The European Union, the UK National Institute for Health Research, the Wellcome Trust, the UK Medical Research Council, Action on Hearing Loss, the UK Biotechnology and Biological Sciences Research Council, the Oak Foundation, the Economic and Social Research Council, Helmholtz Zentrum Munchen, the German Research Center for Environmental Health, the German Federal Ministry of Education and Research, the German Center for Diabetes Research, the Munich Center for Health Sciences, the Ministry of Science and Research of the State of North Rhine-Westphalia, and the German Federal Ministry of Health.

Several studies have recently identified strong epigenetic signals related to tobacco smoking. However, an aspect that did not receive much attention is the evolution of epigenetic changes with time since smoking cessation. We conducted a series of epigenome-wide association studies to capture the dynamics of smoking-induced epigenetic changes after smoking cessation, using genome-wide methylation profiles obtained from blood samples in 745 women from 2 European populations. Two distinct classes of CpG sites were identified: sites whose methylation reverts to levels typical of never smokers within decades after smoking cessation, and sites remaining differentially methylated, even more than 35 years after smoking cessation. Our results suggest that the dynamics of methylation changes following smoking cessation are driven by a differential and site-specific magnitude of the smoking-induced alterations (with persistent sites being most affected) irrespective of the intensity and duration of smoking. Analyses of the link between methylation and expression levels revealed that methylation predominantly and remotely down-regulates gene expression. Among genes whose expression was associated with our candidate CpG sites, LRRN3 appeared to be particularly interesting as it was one of the few genes whose methylation and expression were directly associated, and the only gene in which both methylation and gene expression were found associated with smoking. Our study highlights persistent epigenetic markers of smoking, which can potentially be detected decades after cessation. Such historical signatures are promising biomarkers to refine individual risk profiling of smoking-induced chronic disease such as lung cancer.

We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10−11 to 5.0 × 10−21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10−6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.

DNA hypomethylation in certain genes is associated with tobacco exposure but it is unknown whether these methylation changes translate into increased lung cancer risk. In an epigenome-wide study of DNA from pre-diagnostic blood samples from 132 case–control pairs in the NOWAC cohort, we observe that the most significant associations with lung cancer risk are for cg05575921 in AHRR (OR for 1 s.d.=0.37, 95% CI: 0.31–0.54, P-value=3.3 × 10−11) and cg03636183 in F2RL3 (OR for 1 s.d.=0.40, 95% CI: 0.31–0.56, P-value=3.9 × 10−10), previously shown to be strongly hypomethylated in smokers. These associations remain significant after adjustment for smoking and are confirmed in additional 664 case–control pairs tightly matched for smoking from the MCCS, NSHDS and EPIC HD cohorts. The replication and mediation analyses suggest that residual confounding is unlikely to explain the observed associations and that hypomethylation of these CpG sites may mediate the effect of tobacco on lung cancer risk.

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre‐diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case‐control study nested within the EPIC‐Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case‐control pairs). We validated the top signals in 429 case‐control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p‐valuepooled = 4 × 10−17), cg03636183 in the F2RL3 gene (p‐valuepooled = 2 × 10 − 13), cg21566642 and cg05951221 in 2q37.1 (p‐valuepooled = 7 × 10−16 and 1 × 10−11 respectively), cg06126421 in 6p21.33 (p‐valuepooled = 2 × 10−15) and cg23387569 in 12q14.1 (p‐valuepooled = 5 × 10−7). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p‐valuesheterogeneity ≤ 1.8 x10 − 7), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p‐values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack‐years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.

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Our findings support the hypothesis that the social environment leaves an epigenetic signature in cells. Although the functional significance of SES-related DNA methylation is still unclear, we hypothesize that it may link SES to chronic disease risk.

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.