Search

ObjectivesThe main objective of this study was to determine whether the DNA methylation profile of children born to mothers with rheumatoid arthritis (RA) is different from that of children born to mothers from the general population. In addition, we aimed to determine whether any differences in methylation are associated with maternal RA disease activity or medication use during pregnancy.MethodsFor this study, genome-wide DNA methylation was measured at cytosine-phosphate-guanine (CpG) sites, using the Infinium Illumina HumanMethylation 450K BeadChip, in 80 blood samples from children (mean age=6.8 years) born to mothers with RA. As controls, blood samples from 354 children (mean age=6.0 years) from the population-based Generation R Study were used. Linear mixed models were performed to investigate differential methylation between the groups, corrected for relevant confounders.ResultsA total of 147 CpGs were differentially methylated between blood samples of children born to mothers with RA and the control blood samples. The five most significantly associated CpGs were cg06642177, cg08867893, cg06778273, cg07786668 and cg20116574. The differences in methylation were not associated with maternal RA disease activity or medication use during pregnancy.ConclusionsDNA methylation at 147 CpGs differed between children born to mothers with RA and children born to mothers from the general population. It remains unknown whether the identified associations are causal, and if so whether they are caused by the disease or treatment. More research, including replication of these results, is necessary in order to strengthen the relevance of our findings for the later-life health of children born to mothers with RA.

One-carbon metabolism provides a direct link among dietary folate/vitamin B-12 exposure, the activity of the enzyme methylenetetrahydrofolate reductase (MTHFR), and epigenetic regulation of the genome via DNA methylation. Previously, it has been shown that the common c.677C > T polymorphism in MTHFR influences global DNA methylation status through a direct interaction with folate status and (indirectly) with total homocysteine (tHcy) levels. To build on that and other more recent observations that have further highlighted associations among MTHFR c.677C > T, tHcy, and aberrations in DNA methylation, we investigated whether the interaction between mildly elevated plasma tHcy and the c.677C > T polymorphism is associated with site-specific changes in DNA methylation in humans. We used data on plasma tHcy levels, c.677C > T polymorphism, and site-specific DNA methylation levels for a total of 915 white women and 335 men from the TwinsUK registry (n = 610) and the Rotterdam study (n = 670). We performed methylome-wide association analyses in each cohort to model the interaction between levels of tHcy and c.677C > T genotypes on DNA methylation values. Our meta-analysis identified 13 probes significantly associated with rs1801133 x tHcy levels [false-discovery rate (FDR) < 0.05]. The most significant associations were with a cluster of probes at the AGTRAP-MTHFR-NPPA/B gene locus on chromosome 1 (FDR = 1.3E-04), with additional probes on chromosomes 2, 3, 4, 7, 12, 16, and 19. Our top 2 hits on chromosome 1 were functionally associated with variability in expression of the TNF receptor superfamily member 8 (TNFRSF8) gene/locus on that chromosome. This is the first study, to our knowledge, to provide a direct link between perturbations in 1-carbon metabolism, through an interaction of tHcy and the activity of MTHFR enzyme on epigenetic regulation of the genome via DNA methylation.Nash, A. J., Mandaviya, P. R., Dib, M.-J., Uitterlinden, A. G., van Meurs, J., Heil, S. G., Andrew, T., Ahmadi, K. R. Interaction between plasma homocysteine and the MTHFR c.677C>T polymorphism is associated with site-specific changes in DNA methylation in humans.

AimTo identify differentially methylated positions (DMPs) and regions (DMRs) that predict response to Methotrexate (MTX) in early rheumatoid arthritis (RA) patients.Materials and methodsDNA from baseline peripheral blood mononuclear cells was extracted from 72 RA patients. DNA methylation, quantified using the Infinium MethylationEPIC, was assessed in relation to response to MTX (combination) therapy over the first 3 months.ResultsBaseline DMPs associated with response were identified; including hits previously described in RA. Additionally, 1309 DMR regions were observed. However, none of these findings were genome-wide significant. Likewise, no specific pathways were related to response, nor could we replicate associations with previously identified DMPs.ConclusionNo baseline genome-wide significant differences were identified as biomarker for MTX (combination) therapy response; hence meta-analyses are required.

DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs. Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.

