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W4386744185 abstract "Full text Figures and data Side by side Abstract Editor's evaluation eLife digest Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Smoking-associated DNA methylation levels identified through epigenome-wide association studies (EWASs) are generally ascribed to smoking-reactive mechanisms, but the contribution of a shared genetic predisposition to smoking and DNA methylation levels is typically not accounted for. Methods: We exploited a strong within-family design, that is, the discordant monozygotic twin design, to study reactiveness of DNA methylation in blood cells to smoking and reversibility of methylation patterns upon quitting smoking. Illumina HumanMethylation450 BeadChip data were available for 769 monozygotic twin pairs (mean age = 36 years, range = 18–78, 70% female), including pairs discordant or concordant for current or former smoking. Results: In pairs discordant for current smoking, 13 differentially methylated CpGs were found between current smoking twins and their genetically identical co-twin who never smoked. Top sites include multiple CpGs in CACNA1D and GNG12, which encode subunits of a calcium voltage-gated channel and G protein, respectively. These proteins interact with the nicotinic acetylcholine receptor, suggesting that methylation levels at these CpGs might be reactive to nicotine exposure. All 13 CpGs have been previously associated with smoking in unrelated individuals and data from monozygotic pairs discordant for former smoking indicated that methylation patterns are to a large extent reversible upon smoking cessation. We further showed that differences in smoking level exposure for monozygotic twins who are both current smokers but differ in the number of cigarettes they smoke are reflected in their DNA methylation profiles. Conclusions: In conclusion, by analysing data from monozygotic twins, we robustly demonstrate that DNA methylation level in human blood cells is reactive to cigarette smoking. Funding: We acknowledge funding from the National Institute on Drug Abuse grant DA049867, the Netherlands Organization for Scientific Research (NWO): Biobanking and Biomolecular Research Infrastructure (BBMRI-NL, NWO 184.033.111) and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995); epigenetic data were generated at the Human Genomics Facility (HuGe-F) at ErasmusMC Rotterdam. Cotinine assaying was sponsored by the Neuroscience Campus Amsterdam. DIB acknowledges the Royal Netherlands Academy of Science Professor Award (PAH/6635). Editor's evaluation This study presents valuable findings regarding how smoking can leave a lasting imprint on the human genome. The twin pairs study design is unique, and the methods applied by the authors are solid, providing an excellent starting point for large translational studies with rigorous laboratory approaches. This work will be of interest to geneticists and genetic epidemiologists. https://doi.org/10.7554/eLife.83286.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest The genetic information of people who smoke present distinctive characteristics. In particular, previous research has revealed differences in patterns of DNA methylation, a type of chemical modification that helps cells switch certain genes on or off. However, most of these studies could not establish for sure whether these changes were caused by smoking, predisposed individuals to smoke, or were driven by underlying genetic variation in the DNA sequence itself. To investigate this question, van Dongen et al. examined DNA methylation data from the blood cells of over 700 pairs of identical twins. These individuals share the exact same genetic information, making it possible to better evaluate the impact of lifestyle on DNA modifications. The analyses identified differences in methylation at 13 DNA locations in pairs of twins where one was a current smoker and their sibling had never smoked. Two of the genes code for proteins involved in the response to nicotine, the primary addictive chemical in cigarette smoke. The differences were smaller if one of the twins had stopped smoking, suggesting that quitting can help to reverse some of these changes. These findings confirm that DNA methylation in blood cells is influenced by cigarette smoke, which could help to better understand smoking-associated diseases. They also demonstrate how useful identical twins studies can be to identify methylation changes that are markers of lifestyle. Introduction Epigenome-wide association studies (EWASs) have identified robust differences in DNA methylation between smokers and non-smokers (Gao et al., 2015; Heikkinen et al., 2022). In a meta-analysis of blood-based DNA methylation studies (N = 15,907 individuals; the largest EWAS of smoking to date), 2623 CpG sites passed the Bonferroni threshold for genome-wide significance in