research articles on dna

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Publications
• Account settings
• Advanced Search
• Journal List

Genetic impacts on DNA methylation: research findings and future perspectives

Sergio villicaña, jordana t bell.

• Author information
• Article notes
• Copyright and License information

Corresponding author.

Received 2020 Oct 12; Accepted 2021 Apr 9; Collection date 2021.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-021-02347-6).

Keywords: DNA methylation, Heritability, GWAS, Methylation quantitative trait loci, meQTL

Introduction

The complexity of the human genome lies not only in its composition of billions of base pairs, but also in the chemical modifications that make it interpretable to enzymes and other molecular factors, through epigenetic mechanisms. DNA methylation has been the most widely studied epigenetic mark since 1948 when it was first reported [ 1 ]. In humans, DNA methylation consists of the covalent addition of a methyl group to cytosine residues—predominantly at CpG sites—by a family of enzymes called DNA methyltransferases (DNMTs) [ 2 , 3 ]. DNA methylation plays an important role in multiple processes during human development and over the life course, such as the regulation of transcription [ 4 – 6 ], genomic imprinting [ 2 , 4 ], maintenance of X-chromosome inactivation [ 7 ], chromosomal maintenance, and genomic stability [ 8 ].

With advances in high-throughput molecular techniques our understanding of DNA methylation has greatly increased in the past few decades. Multiple methods have been developed for profiling DNA methylation patterns across the human genome. Currently, the gold standard is bisulfite conversion of DNA followed by deep sequencing or whole genome bisulfite sequencing (WGBS, Table  1 ). However, the most extensively used methylation profiling technologies are microarrays assessing DNA methylation at a proportion of the 28 million CpG sites in the genome. To date, Illumina bead-chip platforms have been most popular, where pre-designed probes target bisulfite-converted DNA, followed by hybridization, single-base extension, and its detection [ 9 ]. Early models included arrays such as the Infinium HumanMethylation27 BeadChip (27K), targeting around 27,000 sites (0.1% of total CpGs) mainly in CpG islands (CGIs) within promoters [ 9 ], followed by the widely used Infinium HumanMethylation450 array (450K), targeting ∼480,000 sites (1.7% of total CpGs) consisting of the 27K sites and increased coverage in non-CGIs and intergenic regions [ 10 ]. A more recent version is the Infinium MethylationEPIC BeadChip (EPIC), targeting ∼850,000 sites (3% of total CpGs), which include almost all of the 450K sites, with additional CpG sites in enhancers [ 11 ].

Glossary of commonly used terms

Unlike DNA sequence, genomic methylation patterns are not directly inherited during meiosis [ 12 ], but are mostly reprogrammed in two waves during embryogenesis [ 13 – 15 ]. Following this, DNA methylation modifications can be both stable and dynamic during mitosis events that accumulate over the life course [ 16 , 17 ]. These observations suggest that the environment may be a key driving force behind changes in mitotic DNA methylation [ 17 – 20 ]. However, growing evidence now shows that genetic variation also plays a role in the establishment of DNA methylation marks, independently of or in contribution with environmental exposures.

Research interest in genetic impacts on DNA methylation variation is especially relevant in context of methylome changes observed in disease [ 16 , 21 – 23 ], alongside results from genome-wide association studies (GWASs). Although many genetic associations have been identified from GWASs, there remain important unanswered questions about candidate causal variants and their functional consequences, as GWAS signals tend to fall in non-coding regions [ 24 ]. Methylome analyses can provide a valuable piece of information as a post-GWAS resource, giving insights into regulatory genomic potential of GWAS signals and helping to prioritize loci to further follow-up [ 25 – 27 ].

Given these considerations, here, we present an overview of results identifying genetic drivers of DNA methylation variation. We discuss methylation heritability findings, and then focus on genome-wide studies that have identified genetic variants as meQTLs for DNA methylation profiles. We also discuss cellular mechanisms that may explain genetic impacts on DNA methylation levels. Lastly, we consider challenges of meQTL analyses, as well as novel applications and further research directions.

DNA methylation heritability

A fundamental question in the study of human traits is to assess the extent to which a phenotype is under the influence of genetic factors, that is, how heritable it is. Heritability refers to the proportion of phenotypic variance attributed to either total genetic effects (broad-sense heritability, H 2 ), or additive genetic effects (narrow-sense heritability, h 2 ) [ 28 ], where the latter is most commonly estimated in context of DNA methylation analyses.

For the estimation of DNA methylation heritability, most studies apply twin-based study designs. The underlying premise of the twin design is based on trait comparison between monozygotic (MZ) twin pairs who share typically 100% of their genome variation, compared to dizygotic (DZ) twins who share on average only 50% of genetic variation. The narrow-sense heritability is then calculated by comparing the correlation of a trait—here level of DNA methylation at a genomic region—between MZ and DZ twins [ 29 ], following a series of assumptions. In a recent study in whole blood samples from 2603 individuals from the Netherlands Twin Registry, van Dongen et al. [ 30 ] estimated the individual CpG site heritability to range from 0 to 0.99 at each CpG site profiled on the Illumina 450K array, where the mean genome-wide heritability averaged over all CpG sites tested was h 2 ¯ ^ = 0.19 ( h 2 ¯ ^ = 0.20 with the classical twin method). The estimate of the average CpG site heritability across the methylome as 0.19 is in agreement with previous twin methylation heritability studies using the 450K [ 31 ] and 27K arrays [ 32 ]. Furthermore, the study estimated that approximately 41% of Illumina 450K sites had significant evidence for additive genetic effects and suggested that heritability at a proportion of DNA methylation sites is sex- and age-specific.

Fewer studies estimate DNA methylation heritability using other approaches, for example, using familial clustering models in extended families. The advantage of such methods is their wider applicability to multiple types of relatives beyond twins and circumventing key assumptions of the classical twin model such as equal influence of common environment for MZ and DZ twins and independence of genetic and environmental factors. Despite this, DNA methylation heritability estimates from familial clustering studies are consistent with those obtained from classical twin models. McRae et al. [ 33 ] estimated the heritability of DNA methylation measured using the Illumina 450K array in 614 peripheral blood leukocyte samples from twin pairs, their siblings and fathers, in altogether 117 families of European descent from the Brisbane System Genetics Study. The estimates of heritability across the Illumina 450K probes give a similar mean CpG site genome-wide estimate of h 2 ¯ ^ = 0.187 , ignoring probes with known genetic variants ( h 2 ¯ ^ = 0.199 , if all probes included). Using a different approach, Nustad et al. [ 34 ] designed a Bayesian mixed model that could include pedigree structure for estimating heritability in two sets of CD4 + T cell samples ( n =995 and n =530) from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, profiled on the 450K array. Here, the mean heritability point estimates across the genome ( h 2 ¯ ^ = 0.33 and h 2 ¯ ^ = 0.36 ) are slightly higher compared to other studies, potentially because the mean was calculated only considering CpG sites with strong evidence for non-zero heritability, as well as lack of precise estimates of shared environmental effects. Other studies using the 450K array have found comparable average heritability estimates based on family clustering ( h 2 ¯ ^ = 0.09 [ 35 ] and H 2 ¯ ^ = 0.13 [ 36 ]), or other methods applicable to unrelated individuals such as SNP-based heritability, calculated using all genetic variants [ 30 , 37 ]. For instance, in 3948 blood samples from the Avon Longitudinal Study of Parents and Children (ALSPAC), Gaunt et al. [ 37 ] estimated the genome-wide average SNP-based heritability for 450K array probes in blood at different time points over the life course to range between 0.20 and 0.25, based on a panel of 1.2 million common SNPs. The majority of methylation variance was explained by SNPs located over 1 Mbp away from the methylation site (or in trans ).

Overall, these heritability studies indicate that DNA methylation profiles have a genetic basis, which expressed as the average heritability across all CpG site in the genome profiled by the Illumina 450K array, ranges from 0.1 to over 0.3. Although this genome-wide mean estimate of methylation heritability could be considered moderate or low, the heritability distribution at specific CpG sites ranges from 0 to 1, and at least one tenth of profiled sites are highly heritable ( h 2 ^ > 0.5 ) [ 30 , 31 , 33 ]. Furthermore, because genetic variability differs across populations and over time, heritability estimates are population- and age-specific, which may explain some of the differences in reported mean DNA methylation heritability estimates so far [ 28 ]. Another factor to consider when interpreting the heritability estimates is that they may vary according to DNA methylation platform [ 38 , 39 ], as array technologies only cover a limited proportion of CpGs out of the 28 million CpGs genome-wide (approximately 1.7% for 450K, 3% for EPIC) and regulatory elements tend to be underrepresented (see the “ DNA methylation profiling ” section).

An outstanding research question has considered evidence for transgenerational transmission of DNA methylation patterns independent of genetic variation or transgenerational epigenetic inheritance. In model organisms such as mice and rats, several phenotypes have been linked to DNA methylation transgenerational inheritance. Examples include a kinked tail phenotype caused by methylation in a retrotransposon within the axin-fused allele in mice [ 40 ], and metabolic phenotypes in male rats linked to in utero nutritional deficiencies and alterations in the sperm methylome [ 41 ]. In contrast, human transgenerational epigenetic inheritance studies are limited and show negative results, suggesting that genetic variants likely fully explain the observed methylation heritability. In a study aiming to test whether methylation levels at certain CpG sites are inherited in a Mendelian fashion through multiple generations in 16 families (123 subjects) from the Arab population, Zaghlool et al. [ 42 ] inspected loci where blood DNA methylation levels followed a trimodal distribution, that is, with peaks around 0 (unmethylated), 0.5 (hemi-methylated), or 1 (methylated). Although about a thousand CpG sites from the 450K followed such patterns, in almost all cases, DNA methylation changes were associated with nearby genetic variants (within 1 Mbp or less), discarding a direct mechanism of transmission that is independent of genetic variation. Importantly, the trimodal loci had high mean heritable values (0.8±0.18), and almost half were associated with expression quantitative trait loci (eQTLs). McRae et al. [ 33 ] reached similar conclusions, noting that the transgenerational inheritance of DNA methylation is mainly attributable to genetic heritability. Therefore, so far, there is no robust evidence in humans to indicate that DNA methylation heritability may be attributed to non-genetic effects, such as evidence for transgenerational epigenetic inheritance as reported in other species [ 12 ].

Methylation quantitative trait loci

Given the observed evidence for DNA methylation heritability, much interest has focused on identifying specific genetic variants that influence DNA methylation variation across the genome. Multiple studies have explored the correlation between DNA methylation levels and genetic variants across the genome (typically single nucleotide polymorphisms, SNPs), to identify DNA methylation quantitative trait loci or meQTLs (also referred to as mQTLs or metQTLs). Although several early papers tackled meQTLs identification over limited target sites [ 43 – 46 ], it was not until the early 2010’s that initial genome-wide efforts identified meQTLs on the 27K methylome and across multiple tissues (Gibbs et al. [ 47 ], Zhang et al. [ 48 ] and Bell et al. [ 49 ]).

Studies to date have reported an influence of meQTLs on up to 45% of CpG sites profiled by the Illumina 450K array across the genome [ 31 , 35 , 50 , 51 ], with more than 90% of meQTLs acting on nearby methylation sites (in cis ) [ 38 , 50 , 52 ]. CpG sites that have higher heritability estimates are more likely to be associated with meQTLs in cis , trans , or both, and have a clear polygenic architecture [ 35 , 38 , 50 ]. Some studies also include replication in independent sample sets, although overall a direct comparison of meQTL signals can be challenging because studies do not systematically report meQTL effect sizes. Despite observations that meQTLs tend to have moderate to large effects, the “missing heritability” issue has also been raised in the context of meQTLs. That is, family-based heritability estimates of DNA methylation are greater than the proportion of variance explained by meQTLs, especially for distal associations [ 37 , 53 – 56 ].

Detecting meQTLs

MeQTL identification is based on association tests between genetic variation genome-wide and DNA methylation levels at a specific CpG site (Fig.  1 ). As for other quantitative trait analyses, the majority of meQTL detection approaches apply linear models, where the DNA methylation level at a CpG site is the response variable and genetic variants are predictors along with technical and biological covariates, such as smoking and age. Other statistical tests employed include non-parametric methods such as Spearman rank correlation [ 51 , 57 – 59 ] and Kruskal-Wallis rank test [ 60 ], that do not make assumptions about the distribution of variables, or even machine learning approaches such as random forests [ 61 ].

A typical workflow for meQTL identification. Step 1 is DNA methylation profiling. The most commonly applied methylation profiling technologies in meQTL studies are Illumina methylation arrays and whole genome bisulfite sequencing (WGBS). In both approaches, DNA is treated with bisulfite, converting unmethylated cytosines into uracils, and leaving methylated cytosines unchanged. DNA can then be profiled by sequencing or by Illumina array technologies, consisting of pre-designed probes. In step 2 , DNA methylation levels at each CpG site are quantified, typically either as percentage (0–100%, e.g., in WGBS) or proportion methylation (0–1, e.g., in the Illumina technology methylation β -value). The example shows the distribution of methylation β -values for one CpG site ( m 1 ) across all profiled samples. Step 3 is the association of a set of genetic variants (coded as allele dosages at each locus) with methylation values at each CpG site, usually using linear models. In this example, after the association test at site m 1 with a set of i genetic variants (shown in the Manhattan plot), g 1 was found to be significantly associated with m 1 (shown in the boxplot). Finally, step 4 represents the extension of the genetic association test to all profiled CpG sites genome-wide and the identification of genome-wide meQTLs after setting an appropriate threshold for statistical significance. The resulting meQTL associations can be either short-range, in cis (shown in heatmap for a few Mbp), or long-range or on different chromosomes, in trans (shown in Circos plot with all chromosomes)

In most studies the focus is on detecting evidence for additive genetic effects alone, where the genetic predictor is the dose of the alternative allele, for example, 0 for genotype “AA”, 1 for “Aa” and 2 for genotype “aa”. To date and to our knowledge, full genome-wide meQTL analyses have not yet considered genetic association models including dominance effects or overall genotype effects. However, Zeng et al. [ 62 ] explored meQTLs at 984 CpGs with parent-of-origin effects (POE) in 5101 individuals from Scottish families. The model included additive effects (coded as the dosage of alternative allele), dominance effects (coded 1 for heterozygotes and 0 for homozygotes), and POE effects (coded 0, −1, and 1 for homozygotes, “Aa” and “aA”, respectively). Likewise, some studies focusing on subsets of CpGs have identified meQTLs in gene interaction models, specifically gene-by-gene (G ×G) and gene-by-environment (G ×E) (see the “ Gene–environment interactions ” section).

The majority of studies discussed here apply Illumina DNA methylation arrays. In these platforms, the DNA methylation level at a CpG site is quantified through the Illumina methylation β -value, defined as the intensity measured in the methylated probes for that CpG over the total intensity across all probes for the CpG and a constant. The methylation β -value is often interpreted as the probability of methylation at a given site, or the proportion of methylated cells in the sample (Fig.  1 ). Some studies apply transformations of methylation β -values—such as the logit transformation or M -value—which are more appropriate to control for heteroskedasticity but are perhaps less biologically interpretable [ 63 , 64 ].

In addition to meQTL studies that explore DNA methylation levels using Illumina arrays, several meQTL approaches have also applied sequencing technologies. To date, only one study has used sequencing techniques to detect meQTLs across the full genome, rather than focusing on specific genomic subsets. In a sample of 697 Swedish subjects, McClay et al. [ 52 ] used methyl-CpG-binding domain (MBD)-enriched sequencing (MBS-seq) genome-wide and profiled ∼13M CpGs collapsed into 4.5M loci across the genome. DNA methylation was quantified by estimating the coverage at each CpG. The results show that 15% of methylation loci have meQTLs (primarily within 1 Mbp), and 98% of the tested SNPs were associated with at least one CpG. Other studies have employed strategies such as targeted bisulfite sequencing of a pre-designed panel with informative genomic regions [ 65 ], MeDIP-sequencing at candidate regions [ 66 ], and meQTL replication in WGBS data [ 67 ]. Several studies have also explored sequence-dependent allele-specific methylation (ASM), which represents a specific type of meQTL effect in cis . In contrast to meQTL analysis, ASM discovery is restricted to heterozygous regions within single samples, and comparison of differentially methylated CpG sites (DMSs) between the two distinct alleles, for example, using Fisher’s exact test or equivalent. ASM studies to date have been carried out using bisulfite sequencing in a moderate number of samples (less than 100). ASM results show that around 10% of the explored CpGs exhibit allelic imbalance at heterozygous regions [ 65 , 68 – 70 ], which is consistent with meQTLs results.

Distribution of meQTLs across the genome

Meqtls can have local or distal effects.

