Inactive or moderately active human promoters are enriched for inter-individual epialleles
- Carolina Gemma†1,
- Sreeram V Ramagopalan†1, 2Email author,
- Thomas A Down†3,
- Huriya Beyan†1,
- Mohammed I Hawa1,
- Michelle L Holland1,
- Paul J Hurd4,
- Gavin Giovannoni1,
- R David Leslie1,
- George C Ebers5 and
- Vardhman K Rakyan1Email author
© Gemma et al.; licensee BioMed Central Ltd. 2013
Received: 9 July 2012
Accepted: 25 May 2013
Published: 25 May 2013
Inter-individual epigenetic variation, due to genetic, environmental or random influences, is observed in many eukaryotic species. In mammals, however, the molecular nature of epiallelic variation has been poorly defined, partly due to the restricted focus on DNA methylation. Here we report the first genome-scale investigation of mammalian epialleles that integrates genomic, methylomic, transcriptomic and histone state information.
First, in a small sample set, we demonstrate that non-genetically determined inter-individual differentially methylated regions (iiDMRs) can be temporally stable over at least 2 years. Then, we show that iiDMRs are associated with changes in chromatin state as measured by inter-individual differences in histone variant H2A.Z levels. However, the correlation of promoter iiDMRs with gene expression is negligible and not improved by integrating H2A.Z information. We find that most promoter epialleles, whether genetically or non-genetically determined, are associated with low levels of transcriptional activity, depleted for housekeeping genes, and either depleted for H3K4me3/enriched for H3K27me3 or lacking both these marks in human embryonic stem cells. The preferential enrichment of iiDMRs at regions of relative transcriptional inactivity validates in a larger independent cohort, and is reminiscent of observations previously made for promoters that undergo hypermethylation in various cancers, in vitro cell culture and ageing.
Our work identifies potential key features of epiallelic variation in humans, including temporal stability of non-genetically determined epialleles, and concomitant perturbations of chromatin state. Furthermore, our work suggests a novel mechanistic link among inter-individual epialleles observed in the context of normal variation, cancer and ageing.
KeywordsEpigenetics DNA methylation epialleles
Epialleles are genomic loci at which the epigenetic state can stably vary among individuals in a given population . Although first described and still best understood in plants [2–4], in recent years we have come to realise that epigenomic landscapes in mammals can also show considerable inter-individual variation (reviewed in [1, 5]). Mammalian epialleles could arise through the action of cis- or trans-genetic influences [6, 7], or have non-genetic origins as a result of: (1) potential stochastic events [8, 9]; (2) exposure to a compromised in utero environment as has been shown in rodent and human studies [10–12]; (3) or adult life-style associated factors such as smoking . Despite these and other previous studies, the molecular nature of mammalian epialleles, in particular those induced by non-genetic factors, has remained controversial . To a large extent, this is due to the DNA methylation-focus of previous investigations . Incorporation of information about the chromatin state would refine our understanding of the molecular nature and ultimately functionality of epialleles in the context of normal variation or disease states.
Here we describe the first systematic interrogation of mammalian epialleles that integrates genomic, methylomic, transcriptomic and chromatin state information. Using a combination of experimental and computational analyses we identify key features of epiallelic variation in humans, including demonstrating that even non-genetically determined epialleles can be temporally stable, and that DNA methylation variability at epialleles is associated with concomitant perturbations in chromatin state. Most notably, we find that promoter-associated epiallelic variation is predominantly associated with developmentally important and/or tissue-restricted genes. A similar category of genes is preferentially hyper-methylated in various cancers, in vitro cellular transformation, and human chronological ageing, potentially pointing to a novel mechanistic link between these processes and human inter-individual epiallelic variation.