Background Cutaneous squamous cell carcinoma (cSCC) occurs 65–200 times more in immunosuppressed organ transplant patients than in the general population. T cells, which are targeted by the given immunosuppressive drugs, are involved in anti-tumor immune surveillance and are functionally regulated by DNA methylation. Prior to kidney transplantation, we aim to discover differentially methylated regions (DMRs) in T cells involved in de novo post-transplant cSCC development. Methods We matched 27 kidney transplant patients with a future de novo cSCC after transplantation to 27 kidney transplant patients without cSCC and studied genome-wide DNA methylation of T cells prior to transplantation. From 11 out of the 27 cSCC patients, the DNA methylation of T cells after transplantation was also examined to assess stability of the observed differences in DNA methylation. Raw methylation values obtained with the 450k array were confirmed with pyrosequencing. Results We found 16 DMRs between patients with a future cSCC and those who do not develop this complication after transplantation. The majority of the DMRs were located in regulatory genomic regions such as flanking bivalent transcription start sites and bivalent enhancer regions, and most of the DMRs contained CpG islands. Examples of genes annotated to the DMRs are ZNF577, coding for a zinc-finger protein, and FLOT1, coding for a protein involved in T cell migration. The longitudinal analysis revealed that DNA methylation of 9 DMRs changed significantly after transplantation. DNA methylation of 5 out of 16 DMRs was relatively stable, with a variation in beta-value lower than 0.05 for at least 50% of the CpG sites within that region. Conclusions This is the first study demonstrating that DNA methylation of T cells from patients with a future de novo post-transplant cSCC is different from patients without cSCC. These results were obtained before transplantation, a clinically relevant time point for cSCC risk assessment. Several DNA methylation profiles remained relatively stable after transplantation, concluding that these are minimally affected by the transplantation and possibly have a lasting effect on post-transplant cSCC development. Electronic supplementary material The online version of this article (10.1186/s13148-018-0519-7) contains supplementary material, which is available to authorized users.

Background A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies. Results We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes. Conclusions Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.

Aim: Cigarette smoking influences DNA methylation genome wide, in newborns from pregnancy exposure and in adults from personal smoking. Whether a unique methylation signature exists for in utero exposure in newborns is unknown. Materials & methods: We separately meta-analyzed newborn blood DNA methylation (assessed using Illumina450k Beadchip), in relation to sustained maternal smoking during pregnancy (9 cohorts, 5648 newborns, 897 exposed) and adult blood methylation and personal smoking (16 cohorts, 15907 participants, 2433 current smokers). Results & conclusion: Comparing meta-analyses, we identified numerous signatures specific to newborns along with many shared between newborns and adults. Unique smoking-associated genes in newborns were enriched in xenobiotic metabolism pathways. Our findings may provide insights into specific health impacts of prenatal exposure on offspring.

BACKGROUND: Folate and vitamin B-12 are essential micronutrients involved in the donation of methyl groups in cellular metabolism. However, associations between intake of these nutrients and genome-wide DNA methylation levels have not been studied comprehensively in humans.OBJECTIVE: The aim of this study was to assess whether folate and/or vitamin B-12 intake are asssociated with genome-wide changes in DNA methylation in leukocytes.METHODS: A large-scale epigenome-wide association study of folate and vitamin B-12 intake was performed on DNA from 5841 participants from 10 cohorts using Illumina 450k arrays. Folate and vitamin B-12 intakes were calculated from food-frequency questionnaires (FFQs). Continuous and categorical (low compared with high intake) linear regression mixed models were applied per cohort, controlling for confounders. A meta-analysis was performed to identify significant differentially methylated positions (DMPs) and regions (DMRs), and a pathway analysis was performed on the DMR annotated genes.RESULTS: The categorical model resulted in 6 DMPs, which are all negatively associated with folate intake, annotated to FAM64A, WRAP73, FRMD8, CUX1, and LCN8 genes, which have a role in cellular processes including centrosome localization, cell proliferation, and tumorigenesis. Regional analysis showed 74 folate-associated DMRs, of which 73 were negatively associated with folate intake. The most significant folate-associated DMR was a 400-base pair (bp) spanning region annotated to the LGALS3BP gene. In the categorical model, vitamin B-12 intake was associated with 29 DMRs annotated to 48 genes, of which the most significant was a 1100-bp spanning region annotated to the calcium-binding tyrosine phosphorylation-regulated gene (CABYR). Vitamin B-12 intake was not associated with DMPs.CONCLUSIONS: We identified novel epigenetic loci that are associated with folate and vitamin B-12 intake. Interestingly, we found a negative association between folate and DNA methylation. Replication of these methylation loci is necessary in future studies.

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D., Our understanding of the functional link between differential DNA methylation and type 2 diabetes and obesity remains limited. Here the authors present a blood-based EWAS of fasting glucose and insulin among 4808 non-diabetic Europeans and identify nine CpGs not previously implicated in glucose, insulin homeostasis and diabetes.

Background: Biologic age may better reflect an individual's rate of aging than chronologic age.Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array.Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05).Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.