MeQTLs can be divided into two classes based on the proximity of the genetic variant to the CpG site. Cis -meQTLs are genetic variants near to or proximal to the target CpG site, and trans -meQTLs are separated by one or more Mbp from the target CpG or located on different chromosomes. Identification of cis and trans -meQTLs includes testing for associations across all possible pairs of SNPs-CpGs. Pairs can be categorized into “proximal” or “distal” and multiple testing correction can be applied for each group independently, or they can be analyzed together and annotated post hoc [ 38 , 47 , 71 ]. Correcting for multiple testing burden is a crucial step for the definition of genome-wide significant p -value thresholds. Published thresholds are typically of the order of p <1×10 −5 for cis effects and p <1×10 −9 for trans effects, based on applying permutation-based approaches to estimate the false discovery rate (FDR) or Bonferroni correction to control the family-wise error rate (FWER). The exact multiple testing correction threshold clearly depends on the methylation array and genotype coverage, methylation, and genotype distributions, as well as sample structure and sample size if permutation-based approaches are applied (see the “ Multiple-testing correction ” section). Some studies limit the search to cis -meQTLs alone, reducing the number of total tests, or carry out trans associations only for selected SNP-CpG pairs [ 51 , 60 ].

To date, the primary focus has been on cis -meQTLs identification. In general, studies with large sample sizes (> 1000) have estimated that at least 10% [ 38 , 53 – 55 ] and up to 45% [ 35 , 50 , 51 ] of the methylome is influenced by nearby meQTLs. A consideration in cis -meQTL analysis protocols is the maximum distance between genetic variants and CpG sites. Published studies have applied a range from a few kbp to 1 Mbp, but in almost all cases it has been observed that the strength of the cis -meQTL effect is inversely proportional to the distance between genetic variant and CpG site. For example, in one of the early genome-wide meQTL analysis using 27K DNA methylation levels in lymphoblastoid cell lines (LCLs), 37 CpG sites had meQTLs in genome-wide analyses across all possible SNP-CpG pairs, but for 27 of these sites the most significant meQTL was located within 50 kbp of the CpG site [ 49 ]. More recently, Hannon et al. [ 38 ] conducted a genome-wide analysis across all SNPs-CpGs using EPIC DNA methylation levels in 1111 blood samples. The results identified meQTLs at 12% of assayed methylation sites, and again a predominance of these associations occurred in cis . Higher effect sizes were observed for genetic variants within a maximum of 500 kbp from the CpG site (in cis ), where the average of the change in DNA methylation per allele was of 3.48%, compared to 3.26% in trans .

Conversely, meQTL genome-wide association analyses to date agree that no more than 5% of total CpGs show evidence for trans -meQTLs. The exception to this are the results from Gong et al. [ 72 ] estimating meQTLs in different cancer tissues samples. The observation of higher trans -meQTL proportions here (more than 10% of total CpG sites are associated with trans -meQTLs in eight cancer types) suggests that under certain conditions, the effects of the distal associations could be enhanced. Furthermore, although trans -meQTL are relatively rare genome-wide, these effects also tend to target specific genomic regions (see the “ MeQTLs are differentially distributed across the genome ” section).

The physical threshold for categorizing a meQTL as cis or trans matters. Insights into the distance between cis -meQTLs and the target CpG sites were gained by Banovich et al. [ 67 ] who used a relatively small cis window of 6 kbp to detect meQTLs in LCLs. The authors estimated the median distance of putative causal cis associations as 76 bp, with 87% of the meQTLs located within 3 kbp of the CpGs. At the other end of this spectrum, Huan et al. [ 35 ] report that 70% of intra-chromosomal trans -meQTLs were within 5 Mbp of the target CpG, leading to the conclusion that such associations may act as long-range cis -meQTLs, rather than as trans . In contrast, inter-chromosomal associations are the most commonly reported trans -meQTLs, accounting for at least 65% of the trans -meQTLs [ 35 , 37 , 50 , 51 , 55 ]. Another factor to take into account is that some trans associations could be SNPs in long-range linkage disequilibrium (LD) with “real” cis -meQTLs—as observed for 17% of intra-chromosomal SNP-CpG associations in lung tissue, after conditional analysis [ 56 ].

MeQTLs are differentially distributed across the genome

Early efforts exploring the correlation between genetic variants and DNA methylation showed evidence that meQTLs and their target DNA methylation sites are not randomly distributed in the genome. Non-genic regions and enhancers appear to be hotspots for CpG sites associated with cis -meQTLs, while CpG islands (CGIs), 5 ′ untranslated regions (UTRs) and regions upstream of the transcription start sites (TSSs) show depletion of CpG sites with cis -meQTLs. In contrast, the opposite pattern is observed for CpGs with trans -meQTLs, which are enriched in CGIs and in promoters and regions surrounding the TSSs, and are underrepresented in gene bodies, 3 ′ UTRs, and heterochromatin regions [ 35 , 37 , 50 , 51 , 53 , 56 , 67 , 73 ]. This genomic distribution of meQTL-related CpGs appears to be quite stable during several life stages [ 37 ] and across tissues [ 73 ].

The underrepresentation of CpGs with cis -meQTLs in CGIs is related to the observation that most of the tested CpGs in CGIs fall in gene promoters, where they tend to be constitutively hypomethylated and have lower DNA methylation variances [ 5 , 6 ]. As hypothesized by Do et al. [ 70 ], meQTL-associated CpGs may be located in areas with more flexible evolutionary constrains, in contrast to typically hypomethylated CGIs which are conserved across vertebrate promoters [ 74 ]. This hypothesis is also supported by results from Husquin et al. [ 60 ] who observed that DMSs in monocytes between two populations (78 samples of African descent and 78 of European descent) are enriched to harbor cis -meQTLs (70.2% of DMSs have cis -meQTLs) compared to the genome-wide meQTL proportion (12.6% of EPIC sites had cis -meQTLs). Hence, CpGs, where methylation patterns are less conserved across different populations, have a higher probability of being under the influence of meQTLs.

The genetic variants driving meQTL effects also exhibit non-random genomic distributions. Min et al. [ 50 ] found that active chromatin domains and genic regions were enriched for meQTLs that act in cis only or both in cis and trans , while heterochromatin and intergenic regions were enriched for trans only meQTLs. Using a different approach, an analysis at 11.5 million DNA methylation sites profiled by WGBS in 34 samples [ 75 ] identified 221 de novo DNA motifs associated with unmethylated regions, and 92 motifs associated with methylated regions. Using data from previously published studies, the authors found that DNA motifs associated with methylation were enriched in meQTLs variants, especially near TSSs.

Lastly, trans -meQTLs results show that the number of inter-chromosomal trans -meQTLs is usually proportional to the number of genes in a chromosome, except for chromosomes 16 and 19 which are highly enriched for trans -meQTLs and chromosome 1 which is depleted for trans -meQTLs [ 37 , 53 ]. Also, McRae et al. [ 53 ] estimated that almost 25% of trans -meQTLs are located in telomeres and sub-telomeres. The major histocompatibility complex (MHC) region is another locus that harbors highly heritable CpGs and meQTLs associated with multiple CpGs [ 33 , 76 , 77 ].

Tissue-specificity of meQTL effects

DNA methylation plays an important role in cell lineage and tissue differentiation, resulting in tissue-specific methylation profiles over a considerable proportion of the methylome. Most meQTL studies explore whole blood, but analyses within specific cell types or bulk tissue have also been carried out.

MeQTLs in blood-based samples

Most studies have identified meQTLs in blood and blood-derived cells, including whole blood, LCLs, peripheral blood mononuclear cells (PBMC), and leukocytes (see Table  2 ). Blood-based meQTLs studies are most common to date, have larger sample sizes, and have shown high replicability. The majority of blood reports are not limited to the discovery of novel meQTLs alone, but also include study designs that integrate DNA methylation findings with GWAS results or other biological data. In the largest study to date, the Genetics of DNA Methylation Consortium (GoDMC), a multi-cohort meta-analysis meQTL resource, combined data from 32,851 blood samples across different population cohorts and found that 45.2% of CpGs in the 450K array have meQTLs, with greater effect sizes for cis associations [ 50 ]. Additionally, the authors detected substantial sharing between meQTLs and GWAS signals, and constructed a network of CpG sites that share meQTLs, identifying 405 highly interconnected genomic communities enriched for regulatory genomic features and links to complex traits. Huan et al. [ 35 ] performed an analysis in 4170 whole blood samples, identifying 4.7 million cis -meQTLs (within 1 Mbp of target CpG) and 706 thousand trans -meQTLs. After a follow-up analysis, the authors found 92 CpGs with a likely causal role in cardiovascular disease, as well as supporting evidence of CpG-expression contribution to these putative causal pathways. Likewise, Bonder et al. [ 51 ] studied trans -meQTLs focusing on SNPs previously associated with complex traits. Using 3841 whole blood samples from the Netherlands, they showed that one-third of the analyzed SNPs affect DNA methylation levels at 10,141 CpG sites in trans , and where 95% of trans -meQTLs were validated in external data from 1748 lymphocytes. Furthermore, the authors provided several examples of trans -meQTLs with effects on specific transcription factors levels as well as methylation of their binding sites across the genome. Chen et al. [ 78 ] identified cis -meQTLs in immune cells (CD14 + monocytes, CD16 + neutrophils, and naive CD4 + T cells) at almost 10% of the CpG sites from the Illumina 450K, and estimated relatively low blood cell specificity of meQTLs especially between myeloid cells.

Blood-based genome-wide meQTL studies (sample size > 100) in whole blood or blood-derived cell samples

a If not specified, the sample type is whole blood. If more than one sample per analysis, the pooled size and number of samples is reported

b In parenthesis, maximum or minimum distances are indicated for cis and trans analysis, respectively. The range of results is presented if more than one analysis was done (unless otherwise stated)

c Multiple-testing criteria, with the corresponding p -value threshold for cis and trans meQTLs (where it differs). Different approaches to estimate FWER and FDR are as follows:

1 FWER based on Bonferroni correction

2 FWER based on Holm-Bonferroni correction

3 FDR based on permutations

4 FDR based on Benjamini-Hochberg correction

d Reference panel for imputations

e Database or biobank

FWER family-wise error rate, FDR false discovery rate, LCL lymphoblastoid cell lines, WGS whole genome sequencing, MCC-seq methylC-capture sequencing, WGBS whole genome bisulfite sequencing, MBD-seq methyl-CpG-binding domain sequencing, 1000G 1000 genotypes, GoNL Genome of the Netherlands, TF transcription factor, ASM allele-specific methylation

MeQTLs in non-blood-based tissues and cells

Genome-wide meQTLs have also been identified in a range of tissues including several regions of the brain, lung, skeletal muscle, buccal and saliva samples, placenta, and adipose tissue (Table  3 ). The discovery of meQTLs across brain regions [ 57 , 71 , 79 , 80 ], their overlap with non-brain tissue findings [ 70 , 73 ] (see the “ Tissue-shared meQTLs-CpGs ” section), and their co-localization with other molecular QTLs [ 81 ] has initiated further studies to identify and characterize the role of genetic variants underlying neurological disorders. In lung tissue, Morrow et al. [ 82 ] investigated meQTLs that may impact the pathogenesis of chronic obstructive pulmonary disease in 90 cases and 36 controls. The authors found cis -meQTLs at 10% of the 450K CpGs, and significant overlaps with GWAS signals for the disease. In parallel, Taylor et al. [ 83 ] assessed 282 samples of skeletal muscle on the Illumina EPIC array and found cis -meQTLs for almost 21% of CpGs. In adipose tissue, Grundberg et al. [ 31 ] ( n =603, from UK females) and Volkov et al. [ 76 ] ( n =119, from Scandinavian males) identified the cis and trans genetic effects on the methylome profiled by the 450K array. Both studies identified meQTLs that may also be involved in metabolic traits, such as variants in the ADCY3 gene, associated with obesity and BMI.

Overview of published genome-wide DNA methylation quantitative trait loci studies in blood-independent sample types

a We account for the different association analyses, even if they are published in the same paper

b If more than one analyses is available, the range is presented

Tissue-shared meQTLs-CpGs

The majority of DNA methylation signatures are tissue-specific and reflect the developmental trajectories of each cell line [ 13 ]. However, when DNA methylation levels are partially or fully driven by genetic variants, DNA methylation levels and meQTLs effects can be tissue-specific or they can also be shared across tissues. Several studies have explored this question, focusing on how easily accessible tissues such as blood may be used as proxies for the indirect study of difficult-to-reach tissues. In a report including samples from T cells, temporal cortex, neurons, glia, and placenta profiled with the 450K array, Do et al. [ 70 ] found good overlap in the percentage of meQTL-associated CpGs between temporal cortex with those in neurons/glia (61%) as expected, but not with T cells (28%) or placenta (12%). However, the study explored in a small to moderate sample size ( n ≤54 for each sample type), and consequently had limited power for detection of modest effects and their tissue-specificity assessment. Lin et al. [ 73 ] explored meQTLs in 197 saliva samples from control and schizophrenia/schizoaffective disorder patients and compared their results with two previous studies in brain and blood samples. They estimated that 38–73% of the meQTL variants in each tissue are shared with another and that most have a consistent effect direction across tissues. They found that 31–68% of the significant CpGs harboring meQTLs in a certain tissue are also significant in at least one other tissue. From these results, the tissues that share most meQTLs or most CpGs with meQTLs—with at least one other tissue—were blood and saliva. Another interesting observation was that tissue-shared signals were enriched in genetic risk loci of diseases such as schizophrenia, as well as in cross-tissue eQTLs (i.e., eQTLs significant in both blood and brain tissue). Similarly, Qi et al. [ 84 ] assessed the correlation of genetic effects at the peak cis -meQTLs in blood and brain from five data sets profiled on the Illumina 450K array. The correlation of meQTL effects between two sets of samples profiled in the same tissue was strong (correlation coefficient r b ^ = 0.92 for both blood and brain sample types), and lower, although still considerable, between brain and blood samples ( r b ^ = 0.78 ). Other cross-tissue meQTL analyses have also included comparisons between blood, brain, adipose tissue, breast, kidney, and lung samples [ 50 , 56 , 65 , 85 , 86 ].

Although no clear consensus currently exists in the estimated proportion of tissue-shared meQTLs, increasing evidence shows that a major subset of meQTL-CpG pairs are indeed shared among multiple tissues and cell types.

MeQTLs databases

Several efforts have attempted to create databases of meQTL findings. One of the first online repositories that incorporated results from GWAS of DNA methylation was GRASP, where the current build has 52,419 meQTLs records [ 87 , 88 ]. In 2015, Relton et al. [ 89 ] constructed the Accessible Resource for Integrated Epigenomic Studies (ARIES), summarizing findings from DNA methylation analysis of 1018 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC). The resource also includes one of the few longitudinal meQTL studies to date, complementing the original database [ 37 ]. The Brain xQTL Serve is another resource that reports results of genetic variation in three molecular traits—gene expression, DNA methylation, and histone acetylation—from prefrontal cortex samples of two longitudinal aging cohorts [ 57 ]. In cancer research, the Pancan-meQTL [ 72 ] and DNMIVD (for DNA Methylation Interactive Visualization Database) [ 90 ] use data from The Cancer Genome Atlas (TCGA). Pancan-meQTL reports 8028 cis and trans -meQTLs identified in 7242 samples from 23 different tumor types, while DNMIVD complements the Pancan-meQTL findings with additional analyses, such as diagnostic and prognostic models, and pathway-meQTL. Hannon et al. [ 38 ] published an interactive database of meQTLs from a blood-based study in 1111 samples, along with putative pleiotropic associations of meQTLs and multiple traits. Altogether, QTLbase is probably the most comprehensive resource to date in different sample types. It compiles summary statistics for molecular QTLs from 233 studies, with meQTL associations representing 16% of the database and summarizing results from 39 meQTLs publications in different tissue types [ 91 ]. In blood specifically, the GoDMC resource [ 50 ] includes an online searchable tool with a full list of meQTLs from the largest blood meQTL study to date (see the “ MeQTLs in blood-based samples ” section).

Genetic effects on DNA methylation: potential underlying mechanisms

Cis -meqtl mechanisms.

Despite the identification of hundreds of thousands of associations between meQTLs and CpGs, the molecular mechanisms underlying meQTLs are not well characterized. The leading hypothesis to explain cis -meQTL effects is that SNPs in protein binding sites alter or disrupt the activity of sequence-specific binding proteins—such as transcription factors (TFs)—and change methylation patterns of nearby CpGs, either directly or through a signaling cascade [ 59 , 67 , 70 , 92 , 93 ]. In support of this hypothesis, Banovich et al. [ 67 ] showed that for meQTLs in TF binding sites (TFBSs), different alleles predicted to affect affinity of TF binding were correlated with methylation levels at nearby CpG sites. Wang et al. [ 75 ] also showed consistent findings by identifying DNA motifs associated with methylation levels, as previously described (see the “ MeQTLs are differentially distributed across the genome ” section). The authors profiled binding profiles of 845 TFs and concluded that TFs can interact with DNA motifs that are also associated with DNA methylation levels. These results are also in concordance with mechanisms reported to underlie other DNA regulatory pathways and their QTLs, such as histone modifications and RNA polymerase II [ 94 ].