a comparison of current and never smokers (Joehanes et al., 2016). Based on comparison with loci identified in large genome-wide association studies (GWASs), differentially methylated sites were significantly enriched in genes implicated in well-established smoking-associated diseases, such as cancer, cardiovascular disease, inflammatory disease, and lung disease, as well as in genes associated with schizophrenia and educational attainment (Joehanes et al., 2016). It has been hypothesized that smoking-induced methylation changes might also contribute to the addictive effect of smoking (Zillich et al., 2022). Importantly, smoking-associated DNA methylation levels, as established in human EWA studies, may reflect different mechanisms. They may reflect causal effects of smoking on methylation, causal effects of methylation on smoking behaviour, methylation differences associated with epiphenomena of other exposures that correlate with smoking e.g. alcohol use (Liu et al., 2018) , or they may reflect a shared genetic predisposition to smoking and methylation level. To distinguish these different mechanisms require incisive study designs (Vink et al., 2017). Establishing whether methylation levels in smokers revert to levels of never smokers upon smoking cessation is a first step. A previous study of 2648 former smokers with cross-sectional methylation data from the Framingham Heart Study suggested that methylation levels at most CpGs return to the level of never smokers within 5 years after quitting smoking, but 36 CpGs were still differentially methylated in former smokers, who had quit smoking for 30 years (Joehanes et al., 2016). In the large EWAS meta-analysis of smoking (Joehanes et al., 2016), 185 CpGs were differentially methylated between former and never smokers (compared to 2623 between current and never smokers). In addition, differences between former and never smokers were smaller than between current and never smokers. Reversible DNA methylation patterns may suggest that DNA methylation is reactive to smoking. However, it is also possible that the different methylation level in current smokers reflects a higher genetic liability to smoking behaviour (that makes them more likely to initiate and keep smoking). Similarly, differences between former smokers and never smokers could reflect that smoking has caused a persistent methylation change but can also be driven by genetic factors. In population-based studies, smoking cases and non-smoking individuals may differ on many aspects, including their genetic predisposition to smoking. On the other hand, monozygotic twins are genetically identical (except for de novo mutations, but these are rare [Jonsson et al., 2021; Ouwens et al., 2018]), share a womb, and are matched on sex, age, and childhood environment. They have been exposed to similar prenatal conditions, which may include second hand smoke from smoking mothers and others. Differences in prenatal environment of monozygotic twins due to for instance unequal vascular supply are also recognized (Hall, 1996; Martin et al., 1997), although it remains to be investigated to what extent the impact of prenatal smoke exposure might differ between monozygotic twins. Smoking discordant monozygotic twin pairs offer a unique opportunity to assess smoking-reactive DNA methylation patterns (Leeuwen et al., 2007; Vink et al., 2017). Despite the large number of previous population-based smoking EWASs, only one previous study compared genome-wide DNA methylation in smoking discordant monozygotic twin pairs (Allione et al., 2015). This study analysed whole-blood Illumina 450k array methylation data from 20 discordant pairs, and reported 22 top loci, many of which had been previously associated with cigarette smoking in previous studies. However, following the correction for multiple testing, none of the differentially methylated loci were statistically significant, and this previous twin study did not examine reversibility of smoking effects, that is, where methylation status changes again following smoking cessation. Here, we analyse unique data from a large cohort of monozygotic twin pairs. This cohort is sufficiently large to include current smoking discordant and concordant pairs, as well as pairs discordant for former smoking (Figure 1). These groups allow identification of loci that are reactive to smoking, and examination of the extent to which effects are reversible upon quitting smoking. Monozygotic pairs in which both twins are current smokers, but who differ in quantity smoked, enable examination of the effects of smoking intensity. Finally, concordant pairs who never smoked allow assessment of the amount of DNA methylation variation at smoking-reactive loci that is due to non-genetic sources of variation other than smoking. In secondary enrichment analyses, we examined whether smoking-reactive methylation patterns are