Esophageal cancer (EC) is the eighth most aggressive malignancy and its treatment remains a challenge due to the lack of biomarkers that can facilitate early detection. EC is identified in two major histological forms namely - Adenocarcinoma (EAC) and Squamous cell carcinoma (ESCC), each showing differences in the incidence among populations that are geographically separated. Hence the detection of potential drug target and biomarkers demands a population-centric understanding of the molecular and cellular mechanisms of EC. To provide an adequate impetus to the biomarker discovery for ESCC, which is the most prevalent esophageal cancer worldwide, here we have developed ESCC ATLAS, a manually curated database that integrates genetic, epigenetic, transcriptomic, and proteomic ESCC-related genes from the published literature. It consists of 3475 genes associated to molecular signatures such as, altered transcription (2600), altered translation (560), contain copy number variation/structural variations (233), SNPs (102), altered DNA methylation (82), Histone modifications (16) and miRNA based regulation (261). We provide a user-friendly web interface (http://www.esccatlas.org, freely accessible for academic, non-profit users) that facilitates the exploration and the analysis of genes among different populations. We anticipate it to be a valuable resource for the population specific investigation and biomarker discovery for ESCC.

Background: DNA methylation at GFI1-locus has been repeatedly associated with exposure to smoking from foetal period onwards. We explored whether DNA methylation maybe a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health. Methods: We meta-analysed the association between methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n=18212). DNA methylation at GFI1-locus was measured in whole-blood. Multivariable regression analyses were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), diastolic, and systolic blood pressure (BP). Findings: Hypomethylation at four out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Cg14179389 hypomethylation, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, hypomethylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 x 10 - 7 < P < 0·01). Similarly, hypo-methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 x 10 - 8 < P < 0·001). Hypomethylation at all the CpGs was consistently associated with higher TG levels. Interpretation: Epigenetic changes at GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. This introduces a molecular mechanism and biomarker of smoking exposure. Funding: Not applicable Declaration of interests: The authors declare no competing interests. Ethical Approval Statement: Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees.

The epigenome has been shown to be influenced by biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption, and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here, we provide evidence on the associations between epigenetic modifications—in our case, CpG methylation—and educational attainment (EA), a biologically distal environmental factor that is arguably among of the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10,767 individuals. While we find that 9 CpG probes are significantly associated with EA, only two remain associated when we restrict the sample to never-smokers. These two are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, their effect sizes on EA are far smaller than the known associations between CpG probes and biologically proximal environmental factors. Two analyses that combine the effects of many probes—polygenic methylation score and epigenetic-clock analyses—both suggest small associations with EA. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

Homocysteine (Hcy) is a sulfur-containing non-protein forming amino acid, which is synthesized from methionine as an important intermediate in the one-carbon pathway. High concentrations of Hcy in a condition called hyperhomocysteinemia (HHcy) are an independent risk factor for several disorders including cardiovascular diseases and osteoporotic fractures. Since Hcy is produced as a byproduct of the methyltransferase reaction, alteration in DNA methylation is studied as one of the underlying mechanisms of HHcy-associated disorders. In animal models, elevated Hcy concentrations are induced either by diet (high methionine, low B-vitamins, or both), gene knockouts (Mthfr, Cbs, Mtrr or Mtr) or combination of both to investigate their effects on DNA methylation or its markers. In humans, most of the literature involves case-control studies concerning patients. The focus of this review is to study existing literature on HHcy and its role in relation to DNA methylation. Apart from this, a few studies investigated the effect of Hcy-lowering trials on restoring DNA methylation patterns, by giving a folic acid or B-vitamin supplemented diet. These studies which were conducted in animal models as well as humans were included in this review. (C) 2014 Elsevier Inc. All rights reserved.

RationaleWe aimed to identify differentially methylated regions (DMRs) in cord blood DNA associated with childhood lung function, asthma and chronic obstructive pulmonary disease (COPD) across the life course.MethodsWe meta-analysed epigenome-wide data of 1688 children from five cohorts to identify cord blood DMRs and their annotated genes, in relation to forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity (FVC) ratio and forced expiratory flow at 75% of FVC at ages 7–13 years. Identified DMRs were explored for associations with childhood asthma, adult lung function and COPD, gene expression and involvement in biological processes.ResultsWe identified 59 DMRs associated with childhood lung function, of which 18 were associated with childhood asthma and nine with COPD in adulthood. Genes annotated to the top 10 identified DMRs were HOXA5, PAOX, LINC00602, ABCA7, PER3, CLCA1, VENTX, NUDT12, PTPRN2 and TCL1A. Differential gene expression in blood was observed for 32 DMRs in childhood and 18 in adulthood. Genes related with 16 identified DMRs were associated with respiratory developmental or pathogenic pathways.InterpretationOur findings suggest that the epigenetic status of the newborn affects respiratory health and disease across the life course.

Introduction: The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes. Implementation: We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker) a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers. Results: Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation. Conclusions: CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network analysis approaches. Visualization options enable biological interpretation of complex regulatory networks in a graphical way. Importantly the incorporation of our tool into the Cytoscape framework allows the application of CyTargetLinker in combination with a wide variety of other apps for state-ofthe- art network analysis. Copyright: © 2013 Kutmon et al.