The signaling pathways triggered by sequence-specific binding proteins are still under discussion, but the main premise is that if a TFBS is occupied, this could be enough to prevent DNA methylation changes in the vicinity of this TFBS. This would represent a form of passive control of genetic variation on DNA methylation, via TFBS occupancy (Fig.  2 a). Alternatively, TFs could recruit DNMT3A and TET enzymes for active methylation or demethylation (Fig.  2 b). This is supported by the observation of an overlap of TFBS with methylation-associated DNA sequence motifs [ 75 ].

Mechanisms underlying cis -meQTL effects. a Passive mechanism. Under normal conditions a sequence-specific binding protein (such as CTCF) can bind to its target and prevent methylation changes at surrounding CpG sites due to its occupancy. If a meQTL disrupts the site, the protein cannot bind successfully, and the CpG sites are prone to change in baseline methylation status. b Active mechanism. If a meQTL is located in a TFBS, lack of TF binding can promote the recruitment of DNMT or TET enzymes, and thus modify the methylation status of nearby CpG sites

One of the main examples in support of the hypothesis of passive genetic control on DNA methylation is CTCF (CCCTC-binding factor), which is an insulator involved in chromatin regulation, forming loops and bringing together genetic elements that may be physically far apart. CTCF binding sites usually contain CGI motifs and have to be poorly methylated to allow for the recruitment of the protein [ 95 ]. The occurrence of a meQTL within the CTCF binding site may result in a decrease or even annulment of CTCF binding affinity, which in turn can lead to an increase in DNA methylation of nearby CpG sites, as shown in the mouse methylome [ 96 ]. Multiple studies have now highlighted CTCF binding as a key example of cis genetic-epigenetic interactions [ 56 , 70 , 71 , 79 , 97 ].

An example of cis -meQTL active mechanisms involves a genetic variant within the gene underlying a clinical subgroup of colorectal cancer known as MSI+ (or microsatellite-unstable cancer). Here, decrease of gene expression of the DNA mismatch repair gene MutL homolog 1, MLH1 , is due to hypermethylation of its promoter. The A allele of variant rs1800734 in the 5 ′ UTR of MLH1 modifies the binding of TFAP4 activating the BRAF/MAFG pathway, which increases DNMT3B-mediated methylation of the MLH1 promoter [ 98 ]. Another example of active genetic-methylation interplay is a mechanism suggested to underlie a type 2 diabetes (T2D) susceptibility locus [ 99 ]. The T allele of rs11257655 in the CAMK1D gene decreases DNA methylation in CAMK1D promoter as a meQTL, increases CAMK1D expression as an eQTL, and increases T2D risk as T2D GWAS signal. The authors propose that in the presence of the T allele at rs11257655, a protein complex formed by FOXA1/FOXA2 and other TFs binds to an enhancer of CAMK1D , which leads to demethylation of cg03575602 in the CAMK1D promoter and in turn upregulates its expression.

Trans -meQTL mechanisms

Many mechanisms have been hypothesized to underlie trans -acting meQTLs effects, but to date, very few clear examples have been uncovered. The simplest hypothesis is that SNPs that act as eQTLs of global methylation regulators, or their associated elements, have downstream effects as meQTLs at multiple CpG sites genome-wide (Fig.  3 a). For example, Lemire et al. [ 55 ] documented the case of SUMO-specific protease 7 (SENP7), which interacts with epigenetic repression proteins. Intronic variants located in SENP7 gene are cis -eQTLs, and high levels of the transcript decrease methylation at several trans -CpGs. Another case is variant rs12933229 associated with expression of RRN3P2 , a pseudogene that regulates DNA methylation through piwi-interacting RNAs (piRNAs).

Mechanisms underlying trans -meQLTs effects. a eQTL-mediated mechanism. If a SNP acts as an eQTL for a gene that regulates DNA methylation, the SNP can have an indirect effect on multiple CpG sites in trans . b Cis -meQTL-mediated mechanism. If a SNP is a cis -meQTL for nearby CpG sites, which in turn impact the expression of genes involved in epigenetic regulatory processes, the SNP can ultimately alter DNA methylation levels at CpG sites in trans . c 3D organization mechanism. In the 3D genome, distal sites can move in close proximity, whereby a SNP can affect a DNA methylation levels at CpG sites in trans , acting either through cis -meQTL mechanisms, or by disrupting the formation of structural loops. d SNPs in the coding regions of methyl-specific binding proteins (such as MeCP2) can alter their specificity and function, and therefore passively or actively (by recruiting DNMTs or TETs) modify DNA methylation of their binding sites

Other findings suggest that distal effects may be mediated, in total or in part, by cis -meQTL-associated CpGs (Fig.  3 b). For instance, one-third of the 585 trans -meQTL-CpG pairs identified by Shi et al. [ 56 ] in lung tissue showed weaker associations after conditional analyses, conditioning on the cis -regulated CpGs by the same SNPs. In 166 trans -meQTL associations, the authors found a partial mediation of cis effects, with lower but still significant partial correlations compared to marginal correlations, and in 30 associations, they found a full mediation, with no significant correlations after conditioning for cis -meQTLs. Genes for GTPase or related enzymes involved in DNA methylation regulation, were over-represented for such cis -CpGs. Therefore, one potential mechanism underlying trans -meQTL effects is that a meQTL may act on nearby CpGs, which then impact the expression of genes that eventually may modify DNA methylation levels at distal sites.

Three-dimensional (3D) genome conformation changes would be an alternative track for the action of trans -meQTLs, since distal loci can be brought into physical proximity by 3D structures [ 100 ]. Hence, either SNPs in TFBSs acting as cis -meQTLs, or SNPs in sites that anchor cohesins and CTCF that integrate topologically associating domains (TADs) and loops, could have an impact on remote CpGs as they move in closer proximity in complex 3D DNA structures (Fig.  3 c) [ 26 ]. Furthermore, the 3D organization of the DNA includes inter-chromosomal contact, which would be the source of a fraction of meQTLs associations—as demonstrated by high-resolution Hi-C data that CpGs overlap with binding sites of architectural proteins (e.g., CTCF, RAD21, and SMC3) [ 51 ], and with a two-dimensional functional enrichment [ 50 ].

Other explanations involve sequence-specific binding proteins, similar to mechanisms for cis -meQTLs, but instead of the genetic variant being located in TFBS, here, the SNPs interfere with the coding or cis -regulatory regions of the TFs, and thus their subsequent expression, coupling, and function (Fig.  3 d). The results of Bonder et al. [ 51 ] point in this direction. The authors found that 13.1% of the trans -meQTLs that they detected also altered the expression of TFs, and those affecting multiple CpGs had consistent direction of effects, either increasing or decreasing methylation at most of CpGs. A representative example is rs3774937 in the intron of the TF NFKB1 , which is a trans -meQTL for 413 CpG sites genome-wide. In 380 CpG sites, the rs3774937 alternative allele was associated with lower methylation levels, and 147 of those CpG sites were in NF- κ B binding sites.

The same mechanism could also apply to the activity of proteins other than sequence-specific binding proteins, although this theory remains mostly unexplored so far. For example, it is well known that DNA binding of some proteins is methylation-dependent through a methyl-CpG-binding domain (MBD), such as for the MeCP2 (methyl-CpG-binding protein 2). MeCP2 regulates DNMT3A allosterically, acting as a repressor or an activator of the methylation process [ 101 ]. However, some mutations in the MeCP2 gene decrease selectivity of the MeCP2 binding [ 102 ], and consequently, could lead to untargeted methylation at several distal sites. This idea can also be extended to proteins without MBD, as emerging evidence suggests [ 103 ]. This may also complement the sequence-specific binding sites theory and thus explain more trans -meQTLs.

MeQTLs and mechanisms underlying human disease

Many research efforts have linked meQTLs to genetic variation underlying human complex traits. MeQTLs are significantly enriched for GWAS signals, with evidence for shared genetic effects [ 50 ]. Multiple studies have explored the directionality of shared genetic associations, applying causal inference approaches typically exploring the potential role of DNA methylation as a mediator of genetic effects on phenotypes [ 38 , 50 , 104 ]. However, despite a substantial sharing of genetic effects, the findings reveal a more complex genetic architecture including putative evidence for both mediation effects of DNA methylation on phenotypes, as well as effects of complex traits on methylation (see the “ MeQTLs and GWAS ” section).

Nevertheless, the discovery of meQTLs has contributed to the advancement of our understanding of the molecular pathways underlying certain human phenotypic traits and diseases, which may eventually help towards the development of therapeutic targets. Examples include a thorough investigation of previously identified genetic signals for Alzheimer’s disease involving the promoter region of gene PM20D1 and meQTLs (rs708727–rs960603 haplotype) [ 105 ]. With a series of in silico, in vitro, and in vivo experiments, Sanchez-Mut et al. determined that meQTLs interact with the promoter of PM20D1 through haplotype-dependent 3D chromatin conformations via CTCF, changing DNA methylation levels, altering gene expression, and ultimately protecting or aggravating neurodegeneration. In another study focused on characterizing osteoarthritis risk variants in cartilage samples, Rice et al. [ 106 ] found four meQTLs for 17 CpGs. In vitro studies of the prioritized locus suggest potential DNA methylation and gene expression mechanisms altering the function of the PLEC and GRINA genes, which have not been previously described in context of osteoarthritis. Similarly, meQTLs have helped to elucidate biological pathways underlying other diseases such as Parkinson’s disease [ 107 ], multiple sclerosis [ 108 ], colorectal cancer [ 98 ], and T2D [ 99 ] (see the “ Cis -meQTL mechanisms ” section), along with complex phenotypes such as platelet function [ 109 ], fatty acid levels [ 110 ], and others.

Challenges and future directions

Methodological and statistical caveats, dna methylation profiling.

The vast majority of meQTL studies to date explore DNA methylation levels profiled by Illumina DNA methylation arrays, which are relatively low-cost and highly standardized. However, array-based DNA methylation profiles can be subject to bias introduced by errors from cross-hybridization events, as well as batch and positional effects. For example, positional effects have been reported to impact a larger proportion of 450K probes, compared to 27K probes [ 111 ]. In addition, both the 450K and EPIC arrays contain two different types of probes with different dynamic ranges [ 112 ]. Several methods have been developed to minimize bias introduced by these potential array effects [ 113 – 116 ], as well as comparisons across methods, which provide useful frameworks for the design of quality control and normalization of Illumina-based DNA methylation profiles [ 117 – 119 ]. Further work has also focused on guidelines for exclusion criteria of low-performing probes [ 120 – 122 ], or has explicitly flagged unreliable probes due to cross-reactive events or underlying genetic variation [ 123 ].

As previously discussed, genome coverage is a key consideration in DNA methylation profiling technologies, and here the ultimate aim is to characterize meQTLs across the entire methylome. With most studies based on array DNA methylation profiles, the EPIC array provides a reasonable cost-coverage balance with increased coverage of regulatory elements compared to the 450K. Despite the improvement in coverage by the EPIC array, regulatory regions included on the EPIC only comprise 27% of cis and 7% of trans regions characterised by ENCODE [ 123 ]. This, combined with the limited methylome coverage, should also be considered when generalizing meQTL findings to whole genome.

On the other hand, WGBS allows for comprehensive profiling of the methylome, but the high costs are still restrictive and prevent its broad application in meQTL studies. Also, some genomic regions and difficult to sequence and library preparation protocols are technically complex and may be subject to bias from multiple sources, such as bisulfite conversion, PCR amplification, DNA modifications, and degradation [ 124 , 125 ]. An important parameter to define in a WGBS experiment is the sequencing depth. The recommended depth coverage based on data from the NIH Roadmap Epigenomics Project [ 126 ] and the International Human Epigenome Consortium (IHEC) [ 127 ] is 30×. In order to optimize costs while maintaining acceptable rates of specificity and sensitivity, Ziller et al. [ 128 ] proposed a minimum coverage per sample of 5–15 × for the discovery of differentially methylated regions (DMRs). Nonetheless, coverage of 100× would be required to have similar precision to that in Illumina arrays [ 124 ]. In light of these estimates, WGBS in large-scale samples currently still poses significant challenges, but represents a promising method for future meQTL analyses, especially for studying regions of the genome underrepresented in microarrays.

Statistical models

The choice of statistical model for meQTL analysis is important. Most meQTL studies apply linear regression, but at many CpG sites the distribution of DNA methylation values does not meet its assumptions, which may in turn increase the error rate (both type I and II). Recently, Mansell et al. [ 129 ] quantified the extent of bias in epigenome-wide association studies (EWAS) using the EPIC array due to non-linearity between variables, non-normal distribution of residuals (skewness and kurtosis), and heteroskedasticity. The authors concluded that even CpG sites with extreme deviation to linear regression assumptions do not result in major bias. By extension, this observation could also apply to meQTL studies. Interestingly, the same study did not find better performance when using M -values instead of β -values in DNA methylation analysis. Ultimately, a higher selectivity of the CpG sites to test—such as filtering out probes with low β -values variability—would leverage the statistical confidence of the models and maximize reproducibility of results, as recommended by Logue et al. [ 122 ].

Multiple-testing correction

One major consideration in meQTL analyses is the multiple-testing correction, given the large number of tests in comparing millions of genetic variants against typically at least hundreds of thousands of CpG sites. On the one hand, the multiple testing correction must be computationally efficient, and on the other hand, the aim is to maximize statistical power to detect low or modest effects.

A considerable amount of studies apply permutation-based multiple testing thresholds to quantify an empirical false discovery rate (FDR) [ 130 ]. Typically, this approach consists of randomizing the genotypes to generate an approximate null distribution of p -values obtained in a large number of association tests between the permuted genotypes and CpG sites. The FDR is the ratio of associations in the permuted data to those observed at a specific nominal significance threshold [ 131 ]. Because permutations are computationally demanding, methods as FastQTL and QTLtools [ 132 , 133 ] have proposed variations of the original technique, such as drawing a few thousand permutations and modeling the resulting p -values with a beta distribution to approximate the null distribution. This approach has been adopted by some meQTLs analyses [ 83 , 93 ]. Moreover, some analyses have reduced the number of permutations, for example to a hundred [ 60 ] or even ten [ 51 , 82 ], a decision supported by eQTL results about the stability of the FDR value with as few as five permutations [ 134 , 135 ]. For example, cis -meQTL analysis by van Dongen et al. [ 39 ] in buccal cells of MZ twins applied the aforementioned method with ten permutations, conserving relatedness between twins by permuting twin pairs samples rather than individuals.

Other studies have applied the conservative Bonferroni multiple testing correction to control the family-wise error rate (FWER), adjusting for the total number of SNPs-CpGs pairs tested, resulting in stringent multiple testing significant thresholds (e.g., p <1×10 −10 ) [ 35 , 37 , 136 ]. However, the Bonferroni multiple testing correction does not take into account linkage disequilibrium (LD) between genetic variants or patterns of co-methylation, that is, the correlation in DNA methylation levels at nearby CpG sites [ 137 ]. To tackle LD, McRae et al. [ 53 ] and Hannon et al. [ 38 ] employed a Bonferroni threshold based on the GWAS canonical value 5×10 −8 —which accounts for LD blocks—and divided this threshold by the number of tested probes, while Smith et al. [ 86 ] adopted a Holm-Bonferroni method—or a step-down Bonferroni—which increases the power. To take into account co-methylation, it has been proposed that for the Illumina EPIC array in whole blood, an appropriate choice of the number of independent probes to control the FWER would be 530,639 (66% of total sites) [ 129 ].

MeQTL analysis strategies that use the Bayesian framework [ 138 ] or a multivariate normal distribution [ 139 ] have also been applied to other molecular QTL studies, and appear promising to explore in future meQTLs analyses.

Detection of common and rare variant meQTLs

Since publication of the first genome-wide meQTL studies, sample sizes have increased dramatically and with them power to detect small effects of common genetic variants on methylation. However, the detection of rare genetic variants is still a major challenge in meQTL studies. Almost all genome-wide meQTL studies discard SNPs with a minor allele frequency (MAF) less than 0.05 or 0.01, while the high penetrance of rare variants in certain complex traits highlights their biological importance [ 140 ].

The most widely implemented approaches for assessing effects of rare genetic variants on human complex traits are collapsing methods. The premise is that all the variants within the boundaries of a functionally meaningful locus would induce the same phenotypic change [ 140 ]. To our knowledge, only the study by Richardson et al. [ 141 ] has so far examined rare variants in meQTL analysis in blood samples. The authors collapsed variants with MAF ≤0.05 around CGIs (alone and with flanking shores/shelves) and carried out the Sequence Kernes Association Test (SKAT) testing for genetic influences on CpG sites from the 450K array. The results identified 94 unique cis -acting and one trans -acting regions, which were not previously linked to methylation. This novel approach can be leveraged in future meQTL analyses by the definition of other functional units for collapsing regions, testing previously identified meQTL regions after conditional analysis, and application to data from the EPIC array or WGBS.