enriched (1) at loci detected in previous EWASs of other traits and exposures, (2) at loci detected in a previous large GWAS meta-analysis of smoking initiation (Liu et al., 2019) – these loci are presumed to have a causal effect on smoking behaviour, and (3) within Gene Ontology and Kegg pathways. Finally, we examined the relationship between DNA methylation and RNA transcript levels in blood for smoking-reactive loci. Figure 1 Download asset Open asset DNA methylation analysis in smoking discordant and smoking concordant monozygotic twin pairs. Blood DNA methylation profiles (Illumina 450k array) from six groups of monozygotic twin pairs were analysed. Methods Participants In the Netherlands Twin Register (NTR), DNA methylation data are available for 3089 whole-blood samples from 3057 individuals in twin families, as described in detail previously (van Dongen et al., 2016). The samples were obtained from twins and family members, who participated in NTR longitudinal survey studies (Ligthart et al., 2019) and the NTR biobank project (Willemsen et al., 2010). In the current study, methylation data from monozygotic twin pairs were analysed. Among 768 monozygotic twin pairs with genome-wide methylation data and information on smoking and covariates, we identified the following discordant pairs: 53 discordant pairs, in which one twin was a current smoker at blood draw and the other never smoked, 72 discordant pairs, in which one twin was a former smoker at blood draw and the other never smoked, 66 discordant pairs of which one twin was a former smoker and the other a current smoker at blood draw. In addition, we identified the following concordant pairs: 83 twin pairs concordant for current smoking, 88 twin pairs concordant for former smoking, and 406 concordant twin pairs who never smoked. A flowchart is provided in Figure 2. Informed consent was obtained from all participants. The twin pairs were primarily of Dutch-European ancestry. For 753 of the 768 MZ pairs who are included in the current study, ancestry could be derived from principal components (PCs) calculated from genome-wide Single Nucleotide Polymorphism (SNP) array data that were available for the twins (750 pairs) or for both of their parents (3 pairs). According to the genotype data PCs, 4.5% of the pairs classify as ancestry outliers.The study was approved by the Central Ethics Committee on Research Involving Human Subjects of the VU University Medical Centre, Amsterdam, an Institutional Review Board certified by the U.S. Office of Human Research Protections (IRB number IRB00002991 under Federal-wide Assurance – FWA00017598; IRB/institute code, NTR 03-180). Figure 2 Download asset Open asset Study flowchart. Peripheral blood DNA methylation and cell counts Genome-wide DNA methylation in whole blood was measured by the Human Genomics facility (HugeF) of ErasmusMC, the Netherlands (http://www.glimdna.org/). DNA methylation was assessed with the Infinium HumanMethylation450 BeadChip Kit (Illumina, San Diego, CA, USA). Genomic DNA (500 ng) from whole blood was bisulfite treated using the Zymo EZ DNA Methylation kit (Zymo Research Corp, Irvine, CA, USA), and 4 μl of bisulfite-converted DNA was measured on the Illumina 450k array (Bibikova et al., 2011) following the manufacturer’s protocol. A custom pipeline for quality control and normalization of the methylation data was developed by the BIOS consortium. First, sample quality control was performed using MethylAid (van Iterson et al., 2014). Next, probe filtering was applied with DNAmArray (Sinke et al., 2019) to remove: ambiguously mapped probes (Chen et al., 2013), probes with a detection p-value >0.01, or bead number <3, or raw signal intensity of zero. After these probe filtering steps, probes and samples with a success rate <95% were removed. Next, the DNA methylation data were normalized using functional normalization (Fortin et al., 2014), as implemented in DNAmArray (Sinke et al., 2019) using the cohort-specific optimum number of control probe-based PCs. Probes containing an SNP, identified in a DNA sequencing project in the Dutch population (The Genome of the Netherlands Consortium, 2014), within the CpG site (at the C or G position) were excluded irrespective of minor allele frequency, and only autosomal probes were analysed, leading to a total number of 411,169 methylation sites. The following subtypes of white blood cells were counted in blood samples: neutrophils, lymphocytes, monocytes, eosinophils, and basophils (Willemsen et al., 2010). Smoking and other phenotypes Information on smoking behaviour was obtained by interview during the home visit for blood collection as part of the NTR biobank project (2004–2008 and 2010–2011). The questions are included in Supplementary file 1. Participants were asked: ‘Did you ever smoke?’, with answer categories: (1) no, I never smoked, (2) I’m a former smoker, and (3) yes. Current smokers were asked how many years they smoked and how many cigarettes per day they smoked at present, while ex-smokers were asked how many years ago they quit, for how many years they smoked and how many cigarettes per day they smoked (note that the question on cigarettes per day to former smoker did not specify a particular time period, which may introduce variation in responses). Data were checked for consistencies and missing data were completed by linking this information to data from surveys filled out close to the time of biobanking within the longitudinal survey study of the NTR. More details on these checks are described in Supplementary file 1. Packyears were calculated as the (number of cigarettes smoked per day/20) × number of years smoked. Plasma cotinine level measurements have been described previously (Bot et al., 2013). Body mass index (BMI) was obtained at blood draw. Educational attainment was obtained in multiple longitudinal surveys and was defined as the highest completed level of education at the age of 25 or higher. It was classified on a 7-point scale: 1 = primary school only, 2 = lower vocational schooling, 3 = lower secondary schooling (general), 4 = intermediate vocational schooling, 5 = intermediate/higher secondary schooling (general), 6 = higher vocational schooling, 7 = university. Statistical analyses Overview and hypotheses All analyses were performed in R (R Development Core Team, 2013). Analyses were performed in six groups of monozygotic twin pairs (Figure 1). To identify DNA methylation differences in smoking-discordant monozygotic twin pairs, we first compared the twin pairs, in which one twin had never smoked, and the other was a current smoker at the time of blood sampling. Second, to identify which of these DNA methylation differences might be reversible, we analysed data from (1) monozygotic pairs in which one twin had never smoked, and the other was a former smoker at the time of blood sampling, (2) from monozygotic pairs in which one twin was a current smoker, and the other was a former smoker at the time of blood sampling, and (3) from monozygotic pairs who were both former smokers. Third, to quantify within-pair methylation differences that occur by chance alone, we compared the within-pair differences monozygotic twins concordant for never having smoked. Forth, data from monozygotic twins concordant for current smoking were analysed to examine the effects of smoking intensity. Our hypotheses were as follows: (1) if DNA methylation level is reactive to cigarette smoking, methylation differences will be present between smokers and non-smokers after ruling out genetic differences, that is in smoking-discordant monozygotic twin pairs, and these differences will be larger than in monozygotic pairs concordant for never smoking, (2) if DNA methylation patterns are reversible upon quitting smoking, methylation differences (∆M) in monozygotic pairs will show the following pattern: ∆M discordant current-never > ∆M discordant current-former and ∆M discordant former-never > ∆M concordant never, (3) a correlation between time since quitting smoking and ∆M in pairs discordant for former smoking is consistent with a gradual reversibility of methylation levels upon quitting smoking, and (4) a correlation between ∆M and the difference in number of cigarettes smoked per day in smoking concordant pairs is consistent with smoking-reactive methylation patterns that show a dose–response relationship with amount of cigarettes smoked. Epigenome-wide association study In the entire dataset of 3089 blood samples, we used linear regression analysis to correct the DNA methylation levels (β-values) for commonly used covariates (van Rooij et al., 2019), including HM450k array row, bisulphite plate (dummy-coding) and white blood cell percentages (% neutrophils, % monocytes, and % eosinophils). White blood cell percentages were included to account for variation in cellular composition between whole-blood samples. Lymphocyte percentage was not included in models because it was strongly correlated with neutrophil percentage (r = −0.93), and basophil percentage was not included because it showed little variation between subjects, with a large number of subjects having 0% of basophils. We did not adjust for sex and age, because monozygotic twins have the same sex and age. The residuals from this regression analysis were used in the within-pair EWAS analyses. Specifically, the residuals were used as input for paired t-tests to compare the methylation of the smoking twins with that of their non-smoking co-twins. Similarly, paired t-tests were applied to data from smoking concordant pairs. Statistical significance was assessed following stringent Bonferroni correction for the number of methylation sites tested (α = 0.05/411,169 = 1.2 × 10−7). For each EWAS analysis, the R package Bacon was used to