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.(Extended Data Fig. 5). These results show the value of large sample sizes in blood to detect trans-mQTLs regardless of the tissue. Trans-mQTL SNPs and DNAm exhibit patterned TF binding.Recent studies have uncovered multiple types of transcription factor (TF)-DNA interactions influenced by DNAm, including the binding of DNAm-sensitive TFs [26][27][28] and cooperativity between TFs 27,29 . To gain insights into how SNPs induce long-range DNAm changes, we mapped enrichments for DNAm sites and SNPs across binding sites for 171 TFs in 27 cell types 30,31 . We found strong enrichments for most TFs and cell types among DNAm sites with a trans association (cis + trans: 55%; trans only: 80%; cis only: 18%) and among cis-acting SNPs (cis only: 96%, cis + trans: 91%, trans only: 1%; Fig. 2b, Supplementary Tables 7 and 8, and Supplementary Figs. 22 and 23). Consistent with the observation that trans-only DNAm sites are enriched for CpG islands (Supplementary Fig. 13), DNAm sites that overlap TF-binding sites (TFBSs) were relatively hypomethylated (weighted mean DNAm levels = 21% versus 52%, P < 2.2 × 10 −16 ; Supplementary Fig. 24).Next, we hypothesized that, if a trans-mQTL is driven by TF activity 8,10 , then particular TF-TF pairs may exhibit preferential enrichment 32 . An mQTL has a pair of TFBS annotations 31 , one for the SNP and one for the DNAm site. We evaluated whether the annotation pairs among 18,584 interchromosomal trans-mQTLs were associated with TF binding in a nonrandom pattern (Supplementary Note and Extended Data Fig. 6a,b). We found that 6.1% (22,962 of 378,225) of possible pairwise combinations of SNP-DNAm site annotations were more over-or underrepresented than expected by chance after strict multiple testing correction (Supplementary Note, Supplementary Table 9 and Extended Data Fig. 6c).After accounting for abundance and other characteristics, the strongest pairwise enrichments involved sites close to TFBSs for proteins in the cohesin complex, ...

BackgroundCells can be primed by external stimuli to obtain a long-term epigenetic memory. We hypothesize that long-term exposure to elevated blood lipids can prime circulating immune cells through changes in DNA methylation, a process that may contribute to the development of atherosclerosis. To interrogate the causal relationship between triglyceride, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels and genome-wide DNA methylation while excluding confounding and pleiotropy, we perform a stepwise Mendelian randomization analysis in whole blood of 3296 individuals.ResultsThis analysis shows that differential methylation is the consequence of inter-individual variation in blood lipid levels and not vice versa. Specifically, we observe an effect of triglycerides on DNA methylation at three CpGs, of LDL cholesterol at one CpG, and of HDL cholesterol at two CpGs using multivariable Mendelian randomization. Using RNA-seq data available for a large subset of individuals (N = 2044), DNA methylation of these six CpGs is associated with the expression of CPT1A and SREBF1 (for triglycerides), DHCR24 (for LDL cholesterol) and ABCG1 (for HDL cholesterol), which are all key regulators of lipid metabolism.ConclusionsOur analysis suggests a role for epigenetic priming in end-product feedback control of lipid metabolism and highlights Mendelian randomization as an effective tool to infer causal relationships in integrative genomics data.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1000-6) contains supplementary material, which is available to authorized users.

DNA methylation mediates the association of prenatal famine exposure with higher adult BMI and serum triglyceride levels.

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.

Educational attainment is a key behavioural measure in studies of cognitive and physical health, and socioeconomic status. We measured DNA methylation at 410,746 CpGs (N = 4152) and identified 58 CpGs associated with educational attainment at loci characterized by pleiotropic functions shared with neuronal, immune and developmental processes. Associations overlapped with those for smoking behaviour, but remained after accounting for smoking at many CpGs: Effect sizes were on average 28% smaller and genome-wide significant at 11 CpGs after adjusting for smoking and were 62% smaller in never smokers. We examined sources and biological implications of education-related methylation differences, demonstrating correlations with maternal prenatal folate, smoking and air pollution signatures, and associations with gene expression in cis, dynamic methylation in foetal brain, and correlations between blood and brain. Our findings show that the methylome of lower-educated people resembles that of smokers beyond effects of their own smoking behaviour and shows traces of various other exposures.

Human gut microbiota produce a variety of molecules, some of which enter the bloodstream and impact health. Conversely, dietary or pharmacological compounds may affect the microbiota before entering the circulation. Characterization of these interactions is an important step towards understanding the effects of the gut microbiota on health. In this cross-sectional study, we used deep metagenomic sequencing and ultra-high-performance liquid chromatography linked to mass spectrometry for a detailed characterization of the gut microbiota and plasma metabolome, respectively, of 8583 participants invited at age 50 to 64 from the population-based Swedish CArdioPulmonary bioImage Study. Here, we find that the gut microbiota explain up to 58% of the variance of individual plasma metabolites and we present 997 associations between alpha diversity and plasma metabolites and 546,819 associations between specific gut metagenomic species and plasma metabolites in an online atlas (https://gutsyatlas.serve.scilifelab.se/). We exemplify the potential of this resource by presenting novel associations between dietary factors and oral medication with the gut microbiome, and microbial species strongly associated with the uremic toxin p-cresol sulfate. This resource can be used as the basis for targeted studies of perturbation of specific metabolites and for identification of candidate plasma biomarkers of gut microbiota composition.

Background: DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. Results: By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. Conclusion: We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation.

Background Epigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks. Results We perform a functional genomics analysis on four epigenetic clocks, including Hannum’s blood predictor and Horvath’s multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expression in trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells. Conclusions The ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.