Gene–environment interactions

Environmental exposures can leave a clear signature on DNA methylation patterns, as observed for smoking [ 18 , 142 , 143 ] and alcohol consumption [ 19 ]. Some environmental exposure or lifestyle factors and behaviors also have a genetic component that explains a proportion of their variance [ 144 , 145 ]. Therefore, to explore the interplay between genetic variation, environmental exposures, and epigenetic changes, some studies have considered gene–environment (G ×E) interaction terms in meQTL analyses.

To date and to our knowledge, no genome-wide G ×E analysis of DNA methylation has yet been published. However, G ×E analyses at candidate genomic regions have been described in several studies. For example, Teh et al. [ 146 ] studied the interaction of genetic effects and in utero environment in 237 umbilical cord samples in Asian neonates. Firstly, the authors identified 1423 variably methylated regions (VMRs) across individuals, based on the median absolute deviation of the DNA methylation levels in each CpG site. Then, to explore triggers of DNA methylation changes at each VMR, the authors assessed DNA methylation effects as a function of (1) genotype alone, (2) intra-uterine environment alone, or (3) the G ×E interaction. The intra-uterine environment was quantified through 19 parameters, including maternal smoking, maternal depression, and concentrations of compounds in maternal serum. Interaction models of genotype with different in utero environments had better performance at 75% of the VMRs compared to main-effects models, and therefore better explained the variability in DNA methylation. In two other studies [ 60 , 147 ] of monocyte samples from European and African populations, the authors suggested that some of the meQTLs may in fact be occurrences of G ×G and G ×E interactions (see the “ MeQTLs are differentially distributed across the genome ” section). The cis analysis uncovered 69,702 CpGs with meQTLs, and of these, 4.1% displayed different effects across the two populations, which may reflect G ×G or G ×E interactions.

Several studies have explored G ×E effects involving smoking status and genetic variants at candidate loci in the context of meQTLs and complex disease. Meng et al. [ 148 ] provide an example of a candidate G ×E effects linked to rheumatoid arthritis, involving genetic variants in the MHC and smoking status. They observed an effect of rs6933349 on cg21325723 (located in the body of the TSBP1 gene), only in current smokers. Further examples include the study of Klengel et al. [ 149 ] who investigated a G ×E interaction in FKBP5 , a gene that regulates the glucocorticoid receptor—a major component of the stress hormone system. The transcriptional activation of FKBP5 as a response to childhood abuse depends on genetic variants (rs1360780) that alter the 3D conformations of the locus; the expression of the gene is mediated by the demethylation in intron 7, a change that is long-term stable and has implications in stress disorders. A similar pathway potentially underlies methylation at SLC6A4 (serotonin transporter gene) [ 150 ].

The implementation of G ×E meQTL analyses entails challenges, such as substantially larger multiple testing burden and limited power [ 151 ]; however, it represents a promising niche to explore that may account for a fraction of missing heritability in DNA methylation.

MeQTL impacts on DNA methylation variance

Conventional statistical methods applied to QTL analysis aim to identify significant deviations of the trait mean between the subjects in different genotype groups, typically with the assumption of equal variance across groups (i.e., homoskedasticity). Recently, new perspectives in QTL studies have explored QTLs that influence the phenotypic variability across genotype groups, or variance QTLs (vQTLs/varQTLs). VarQTL may capture interaction effects, such as epistatic (G ×G) or G ×E [ 152 ]. Methods for detecting varQTLs include parametric and non-parametric tests, including Bayesian and family-based approaches [ 152 – 154 ]. One of the most comprehensive studies on varQTLs was carried out recently by Wang et al. [ 155 ], with genotype data from 348,501 participants from the UK Biobank and across 13 quantitative traits—including obesity-related, height, and lung function measures. The results show a total of 75 varQTLs for nine traits (54 varQTLs related to obesity) located in 41 nearly independent loci. Moreover, the authors found two varQTLs with possible non-additive effects on the variance, 66 varQTLs that also have an effect on the mean of the trait with the same direction, and 16 varQTLs that are explained by G ×E interaction models.

So far, only few studies have explored varQTLs in the context of molecular datasets, such as DNA methylation or gene expression profiles. For example, Brown et al. [ 156 ] examined variability of 13,660 genes in 765 LCL samples from the TwinsUK cohort, identifying 508 var-eQTLs in cis , of which 36% were also eQTLs. They then searched for variants interacting with each of the var-eQTLs within the same cis window in order to identify epistatic interactions, and found 256 G ×G signals, of which 57 replicated in another cohort. They also suggested that 70% of var-eQTLs may be the result of G ×E interactions based on analyses focusing on gene expression differences in MZ twins. In a methylome analysis with the 450K array in 729 peripheral blood leukocytes samples from individuals of Swedish descent, Ek et al. [ 157 ] estimated a total of 374,252 CpG-var-meQTL pairs, or 7195 unique CpGs with at least one var- cis -meQTLs. At almost all of these CpGs, there was also evidence of cis -meQTL effects, and after adjusting methylation levels for cis -meQTLs, the authors no longer found variance heterogeneity at the majority of CpGs. As a result, they conclude that a considerable proportion of varQTLs (92%) may be statistical artifacts attributed to SNPs in LD, rather than real biological interactions, and that var-meQTLs are unlikely to explain missing heritability.

Future studies are needed to replicate these var-meQTLs results, explore mechanisms driving these effects, and potentially identify novel signals.

Integrating meQTL results in association studies

Meqtls and ewas.

EWASs aim to systematically associate variation in DNA methylation levels across the genome with variation in phenotypes or environmental exposures. However, significant associations between DNA methylation levels and phenotypes may arise due to confounding effects of meQTLs, and most EWASs do not take meQTLs into account. Adjustment of DNA methylation values for meQTL effects prior to EWAS has been proposed to tackle this issue [ 158 , 159 ]. Chen et al. [ 78 ] applied this approach in EWASs of gene expression levels genome-wide, or in expression quantitative trait methylation (eQTM) analyses. The authors quantified the contribution of DNA methylation to gene expression variance through a variance decomposition model and found that DNA methylation explained a lower proportion of the variance in models adjusted for underlying genetic effects, compared to unadjusted models. Subsequently, they performed EWASs with two models—either not correcting for or correcting for cis -genetic effects. Over half of the genes associated with epigenetic marks in the uncorrected model did not reach significance in the corrected model. Although meQTLs effects were not directly assessed in this study, these findings may extend to meQTLs. In another study, Krause et al. [ 160 ] aimed to validate two candidate CpGs associated with T2D, but found a significant association between BMI and blood methylation only after correcting for genotype at rs9982016, which was a cis -meQTL at one of the candidate CpGs.

Another relevant application of integrating meQTLs in EWAS is to gain insight into the putative causal direction of association between DNA methylation signals and the associated phenotypes by using Mendelian randomization (MR). MR evaluates the likelihood that a phenotype is the consequence of an exposure, which in turn is the result of genetic variation (or the instrumental variable) [ 161 ]. In context of epigenetic analyses, meQTLs are instrumental variables, DNA methylation levels are exposures, and diseases or phenotypes are the outcomes [ 104 ]. Multiple studies have applied MR using meQTLs in EWAS across a range of phenotypes [ 162 – 166 ]. For example, in an EWAS of BMI in 3743 blood 450K methylomes from older adults and with replication, Mendelson et al. [ 167 ] identified 83 DMSs and their associated meQTLs. Follow on MR identified two CpGs (cg11024682 in SREBF1 , and cg07730360, unannotated) with nominally significant putative causal effects of DNA methylation on BMI. In contrast, they identified 16 CpG where DNA methylation levels are likely mediated by BMI after a reverse MR model. Using a similar approach, Dekkers et al. [ 168 ] analyzed if exposure to elevated blood lipids affected DNA methylation levels in immune cells, in 3296 450K methylomes from six Dutch biobanks. The authors identified 21 DMSs for triglycerides (TG) levels, three for low-density lipoprotein cholesterol (LDL-C) and four for high-density lipoprotein cholesterol (HDL-C). Follow on MR analysis identified putative causal effects of lipid levels on 13 DMSs. To exclude pleiotropy (SNPs acting as QTLs for multiple lipid levels, or as cis -meQTLs in DMSs) and reverse causation ( cis -meQTLs affecting DMSs, and DMSs affecting lipid levels), the authors conducted secondary MR analysis. The results confirmed that TG likely induced differential methylation at three CpGs, LDL-C at one, and either TG or HDL-C at two. Mendelian randomization has also been applied when integrating meQTL results and GWAS (see the “ MeQTLs and GWAS ” section).

In addition, the combination of EWAS and meQTL signals can be used to explore G ×E interactions. For instance, Tsaprouni et al. [ 169 ] found that almost half of smoking-associated loci have meQTLs. Subsequent analyses fitting G ×E interaction effects identified a CpG (cg03329539 located in chromosome 2) where methylation response to cigarette smoking was modulated by rs62192178 genotype.

MeQTLs and GWAS

Although thousands of GWAS results have been published to date, the identification of causal variants and their functional interpretation remains mostly outstanding. Furthermore, GWASs also face the “missing heritability” problem and epigenetic signals (potentially, through meQTLs) might explain a proportion of the phenotype missing heritability [ 27 , 170 ]. Therefore, integrating meQTL findings as a post-GWAS analysis can help to address some of these challenges.

One approach for this integration is to use meQTLs findings to prioritize GWAS signals for follow on analysis, for example, as applied in a study of autism spectrum disorders in 1263 infants by Hannon et al. [ 171 ]. The authors estimated that 91 SNPs associated with the disease were also meQTLs, based on a Bayesian co-localization analysis. Their results highlight specific variants to target in subsequent studies since they may have a functional role in autism pathophysiology. Morrow et al. [ 82 ] implemented a similar Bayesian framework to identify meQTLs that are also chronic obstructive pulmonary disease (COPD) GWAS signals (see the “ MeQTLs in non-blood-based tissues and cells ” section). Their findings identified 20 SNPs with suggestive evidence of co-localization, highlighting novel regions of interest in addition to previously identified COPD signals, such as KCNK3 and EEFSEC .

MR analyses have also been adopted to integrate meQTL and GWAS results. Richardson et al. [ 172 ] assessed putative causal effects at 30,328 CpGs in 139 complex traits based on previously published cis -meQTL and GWAS results. The authors assessed the fit of several models spanning: (1) a forward MR model where the DNA methylation level impacts the phenotype; (2) a joint likelihood mapping, to exclude genetic variants in LD independently influencing DNA methylation and phenotype; and (3) a reverse MR model to exclude cases where DNA methylation is the outcome. A final set of 346 CpG sites were identified as potentially causal across 46 traits, ultimately highlighting specific biological pathways and suggesting potential drug targets. Similar analyses have also been undertaken within specific phenotype domains by multiple other studies, including Huan et al. [ 35 ], Bonder et al. [ 51 ] and Chen et al. [ 78 ]. In the largest analysis so far, Min et al. [ 50 ] found a significant substantial enrichment of meQTLs with the GWAS signals in 13 of 37 phenotypes GWAS datasets assessed, especially for SNPs acting as both cis and trans -meQTLs. However, after multiple causal inference analyses, the authors observed that only for a minority of cases DNA methylation exhibited mediating effects of GWAS signals in complex traits, and vice versa. These directionality results have several interpretations, including the possibility that other molecular mechanisms may explain a proportion of the observed shared genetic signals.

Shared QTL effects on multiple regulatory genomic processes

Regulatory genomic changes capture multiple molecular processes across different layers of epigenetic data. Comparison of meQTLs with QTLs for different biological profiles is a promising route to infer regulatory potential. In spite of the considerable amount of studies that jointly consider DNA methylation and gene expression data, relatively few studies have explicitly compared eQTLs and meQTLs genome-wide. Such comparisons have been based on either summary statistics of published studies [ 92 , 173 ] or de novo associations [ 49 , 51 , 59 , 67 , 78 , 83 , 93 ]. Overlapping results can be used to identify pleiotropic effects for DNA methylation and expression and explore directionality of these effects, such as SNP →methylation →expression (active) or SNP →expression →methylation (passive). For example, Gutierrez-Arcelus et al. [ 59 ] inferred that DNA methylation can have both active and passive roles in gene expression regulation across fibroblasts, T cells and lymphoblastoid cells from the umbilical cords of 204 babies. Furthermore, comparison of meQTLs with other epigenetic data QTLs may also give further insights into regulatory epigenetic processes. Banovich et al. [ 67 ] compared meQTLs with QTLs for histone modifications, PolII occupancy and DNAse I hypersensitivity, and based on the extent of overlap observed they hypothesized that coordinated regulatory changes may be explained by modified TF binding affinities. Chen et al. [ 78 ] explored similar questions in three different immune cell types ( n =525), where 43.3% of the genetic variants identified as eQTLs were either found to have a coordinated effect as meQTLs or to be in high LD with a meQTL. However, the effect sizes were weakly negatively correlated, which the authors interpreted as a partial uncoupling between methylation and expression. The study also included analysis of histone modification QTLs (hQTLs for H3K4me1 and H3K27ac), where again 43.3% of eQTLs and hQTLs overlapped with strong positive correlation in effect sizes, suggesting an active role for histone modifications on expression.

As additional data are being generated on multiple epigenetic and expression layers of data, future analyses will have greater power to explore the regulatory nature of meQTLs. However, co-localization results should be interpreted with caution, as the intersection of QTLs does not imply a causal relationship or direct association due to LD or statistical artifacts. Additionally, if summary statistics are obtained from databases with different reference populations, the significant signals may not be comparable [ 25 ].

In conclusion, the identification of methylation quantitative trait loci genome-wide has significantly increased our knowledge of the factors driving DNA methylation variation in humans, and holds value for integrating genomics and epigenomics in the context of disease.

Acknowledgements

SV acknowledges the financial support of the Mexican National Council of Science and Technology (CONACYT).

Review history

The review history is available as additional file 1 .

Authors’ contributions

Both authors wrote and approved the final version of the manuscript.

The work was supported by the ESRC (ES/N000404/1 to JTB), JPI-HDHL through BBSRC (BB/S020845/1 to JTB), and CONACYT doctoral fellowship (2019-000021-01EXTF-00323 to SV).

Declarations

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sergio Villicaña, Email: [email protected].