compute the Bayesian inflation factor (van Iterson et al., 2017). A previous power analysis for DNA methylation studies in discordant monozygotic twins indicated that with 50 discordant pairs, there is 80% power to detect methylation differences of 15% (at epigenome-wide significance; that is following multiple testing correction) (Tsai and Bell, 2015). Power quickly drops for smaller effect sizes; for example, with 50 discordant pairs, the power to detect a 10% methylation difference is 10% and the power to detect a methylation difference of 5% approaches alpha (Tsai and Bell, 2015). We tested for within-pair differences in demographics (e.g. BMI, educational attainment) and smoking characteristics (e.g. amount of cigarettes per day) with paired t-tests (continuous data) and Wilcoxon Signed Ranks tests (ordinal data) in R. Dose–response relationships For significant CpGs from the EWAS of discordant monozygotic twin pairs, we examined dose–response relationships in smoking concordant pairs (both twins were current smokers) by correlating within-pair differences in DNA methylation with within-pair differences in smoking packyears and cigarettes per day. All correlations reported in this paper are Pearson correlations. Secondly, in twin pairs discordant for former smoking (one twin never smoked and the other one is a former smoker), we correlated and plotted within-pair differences in DNA methylation with the time since quitting smoking to assess the relationship between time since quitting smoking and reversal of methylation differences within monozygotic twin pairs. Enrichment analyses We used the EWAS Toolkit from the EWAS atlas (Li et al., 2019) to perform enrichment analyses of Gene Ontology Terms, Kegg pathways, and previously associated traits among top sites from the EWAS in discordant monozygotic twin pairs (current versus never). With the trait enrichment tool of the EWAS analysis, we tested for enrichment of all traits (680) that were present in the atlas on 26 April 2022. Because the software requires a minimum of 20 input CpGs, we selected the top 20 CpGs from the EWAS in discordant monozygotic pairs for the enrichment analyses using the EWAS toolkit. To study overlap of EWAS signal with genetic findings for smoking, we compared our EWAS results against GWAS results from the largest GWAS meta-analysis of smoking phenotypes. This is the meta-analysis of smoking initiation by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) (Liu et al., 2019). We obtained leave-one out meta-analysis results with NTR excluded. From the GWAS, we selected all SNPs with a p-value <5.0 × 10−8 and determined the distance of each Illumina 450k methylation site to each SNP. We then tested whether methylation sites within 1 Mb of genome-wide significant SNPs from the GWAS showed a stronger signal in the within-pair EWAS of smoking discordant monozygotic pairs compared to other genome-wide methylation sites, by regressing the EWAS test statistics on a variable (GWAS locus) indicating if the CpG is located within a 1 Mb window from SNPs associated with smoking initiation (1 = yes, 0 = no): t=Intercept+βGWASlocus*GWASlocus where t represents the absolute t-statistic from the paired t-test comparing within-pair methylation differences in smoking discordant pairs and βGWASlocus represents the estimate for GWASlocus, that is the change in the t-test statistic associated with a one-unit change in the variable GWASlocus (e.g. being within 1 Mb of SNPs associated with smoking initiation). For each enrichment test, bootstrap standard erors were computed with 2000 bootstraps with the R-package ‘simpleboot’. Gene expression For significant CpGs from the EWAS of discordant monozygotic twin pairs (current versus never), we examined whether the DNA methylation was associated with gene expression levels in cis. To this end, we used an independent whole-blood RNA-sequencing dataset from the Biobank-based Integrative Omics Study (BIOS) consortium that did not include NTR, and tested associations between genome-wide CpGs and transcripts in cis (<250 kb), as described in detail previously (the BIOS Consortium et al., 2017). In short, methylation and expression levels in whole-blood samples (n = 2101) were quantified with Illumina Infinium HumanMethylation450 BeadChip arrays and with RNA-seq (2 × 50 bp paired-end, Hiseq2000, >15 M read pairs per sample). For each target CpG (epigenome-wide significant differentially methylated positions [DMPs]), we identified transcripts in cis (<250 kb), for which methylation levels were significantly associated with gene expression levels at the experiment-wide threshold applied by this study (False Discovery Rate (FDR) <5.0%), after regressing out methylation Quantitative Trait Locus (mQTL) and expression Quantitative Trait Locus (eQTL) effects. We also examined whether significant CpGs from the EWAS of discordant