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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., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Motivation: The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data., Results: Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data.We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform., Availability and Implementation: The bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press.)

Accelerated DNA methylation age is linked to all-cause mortality and environmental factors, but studies of associations with socioeconomic position are limited. Researchers generally use small selected samples, and it is unclear how findings obtained with 2 commonly used methods for calculating methylation age (the Horvath method and the Hannum method) translate to general population samples including younger and older adults. Among 1,099 United Kingdom adults aged 28-98 years in 2011-2012, we assessed the relationship of Horvath and Hannum DNA methylation age acceleration with a range of social position measures: current income and employment, education, income and unemployment across a 12-year period, and childhood social class. Accounting for confounders, participants who had been less advantaged in childhood were epigenetically "older" as adults: In comparison with participants who had professional/managerial parents, Hannum age was 1.07 years higher (95% confidence interval: 0.20, 1.94) for participants with parents in semiskilled/unskilled occupations and 1.85 years higher (95% confidence interval: 0.67, 3.02) for those without a working parent at age 14 years. No other robust associations were seen. Results accord with research implicating early life circumstances as critical for DNA methylation age in adulthood. Since methylation age acceleration as measured by the Horvath and Hannum estimators appears strongly linked to chronological age, researchers examining associations with the social environment must take steps to avoid age-related confounding.

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10 -14 ) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Exposure of a developing foetus to maternal gestational diabetes (GDM) has been shown to programme future risk of diabetes and obesity. Epigenetic variation in foetal tissue may have a mechanistic role in metabolic disease programming through interaction of the pregnancy environment with gene function. We aimed to identify genome-wide DNA methylation variation in cord blood and placenta from offspring born to mothers with and without GDM. Pregnant women of South Asian origin were studied and foetal tissues sampled at term delivery. The Illumina HumanMethylation450 BeadChip was used to assay genome-wide DNA methylation in placenta and cord blood from 27 GDM exposed and 21 unexposed offspring. We identified 1485 cord blood and 1708 placenta methylation variable positions (MVPs) achieving genome-wide significance (adjusted P-value <0.05) with methylation differences of >5%. MVPs were disproportionately located within first exons. A bioinformatic co-methylation algorithm was used to detect consistent directionality of methylation in 1000 bp window around each MVP was observed at 74% of placenta and 59% of cord blood MVPs. KEGG pathway analysis showed enrichment of pathways involved in endocytosis, MAPK signalling and extracellular triggers to intracellular metabolic processes. Replication studies should integrate genomics and transcriptomics with longitudinal sampling to elucidate stability, determine causality for translation into biomarker and prevention studies., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Fetal growth is determined by the feto-placental genome interacting with the maternal in utero environment. Failure of this interplay leads to poor placental development and fetal growth restriction (FGR), which is associated with future metabolic disease. We investigated whether whole genome methylation differences existed in umbilical cord blood and placenta, between gestational-matched, FGR, and appropriately grown (AGA) neonates. Using the Infinium HumanMethylation450 BeadChip®, we found that DNA from umbilical cord blood of FGR born at term (n = 19) had 839 differentially methylated positions (DMPs) that reached genome-wide significance compared with AGA (n = 18). Using gestational age as a continuous variable, we identified 76,249 DMPs in cord blood (adj. P < 0.05) of which 121 DMPs were common to the 839 DMPs and were still evident when comparing 12 FGR with 12 AGA [39.9 ± 1.2 vs. 40.0 ± 1.0 weeks (mean ± SD), respectively]. A total of 53 DMPs had a β methylation difference >10% and 25 genes were co-methylated more than twice within 1000 base pairs. Gene Ontology (GO) analysis of DMPs supported their involvement in gene regulation and transcription pathways related to organ development and metabolic function. A similar profile of DMPs was found across different cell types in the cord blood. At term, no DMPs between FGR and AGA placentae reached genome-wide significance, validated with an external dataset. GO analysis of 284 pre-term, placental DMPs associated with autophagy, oxidative stress and hormonal responses. Growth restricted neonates have distinct DNA methylation profiles in pre-term placenta and in cord blood at birth, which may predispose to future adult disease.

Success in the identification of genetic variants associated with neuropsychiatric disorders is one of the major achievements in contemporary biomedical research. Most genetic variants identified in Genome-Wide Association Studies (GWAS) of complex traits are thought to act via effects on gene regulation rather than directly altering the protein product. As a consequence, the actual genes involved in disease are not necessarily the most proximal to the associated variants. By integrating data from GWAS analyses with that from genetic studies of regulatory variation, it is possible to identify variants pleiotropically-associated with both a complex trait and measures of gene regulation. In this study, we use Summary data–based Mendelian Randomization (SMR), a method developed to identify variants pleiotropically associated with both complex traits and gene expression, to identify associations between neuropsychiatric disorders and DNA methylation. Building on our previous efforts, we increased our catalogue of DNA Methylation Quantitative Trait Loci (mQTL) in whole blood using the Illumina EPIC HumanMethylation array that interrogates over 800,000 genomic loci. These data along with mQTL data identified previously in human fetal brain were used to prioritize genes for psychiatric disorders using GWAS data from the Psychiatric Genomics Consortium (PGC). In this study, we apply the SMR approach to test 129,469 DNA methylation sites against five psychiatric phenotypes (schizophrenia, bipolar disorder, major depressive disorder, autism, ADHD) with robust GWAS data available from the PGC using mQTLs identified in whole blood (n=1,175; mQTL P < 1×10-10) to identify novel associations with psychiatric traits. In addition, we tested 9,261 DNA methylation sites using mQTL identified in fetal brain (n=166; mQTL P < 1×10-8). In total, we identified 107 associations with 37 satisfied addition criteria to be defined as pleiotropic and not an artefact of linkage disequilibrium. We identify multiple examples of variable DNA methylation associated with GWAS variants across the five psychiatric disorders, demonstrating the utility of the SMR approach for refining genetic association signals. [ABSTRACT FROM AUTHOR]