Jordana T. Bell, Email: [email protected]

• 1. Hotchkiss RD. The quantitative separation of purines, pyrimidines, and nucleosides by paper chromatography. J Biol Chem. 1948;175(1):315–32. doi: 10.1016/S0021-9258(18)57261-6. [ DOI ] [ PubMed ] [ Google Scholar ]
• 2. Bourc’his D. Dnmt3L and the establishment of maternal genomic imprints. Science. 2001;294(5551):2536–9. doi: 10.1126/science.1065848. [ DOI ] [ PubMed ] [ Google Scholar ]
• 3. Song J, Rechkoblit O, Bestor TH, Patel DJ. Structure of DNMT1-DNA complex reveals a role for autoinhibition in maintenance DNA methylation. Science. 2011;331(6020):1036–40. doi: 10.1126/science.1195380. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 4. Pervjakova N, Kasela S, Morris AP, Kals M, Metspalu A, Lindgren CM, Salumets A, Mägi R. Imprinted genes and imprinting control regions show predominant intermediate methylation in adult somatic tissues. Epigenomics. 2016;8(6):789–99. doi: 10.2217/epi.16.8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 5. van Eijk KR, de Jong S, Boks MPM, Langeveld T, Colas F, Veldink JH, de Kovel CGF, Janson E, Strengman E, Langfelder P, Kahn RS, van den Berg LH, Horvath S, Ophoff RA. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012;13(1):636. doi: 10.1186/1471-2164-13-636. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 6. Maunakea AK, Nagarajan RP, Bilenky M, Ballinger TJ, D’Souza C, Fouse SD, Johnson BE, Hong C, Nielsen C, Zhao Y, Turecki G, Delaney A, Varhol R, Thiessen N, Shchors K, Heine VM, Rowitch DH, Xing X, Fiore C, Schillebeeckx M, Jones SJM, Haussler D, Marra MA, Hirst M, Wang T, Costello JF. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature. 2010;466(7303):253–7. doi: 10.1038/nature09165. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 7. Sharp AJ, Stathaki E, Migliavacca E, Brahmachary M, Montgomery SB, Dupre Y, Antonarakis SE. DNA methylation profiles of human active and inactive X chromosomes. Genome Res. 2011;21(10):1592–600. doi: 10.1101/gr.112680.110. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 8. Vilain A, Bernardino J, Gerbault-Seureau M, Vogt N, Niveleau A, Lefrançois D, Malfoy B, Dutrillaux B. DNA methylation and chromosome instability in lymphoblastoid cell lines. Cytogenet Genome Res. 2000;90(1-2):93–101. doi: 10.1159/000015641. [ DOI ] [ PubMed ] [ Google Scholar ]
• 9. Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, Gunderson KL. Genome-wide DNA methylation profiling using Infinium Ⓡ assay. Epigenomics. 2009;1(1):177–200. doi: 10.2217/epi.09.14. [ DOI ] [ PubMed ] [ Google Scholar ]
• 10. Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, Esteller M. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics. 2011;6(6):692–702. doi: 10.4161/epi.6.6.16196. [ DOI ] [ PubMed ] [ Google Scholar ]
• 11. Moran S, Arribas C, Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2016;8(3):389–99. doi: 10.2217/epi.15.114. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 12. Horsthemke B. A critical view on transgenerational epigenetic inheritance in humans. Nat Commun. 2018;9(1):1–4. doi: 10.1038/s41467-018-05445-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 13. Slieker RC, Roost MS, van Iperen L, Suchiman HED, Tobi EW, Carlotti F, de Koning EJP, Slagboom PE, Heijmans BT, Chuva de Sousa Lopes SM. DNA methylation landscapes of human fetal development. PLoS Genet. 2015;11(10):1005583. doi: 10.1371/journal.pgen.1005583. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 14. Smith ZD, Chan MM, Humm KC, Karnik R, Mekhoubad S, Regev A, Eggan K, Meissner A. DNA methylation dynamics of the human preimplantation embryo. Nature. 2014;511(7511):611–5. doi: 10.1038/nature13581. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 15. Guo H, Zhu P, Yan L, Li R, Hu B, Lian Y, Yan J, Ren X, Lin S, Li J, Jin X, Shi X, Liu P, Wang X, Wang W, Wei Y, Li X, Guo F, Wu X, Fan X, Yong J, Wen L, Xie SX, Tang F, Qiao J. The DNA methylation landscape of human early embryos. Nature. 2014;511(7511):606–10. doi: 10.1038/nature13544. [ DOI ] [ PubMed ] [ Google Scholar ]
• 16. Ziller MJ, Gu H, Müller F, Donaghey J, Tsai LTY, Kohlbacher O, De Jager PL, Rosen ED, Bennett DA, Bernstein BE, Gnirke A, Meissner A. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013;500(7463):477–81. doi: 10.1038/nature12433. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 17. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL, Nelson HH, Karagas MR, Padbury JF, Bueno R, Sugarbaker DJ, Yeh R-F, Wiencke JK, Kelsey KT. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009;5(8):1000602. doi: 10.1371/journal.pgen.1000602. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 18. Tsai P-C, Glastonbury CA, Eliot MN, Bollepalli S, Yet I, Castillo-Fernandez JE, Carnero-Montoro E, Hardiman T, Martin TC, Vickers A, Mangino M, Ward K, Pietiläinen KH, Deloukas P, Spector TD, Viñuela A, Loucks EB, Ollikainen M, Kelsey KT, Small KS, Bell JT. Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health. Clin Epigenetics. 2018;10(1):126. doi: 10.1186/s13148-018-0558-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 19. Mahna D, Puri S, Sharma S. DNA methylation signatures: Biomarkers of drug and alcohol abuse. Mutat Res Rev Mutat Res. 2018;777:19–28. doi: 10.1016/j.mrrev.2018.06.002. [ DOI ] [ PubMed ] [ Google Scholar ]
• 20. Martin EM, Fry RC. Environmental influences on the epigenome: exposure-associated DNA methylation in human populations. Annu Rev Public Health. 2018;39(1):309–33. doi: 10.1146/annurev-publhealth-040617-014629. [ DOI ] [ PubMed ] [ Google Scholar ]
• 21. Sanchez-Mut JV, Heyn H, Vidal E, Moran S, Sayols S, Delgado-Morales R, Schultz MD, Ansoleaga B, Garcia-Esparcia P, Pons-Espinal M, de Lagran MM, Dopazo J, Rabano A, Avila J, Dierssen M, Lott I, Ferrer I, Ecker JR, Esteller M. Human DNA methylomes of neurodegenerative diseases show common epigenomic patterns. Transl Psychiatry. 2016;6(1):718. doi: 10.1038/tp.2015.214. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 22. Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, Reinius L, Acevedo N, Taub M, Ronninger M, Shchetynsky K, Scheynius A, Kere J, Alfredsson L, Klareskog L, Ekström TJ, Feinberg AP. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31(2):142–7. doi: 10.1038/nbt.2487. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 23. Kim M, Long TI, Arakawa K, Wang R, Yu MC, Laird PW. DNA methylation as a biomarker for cardiovascular disease risk. PLoS ONE. 2010;5(3):9692. doi: 10.1371/journal.pone.0009692. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 24. Gallagher MD, Chen-Plotkin AS. The post-GWAS era: from association to function. Am J Hum Genet. 2018;102(5):717–30. doi: 10.1016/j.ajhg.2018.04.002. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 25. Vandiedonck C. Genetic association of molecular traits: a help to identify causative variants in complex diseases. Clin Genet. 2018;93(3):520–32. doi: 10.1111/cge.13187. [ DOI ] [ PubMed ] [ Google Scholar ]
• 26. Do C, Shearer A, Suzuki M, Terry MB, Gelernter J, Greally JM, Tycko B. Genetic–epigenetic interactions in cis: a major focus in the post–GWAS era. Genome Biol. 2017;18(1):1–22. doi: 10.1186/s13059-017-1250-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 27. Trerotola M, Relli V, Simeone P, Alberti S. Epigenetic inheritance and the missing heritability. Hum Genomics. 2015;9(1):17. doi: 10.1186/s40246-015-0041-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 28. Visscher PM, Hill WG, Wray NR. Heritability in the genomics era – concepts and misconceptions. Nat Rev Genet. 2008;9(4):255–66. doi: 10.1038/nrg2322. [ DOI ] [ PubMed ] [ Google Scholar ]
• 29. Verweij KJ, Mosing MA, Zietsch BP, Medland SE. Estimating heritability from twin studies. In: Statistical Human Genetics. Springer: 2012. p. 151–70. [ DOI ] [ PubMed ]
• 30. van Dongen J, Nivard MG, Willemsen G, Hottenga J-J, Helmer Q, Dolan CV, Ehli EA, Davies GE, van Iterson M, Breeze CE, Beck S, Suchiman HE, Jansen R, van Meurs JB, Heijmans BT, Slagboom PE, Boomsma DI. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nat Commun. 2016;7(1):11115. doi: 10.1038/ncomms11115. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 31. Grundberg E, Meduri E, Sandling JK, Hedman ÅK, Keildson S, Buil A, Busche S, Yuan W, Nisbet J, Sekowska M, Wilk A, Barrett A, Small KS, Ge B, Caron M, Shin S-Y, Lathrop M, Dermitzakis ET, McCarthy MI, Spector TD, Bell JT, Deloukas P, Ahmadi KR, Ainali C, Barrett A, Bataille V, Bell JT, Buil A, Deloukas P, Dermitzakis ET, Dimas AS, Durbin R, Glass D, Grundberg E, Hassanali N, Hedman ÅK, Ingle C, Knowles D, Krestyaninova M, Lindgren CM, Lowe CE, McCarthy MI, Meduri E, di Meglio P, Min JL, Montgomery SB, Nestle FO, Nica AC, Nisbet J, O’Rahilly S, Parts L, Potter S, Sandling J, Sekowska M, Shin S-Y, Small KS, Soranzo N, Spector TD, Surdulescu G, Travers ME, Tsaprouni L, Tsoka S, Wilk A, Yang T-P, Zondervan KT. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am J Hum Genet. 2013;93(5):876–90. doi: 10.1016/j.ajhg.2013.10.004. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 32. Bell JT, Tsai P-C, Yang T-P, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, Zhang F, Valdes A, Shin S-Y, Dempster EL, Murray RM, Grundberg E, Hedman AK, Nica A, Small KS, Dermitzakis ET, McCarthy MI, Mill J, Spector TD, Deloukas P. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012;8(4):1002629. doi: 10.1371/journal.pgen.1002629. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 33. McRae AF, Powell JE, Henders AK, Bowdler L, Hemani G, Shah S, Painter JN, Martin NG, Visscher PM, Montgomery GW. Contribution of genetic variation to transgenerational inheritance of DNA methylation. Genome Biol. 2014;15(5):73. doi: 10.1186/gb-2014-15-5-r73. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 34. Nustad HE, Page CM, Reiner AH, Zucknick M, LeBlanc M. A Bayesian mixed modeling approach for estimating heritability. BMC Proceedings. 2018;12(S9):31. doi: 10.1186/s12919-018-0131-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 35. Huan T, Joehanes R, Song C, Peng F, Guo Y, Mendelson M, Yao C, Liu C, Ma J, Richard M, Agha G, Guan W, Almli LM, Conneely KN, Keefe J, Hwang S-J, Johnson AD, Fornage M, Liang L, Levy D. Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat Commun. 2019;10(1):1–14. doi: 10.1038/s41467-019-12228-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 36. Day K, Waite LL, Alonso A, Irvin MR, Zhi D, Thibeault KS, Aslibekyan S, Hidalgo B, Borecki IB, Ordovas JM, Arnett DK, Tiwari HK, Absher DM. Heritable DNA methylation in CD4+ cells among complex families displays genetic and non-genetic effects. PLoS ONE. 2016;11(10):0165488. doi: 10.1371/journal.pone.0165488. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 37. Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, Zheng J, Duggirala A, McArdle WL, Ho K, Ring SM, Evans DM, Davey Smith G, Relton CL. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016;17(1):1–14. doi: 10.1186/s13059-016-0926-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 38. Hannon E, Gorrie-Stone TJ, Smart MC, Burrage J, Hughes A, Bao Y, Kumari M, Schalkwyk LC, Mill J. Leveraging DNA-methylation quantitative-trait loci to characterize the relationship between methylomic variation, gene expression, and complex traits. Am J Hum Genet. 2018;103(5):654–65. doi: 10.1016/j.ajhg.2018.09.007. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 39. van Dongen J, Ehli EA, Jansen R, van Beijsterveldt CEM, Willemsen G, Hottenga JJ, Kallsen NA, Peyton SA, Breeze CE, Kluft C, Heijmans BT, Bartels M, Davies GE, Boomsma DI. Genome-wide analysis of DNA methylation in buccal cells: a study of monozygotic twins and mQTLs. Epigenetics Chromatin. 2018;11(1):54. doi: 10.1186/s13072-018-0225-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 40. Rakyan VK, Chong S, Champ ME, Cuthbert PC, Morgan HD, Luu KVK, Whitelaw E. Transgenerational inheritance of epigenetic states at the murine AxinFu allele occurs after maternal and paternal transmission. Proc Natl Acad Sci. 2003;100(5):2538–43. doi: 10.1073/pnas.0436776100. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 41. Radford EJ, Ito M, Shi H, Corish JA, Yamazawa K, Isganaitis E, Seisenberger S, Hore TA, Reik W, Erkek S, Peters AHFM, Patti M-E, Ferguson-Smith AC. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science. 2014;345(6198):1255903. doi: 10.1126/science.1255903. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 42. Zaghlool SB, Al-Shafai M, Al Muftah WA, Kumar P, Gieger C, Waldenberger M, Falchi M, Suhre K. Mendelian inheritance of trimodal CpG methylation sites suggests distal cis-acting genetic effects. Clin Epigenetics. 2016;8(1):124. doi: 10.1186/s13148-016-0295-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 43. Heijmans BT, Kremer D, Tobi EW, Boomsma DI, Slagboom PE. Heritable rather than age-related environmental and stochastic factors dominate variation in DNA methylation of the human IGF2/H19 locus. Hum Mol Genet. 2007;16(5):547–54. doi: 10.1093/hmg/ddm010. [ DOI ] [ PubMed ] [ Google Scholar ]
• 44. Kerkel K, Spadola A, Yuan E, Kosek J, Jiang L, Hod E, Li K, Murty VV, Schupf N, Vilain E, et al. Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet. 2008;40(7):904. doi: 10.1038/ng.174. [ DOI ] [ PubMed ] [ Google Scholar ]
• 45. Boks MP, Derks EM, Weisenberger DJ, Strengman E, Janson E, Sommer IE, Kahn RS, Ophoff RA. The relationship of dna methylation with age, gender and genotype in twins and healthy controls. PLoS ONE. 2009;4(8):6767. doi: 10.1371/journal.pone.0006767. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 46. Schalkwyk LC, Meaburn EL, Smith R, Dempster EL, Jeffries AR, Davies MN, Plomin R, Mill J. Allelic skewing of DNA methylation is widespread across the genome. Am J Hum Genet. 2010;86(2):196–212. doi: 10.1016/j.ajhg.2010.01.014. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 47. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai S-L, Arepalli S, Dillman A, Rafferty IP, Troncoso J, Johnson R, Zielke HR, Ferrucci L, Longo DL, Cookson MR, Singleton AB. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6(5):1000952. doi: 10.1371/journal.pgen.1000952. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 48. Zhang D, Cheng L, Badner JA, Chen C, Chen Q, Luo W, Craig DW, Redman M, Gershon ES, Liu C. Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet. 2010;86(3):411–9. doi: 10.1016/j.ajhg.2010.02.005. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 49. Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, Gilad Y, Pritchard JK. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 2011;12(1):10. doi: 10.1186/gb-2011-12-1-r10. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 50. Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Khleifat AA, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Chalabi AA, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell C, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Smith GD, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, Relton CL. Genomic and phenomic insights from an atlas of genetic effects on DNA methylation. Nat Genet:1–30. (in press).
• 51. Bonder MJ, Luijk R, Zhernakova DV, Moed M, Deelen P, Vermaat M, Van Iterson M, Van Dijk F, Van Galen M, Bot J, Slieker RC, Jhamai PM, Verbiest M, Suchiman HED, Verkerk M, Van Der Breggen R, Van Rooij J, Lakenberg N, Arindrarto W, Kielbasa SM, Jonkers I, Van’t Hof P, Nooren I, Beekman M, Deelen J, Van Heemst D, Zhernakova A, Tigchelaar EF, Swertz MA, Hofman A, Uitterlinden AG, Pool R, Van Dongen J, Hottenga JJ, Stehouwer CDA, Van Der Kallen CJH, Schalkwijk CG, Van Den Berg LH, Van Zwet EW, Mei H, Li Y, Lemire M, Hudson TJ, Slagboom PE, Wijmenga C, Veldink JH, Van Greevenbroek MMJ, Van Duijn CM, Boomsma DI, Isaacs A, Jansen R, Van Meurs JBJ, Hoen’t PAC, Franke L, Heijmans BT. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2017;49(1):131–8. doi: 10.1038/ng.3721. [ DOI ] [ PubMed ] [ Google Scholar ]
• 52. McClay JL, Shabalin AA, Dozmorov MG, Adkins DE, Kumar G, Nerella S, Clark SL, Bergen SE, Hultman CM, Magnusson PKE, Sullivan PF, Aberg KA, van den Oord EJCG. High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction. Genome Biol. 2015;16(1):291. doi: 10.1186/s13059-015-0842-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 53. McRae AF, Marioni RE, Shah S, Yang J, Powell JE, Harris SE, Gibson J, Henders AK, Bowdler L, Painter JN, Murphy L, Martin NG, Starr JM, Wray NR, Deary IJ, Visscher PM, Montgomery GW. Identification of 55,000 replicated DNA methylation QTL. Sci Rep. 2018;8(1):1–9. doi: 10.1038/s41598-018-35871-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 54. Kulkarni H, Kos MZ, Neary J, Dyer TD, Kent JW, Göring HHH, Cole SA, Comuzzie AG, Almasy L, Mahaney MC, Curran JE, Blangero J, Carless MA. Novel epigenetic determinants of type 2 diabetes in Mexican-American families. Hum Mol Genet. 2015;24(18):5330–44. doi: 10.1093/hmg/ddv232. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 55. Lemire M, Zaidi SHE, Ban M, Ge B, Aïssi D, Germain M, Kassam I, Wang M, Zanke BW, Gagnon F, Morange P-E, Trégouët D-A, Wells PS, Sawcer S, Gallinger S, Pastinen T, Hudson TJ. Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci. Nat Commun. 2015;6(1):6326. doi: 10.1038/ncomms7326. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 56. Shi J, Marconett CN, Duan J, Hyland PL, Li P, Wang Z, Wheeler W, Zhou B, Campan M, Lee DS, Huang J, Zhou W, Triche T, Amundadottir L, Warner A, Hutchinson A, Chen P-H, Chung BSI, Pesatori AC, Consonni D, Bertazzi P. A. l., Bergen AW, Freedman M, Siegmund KD, Berman BP, Borok Z, Chatterjee N, Tucker MA, Caporaso NE, Chanock SJ, Laird-Offringa IA, Landi MT. Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue. Nat Commun. 2014;5(1):3365. doi: 10.1038/ncomms4365. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 57. Ng B, White CC, Klein HU, Sieberts SK, McCabe C, Patrick E, Xu J, Yu L, Gaiteri C, Bennett DA, Mostafavi S, De Jager PL. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat Neurosci. 2017;20(10):1418. doi: 10.1038/nn.4632. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 58. Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15(2):37. doi: 10.1186/gb-2014-15-2-r37. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 59. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, Bryois J, Giger T, Romano L, Planchon A, Falconnet E, Bielser D, Gagnebin M, Padioleau I, Borel C, Letourneau A, Makrythanasis P, Guipponi M, Gehrig C, Antonarakis SE, Dermitzakis ET. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife. 2013;2:e00523. doi: 10.7554/eLife.00523. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 60. Husquin LT, Rotival M, Fagny M, Quach H, Zidane N, McEwen LM, MacIsaac JL, Kobor MS, Aschard H, Patin E, Quintana-Murci L. Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation. Genome Biol. 2018;19(1):222. doi: 10.1186/s13059-018-1601-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 61. Heyn H, Sayols S, Moutinho C, Vidal E, Sanchez-Mut JV, Stefansson OA, Nadal E, Moran S, Eyfjord JE, Gonzalez-Suarez E, Pujana MA, Esteller M. Linkage of DNA methylation quantitative trait loci to human cancer risk. Cell Rep. 2014;7(2):331–8. doi: 10.1016/j.celrep.2014.03.016. [ DOI ] [ PubMed ] [ Google Scholar ]
