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Abstract Background B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. Results Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p

Abstract Background Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. Results We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1520 co-localisations across 1325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs (e.g. through disruption of transcription factor binding sites for CTCF and SMC3) and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites). Conclusions Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk .

Abstract Background There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, a robust methylation signature has been associated with body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study, we dissect the subcutaneous adipose tissue (SAT) methylome signature relevant to metabolic health by focusing on VF as the major risk factor of metabolic disease. We integrate results with genetic, blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data to assess and quantify genetic and environmental drivers of the identified signals, as well as their potential functional roles. Methods Epigenome-wide association analyses were carried out to determine visceral fat mass-associated differentially methylated positions (VF-DMPs) in SAT samples from 538 TwinsUK participants. Validation and replication were performed in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including android:gynoid fat ratio (AGR), lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health. Results We identified 1181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet, and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC and CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone. Conclusions Our findings help characterise the adiposity-associated methylation signature of SAT, with insights for metabolic disease risk.

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Abstract Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

Abstract Background Although the genomes of monozygotic twins are practically identical, their methylomes may evolve divergently throughout their lifetime as a consequence of factors such as the environment or aging. Particularly for young and healthy monozygotic twins, DNA methylation divergence, if any, may be restricted to stochastic processes occurring post-twinning during embryonic development and early life. However, to what extent such stochastic mechanisms can systematically provide a stable source of inter-individual epigenetic variation remains uncertain until now. Results We enriched for inter-individual stochastic variation by using an equivalence testing-based statistical approach on whole blood methylation microarray data from healthy adolescent monozygotic twins. As a result, we identified 333 CpGs displaying similarly large methylation variation between monozygotic co-twins and unrelated individuals. Although their methylation variation surpasses measurement error and is stable in a short timescale, susceptibility to aging is apparent in the long term. Additionally, 46% of these CpGs were replicated in adipose tissue. The identified sites are significantly enriched at the clustered protocadherin loci, known for stochastic methylation in developing neurons. We also confirmed an enrichment in monozygotic twin DNA methylation discordance at these loci in whole genome bisulfite sequencing data from blood and adipose tissue. Conclusions We have isolated a component of stochastic methylation variation, distinct from genetic influence, measurement error, and epigenetic drift. Biomarkers enriched in this component may serve in the future as the basis for universal epigenetic fingerprinting, relevant for instance in the discrimination of monozygotic twin individuals in forensic applications, currently impossible with standard DNA profiling.

Abstract Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

Abstract Background Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear. Methods We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat. Results We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation. Conclusions Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung.

Abstract Background Rheumatoid arthritis is a common autoimmune disorder influenced by both genetic and environmental factors. Epigenome-wide association studies can identify environmentally mediated epigenetic changes such as altered DNA methylation, which may also be influenced by genetic factors. To investigate possible contributions of DNA methylation to the aetiology of rheumatoid arthritis with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. Methods Genome-wide DNA methylation was assessed in 79 monozygotic twin pairs discordant for rheumatoid arthritis using the HumanMethylation450 BeadChip array (Illumina). Discordant twins were tested for both differential DNA methylation and methylation variability between rheumatoid arthritis and healthy twins. The methylation variability signature was then compared with methylation variants from studies of other autoimmune diseases and with an independent healthy population. Results We have identified a differentially variable DNA methylation signature that suggests multiple stress response pathways may be involved in the aetiology of the disease. This methylation variability signature also highlighted potential epigenetic disruption of multiple RUNX3 transcription factor binding sites as being associated with disease development. Comparison with previously performed epigenome-wide association studies of rheumatoid arthritis and type 1 diabetes identified shared pathways for autoimmune disorders, suggesting that epigenetics plays a role in autoimmunity and offering the possibility of identifying new targets for intervention. Conclusions Through genome-wide analysis of DNA methylation in disease-discordant monozygotic twins, we have identified a differentially variable DNA methylation signature, in the absence of differential methylation in rheumatoid arthritis. This finding supports the importance of epigenetic variability as an emerging component in autoimmune disorders.

Abstract Background In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. Results Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. Conclusion We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.