In the early 2000s, microRNAs (miRNAs) were discovered as segments of a new class of highly conserved and small non-coding RNA molecules of 20-25 nucleotides that are transcribed from DNA.They do not translation into proteins, rather they inhibit protein expression by binding to the 3’untranslated regions (3’ UTRs) of specific mRNA targets (that is/are complementary to them) and guiding their translational repression or complete degradation and gene silencing. With this, miRNAs provide a second level of regulation beyond primary gene expression. Integrative study of cellular pathways is pivotal to understanding the functions of individual genes and proteins in terms of systems and processes that contribute to normal physiology and to disease. "WikiPathways":http://wikipathways.org is an open, collaborative platform dedicated to the curation of biological pathways by and for the scientific community. The collection of pathways is publicly available to the researchers. The miRNA’s predicted by TargetScan in cardiomyocytes hypertrophy pathway has already been visualized on WikiPathways (WP1560). Since more studies investigate miRNAs using microarray technologies it would be desirable to be able to use information about miRNA’s in that analysis. One way to do that is to add the miRNA’s to all pathways. Therefore, we are integrating both validated and predicted miRNA information into biological pathways and making them available in WikiPathways. Initially, we focused on pathways related to the heart because miRNAs created a true revolution in the cardiovascular research field. The validated miRNAs have been downloaded from miRNA databases such as TarBase or miRTarbase. In order to link the validated miRNA targets to the genes in the pathways of our interest, we use "BridgeDb":http://www.bridgedb.org for identifier mapping. BridgeDb is a middleware between the relational databases, files and mapping services. BridgeDb is available in two forms. The first is a framework suitable for integration in Java applications. The other is based on Representational State Transfer (REST) webservices and is suitable for all other programming languages. The identifier mapping has been done in the R statistical environment as the connected Bioconductor repository has many pre-existing packages for microarray data analysis. For now we used the REST interface from R but we will also submit BridgeDb R package to Bioconductor.Predicted miRNA targets by different prediction algorithms were verified by co evaluating miRNA and mRNA expression using microarray analysis. Quality control and normalization of the microarray datasets was done using the current functionality of the arrayanalysis.org web portal. Statistical analysis was done using Limma and the miRNAs were visualized in the pathways of interest using "PathVisio":http://www.pathvisio.org. Modules for statistical and pathway analysis have been developed which will be added to the "arrayanalysis.org":http://www.arrayanalysis.org portal. This also required connecting R to PathVisio, for which a new XMLRPC interface was developed. Through this PathVisio can be controlled by R scripts.In conclusion, these R tools can help to integrate information about miRNAs with other knowledge about biological pathways and used for research purposes.

Background: DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health.Methods: We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n= 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), diastolic, and systolic blood pressure (BP).Findings: Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P Interpretation: Epigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. Fund: European Union's Horizon 2020 research and innovation programme under grant agreement no. 633595 DynaHEALTH. (c) 2018 The Authors. Published by Elsevier B.V.

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of the functional relevance of the phenomenon remains limited. Because obesity is the main risk factor for T2D and a driver of methylation from previous study, we aimed to explore the effect of DNA methylation in the early phases of T2D pathology while accounting for body mass index (BMI). We performed a blood-based epigenome-wide association study (EWAS) of fasting glucose and insulin among 4,808 non-diabetic European individuals and replicated the findings in an independent sample consisting of 11,750 non-diabetic subjects. We integrated blood-based in silico cross-omics databases comprising genomics, epigenomics and transcriptomics collected by BIOS project of the Biobanking and BioMolecular resources Research Infrastructure of the Netherlands (BBMRI-NL), the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium, and the tissue-specific Genotype-Tissue Expression (GTEx) project. We identified and replicated nine novel differentially methylated sites in whole blood (P-value < 1.27 × 10-7): sites in LETM1, RBM20, IRS2, MAN2A2 genes and 1q25.3 region were associated with fasting insulin; sites in FCRL6, SLAMF1, APOBEC3H genes and 15q26.1 region were associated with fasting glucose. The association between SLAMF1, APOBEC3H and 15q26.1 methylation sites and glucose emerged only when accounted for BMI. Follow-up in silico cross-omics analyses indicate that the cis-acting meQTLs near SLAMF1 and SLAMF1 expression are involved in glucose level regulation. Moreover, our data suggest that differential methylation in FCRL6 may affect glucose level and the risk of T2D by regulating FCLR6 expression in the liver. In conclusion, the present study provided nine new DNA methylation sites associated with glycemia homeostasis and also provided new insights of glycemia related loci into the genetics, epigenetics and transcriptomics pathways based on the integration of cross-omics data in silico.