• 62. Zeng Y, Amador C, Xia C, Marioni R, Sproul D, Walker RM, Morris SW, Bretherick A, Canela-Xandri O, Boutin TS, Clark DW, Campbell A, Rawlik K, Hayward C, Nagy R, Tenesa A, Porteous DJ, Wilson JF, Deary IJ, Evans KL, McIntosh AM, Navarro P, Haley CS. Parent of origin genetic effects on methylation in humans are common and influence complex trait variation. Nat Commun. 2019;10(1):1383. doi: 10.1038/s41467-019-09301-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 63. Xie C, Leung Y-K, Chen A, Long D-X, Hoyo C, Ho S-M. Differential methylation values in differential methylation analysis. Bioinformatics. 2019;35(7):1094–7. doi: 10.1093/bioinformatics/bty778. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 64. Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, Lin SM. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11(1):587. doi: 10.1186/1471-2105-11-587. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 65. Cheung WA, Shao X, Morin A, Siroux V, Kwan T, Ge B, Aïssi D, Chen L, Vasquez L, Allum F, Guénard F, Bouzigon E, Simon M-M, Boulier E, Redensek A, Watt S, Datta A, Clarke L, Flicek P, Mead D, Paul DS, Beck S, Bourque G, Lathrop M, Tchernof A, Vohl M-C, Demenais F, Pin I, Downes K, Stunnenberg HG, Soranzo N, Pastinen T, Grundberg E. Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome. Genome Biol. 2017;18(1):50. doi: 10.1186/s13059-017-1173-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 66. Bell CG, Gao F, Yuan W, Roos L, Acton RJ, Xia Y, Bell J, Ward K, Mangino M, Hysi PG, Wang J, Spector TD. Obligatory and facilitative allelic variation in the DNA methylome within common disease-associated loci. Nat Commun. 2018;9(1):8. doi: 10.1038/s41467-017-01586-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 67. Banovich NE, Lan X, McVicker G, van de Geijn B, Degner JF, Blischak JD, Roux J, Pritchard JK, Gilad Y. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 2014;10(9):1004663. doi: 10.1371/journal.pgen.1004663. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 68. Benton MC, Lea RA, Macartney-Coxson D, Sutherland HG, White N, Kennedy D, Mengersen K, Haupt LM, Griffiths LR. Genome-wide allele-specific methylation is enriched at gene regulatory regions in a multi-generation pedigree from the Norfolk Island isolate. Epigenetics Chromatin. 2019;12(1):60. doi: 10.1186/s13072-019-0304-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 69. Onuchic V, Lurie E, Carrero I, Pawliczek P, Patel RY, Rozowsky J, Galeev T, Huang Z, Altshuler RC, Zhang Z, Harris RA, Coarfa C, Ashmore L, Bertol JW, Fakhouri WD, Yu F, Kellis M, Gerstein M, Milosavljevic A. Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci. Science. 2018;361(6409):3146. doi: 10.1126/science.aar3146. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 70. Do C, Lang CF, Lin J, Darbary H, Krupska I, Gaba A, Petukhova L, Vonsattel J-P, Gallagher MP, Goland RS, et al. Mechanisms and disease associations of haplotype-dependent allele-specific DNA methylation. Am J Hum Genet. 2016;98(5):934–55. doi: 10.1016/j.ajhg.2016.03.027. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 71. Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, Troakes C, Turecki G, O’Donovan MC, Schalkwyk LC, Bray NJ, Mill J. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci. 2016;19(1):48–54. doi: 10.1038/nn.4182. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 72. Gong J, Wan H, Mei S, Ruan H, Zhang Z, Liu C, Guo A-Y, Diao L, Miao X, Han L. Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer. Nucleic Acids Res. 2019;47(D1):1066–72. doi: 10.1093/nar/gky814. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 73. Lin D, Chen J, Perrone-Bizzozero N, Bustillo JR, Du Y, Calhoun VD, Liu J. Characterization of cross-tissue genetic-epigenetic effects and their patterns in schizophrenia. Genome Med. 2018;10(1):13. doi: 10.1186/s13073-018-0519-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 74. Long HK, Sims D, Heger A, Blackledge NP, Kutter C, Wright ML, Grützner F, Odom DT, Patient R, Ponting CP, Klose RJ. Epigenetic conservation at gene regulatory elements revealed by non-methylated DNA profiling in seven vertebrates. eLife. 2013;2:e00348. doi: 10.7554/eLife.00348. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 75. Wang M, Zhang K, Ngo V, Liu C, Fan S, Whitaker JW, Chen Y, Ai R, Chen Z, Wang J, Zheng L, Wang W. Identification of DNA motifs that regulate DNA methylation. Nucleic Acids Res. 2019;47(13):6753–68. doi: 10.1093/nar/gkz483. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 76. Volkov P, Olsson AH, Gillberg L, Jørgensen SW, Brøns C, Eriksson K-F, Groop L, Jansson P-A, Nilsson E, Rönn T, Vaag A, Ling C. A genome-wide mQTL analysis in human adipose tissue identifies genetic variants associated with DNA methylation, gene expression and metabolic traits. PLoS ONE. 2016;11(6):0157776. doi: 10.1371/journal.pone.0157776. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 77. Shin J, Bourdon C, Bernard M, Wilson MD, Reischl E, Waldenberger M, Ruggeri B, Schumann G, Desrivieres S, Leemans A, Abrahamowicz M, Leonard G, Richer L, Bouchard L, Gaudet D, Paus T, Pausova Z. Layered genetic control of DNA methylation and gene expression: a locus of multiple sclerosis in healthy individuals. Hum Mol Genet. 2015;24(20):5733–45. doi: 10.1093/hmg/ddv294. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 78. Chen L, Ge B, Casale FP, Vasquez L, Kwan T, Garrido-Martín D, Watt S, Yan Y, Kundu K, Ecker S, Datta A, Richardson D, Burden F, Mead D, Mann AL, Fernandez JM, Rowlston S, Wilder SP, Farrow S, Shao X, Lambourne JJ, Redensek A, Albers CA, Amstislavskiy V, Ashford S, Berentsen K, Bomba L, Bourque G, Bujold D, Busche S, Caron M, Chen S-HH, Cheung W, Delaneau O, Dermitzakis ET, Elding H, Colgiu I, Bagger FO, Flicek P, Habibi E, Iotchkova V, Janssen-Megens E, Kim B, Lehrach H, Lowy E, Mandoli A, Matarese F, Maurano MT, Morris JA, Pancaldi V, Pourfarzad F, Rehnstrom K, Rendon A, Risch T, Sharifi N, Simon M-MM, Sultan M, Valencia A, Walter K, Wang S-YY, Frontini M, Antonarakis SE, Clarke L, Yaspo M-LL, Beck S, Guigo R, Rico D, Martens JHA, Ouwehand WH, Kuijpers TW, Paul DS, Stunnenberg HG, Stegle O, Downes K, Pastinen T, Soranzo N. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell. 2016;167(5):1398–141. doi: 10.1016/j.cell.2016.10.026. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 79. Schulz H, Ruppert A-K, Herms S, Wolf C, Mirza-Schreiber N, Stegle O, Czamara D, Forstner AJ, Sivalingam S, Schoch S, Moebus S, Pütz B, Hillmer A, Fricker N, Vatter H, Müller-Myhsok B, Nöthen MM, Becker AJ, Hoffmann P, Sander T, Cichon S. Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus. Nat Commun. 2017;8(1):1511. doi: 10.1038/s41467-017-01818-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 80. Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, Kleinman JE. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19(1):40–7. doi: 10.1038/nn.4181. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 81. Hatcher C, Relton CL, Gaunt TR, Richardson TG. Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease. Transl Psychiatry. 2019;9(1):105. doi: 10.1038/s41398-019-0437-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 82. Morrow JD, Glass K, Cho MH, Hersh CP, Pinto-Plata V, Celli B, Marchetti N, Criner G, Bueno R, Washko G, Choi AMKK, Quackenbush J, Silverman EK, DeMeo DL. Human lung DNA methylation quantitative trait loci colocalize with chronic obstructive pulmonary disease genome-wide association loci. Am J Respir Crit Care Med. 2018;197(10):1275–84. doi: 10.1164/rccm.201707-1434OC. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 83. Taylor DL, Jackson AU, Narisu N, Hemani G, Erdos MR, Chines PS, Swift A, Idol J, Didion JP, Welch RP, Kinnunen L, Saramies J, Lakka TA, Laakso M, Tuomilehto J, Parker SCJJ, Koistinen HA, Davey Smith G, Boehnke M, Scott LJ, Birney E, Collins FS. Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. Proc Natl Acad Sci. 2019;116(22):10883–8. doi: 10.1073/pnas.1814263116. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 84. Qi T, Wu Y, Zeng J, Zhang F, Xue A, Jiang L, Zhu Z, Kemper K, Yengo L, Zheng Z, et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat Commun. 2018;9(1):2282. doi: 10.1038/s41467-018-04558-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 85. Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Feinberg AP, Arking DE, Ladd-Acosta C, Fallin MD. Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder. Nat Commun. 2017;8(1):1011. doi: 10.1038/s41467-017-00868-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 86. Smith AK, Kilaru V, Kocak M, Almli LM, Mercer KB, Ressler KJ, Tylavsky FA, Conneely KN. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics. 2014;15(1):145. doi: 10.1186/1471-2164-15-145. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 87. Leslie R, O’Donnell CJ, Johnson AD. GRASP: analysis of genotype–phenotype results from 1390 genome-wide association studies and corresponding open access database. Bioinformatics. 2014;30(12):185–94. doi: 10.1093/bioinformatics/btu273. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 88. Eicher JD, Landowski C, Stackhouse B, Sloan A, Chen W, Jensen N, Lien J-P, Leslie R, Johnson AD. GRASP v2. 0: an update on the Genome-Wide Repository of Associations between SNPs and phenotypes. Nucleic Acids Res. 2014;43(D1):799–804. doi: 10.1093/nar/gku1202. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 89. Relton CL, Gaunt T, McArdle W, Ho K, Duggirala A, Shihab H, Woodward G, Lyttleton O, Evans DM, Reik W, et al. Data resource profile: accessible resource for integrated epigenomic studies (ARIES) Int J Epidemiol. 2015;44(4):1181–90. doi: 10.1093/ije/dyv072. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 90. Ding W, Chen J, Feng G, Chen G, Wu J, Guo Y, Ni X, Shi T. DNMIVD: DNA methylation interactive visualization database. Nucleic Acids Res. 2020;48(D1):856–62. doi: 10.1093/nar/gkz830. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 91. Zheng Z, Huang D, Wang J, Zhao K, Zhou Y, Guo Z, Zhai S, Xu H, Cui H, Yao H, Wang Z, Yi X, Zhang S, Sham PC, Li MJ. QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes. Nucleic Acids Res. 2020;48(D1):983–91. doi: 10.1093/nar/gkz888. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 92. Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, Lloyd-Jones LR, Marioni RE, Martin NG, Montgomery GW, Deary IJ, Wray NR, Visscher PM, McRae AF, Yang J. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9(1):1–14. doi: 10.1038/s41467-017-02088-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 93. Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, Sarwar G, Islam MT, Shahriar H, Islam T, Rahman M, Yunus M, Kibriya MG, Chen LS, Ahsan H. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018;9(1):804. doi: 10.1038/s41467-018-03209-9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 94. Pai AA, Pritchard JK, Gilad Y. The genetic and mechanistic basis for variation in gene regulation. PLoS Genet. 2015;11(1):1004857. doi: 10.1371/journal.pgen.1004857. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 95. Liu F, Wu D, Wang X. Roles of CTCF in conformation and functions of chromosome. Semin Cell Dev Biol. 2019;90:168–73. doi: 10.1016/j.semcdb.2018.07.021. [ DOI ] [ PubMed ] [ Google Scholar ]
• 96. Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Schöler A, van Nimwegen E, Wirbelauer C, Oakeley EJ, Gaidatzis D, Tiwari VK, Schübeler D. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480(7378):490–5. doi: 10.1038/nature10716. [ DOI ] [ PubMed ] [ Google Scholar ]
• 97. Quon G, Lippert C, Heckerman D, Listgarten J. Patterns of methylation heritability in a genome-wide analysis of four brain regions. Nucleic Acids Res. 2013;41(4):2095–104. doi: 10.1093/nar/gks1449. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 98. Thomas R, Trapani D, Goodyer-Sait L, Tomkova M, Fernandez-Rozadilla C, Sahnane N, Woolley C, Davis H, Chegwidden L, Kriaucionis S, Maughan T, Leedham S, Palles C, Furlan D, Tomlinson I, Lewis A. The polymorphic variant rs1800734 influences methylation acquisition and allele-specific TFAP4 binding in the MLH1 promoter leading to differential mRNA expression. Sci Rep. 2019;9(1):13463. doi: 10.1038/s41598-019-49952-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 99. Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, Yengo L, Lloyd-Jones LR, Sidorenko J, Wu Y, McRae AF, Visscher PM, Zeng J, Yang J. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 2018;9(1):2941. doi: 10.1038/s41467-018-04951-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 100. Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aiden EL. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell. 2014;159(7):1665–80. doi: 10.1016/j.cell.2014.11.021. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 101. Rajavelu A, Lungu C, Emperle M, Dukatz M, Bröhm A, Broche J, Hanelt I, Parsa E, Schiffers S, Karnik R, Meissner A, Carell T, Rathert P, Jurkowska RZ, Jeltsch A. Chromatin-dependent allosteric regulation of DNMT3A activity by MeCP2. Nucleic Acids Res. 2018;46(17):9044–56. doi: 10.1093/nar/gky715. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 102. Franklin D. P152R mutation within MeCP2 can cause loss of DNA-binding selectivity. Interdiscip Sci Comput Life Sci. 2019;11(1):10–20. doi: 10.1007/s12539-019-00316-z. [ DOI ] [ PubMed ] [ Google Scholar ]
• 103. Zhu H, Wang G, Qian J. Transcription factors as readers and effectors of DNA methylation. Nat Rev Genet. 2016;17(9):551–65. doi: 10.1038/nrg.2016.83. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 104. Relton CL, Davey Smith G. Mendelian randomization: applications and limitations in epigenetic studies. Epigenomics. 2015;7(8):1239–43. doi: 10.2217/epi.15.88. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 105. Sanchez-Mut JV, Heyn H, Silva BA, Dixsaut L, Garcia-Esparcia P, Vidal E, Sayols S, Glauser L, Monteagudo-Sánchez A, Perez-Tur J, Ferrer I, Monk D, Schneider B, Esteller M, Gräff J. PM20D1 is a quantitative trait locus associated with Alzheimer’s disease. Nat Med. 2018;24(5):598–603. doi: 10.1038/s41591-018-0013-y. [ DOI ] [ PubMed ] [ Google Scholar ]
• 106. Rice SJ, Tselepi M, Sorial AK, Aubourg G, Shepherd C, Almarza D, Skelton AJ, Pangou I, Deehan D, Reynard LN, Loughlin J. Prioritization of PLEC and GRINA as osteoarthritis risk genes through the identification and characterization of novel methylation quantitative trait loci. Arthritis Rheum. 2019;71(8):1285–96. doi: 10.1002/art.40849. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 107. Pihlstrøm L, Berge V, Rengmark A, Toft M. Parkinson’s disease correlates with promoter methylation in the α-synuclein gene. Mov Disord. 2015;30(4):577–80. doi: 10.1002/mds.26073. [ DOI ] [ PubMed ] [ Google Scholar ]
• 108. Kular L, Liu Y, Ruhrmann S, Zheleznyakova G, Marabita F, Gomez-Cabrero D, James T, Ewing E, Lindén M, Górnikiewicz B, Aeinehband S, Stridh P, Link J, Andlauer TFM, Gasperi C, Wiendl H, Zipp F, Gold R, Tackenberg B, Weber F, Hemmer B, Strauch K, Heilmann-Heimbach S, Rawal R, Schminke U, Schmidt CO, Kacprowski T, Franke A, Laudes M, Dilthey AT, Celius EG, Søndergaard HB, Tegnér J, Harbo HF, Oturai AB, Olafsson S, Eggertsson HP, Halldorsson BV, Hjaltason H, Olafsson E, Jonsdottir I, Stefansson K, Olsson T, Piehl F, Ekström TJ, Kockum I, Feinberg AP, Jagodic M. DNA methylation as a mediator of HLA-DRB1*15:01 and a protective variant in multiple sclerosis. Nat Commun. 2018;9(1):2397. doi: 10.1038/s41467-018-04732-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 109. Izzi B, Pistoni M, Cludts K, Akkor P, Lambrechts D, Verfaillie C, Verhamme P, Freson K, Hoylaerts MF. Allele-specific DNA methylation reinforces PEAR1 enhancer activity. Blood. 2016;128(7):1003–12. doi: 10.1182/blood-2015-11-682153. [ DOI ] [ PubMed ] [ Google Scholar ]
• 110. He Z, Zhang R, Jiang F, Zhang H, Zhao A, Xu B, Jin L, Wang T, Jia W, Jia W, Hu C. FADS1-FADS2 genetic polymorphisms are associated with fatty acid metabolism through changes in DNA methylation and gene expression. Clin Epigenetics. 2018;10(1):113. doi: 10.1186/s13148-018-0545-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 111. Jiao C, Zhang C, Dai R, Xia Y, Wang K, Giase G, Chen C, Liu C. Positional effects revealed in Illumina methylation array and the impact on analysis. Epigenomics. 2018;10(5):643–59. doi: 10.2217/epi-2017-0105. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 112. Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F. Evaluation of the infinium methylation 450k technology. Epigenomics. 2011;3(6):771–84. doi: 10.2217/epi.11.105. [ DOI ] [ PubMed ] [ Google Scholar ]
• 113. Xu Z, Niu L, Li L, Taylor JA. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44(3):20. doi: 10.1093/nar/gkv907. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 114. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9. doi: 10.1093/bioinformatics/btu049. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 115. Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics. 2013;14(1):1–10. doi: 10.1186/1471-2164-14-293. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 116. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3. doi: 10.1093/bioinformatics/bts034. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 117. Wang Z, Wu X, Wang Y. A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip. BMC Bioinformatics. 2018;19(S5):115. doi: 10.1186/s12859-018-2096-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 118. Wu MC, Kuan PF. DNA Methylation Protocols In: Tost J, editor. New York, NY: Humana Press: 2018. p. 303–30. Chap. 16.
• 119. Liu J, Siegmund KD. An evaluation of processing methods for HumanMethylation450 BeadChip data. BMC Genomics. 2016;17(1):469. doi: 10.1186/s12864-016-2819-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 120. Heiss JA, Just AC. Improved filtering of DNA methylation microarray data by detection p values and its impact on downstream analyses. Clin Epigenetics. 2019;11(1):15. doi: 10.1186/s13148-019-0615-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 121. Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2016;45(4):967. doi: 10.1093/nar/gkw967. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 122. Logue MW, Smith AK, Wolf EJ, Maniates H, Stone A, Schichman SA, McGlinchey RE, Milberg W, Miller MW. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics. 2017;9(11):1363–71. doi: 10.2217/epi-2017-0078. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 123. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, Van Djik S, Muhlhausler B, Stirzaker C, Clark SJ. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208. doi: 10.1186/s13059-016-1066-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 124. Zhou L, Ng HK, Drautz-Moses DI, Schuster SC, Beck S, Kim C, Chambers JC, Loh M. Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing. Sci Rep. 2019;9(1):10383. doi: 10.1038/s41598-019-46875-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 125. Olova N, Krueger F, Andrews S, Oxley D, Berrens RV, Branco MR, Reik W. Comparison of whole-genome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data. Genome Biol. 2018;19(1):33. doi: 10.1186/s13059-018-1408-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 126. NIH Roadmap Epigenomics Mapping Consortium. Standards and guidelines for whole genome shotgun bisulfite sequencing. 2011. http://www.roadmapepigenomics.org/files/protocols/data/dna-methylation . MethylC-SeqStandards_FINAL.pdf. Accessed 21 Sept 2020.
• 127. Libertini E, Heath SC, Hamoudi RA, Gut M, Ziller MJ, Herrero J, Czyz A, Ruotti V, Stunnenberg HG, Frontini M, Ouwehand WH, Meissner A, Gut IG, Beck S. Saturation analysis for whole-genome bisulfite sequencing data. Nat Biotechnol. 2016;34(7):691–3. doi: 10.1038/nbt.3524. [ DOI ] [ PubMed ] [ Google Scholar ]
• 128. Ziller MJ, Hansen KD, Meissner A, Aryee MJ. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat Methods. 2015;12(3):230–2. doi: 10.1038/nmeth.3152. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 129. Mansell G, Gorrie-Stone TJ, Bao Y, Kumari M, Schalkwyk LS, Mill J, Hannon E. Guidance for DNA methylation studies: Statistical insights from the Illumina EPIC array. BMC Genomics. 2019;20(1):1–15. doi: 10.1186/s12864-019-5761-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 130. Westfall PH, Young SS, et al, Vol. 279. Resampling-based multiple testing: examples and methods for P -value adjustment: John Wiley & Sons; 1993.
