSNP.20120608 and BSgenome.Hsapiens.UCSC.hg19, we excluded 4,107 CpG sites that had SNPs with MAF ≥ 0.05 within 5 base pairs of the interrogated CpG sites or overlapped with a repetitive element.  A total of 5,789 CpG sites had SNPs with MAF < 0.05 within 5 base pairs; these were retained in the analysis results. In summary, the cleaned data consisted of 22,375 CpG marks and 85 arrays for analysis.

Genotyping data

Genotyping data were obtained using the Illumina OmniExpress platform and were cleaned as described.50 for the whole COPDGene cohort. For the 85 subjects included in our analysis, we further performed subset-specific quality control using PLINK.51 on the non-imputed genotype data  (imputed data were excluded). A total of 156 SNPs were excluded based on Hardy-Weinberg P<0.001. Another 10 SNPs were excluded due to missingness (i.e., the genotyping call rate ≤ 0.1); 48,898 SNPs with a minor allele frequency < 0.05 were also excluded. After frequency and genotyping pruning, there were 581,796 SNPs remaining for analysis. The physical locations for the SNPs were annotated using hg19.

Statistical analysis

To evaluate the association between genetic factors and DNA methylation, we performed a cis-methylation quantitative trait (cis-mQTL) analysis as follows.  For each CpG site, we first identified SNPs within 50 kb upstream and downstream from the CpG site.33 We performed general linear regression analysis with methylation M-value as the dependent variable and each regional SNP as an independent variable under an additive model, adjusting for age, sex, pack-years of cigarette smoking, current smoking status, methylation array batch number, the top 5 principal components of the genotype data, and the top 4 principal components of the methylation data. Associations with a false discovery rate (FDR)-adjusted P < 0.05 were considered significant.

To evaluate the association of current smoking status with DNA methylation, we performed general linear regression analysis with DNA methylation M-value as the outcome variable and current smoking status as a binary predictor, adjusting for age at enrollment, sex, pack years of cigarette smoking, DNA methylation array batch number, and the top 4 principal components of the methylation data. Tests with an association P < 0.05 were considered significant.

To investigate the overlap between CpG sites associated with genetic variants and CpG sites associated with smoking, we examined the lists of unique CpG sites identified in each of the analyses above and tested for enrichment in the number of overlapping CpG sites using a binomial test. We also evaluated the correlations of ranks, P-values, and effect sizes between mQTL results and the results of smoking-CpG associations to evaluate if the overlap was more significant than the expected number of overlaps by chance. Lastly, we performed a functional enrichment analysis using 52. (The Data-base for Annotation, Visualization and Integrated Discovery) DAVID52on the set of overlapping CpG sites significant in both the mQTL and current smoking analyses.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Supplemental Material

Supplemental data for this article can be accessed on the publisher's website

1106672_Supplemental_Material.docx

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