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Epigenome-wide association study in the European Prospective Investigation Into Cancer And Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking

Epigenetics Unit, Department of Surgery and Cancer, Imperial College London, W12 0NN, UK.
Human Molecular Genetics (Impact Factor: 6.68). 11/2012; 22(5). DOI: 10.1093/hmg/dds488
Source: PubMed

ABSTRACT A single cytosine–guanine dinucleotide (CpG) site within coagulation factor II (thrombin) receptor-like 3 (F2RL3) was recently found to be hypomethylated in peripheral blood genomic DNA from smokers compared with former and non-smokers.
We performed two epigenome-wide association studies (EWAS) nested in a prospective healthy cohort using the Illumina 450K
Methylation Beadchip. The two populations consisted of matched pairs of healthy individuals (n = 374), of which half went on to develop breast or colon cancer. The association was analysed between methylation and smoking
status, as well as cancer risk. In addition to the same locus in F2RL3, we report several loci that are hypomethylated in smokers compared with former and non-smokers, including an intragenic
region of the aryl hydrocarbon receptor repressor gene (AHRR; cg05575921, P = 2.31 × 10−15; effect size = 14–17%), an intergenic CpG island on 2q37.1 (cg21566642, P = 3.73 × 10−13; effect size = 12%) and a further intergenic region at 6p21.33 (cg06126421, P = 4.96 × 10−11, effect size = 7–8%). Bisulphite pyrosequencing validated six loci in a further independent population of healthy individuals
(n = 180). Methylation levels in AHRR were also significantly decreased (P < 0.001) and expression increased (P = 0.0047) in the lung tissue of current smokers compared with non-smokers. This was further validated in a mouse model of
smoke exposure. We observed an association with breast cancer risk for the 2q37.1 locus (P = 0.003, adjusted for the smoking status), but not for the other loci associated with smoking. These data show that smoking
has a direct effect on the epigenome in lung tissue, which is also detectable in peripheral blood DNA and may contribute to
cancer risk.

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    • "Altered methylation of cancer-related genes is, in fact, frequently observed in lung tumors of smokers [8] [9] [10] [11], and a progressive accumulation of epigenetic alterations is also observed in the respiratory epithelium of cancer-free heavy smokers [12] [13] [14] and in exfoliated cells of smokers sputum [15]. Moreover, changes in the methylation profile of cancer related genes have been observed in plasma DNA from cancer-free heavy smokers [16] [17], and smoking-related changes in methylation at a number of CpG sites have been identified in epigenome-wide investigations in blood cells of subjects with different smoking habits [18] [19] [20] [21]. The mechanism(s) by which tobacco smoke could affect DNA methylation is not elucidated. "
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    • "There is evidence that the leading risk factors for lung cancer, tobacco smoke and air pollution, can influence DNA methylation in the lungs and in blood. A study nested within the EPIC cohort reported hypomethylation of F2RL3, AHRR and two intergenic regions in the blood of smokers in comparison to non-smokers [92]. A follow-up study from the same group demonstrated that four loci, including AHRR, may serve as blood-based markers of tobacco smoke exposure [93]. "
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    • "GWAS-based PCs, the top 10 methylation-based PCs pick up some age-associated methylation (10 –26 < P < 10 –7 , Supplementary Table S2), and their inclusion as covariates can thus reduce power to detect association with age. Sets of PCs that are based on CpG sites close to SNPs may be better proxies for genetic variation and thus somewhat less associated with age (as demonstrated in Supplementary Table S2), and methods such as SVA will avoid this issue entirely; this is consistent with the pattern shown in Figure 2. In contrast, because only a few CpG sites associate with smoking, the PCs do not proxy for association between methylation and smoking (P > 0.005 for all PCs tested); in this case, all of the methylation-based PC-based methods perform similarly well, as in Figure 3. Similarly, adjustment via GC led to somewhat reduced significance in the smoking analysis (Fig. 3), but not the large reduction observed in the age analysis (Fig. 2); this is likely because thousands of CpG sites across the genome have been shown to associate with age [Alisch et al., 2012; Christensen et al., 2009; Numata et al., 2012; Rakyan et al., 2010; Teschendorff et al., 2010], while only a handful have been associated with smoking [Breitling et al., 2011, 2012; Shenker et al., 2013; Sun et al., 2013; Wan et al., 2012]. "
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