• 131. Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27(12):1135–7. doi: 10.1038/nbt1209-1135. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 132. Delaneau O, Ongen H, Brown AA, Fort A, Panousis NI, Dermitzakis ET. A complete tool set for molecular QTL discovery and analysis. Nat Commun. 2017;8:1–7. doi: 10.1038/ncomms15452. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 133. Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics. 2016;32(10):1479–85. doi: 10.1093/bioinformatics/btv722. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 134. Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, Christiansen MW, Fairfax BP, Schramm K, Powell JE, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45(10):1238. doi: 10.1038/ng.2756. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 135. Fehrmann RS, Jansen RC, Veldink JH, Westra H-J, Arends D, Bonder MJ, Fu J, Deelen P, Groen HJ, Smolonska A, et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 2011;7(8):1002197. doi: 10.1371/journal.pgen.1002197. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 136. Cox JW, Patel D, Chung J, Zhu C, Lent S, Fisher V, Pitsillides A, Farrer L, Zhang X. An efficient analytic approach in genome-wide identification of methylation quantitative trait loci response to fenofibrate treatment. BMC Proceedings. 2018;12(S9):44. doi: 10.1186/s12919-018-0152-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 137. Affinito O, Palumbo D, Fierro A, Cuomo M, De Riso G, Monticelli A, Miele G, Chiariotti L, Cocozza S. Nucleotide distance influences co-methylation between nearby CpG sites. Genomics. 2020;112(1):144–50. doi: 10.1016/j.ygeno.2019.05.007. [ DOI ] [ PubMed ] [ Google Scholar ]
• 138. Wen X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann Appl Stat. 2016;10(3):1619–38. doi: 10.1214/16-AOAS952. [ DOI ] [ Google Scholar ]
• 139. Sul JH, Raj T, de Jong S, de Bakker PIW, Raychaudhuri S, Ophoff RA, Stranger BE, Eskin E, Han B. Accurate and fast multiple-testing correction in eQTL studies. Am J Hum Genet. 2015;96(6):857–68. doi: 10.1016/j.ajhg.2015.04.012. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 140. Povysil G, Petrovski S, Hostyk J, Aggarwal V, Allen AS, Goldstein DB. Rare-variant collapsing analyses for complex traits: guidelines and applications. Nat Rev Genet. 2019;20(12):747–59. doi: 10.1038/s41576-019-0177-4. [ DOI ] [ PubMed ] [ Google Scholar ]
• 141. Richardson TG, Shihab HA, Hemani G, Zheng J, Hannon E, Mill J, Carnero-Montoro E, Bell JT, Lyttleton O, McArdle WL, Ring SM, Rodriguez S, Campbell C, Smith GD, Relton CL, Timpson NJ, Gaunt TR. Collapsed methylation quantitative trait loci analysis for low frequency and rare variants. Hum Mol Genet. 2016;25(19):4339–49. doi: 10.1093/hmg/ddw283. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 142. Li S, Wong EM, Bui M, Nguyen TL, Joo JHE, Stone J, Dite GS, Giles GG, Saffery R, Southey MC, Hopper JL. Causal effect of smoking on DNA methylation in peripheral blood: a twin and family study. Clin Epigenetics. 2018;10(1):1–12. doi: 10.1186/s13148-017-0434-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 143. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, Guan W, Xu T, Elks CE, Aslibekyan S, Moreno-Macias H, Smith JA, Brody JA, Dhingra R, Yousefi P, Pankow JS, Kunze S, Shah SH, McRae AF, Lohman K, Sha J, Absher DM, Ferrucci L, Zhao W, Demerath EW, Bressler J, Grove ML, Huan T, Liu C, Mendelson MM, Yao C, Kiel DP, Peters A, Wang-Sattler R, Visscher PM, Wray NR, Starr JM, Ding J, Rodriguez CJ, Wareham NJ, Irvin MR, Zhi D, Barrdahl M, Vineis P, Ambatipudi S, Uitterlinden AG, Hofman A, Schwartz J, Colicino E, Hou L, Vokonas PS, Hernandez DG, Singleton AB, Bandinelli S, Turner ST, Ware EB, Smith AK, Klengel T, Binder EB, Psaty BM, Taylor KD, Gharib SA, Swenson BR, Liang L, DeMeo DL, O’Connor GT, Herceg Z, Ressler KJ, Conneely KN, Sotoodehnia N, Kardia SLR, Melzer D, Baccarelli AA, van Meurs JBJ, Romieu I, Arnett DK, Ong KK, Liu Y, Waldenberger M, Deary IJ, Fornage M, Levy D, London SJ. Epigenetic signatures of cigarette smoking. Circ Cardiovasc Genet. 2016;9(5):436–47. doi: 10.1161/CIRCGENETICS.116.001506. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 144. Erzurumluoglu AM, Liu M, Jackson VE, Barnes DR, Datta G, Melbourne CA, Young R, Batini C, Surendran P, Jiang T, Adnan SD, Afaq S, Agrawal A, Altmaier E, Antoniou AC, Asselbergs FW, Baumbach C, Bierut L, Bertelsen S, Boehnke M, Bots ML, Brazel DM, Chambers JC, Chang-Claude J, Chen C, Corley J, Chou YL, David SP, de Boer RA, de Leeuw CA, Dennis JG, Dominiczak AF, Dunning AM, Easton DF, Eaton C, Elliott P, Evangelou E, Faul JD, Foroud T, Goate A, Gong J, Grabe HJ, Haessler J, Haiman C, Hallmans G, Hammerschlag AR, Harris SE, Hattersley A, Heath A, Hsu C, Iacono WG, Kanoni S, Kapoor M, Kaprio J, Kardia SL, Karpe F, Kontto J, Kooner JS, Kooperberg C, Kuulasmaa K, Laakso M, Lai D, Langenberg C, Le N, Lettre G, Loukola A, Luan J, Madden PAF, Mangino M, Marioni RE, Marouli E, Marten J, Martin NG, McGue M, Michailidou K, Mihailov E, Moayyeri A, Moitry M, Müller-Nurasyid M, Naheed A, Nauck M, Neville MJ, Nielsen SF, North K, Perola M, Pharoah PDP, Pistis G, Polderman TJ, Posthuma D, Poulter N, Qaiser B, Rasheed A, Reiner A, Renström F, Rice J, Rohde R, Rolandsson O, Samani NJ, Samuel M, Schlessinger D, Scholte SH, Scott RA, Sever P, Shao Y, Shrine N, Smith JA, Starr JM, Stirrups K, Stram D, Stringham HM, Tachmazidou I, Tardif JC, Thompson DJ, Tindle HA, Tragante V, Trompet S, Turcot V, Tyrrell J, Vaartjes I, van der Leij AR, van der Meer P, Varga TV, Verweij N, Völzke H, Wareham NJ, Warren HR, Weir DR, Weiss S, Wetherill L, Yaghootkar H, Yavas E, Jiang Y, Chen F, Zhan X, Zhang W, Zhao W, Zhao W, Zhou K, Amouyel P, Blankenberg S, Caulfield MJ, Chowdhury R, Cucca F, Deary IJ, Deloukas P, Di Angelantonio E, Ferrario M, Ferrières J, Franks PW, Frayling TM, Frossard P, Hall IP, Hayward C, Jansson JH, Jukema JW, Kee F, Männistö S, Metspalu A, Munroe PB, Nordestgaard BG, Palmer CNA, Salomaa V, Sattar N, Spector T, Strachan DP, van der Harst, Zeggini E, Saleheen D, Butterworth AS, Wain LV, Abecasis GR, Danesh J, Tobin MD, Vrieze S, Liu DJ, Howson JMM. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Mol Psychiatry. 2019;25(10):2392–409. doi: 10.1038/s41380-018-0313-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 145. Hancock DB, Markunas CA, Bierut LJ, Johnson EO. Human genetics of addiction: new insights and future directions. Curr Psychiatr Rep. 2018;20(2):8. doi: 10.1007/s11920-018-0873-3. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 146. Teh AL, Pan H, Chen L, Ong M-L, Dogra S, Wong J, MacIsaac JL, Mah SM, McEwen LM, Saw S-M, Godfrey KM, Chong Y-S, Kwek K, Kwoh C-K, Soh S-E, Chong MFF, Barton S, Karnani N, Cheong CY, Buschdorf JP, Stunkel W, Kobor MS, Meaney MJ, Gluckman PD, Holbrook JD. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res. 2014;24(7):1064–74. doi: 10.1101/gr.171439.113. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 147. Fraser HB, Lam LL, Neumann SM, Kobor MS. Population-specificity of human DNA methylation. Genome Biol. 2012;13(2):8. doi: 10.1186/gb-2012-13-2-r8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 148. Meng W, Zhu Z, Jiang X, Too CL, Uebe S, Jagodic M, Kockum I, Murad S, Ferrucci L, Alfredsson L, Zou H, Klareskog L, Feinberg AP, Ekström TJ, Padyukov L, Liu Y. DNA methylation mediates genotype and smoking interaction in the development of anti-citrullinated peptide antibody-positive rheumatoid arthritis. Arthritis Res Ther. 2017;19(1):1–10. doi: 10.1186/s13075-017-1276-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 149. Klengel T, Mehta D, Anacker C, Rex-Haffner M, Pruessner JC, Pariante CM, Pace TWW, Mercer KB, Mayberg HS, Bradley B, Nemeroff CB, Holsboer F, Heim CM, Ressler KJ, Rein T, Binder EB. Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nat Neurosci. 2013;16(1):33–41. doi: 10.1038/nn.3275. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 150. Palma-Gudiel H, Fañanás L. An integrative review of methylation at the serotonin transporter gene and its dialogue with environmental risk factors, psychopathology and 5-HTTLPR. Neurosci Biobehav Rev. 2017;72:190–209. doi: 10.1016/j.neubiorev.2016.11.011. [ DOI ] [ PubMed ] [ Google Scholar ]
• 151. McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, Jankowska MM, Kerr J, Kraft P, Montgomery SB, Mukherjee B, Papanicolaou GJ, Patel CJ, Ritchie MD, Ritz BR, Thomas DC, Wei P, Witte JS. Current challenges and new opportunities for gene-environment interaction studies of complex diseases. Am J Epidemiol. 2017;186(7):753–61. doi: 10.1093/aje/kwx227. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 152. Rönnegård L, Valdar W. Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genetics. 2012;13(1):63. doi: 10.1186/1471-2156-13-63. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 153. Dumitrascu B, Darnell G, Ayroles J, Engelhardt BE. Statistical tests for detecting variance effects in quantitative trait studies. Bioinformatics. 2019;35(2):200–10. doi: 10.1093/bioinformatics/bty565. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 154. Conley D, Johnson R, Domingue B, Dawes C, Boardman J, Siegal M. A sibling method for identifying vQTLs. PLoS ONE. 2018;13(4):0194541. doi: 10.1371/journal.pone.0194541. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 155. Wang H, Zhang F, Zeng J, Wu Y, Kemper KE, Xue A, Zhang M, Powell JE, Goddard ME, Wray NR, Visscher PM, McRae AF, Yang J. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Sci Adv. 2019;5:3538. doi: 10.1126/sciadv.aaw3538. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 156. Brown AA, Buil A, Viñuela A, Lappalainen T, Zheng H-F, Richards JB, Small KS, Spector TD, Dermitzakis ET, Durbin R. Genetic interactions affecting human gene expression identified by variance association mapping. eLife. 2014;3:e01381. doi: 10.7554/eLife.01381. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 157. Ek WE, Rask-Andersen M, Karlsson T, Enroth S, Gyllensten U, Johansson Å. Genetic variants influencing phenotypic variance heterogeneity. Hum Mol Genet. 2018;27(5):799–810. doi: 10.1093/hmg/ddx441. [ DOI ] [ PubMed ] [ Google Scholar ]
• 158. Lappalainen T, Greally JM. Associating cellular epigenetic models with human phenotypes. Nat Rev Genet. 2017;18(7):441–51. doi: 10.1038/nrg.2017.32. [ DOI ] [ PubMed ] [ Google Scholar ]
• 159. Birney E, Smith GD, Greally JM. Epigenome-wide association studies and the interpretation of disease-omics. PLoS Genet. 2016;12(6):1006105. doi: 10.1371/journal.pgen.1006105. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 160. Krause C, Sievert H, Geißler C, Grohs M, El Gammal AT, Wolter S, Ohlei O, Kilpert F, Krämer UM, Kasten M, Klein C, Brabant GE, Mann O, Lehnert H, Kirchner H. Critical evaluation of the DNA-methylation markers ABCG1 and SREBF1 for type 2 diabetes stratification. Epigenomics. 2019;11(8):885–97. doi: 10.2217/epi-2018-0159. [ DOI ] [ PubMed ] [ Google Scholar ]
• 161. Smith GD, Lawlor DA, Harbord R, Timpson N, Day I, Ebrahim S. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med. 2007;4(12):352. doi: 10.1371/journal.pmed.0040352. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 162. Yu F, Qiu C, Xu C, Tian Q, Zhao LJ, Wu L, Deng HW, Shen H. Mendelian randomization identifies CpG methylation sites with mediation effects for genetic influences on BMD in peripheral blood monocytes. Front Genet. 2020;11:1–14. doi: 10.3389/fgene.2020.00001. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 163. Battram T, Richmond RC, Baglietto L, Haycock PC, Perduca V, Bojesen SE, Gaunt TR, Hemani G, Guida F, Carreras-Torres R, Hung R, Amos CI, Freeman JR, Sandanger TM, Nøst TH, Nordestgaard BG, Teschendorff AE, Polidoro S, Vineis P, Severi G, Hodge AM, Giles GG, Grankvist K, Johansson MB, Johansson M, Davey Smith G, Relton CL. Appraising the causal relevance of DNA methylation for risk of lung cancer. Int J Epidemiol. 2019;48(5):1493–504. doi: 10.1093/ije/dyz190. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 164. Ahsan M, Ek WE, Rask-Andersen M, Karlsson T, Lind-Thomsen A, Enroth S, Gyllensten U, Johansson Å. The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases. PLoS Genet. 2017;13(9):1007005. doi: 10.1371/journal.pgen.1007005. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 165. Richard MA, Huan T, Ligthart S, Gondalia R, Jhun MA, Brody JA, Irvin MR, Marioni R, Shen J, Tsai P-C, Montasser ME, Jia Y, Syme C, Salfati EL, Boerwinkle E, Guan W, Mosley TH, Bressler J, Morrison AC, Liu C, Mendelson MM, Uitterlinden AG, van Meurs JB, Franco OH, Zhang G, Li Y, Stewart JD, Bis JC, Psaty BM, Chen Y-DI, Kardia SLR, Zhao W, Turner ST, Absher D, Aslibekyan S, Starr JM, McRae AF, Hou L, Just AC, Schwartz JD, Vokonas PS, Menni C, Spector TD, Shuldiner A, Damcott CM, Rotter JI, Palmas W, Liu Y, Paus T, Horvath S, O’Connell JR, Guo X, Pausova Z, Assimes TL, Sotoodehnia N, Smith JA, Arnett DK, Deary IJ, Baccarelli AA, Bell JT, Whitsel E, Dehghan A, Levy D, Fornage M, Heijmans BT, ’t Hoen PAC, van Meurs J, Isaacs A, Jansen R, Franke L, Boomsma DI, Pool R, van Dongen J, Hottenga JJ, van Greevenbroek MMJ, Stehouwer CDA, van der Kallen CJH, Schalkwijk CG, Wijmenga C, Zhernakova A, Tigchelaar EF, Slagboom PE, Beekman M, Deelen J, van Heemst D, Veldink JH, van den Berg LH, van Duijn CM, Hofman A, Uitterlinden AG, Jhamai PM, Verbiest M, Suchiman HED, Verkerk M, van der Breggen R, van Rooij J, Lakenberg N, Mei H, van Iterson M, van Galen M, Bot J, van ’t Hof P, Deelen P, Nooren I, Moed M, Vermaat M, Zhernakova DV, Luijk R, Bonder MJ, van Dijk F, Arindrarto W, Kielbasa SM, Swertz MA, van Zwet EW. DNA methylation analysis identifies loci for blood pressure regulation. Am J Hum Genet. 2017;101(6):888–902. doi: 10.1016/j.ajhg.2017.09.028. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 166. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, Tsai P-C, Ried JS, Zhang W, Yang Y, Tan S, Fiorito G, Franke L, Guarrera S, Kasela S, Kriebel J, Richmond RC, Adamo M, Afzal U, Ala-Korpela M, Albetti B, Ammerpohl O, Apperley JF, Beekman M, Bertazzi PA, Black SL, Blancher C, Bonder M-J, Brosch M, Carstensen-Kirberg M, de Craen AJM, de Lusignan S, Dehghan A, Elkalaawy M, Fischer K, Franco OH, Gaunt TR, Hampe J, Hashemi M, Isaacs A, Jenkinson A, Jha S, Kato N, Krogh V, Laffan M, Meisinger C, Meitinger T, Mok ZY, Motta V, Ng HK, Nikolakopoulou Z, Nteliopoulos G, Panico S, Pervjakova N, Prokisch H, Rathmann W, Roden M, Rota F, Rozario MA, Sandling JK, Schafmayer C, Schramm K, Siebert R, Slagboom PE, Soininen P, Stolk L, Strauch K, Tai E-S, Tarantini L, Thorand B, Tigchelaar EF, Tumino R, Uitterlinden AG, van Duijn C, van Meurs JBJ, Vineis P, Wickremasinghe AR, Wijmenga C, Yang T-P, Yuan W, Zhernakova A, Batterham RL, Smith GD, Deloukas P, Heijmans BT, Herder C, Hofman A, Lindgren CM, Milani L, van der Harst P, Peters A, Illig T, Relton CL, Waldenberger M, Järvelin M-R, Bollati V, Soong R, Spector TD, Scott J, McCarthy MI, Elliott P, Bell JT, Matullo G, Gieger C, Kooner JS, Grallert H, Chambers JC. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541(7635):81–6. doi: 10.1038/nature20784. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 167. Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman ÅK, Aslibekyan S, Demerath EW, Guan W, Zhi D, Yao C, Huan T, Willinger C, Chen B, Courchesne P, Multhaup M, Irvin MR, Cohain A, Schadt EE, Grove ML, Bressler J, North K, Sundström J, Gustafsson S, Shah S, McRae AF, Harris SE, Gibson J, Redmond P, Corley J, Murphy L, Starr JM, Kleinbrink E, Lipovich L, Visscher PM, Wray NR, Krauss RM, Fallin D, Feinberg A, Absher DM, Fornage M, Pankow JS, Lind L, Fox C, Ingelsson E, Arnett DK, Boerwinkle E, Liang L, Levy D, Deary IJ. Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a Mendelian randomization approach. PLoS Med. 2017;14(1):1002215. doi: 10.1371/journal.pmed.1002215. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 168. Dekkers KF, van Iterson M, Slieker RC, Moed MH, Bonder MJ, van Galen M, Mei H, Zhernakova DV, van den Berg LH, Deelen J, van Dongen J, van Heemst D, Hofman A, Hottenga JJ, van der Kallen CJH, Schalkwijk CG, Stehouwer CDA, Tigchelaar EF, Uitterlinden AG, Willemsen G, Zhernakova A, Franke L, ’t Hoen PAC, Jansen R, van Meurs J, Boomsma DI, van Duijn CM, van Greevenbroek MMJ, Veldink JH, Wijmenga C, van Zwet EW, Slagboom PE, Jukema JW, Heijmans BT. Blood lipids influence DNA methylation in circulating cells. Genome Biol. 2016;17(1):138. doi: 10.1186/s13059-016-1000-6. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 169. Tsaprouni LG, Yang T-P, Bell J, Dick KJ, Kanoni S, Nisbet J, Viñuela A, Grundberg E, Nelson CP, Meduri E, Buil A, Cambien F, Hengstenberg C, Erdmann J, Schunkert H, Goodall AH, Ouwehand WH, Dermitzakis E, Spector TD, Samani NJ, Deloukas P. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics. 2014;9(10):1382–96. doi: 10.4161/15592294.2014.969637. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 170. Bourrat P, Lu Q, Jablonka E. Why the missing heritability might not be in the DNA. BioEssays. 2017;39(7):1–2. doi: 10.1002/bies.201700067. [ DOI ] [ PubMed ] [ Google Scholar ]
• 171. Hannon E, Schendel D, Ladd-Acosta C, Grove J, Hansen CS, Andrews SV, Hougaard DM, Bresnahan M, Mors O, Hollegaard MV, Bækvad-Hansen M, Hornig M, Mortensen PB, Børglum AD, Werge T, Pedersen MG, Nordentoft M, Buxbaum J, Daniele Fallin M, Bybjerg-Grauholm J, Reichenberg A, Mill J. Elevated polygenic burden for autism is associated with differential DNA methylation at birth. Genome Med. 2018;10(1):19. doi: 10.1186/s13073-018-0527-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 172. Richardson TG, Haycock PC, Zheng J, Timpson NJ, Gaunt TR, Davey Smith G, Relton CL, Hemani G. Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease. Hum Mol Genet. 2018;27(18):3293–304. doi: 10.1093/hmg/ddy210. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 173. Li YI, van de Geijn B, Raj A, Knowles DA, Petti AA, Golan D, Gilad Y, Pritchard JK. RNA splicing is a primary link between genetic variation and disease. Science. 2016;352(6285):600–4. doi: 10.1126/science.aad9417. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 174. Gamazon ER, Badner JA, Cheng L, Zhang C, Zhang D, Cox NJ, Gershon ES, Kelsoe JR, Greenwood TA, Nievergelt CM, Chen C, McKinney R, Shilling PD, Schork NJ, Smith EN, Bloss CS, Nurnberger JI, Edenberg HJ, Foroud T, Koller DL, Scheftner WA, Coryell W, Rice J, Lawson WB, Nwulia EA, Hipolito M, Byerley W, McMahon FJ, Schulze TG, Berrettini WH, Potash JB, Zandi PP, Mahon PB, McInnis MG, Zöllner S, Zhang P, Craig DW, Szelinger S, Barrett TB, Liu C. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiatry. 2013;18(3):340–6. doi: 10.1038/mp.2011.174. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 175. Nicodemus-Johnson J, Myers RA, Sakabe NJ, Sobreira DR, Hogarth DK, Naureckas ET, Sperling AI, Solway J, White SR, Nobrega MA, Nicolae DL, Gilad Y, Ober C. DNA methylation in lung cells is associated with asthma endotypes and genetic risk. JCI Insight. 2016;1(20):1–15. doi: 10.1172/jci.insight.90151. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 176. Delahaye F, Do C, Kong Y, Ashkar R, Salas M, Tycko B, Wapner R, Hughes F. Genetic variants influence on the placenta regulatory landscape. PLoS Genet. 2018;14(11):1007785. doi: 10.1371/journal.pgen.1007785. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