Comprehensive genomic/epigenomic/transcriptomic profiling of monozygotic (MZ) twin pairs
For the initial discovery phase of the study, we originally aimed to generate integrated genomic/epigenomic/transcriptomic profiles for CD14+ cells from three different healthy MZ twin pairs of European ancestry (MZ pair 31/32: female, age at sampling 27 years; MZ pair 21/22: female, age at sampling 27 years; MZ pair, male, 11/12, age at sampling 19 years Additional file 1, Table S2). We focussed on CD14+ cells as they can be obtained to >90% purity  and Additional file 1, Figure S1, and are less likely to harbor post-differentiation, random epigenetic alterations as they have a lifespan of only a few weeks. Genetic profiles were obtained using the Illumina Omni2.5S array that interrogates approximately 2.5 million single nucleotide polymorphisms (SNPs) with a minor allele frequency of down to 1%. DNA methylation was assayed by Illumina450K arrays that provide bisulfite conversion-based, single base resolution methylation measurements at approximately 450,000 different cytosines associated with a range of genomic features such as promoters, enhancers, and CpG islands (CGIs) . Gene expression was profiled using the standard Illumina mRNA-seq protocol. For the analysis of chromatin state we performed ChIP-seq on the histone variant H2A.Z, which is strongly associated with transcriptional activity (but can also be found at transcriptionally silent promoters), and is thought to be environmentally responsive . We obtained 60-80 million mapped 36 bp paired end reads for the ChIP-seq and RNA-seq libraries. All genomic/epigenomic/transcriptomic profiles were generated from the same single sampling of CD14+ cells for each individual. Unfortunately, during the course of processing the samples, the DNA sample that was to be used for subsequent DNA methylation analysis for individual '12' was inadvertently lost. As these twins were recruited because we had methylomic data from a previous time-point to test for temporal stability, as discussed below, it was not feasible to repeat all the functional genomic assays on additional MZ pairs.
Identification of temporally stable inter-individual DMRs (iiDMRs)
Number of iiDMRs identified in the various pairwise comparisons.
31 vs. 32
21 vs. 22
31 vs. 21
31 vs. 11
11 vs. 22
The epigenomic landscape for any given individual is, to an extent, constantly in flux, and many iiDMRs identified from single time-point measurements probably do not have long-term phenotypic consequences. Therefore obtaining some measure of iiDMR temporal stability is critical, especially for non-genetically determined iiDMRs. The CD14+ cells used in this study were obtained from individuals recruited in late 2010. We previously sampled the same individuals in mid-2008 as part of a separate study in which we profiled their CD14+ cells using the Illumina27K array . This array contains probes for 27,578 different CpG sites, largely promoter-associated, and >90% of these CpGs are also represented on the Illumina450K array. For maximum power when comparing to the much less densely spaced probes on the Illumina27K array, we considered all >5% methylation differences, rather than just those in multiple-probe iiDMRs. Comparison of the 2010 with 2008 methylomic profiles, for CpG sites common to both platforms, revealed that a substantial proportion of the epigenetic variation seen in the 2010 samplings were also found in the 2008 samplings, that is, was temporally stable (Figure 2C). Although this is not surprising for comparisons between unrelated individuals, since many of the methylation differences in such cases most likely represent stable genetic effects, temporal stability in the intra-MZ pair comparisons is particularly noteworthy as it means that random or environmental events can induce permanent, or at least semi-permanent, epiallelic variation. Furthermore, given that the lifespan of CD14+ cells is only a few weeks, compared with the approximately 2-year gap between the first and second samplings, these epialleles most probably arise in blood progenitor cells. For the CpGs in common between the second (450K array) and first time-points (27K arrays), the proportion of methylation differences found at the second time-point that were also present at the first time-point is shown in Additional file 1, Figure S2. The number of sites where a difference in the same direction is seen at both sites significantly exceeds what would be expected by chance in all cases (P <3×10-13, χ2 test). This is the first genome-scale demonstration of temporally stable epiallelic variation in mammals that cannot potentially be explained by stable genetic effects (for example, ). For further analyses we did not restrict ourselves to CpG sites common to the Illumina450K and 27K arrays, otherwise we would have been left with too few DMRs for any meaningful analyses, but it is reasonable to assume that the same degree of temporal stability should be present across the entire set of iiDMRs found from the Illumina450K data obtained from the 2010 samplings.
iiDMRs at promoters anti-correlate with H2A.Z and preferentially associate with lowly expressed or silent genes
Interestingly, we observed only a very weak anti-correlation between promoter iiDMRs and RNA-seq gene expression levels (Figure 3B). This was true for both intra- and inter-MZ pair iiDMRs, and not significantly improved even when considering just those iiDMRs that were anti-correlated with H2A.Z, or iiDMRs with a large magnitude, or those found within CpG islands or CpG shores (Figure 3B). Not surprisingly, a direct comparison between intra-/inter-MZ pair H2A.Z variation and expression did not reveal any significant correlations either (Additional file 1, Figure S4).
The relationship between promoter DNA methylation and gene expression is known to be complex, even in contexts where large DNA methylation differences are generally observed, for example, genetically-encoded differentiation programs or cancer . In our case, we found it surprising that even inclusion of H2A.Z information did not improve the strength of the correlations as, theoretically, integration of information from different components of the 'epigenetic' state of a region should yield more robust correlations. To further explore the cause for these observations, we compared mean expression levels of iiDMR promoter-associated genes with all genes in our dataset. We found promoter iiDMR-associated genes to be expressed at significantly lower levels, relative to the other genes, in all intra- and inter-MZ pair comparisons (Figure 3C). In other words, it seems that promoter-iiDMRs are associated with genes that are lowly expressed or silent in CD14+ cells.
These observations were reminiscent of previous results and our own report on human ageing-associated DNA methylation dynamics [20, 21]. In those studies, human promoters that become hypermethylated with chronological age (aDMRs) were also associated with genes expressed at relatively low levels in the analysed tissue. Notably, these aDMRs were strongly enriched for promoters harbouring bivalent chromatin domains in embryonic stem (ES) cells. Bivalent domains harbour both H3K4me3, generally considered an active mark, and H3K27me3, generally considered an inactive mark [22, 23]. Furthermore, bivalent domain promoters are associated with developmentally important and tissue-restricted genes [22, 23]. An analysis of previously published H3K4me3 and H3K27me3 ChIP-seq profiles in human ES cells  revealed that promoter iiDMRs were only moderately enriched for bivalent chromatin domains (relative to genome average, refer to Figure 3D). Surprisingly though, strong statistically significant enrichment was observed for the high H3K27me3/low H3K4me3, or low H3K27me3/low H3K4me3 states in ES cells. Both these chromatin states are also strongly associated with tissue-restricted genes and indeed we found iiDMR promoters were significantly less likely to be associated with house keeping genes (P <10-5 in all five possible pair-wise comparisons, Chi-squared test). Re-analysis of the 500 most age-correlated probes (that is, aDMRs) from  revealed that these probes display significantly more intra- and inter-MZ pair variability than 500 randomly selected probes (P <0.01, bootstrapped for all five comparisons, Figure 3E). So the common property between iiDMR and aDMR promoters seems to be a strong association with genes that are tissue-restricted, but are only moderately active, or inactive, in the analysed tissue.
Analysis of iiDMRs in an independent cohort
Our data reveal several novel and important features of mammalian epialleles. First, we find that even non-genetically determined epialleles can be temporally stable (at least over the course of 2 years). That is, a significant fraction of these epialleles are not just transient epigenetic perturbations with little prospect of influencing molecular function. Second, inter-individual DNA methylation variants are associated with perturbations of chromatin state, a relationship observed for even small differences, for example, down to approximately 5% methylation difference, and therefore can be considered as bona fide epigenetic perturbations. Of course, future studies using bigger sample numbers are needed to further explore our initial findings.
The most significant aspect of our study is the finding that the correlation of iiDMRS with gene expression differences is very weak and that iiDMRs are preferentially found in regions of relative transcriptional inactivity. So what are the implications of this? First, it is possible that some promoter epialleles show inter-related DNA methylation and chromatin state perturbations, but may not impact significantly on genome function, at least as measured by steady state transcriptional activity. In the case of non-genetically determined epialleles, maybe all promoters are potentially subject to epiallelic variation, but the more active ones are 'cleared' of aberrant epigenomic variants, whereas the less active/silent promoters can accumulate epigenetic variation. But the enrichment of epialleles in less active/silent promoters was also found in comparisons between unrelated individuals. Although it is hard to say what proportion of epialleles between unrelated individuals are due to genetic as opposed to environmental differences from our data, the genetic influence on DNA methylation profiles is well documented [3, 4, 25]. Bell and colleagues measured genome-wide methylation in 77 HapMap Yoruba individuals, for which gene expression and genotype data were available, and found a strong genetic component to inter-individual variation in DNA methylation profiles . Although they found a significant enrichment of SNPs that affect both methylation and gene expression, they also noted that the total number of genes showing such a signal is only a small proportion of the total number of methylation variants they identified . A similar conclusion was reached by Myers and colleagues who analysed genome-wide methylation in six members of a three generation family and found that only 22% of genes harbouring genotype-dependent DNA methylation exhibited allele-specific gene expression (albeit more than expected by chance) . Therefore, in both cases the correlation between genetically determined DNA methylation and expression is at best modest, which would be consistent with our results regarding chromatin state.
It is possible that epiallelic variation acts in a manner not evident from simple correlations with steady-state expression levels in a given tissue. First it is possible that these correlations are tissue-restricted as has recently been shown for genetically determined tissue-restricted gene expression . Alternatively, conclusions from two recent studies, although not focusing on DNA methylation/chromatin state in mammals, hint at other potential mechanisms by which epialleles could act. Yvert and colleagues recently compared H3K14 acetylation profiles between two strains of the yeast Saccharomyces cerevisiae, and found 5,442 sites that significantly differed in H3K14ac levels, which they called single nucleosome epi-polymorphisms (SNEPs) . However, higher acetylation in one strain did not always mean higher expression of the relevant gene, for example, in one case the SNEP was associated with the strength of gene activation upon stimulation by heat shock. Secondly, Lindgren and colleagues recently assessed the effect of naturally occurring variation in miRNA expression levels on mRNA levels in humans, but found little correlation . The authors concluded that their findings were more consistent with the primary role of miRNAs being to buffer mRNA levels. A key conclusion therefore is that correlating epialleles with steady-state RNA dynamics, possibly the most common analysis currently presented in papers on epiallelic investigations, may not be particularly fruitful.
Finally, and potentially most importantly, the broadly similar characteristics of iiDMRs and aDMRs (from our previous study  and ) may in fact be a general feature of mammalian epiallelic variation in a variety of contexts. Meissner and colleagues found that aberrant gradual hyper-methylation during in vitro cell culture is found at promoters associated with genes not expressed in that cell type . Additionally, it has been found in a variety of human cancers that bivalent chromatin domains (associated with low transcriptional activity in stem cells) are preferential targets of hyper-methylation [31–33]. The common thread among these seemingly disparate examples of inter-individual epigenetic variation is promoters that are developmentally regulated and tissue-restricted, and are only moderately active, or inactive, in the analysed tissue. We propose that there could be a potentially important mechanistic link between normal/stochastic epiallelic variation and the epigenetic perturbations observed in the context of cancer and ageing.
The existence of mammalian epialleles is not in doubt, but the key challenge now is to characterise epialleles at the molecular level. Our work reveals key and novel properties of epiallelic variation in humans, and further suggests important mechanistic links between normal inter-individual epigenetic variation and epigenetic perturbations observed in cancer and chronological ageing.
Materials and methods
Fresh venous blood was obtained from three pairs (six individuals) of healthy MZ twins (Additional file 1, Table S1). Blood was diluted 1:1 in RPMI media and then peripheral blood mononuclear cells (PBMCs) were separated by Ficoll-Hypaque gradient centrifugation. CD14+ cells were isolated according to manufacturer's instruction using magnetic bead-based positive selection system (Miltenyi Biotech). The purity of the cells was determined by FACS using CD14-FITC antibodies (Additional file 1, Figure S1). All subjects gave informed consent and the study was approved by the Northern and Yorkshire Research Ethics committee (REC Reference Number: 06-MREO-3-22). Validation of iiDMRs was done using whole blood Illumina27K data previously generated . This cohort included 30 different healthy MZ female twin pairs recruited from within the UK as part of the TwinsUK registry.
Illumina 450K array
A total of 500 ng of DNA from CD14+ cells isolated using QIAamp DNA Mini Kit was bisulfite converted using the EZ DNA Methylation kit (Zymo Research). Arrays were processed at the Barts and The London Genome Centre, London, UK according to the manufacturer's recommendations. Methylation scores for each CpG site are called as 'Beta' values (using BeadStudio software from Illumina), that range from 0 (unmethylated (U)) to 1 (fully methylated (M)) on a continuous scale, and are calculated from the intensity of the M and U alleles as the ratio of fluorescent signals.
Illumina Omni2.5S arrays
The arrays were processed according to the manufacturer's instructions using 500 ng of DNA.
ChIP- and RNA-seq
The chromatin immunoprecipitation (ChIP) assay was performed on 5 × 105 CD14+ cells according to previously published protocols with minor modifications . Chromatin was sonicated to get fragments of 100 to 500 bp and immunoprecipitated with 10 uL of anti-H2A.Z antibody (Active Motif, Cat no: 39113). ChIP-seq libraries were prepared following the Illumina protocol and ligated to standard PE adaptors and sequenced on an Illumina GAIIx instrument. For RNA-seq, 200 ng of total RNA was used to prepare RNA-seq libraries using the TruSeq RNA kit from Illumina following the instructions provided in the supplier's manual, and sequenced on an Illumina GAIIx instrument.
Sequence data processing
ChIP-seq reads were mapped to the GRCh37 (hg19) reference genome sequence using MAQ 0.6.6 and mappings with quality scores <10 were discarded. For iiDMR-centric analyses, we counted the numbered of paired end fragments overlapping each probe region on the Illumina array and used that as a ChIP score for that probe. RNA-seq reads were mapped to the reference genome using Tophat 1.3.1, then expression levels (FPKM) were estimated for each Ensembl transcript using Cufflinks 1.0.3. For analyses comparing methylation data to expression, methylation array probes lying within 1 kb of an Ensembl TSS were assigned an 'expression level' equal to that of the transcript associated with the nearest TSS.
Correlation between variables was performed using Spearman's rank test. Confidence intervals for all box/bar plots are obtained by bootstrapping unless otherwise stated. Confidence intervals for the hES cell H3K4me3/H3K27me3 bar charts are estimated from a binomial model. Probes associated with housekeeping genes were defined as in .
For the genomic location enrichment analyses, exon, intron and regulatory features were extracted from Ensembl, and promoters were defined as regions within 1 kb of the TSSs of an Ensembl gene. For each of these categories, we asked what fraction of the probes lying in the selected regions were called as iiDMRs, and plot 95% confidence intervals on this proportion, estimated using a binomial model. For comparison, the feint line indicates the fraction of iiDMRs across the whole dataset, allowing enrichment or depletion to be assessed.
All data are available on GEO [GSE46220].
List of abbreviations
ageing-associated differentially methylated region
embryonic stem cells
fragments per kilobase of exon per million fragments mapped
inter-individual differentially methylated region
peripheral blood mononuclear cells
root mean square
single nucleosome epi-polymorphism
single nucleotide polymorphism
transcriptional start site.
VKR, CG and RDL are supported by the BBSRC, UK (BB/H012494/1). VKR and RDL are also supported by the EU-FP7 'BLUEPRINT' program (282510). RDL is also supported by Juvenile Diabetes Research Foundation International (JDRFI Award 5-2011-145). HB was supported by EFSD/Novo Nordisk Program Grant and Diabetes UK (10/0004107). SVR and GE are funded by the Multiple Sclerosis Society of the United Kingdom. SVR and GG are funded by the Medical Research Council of the United Kingdom (G0801976). TAD is funded by the Wellcome Trust (083563).
- Richards EJ: Population epigenetics. Curr Opin Genet Dev. 2008, 18: 221-226. 10.1016/j.gde.2008.01.014.PubMedView ArticleGoogle Scholar
- Miura K, Agetsuma M, Kitano H, Yoshimura A, Matsuoka M, Jacobsen SE, Ashikari M: A metastable DWARF1 epigenetic mutant affecting plant stature in rice. Proc Natl Acad Sci USA. 2009, 106: 11218-11223. 10.1073/pnas.0901942106.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmitz RJ, Schultz MD, Lewsey MG, O'Malley RC, Urich MA, Libiger O, Schork NJ, Ecker JR: Transgenerational epigenetic instability is a source of novel methylation variants. Science. 2011, 334: 369-373. 10.1126/science.1212959.PubMedPubMed CentralView ArticleGoogle Scholar
- Becker C, Hagmann J, Müller J, Koenig D, Stegle O, Borgwardt K, Weigel D: Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature. 2011, 480: 245-249. 10.1038/nature10555.PubMedView ArticleGoogle Scholar
- Rakyan VK, Down TA, Balding DJ, Beck S: Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011, 12: 529-541. 10.1038/nrg3000.PubMedPubMed CentralView ArticleGoogle Scholar
- 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: 411-419. 10.1016/j.ajhg.2010.02.005.PubMedPubMed CentralView ArticleGoogle Scholar
- 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: R10-R16. 10.1186/gb-2011-12-1-r10.PubMedPubMed CentralView ArticleGoogle Scholar
- Kaminsky ZA, Tang T, Wang SC, Ptak C, Oh GH, Wong AH, Feldcamp LA, Virtanen C, Halfvarson J, Tysk C, McRae AF, Visscher PM, Montgomery GW, Gottesman II, Martin NG, Petronis A: DNA methylation profiles in monozygotic and dizygotic twins. Nature Genet. 2009, 41: 240-245. 10.1038/ng.286.PubMedView ArticleGoogle Scholar
- Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suñer D, Cigudosa JC, Urioste M, Benitez J, Boix-Chornet M, Sanchez-Aguilera A, Ling C, Carlsson E, Poulsen P, Vaag A, Stephan Z, Spector TD, Wu YZ, Plass C, Esteller M: Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci USA. 2005, 102: 10604-10609. 10.1073/pnas.0500398102.PubMedPubMed CentralView ArticleGoogle Scholar
- Sandovici I, Smith NH, Nitert MD, Ackers-Johnson M, Uribe-Lewis S, Ito Y, Jones RH, Marquez VE, Cairns W, Tadayyon M, O'Neill LP, Murrell A, Ling C, Constância M, Ozanne SE: Maternal diet and aging alter the epigenetic control of a promoter-enhancer interaction at the Hnf4a gene in rat pancreatic islets. Proc Natl Acad Sci USA. 2011, 108: 5449-5454. 10.1073/pnas.1019007108.PubMedPubMed CentralView ArticleGoogle Scholar
- Waterland RA, Kellermayer R, Laritsky E, Rayco-Solon P, Harris RA, Travisano M, Zhang W, Torskaya MS, Zhang J, Shen L, Manary MJ, Prentice AM: Season of conception in rural Gambia affects DNA methylation at putative human metastable epialleles. PLoS Genet. 2011, 6: e1001252-View ArticleGoogle Scholar
- Hoile SP, Lillycrop KA, Thomas NA, Hanson MA, Burdge GC: Dietary protein restriction during F0 pregnancy in rats induces transgenerational changes in the hepatic transcriptome in female offspring. PLoS One. 2011, 6: e21668-10.1371/journal.pone.0021668.PubMedPubMed CentralView ArticleGoogle Scholar
- Breitling LP, Yang R, Korn B, Burwinkel B, Brenner H: Tobacco-smoking-related differential DNA methylation: 27K discovery and replication. Am J Hum Genet. 2011, 88: 450-457. 10.1016/j.ajhg.2011.03.003.PubMedPubMed CentralView ArticleGoogle Scholar
- Bird A: Putting the DNA back into DNA methylation. Nat Genet. 2011, 43: 1050-1051. 10.1038/ng.987.PubMedView ArticleGoogle Scholar
- 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: 692-702. 10.4161/epi.6.6.16196.PubMedView ArticleGoogle Scholar
- Conerly ML, Teves SS, Diolaiti D, Ulrich M, Eisenman RN, Henikoff S: Changes in H2A.Z occupancy and DNA methylation during B-cell lymphomagenesis. Genome Res. 2010, 20: 1383-1390. 10.1101/gr.106542.110.PubMedPubMed CentralView ArticleGoogle Scholar
- Rakyan VK, Beyan H, Down TA, Hawa MI, Maslau S, Aden D, Daunay A, Busato F, Mein CA, Manfras B, Dias KR, Bell CG, Tost J, Boehm BO, Beck S, Leslie RD: Identification of type 1 diabetes-associated DNA methylation variable positions that precede disease diagnosis. PLoS Genet. 2011, 7: e1002300-10.1371/journal.pgen.1002300.PubMedPubMed CentralView ArticleGoogle Scholar
- Feinberg AP, Irizarry RA, Fradin D, Aryee MJ, Murakami P, Aspelund T, Eiriksdottir G, Harris TB, Launer L, Gudnason V, Fallin MD: Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci Transl Med. 2010, 2: 49ra67-10.1126/scitranslmed.3001262.PubMedPubMed CentralGoogle Scholar
- Finer S, Holland ML, Nanty L, Rakyan VK: The hunt for the epiallele. Environ Mol Mutagen. 2011, 52: 1-11. 10.1002/em.20590.PubMedView ArticleGoogle Scholar
- Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM, Leslie RD, Deloukas P, Spector TD: Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010, 20: 434-439. 10.1101/gr.103101.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, Savage DA, Mueller-Holzner E, Marth C, Kocjan G, Gayther SA, Jones A, Beck S, Wagner W, Laird PW, Jacobs IJ, Widschwendter M: Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010, 20: 440-446. 10.1101/gr.103606.109.PubMedPubMed CentralView ArticleGoogle Scholar
- Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, Jaenisch R, Wagschal A, Feil R, Schreiber SL, Lander ES: A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell. 2006, 125: 315-326. 10.1016/j.cell.2006.02.041.PubMedView ArticleGoogle Scholar
- Azuara V, Perry P, Sauer S, Spivakov M, Jørgensen HF, John RM, Gouti M, Casanova M, Warnes G, Merkenschlager M, Fisher AG: Chromatin signatures of pluripotent cell lines. Nat Cell Biol. 2006, 8: 532-538. 10.1038/ncb1403.PubMedView ArticleGoogle Scholar
- Zhao XD, Han X, Chew JL, Liu J, Chiu KP, Choo A, Orlov YL, Sung WK, Shahab A, Kuznetsov VA, Bourque G, Oh S, Ruan Y, Ng HH, Wei CL: Whole-genome mapping of histone H3 Lys4 and 27 trimethylations reveals distinct genomic compartments in human embryonic stem cells. Cell Stem Cell. 2007, 1: 286-298. 10.1016/j.stem.2007.08.004.PubMedView ArticleGoogle Scholar
- Gertz J, Varley KE, Reddy TE, Bowling KM, Pauli F, Parker SL, Kucera KS, Willard HF, Myers RM: Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet. 2011, 7: e1002228-10.1371/journal.pgen.1002228.PubMedPubMed CentralView ArticleGoogle Scholar
- Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, Hedman AK, Bataille V, Tzenova Bell J, Surdulescu G, Dimas AS, Ingle C, Nestle FO, di Meglio P, Min JL, Wilk A, Hammond CJ, Hassanali N, Yang TP, Montgomery SB, O'Rahilly S, Lindgren CM, Zondervan KT, Soranzo N, Barroso I, Durbin R, et al: The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 2011, 7: e1002003-10.1371/journal.pgen.1002003.PubMedPubMed CentralView ArticleGoogle Scholar
- Nagarajan M, Veyrieras JB, de Dieuleveult M, Bottin H, Fehrmann S, Abraham AL, Croze S, Steinmetz LM, Gidrol X, Yvert G: Natural single-nucleosome epi-polymorphisms in yeast. PLoS Genet. 2010, 6: e1000913-10.1371/journal.pgen.1000913.PubMedPubMed CentralView ArticleGoogle Scholar
- Parts L, Hedman ÅK, Keildson S, Knights AJ, Abreu-Goodger C, van de Bunt M, Guerra-Assunção JA, Bartonicek N, van Dongen S, Mägi R, Nisbet J, Barrett A, Rantalainen M, Nica AC, Quail MA, Small KS, Glass D, Enright AJ, Winn J, MuTHER Consortium, Deloukas P, Dermitzakis ET, McCarthy MI, Spector TD, Durbin R, Lindgren CM: Extent, causes, and consequences of small RNA expression variation in human adipose tissue. PLoS Genet. 2012, 8: e1002704-10.1371/journal.pgen.1002704.PubMedPubMed CentralView ArticleGoogle Scholar
- Teschendorff AE, Jones A, Fiegl H, Sargent A, Zhuang JJ, Kitchener HC, Widschwendter M: Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Med. 2012, 4: 24-10.1186/gm323.PubMedPubMed CentralView ArticleGoogle Scholar
- Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, Zhang X, Bernstein BE, Nusbaum C, Jaffe DB, Gnirke A, Jaenisch R, Lander ES: Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature. 2008, 454: 766-770.PubMedPubMed CentralGoogle Scholar
- Ohm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, Mohammad HP, Chen W, Daniel VC, Yu W, Berman DM, Jenuwein T, Pruitt K, Sharkis SJ, Watkins DN, Herman JG, Baylin SB: A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet. 2007, 39: 237-242. 10.1038/ng1972.PubMedPubMed CentralView ArticleGoogle Scholar
- Schlesinger Y, Straussman R, Keshet I, Farkash S, Hecht M, Zimmerman J, Eden E, Yakhini Z, Ben-Shushan E, Reubinoff BE, Bergman Y, Simon I, Cedar H: Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer. Nat Genet. 2007, 39: 232-236. 10.1038/ng1950.PubMedView ArticleGoogle Scholar
- Widschwendter M, Fiegl H, Egle D, Mueller-Holzner E, Spizzo G, Marth C, Weisenberger DJ, Campan M, Young J, Jacobs I, Laird PW: Epigenetic stem cell signature in cancer. Nat Genet. 2007, 39: 157-158. 10.1038/ng1941.PubMedView ArticleGoogle Scholar
- Cuddapah S, Jothi R, Schones DE, Roh TY, Cui K, Zhao K: Global analysis of the insulator binding protein CTCF in chromatin barrier regions reveals demarcation of active and repressive domains. Genome Res. 2009, 19: 4-32.Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.