monozygotic twin pairs mapped to genes that were previously reported to be differentially expressed in monozygotic pairs of which one twin never smoked, and the other was a current smoker at the time of blood sampling (based on Affymetrix U219 array data; n = 56 pairs; note: the 53 discordant pairs included in the current study of DNA methylation are a subset of the 56 discordant pairs included in the study of gene expression) (Vink et al., 2017). Results Descriptives of the smoking-discordant and concordant monozygotic twin pairs are given in Table 1. In twin pairs discordant for current smoking status (i.e. one twin a current smoker at the time of blood sampling and the other never initiated regular smoking, N = 53 pairs, mean age = 33 years), the smoking twin on average smoked 8.9 cigarettes per day at the time of blood sampling, and had an average smoking history equivalent to 6.8 packyears. The EWAS analysis in pairs discordant for current smoking status identified 13 epigenome-wide significant (p < 1.20 × 10−7) DMPs (Figure 3a). Genome-wide test statistics were not inflated (Supplementary file 2). Absolute differences in methylation ranged from 2.5% to 13% (0.025–0.13 on the methylation β-value scale), with a mean of 5.4% (Table 2). Eight of the 13 CpGs (61.5%) showed lower methylation in the current smoking twins compared to their non-smoking twins. Pair-level methylation β-values are shown in Figure 3—figure supplement 1 and illustrate large consistency in the direction of effect. For example, at top CpG site cg05575921, for 51 out of the 53 pairs, the smoking twin had a lower methylation level than the non-smoking twin. At 11 of the 13 CpGs, the methylation difference in smoking discordant monozygotic twin pairs was smaller (on average 19.0%, range = 2.2–37.5%) compared to the methylation difference reported previously in an EWAS meta-analysis of smoking (Joehanes et al., 2016). At two CpGs, the methylation difference in smoking discordant monozygotic twins was larger (on average 24.6%). Figure 3 with 1 supplement see all Download asset Open asset Top differentially methylated loci identified in monozygotic twin pairs discordant for current smoking. (a) Manhattan plot of the epigenome-wide association study (EWAS) in 53 smoking discordant monozygotic twin pairs (current versus never). The red horizontal line denotes the epigenome-wide significance threshold (Bonferroni correction) and 13 CpGs with significant differences are highlighted. (b) Mean within-pair differences in monozygotic twin pairs at the 13 CpGs that were epigenome-wide significant in smoking discordant monozygotic pairs. Mean within-pair differences of the residuals obtained after correction of methylation β-values for covariates are shown for 53 monozygotic pairs discordant for current/never smoking, 66 monozygotic pairs discordant for current/former smoking, 72 monozygotic pairs discordant for former/never smoking, 83 concordant current smoking monozygotic pairs, 88 concordant former smoking monozygotic pairs, and 406 concordant never smoking monozygotic pairs. (c) QQ-plot showing p-values from the EWAS in 53 smoking discordant monozygotic twin pairs (current versus never). P-values for CpGs located nearby significant SNPs from the genome-wide association study (GWAS) of smoking initiation are plotted in blue and all other genome-wide CpGs are plotted in orange. Table 1 Descriptive statistics for smoking discordant and concordant monozygotic twin pairs. Discordant current/never(53 pairs)Discordant former/never(72 pairs)Discordant current/former(66 pairs)Concordant current(83 pairs)Concordant never(406 pairs)Concordant former(88 pairs)Current smokerNever-smokerMean diffp-valueFormer smokerNever-smokerMean diffp-valueCurrent smokerFormer smokerMean diffp-valueTwin 1Twin 2Mean diffp-valueTwin 1Twin 2Mean diffp-valueTwin 1Twin 2Mean diffp-value% Female pairs60.4%60.4%n.a.n.a.77.80%77.80%n.a.n.a.69.7%69.7%n.a.n.a.61.4%61.4%n.a.n.a.73.6%73.6%n.a.n.a.64.8%64.8%n.a.n.a.Age at blood sampling, mean (SD)33.1 (8.0)33.0 (7.9)0.100.3441.4 (13.2)41.4 (13.1)0.020.8342.2(12.6)42.2(12.5)−0.060.4533.8(10.3)33.9(10.5)−0.120.1033.1(11.3)33.0(11.2)0.060.0845.2(13.4)45.2(13.4)0.090.29Cigarettes per day at blood sampling, mean (SD), N missings8.9 (6.4), 6n.a.n.a.n.a.n.a.n.a.n.a.n.a.11.9 (7.2), 9n.a.n.a.n.a.11.1(7.0), 210.9(6.9), 10.001.00n.a.n.a.n.a.n.a.n.a.n.a.n.a.n.a.Packyears, mean (SD), N missings6.8 (7.0), 13n.a.n.a.n.a.5.9 (11.1), 15n.a.n.a.n.a.13.6(13.2), 99.3(8.7), 103.90.059.7 (9.3), 108.3 (7.6), 90.220.82n.a.n.a.n.a.n.a.10.6(11.5), 79.8(10.4), 110.780.55Years since quitting smoking, mean" @default.
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W4386744185 title "Decision letter: Effects of smoking on genome-wide DNA methylation profiles: A study of discordant and concordant monozygotic twin pairs" @default.
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