• View on publisher site
• PDF (1.4 MB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

• Download .nbib .nbib
• Format: AMA APA MLA NLM

Add to Collections

• Search Menu
• Sign in through your institution
• Advance Articles
• Editor's Choice
• Information for authors
• Submission Site
• Open Access Options
• Why publish with the journal
• About DNA Research
• About the Kazusa DNA Research Institute
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Advance articles

Time-dependent changes in genome-wide gene expression and post-transcriptional regulation across the post death process in silkworm.

• View article
• Supplementary data

Email alerts

• Author Guidelines
• X (formerly Twitter)

Affiliations

• Online ISSN 1756-1663
• Copyright © 2024 Kazusa DNA Research Institute
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

DNA replication articles from across Nature Portfolio

DNA replication is the biological process by which an exact copy of a deoxyribonucleic acid (DNA) molecule is created and it is the basis for biological inheritance. Each of the two strands of the DNA molecule acts as a template for the synthesis of a complementary strand.

Clamping Pol ε to the leading strand

Two recent studies provide structural insights into how human DNA polymerase ε (Pol ε) interacts with PCNA to form a processive holoenzyme on the leading strand. A series of cryo-EM images offer structural information on the proofreading process, showing how DNA is transferred between the polymerase and exonuclease sites in human Pol ε.

• Noopur Singh
• Erik Johansson

Related Subjects

• DNA synthesis
• Fragile sites
• Origin firing
• Origin selection
• Stalled forks
• Translesion synthesis

Latest Research and Reviews

Darwinian Evolution of Self-Replicating DNA in a Synthetic Protocell

The construction of a synthetic cell will be contingent on the development of an evolvable genetic replicator. Here, authors create a Darwinian protocell that supports protein synthesis, DNA self-replication, and adaptive evolution.

• Zhanar Abil
• Ana María Restrepo Sierra
• Christophe Danelon

High fidelity DNA ligation prevents single base insertions in the yeast genome

DNA ligase 1 finalizes nuclear DNA replication by accurately sealing nicks generated during Okazaki Fragment Maturation. Here, Williams et al employ a low fidelity DNA ligase 1 variant to study mutation rates and specificity across the yeast genome

• Jessica S. Williams
• Scott. A. Lujan
• Thomas A. Kunkel

Catalytic and noncatalytic functions of DNA polymerase κ in translesion DNA synthesis

The authors uncover the roles and regulations of DNA polymerase κ (Polκ) during DNA damage bypass. In addition to a catalytic function across minor groove DNA lesions, Polκ stimulates Polζ-mediated extension past various DNA lesions.

• Selene Sellés-Baiget
• Sara M. Ambjørn
• Julien P. Duxin

Timely lagging strand maturation relies on Ubp10 deubiquitylase-mediated PCNA dissociation from replicating chromatin

Synthesis and maturation of Okazaki fragments is crucial for lagging strand DNA replication. Here the authors find that the ubiquitin-specific protease Ubp10 works with the DNA synthesis machinery to promote Okazaki fragments joining by removal of PCNA through deubiquitylation of PCNA-K164.

• Javier Zamarreño
• Sofía Muñoz
• Avelino Bueno

USP50 suppresses alternative RecQ helicase use and deleterious DNA2 activity during replication

Mammalian DNA replication relies on various helicases and nucleases to ensure accurate genetic duplication, but how these enzymes are properly directed is unclear. Here, the authors identify USP50 as a key protein for promoting ongoing replication, restarting stalled forks, maintaining telomeres, and ensuring cell survival.

• Hannah L. Mackay
• Helen R. Stone
• Joanna R. Morris

Overactive mitochondrial DNA replication disrupts perinatal cardiac maturation

High mitochondrial DNA content is reported to be beneficial for health. However here, Landoni et al . show that boosting mtDNA replication disrupts cardiac maturation and induces the mitochondrial integrated stress response, ferroptosis and neonatal heart failure.

• Juan C. Landoni
• Semin Erkul
• Anu Suomalainen

News and Comment

Fanconi anaemia pathway induces chromosome shattering

Engel et al. conducted a genetic screen in which they identified the Fanconi anaemia (FA) pathway as a driver of chromothripsis, complex genomic rearrangements and generation of extrachromosomal DNA.

• Gabrielle Brewer

Dynamics of DNA replication speeds in single cells

In this Tools of the Trade article, Jeroen van den Berg (van Oudenaarden lab) presents a new method based on pulse-labelling of nascent DNA to study the dynamics of DNA replication in single cells.

• Jeroen van den Berg

Dissecting the mechanism of CRISPR–Cas technologies to design efficient biotechnologies

• Jasleen Gill

Using gene copy flexibility to build adaptable synthetic microbial populations

Plasmid coupling creates heterogeneity in copy number and, therefore, expression across a microbial population. We demonstrate that plasmid-encoded fitness differences between individuals can then lead to adaptation of the population to specific environments and can promote both genetic stability and memory of past environmental exposures.

A leap for directed evolution

• Rita Strack

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies