Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence

  1. Richard Saffery2,3,12
  1. 1Bioinformatics Unit, Murdoch Childrens Research Institute (MCRI), Parkville, Victoria 3052, Australia;
  2. 2Cancer and Developmental Epigenetics Group, MCRI, Parkville, Victoria 3052, Australia;
  3. 3Department of Paediatrics, University of Melbourne, Victoria 3052, Australia;
  4. 4University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland 4102, Australia;
  5. 5Queensland Institute of Medical Research, Brisbane, Queensland 4006, Australia;
  6. 6Hjelt Institute, Department of Public Health, FI-00014 University of Helsinki, Helsinki, Finland;
  7. 7Early Life Epigenetics Group, MCRI, Parkville, Victoria 3052, Australia;
  8. 8Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, USA;
  9. 9Department of Psychiatry & Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia 30322, USA;
  10. 10Department of Psychiatry, University of Wisconsin School of Medicine, Madison, Wisconsin 53719, USA;
  11. 11The Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia

    1. ↵12 These authors contributed equally to this work.

     
    Next Section

    Abstract

    Comparison between groups of monozygotic (MZ) and dizygotic (DZ) twins enables an estimation of the relative contribution of genetic and shared and nonshared environmental factors to phenotypic variability. Using DNA methylation profiling of ∼20,000 CpG sites as a phenotype, we have examined discordance levels in three neonatal tissues from 22 MZ and 12 DZ twin pairs. MZ twins exhibit a wide range of within-pair differences at birth, but show discordance levels generally lower than DZ pairs. Within-pair methylation discordance was lowest in CpG islands in all twins and increased as a function of distance from islands. Variance component decomposition analysis of DNA methylation in MZ and DZ pairs revealed a low mean heritability across all tissues, although a wide range of heritabilities was detected for specific genomic CpG sites. The largest component of variation was attributed to the combined effects of nonshared intrauterine environment and stochastic factors. Regression analysis of methylation on birth weight revealed a general association between methylation of genes involved in metabolism and biosynthesis, providing further support for epigenetic change in the previously described link between low birth weight and increasing risk for cardiovascular, metabolic, and other complex diseases. Finally, comparison of our data with that of several older twins revealed little evidence for genome-wide epigenetic drift with increasing age. This is the first study to analyze DNA methylation on a genome scale in twins at birth, further highlighting the importance of the intrauterine environment on shaping the neonatal epigenome.

    Epigenetics has been defined as “the structural adaptation of chromosomal regions so as to register, signal or perpetuate altered activity states” (Bird 2007). This is exemplified by the epigenetic mark of DNA methylation, which influences a gene's transcriptional potential and plays a role in differentiation (Reik 2007; Brunner et al. 2009; Huang and Fan 2010) and aging (Rakyan et al. 2010; Teschendorff et al. 2010). Disruption of epigenetic profile is a ubiquitous feature of cancers and is likely to play a role in the etiology of other complex diseases (van Vliet et al. 2007; Foley et al. 2009).

    The DNA methylation profile is heritable through mitosis, but the fidelity of this transmission is imperfect (Bennett-Baker et al. 2003) and may contribute to differences in gene expression and phenotype observed between genetically identical individuals, whether isogenic strains of mice (Gartner and Baunack 1981; Pritchard et al. 2006) or human MZ twins (Fraga et al. 2005; Martin 2005; Kuratomi et al. 2008; Kaminsky et al. 2009; Javierre et al. 2010).

    Animal studies have demonstrated that the environment can shape the epigenome, particularly during the intrauterine period, when it demonstrates the greatest plasticity (Gluckman et al. 2007, 2010; Ozanne and Constancia 2007). The importance of this period for human health is well documented, and mounting evidence implicates the intrauterine environment in the fetal “programming” of diseases of later life (Gluckman et al. 2007). Despite this, it remains unclear as to how influential the intrauterine period is in shaping the human epigenome, whether different genomic regions show varying sensitivities to this environment during this period, and the extent to which this interaction is sensitive to genetic influences.

    Twin studies, which have traditionally enabled estimation of genetic and environmental components to phenotypic variance, have been used to estimate the effect of these factors on DNA methylation, at both a gene-specific level (Heijmans et al. 2007; Wong et al. 2010) or throughout the genome (Kuratomi et al. 2008; Kaminsky et al. 2009; Javierre et al. 2010; Rakyan et al. 2011a,b). Such studies are improving our understanding of the processes involved in the regulation of epigenetic variation and are disentangling the relative contributions of epigenetics, environment, and genetic variation, together with stochastic factors, in complex traits (Bell and Saffery 2012). This information is critical to understanding processes of development and evolution (Feinberg and Irizarry 2011) and for future potential epigenetic-based interventions in complex disease.

    To investigate the components of epigenetic variation at birth, we have established a longitudinal cohort of 250 twin pairs with collection of extensive biospecimens and environmental data (Saffery et al. 2012) and have shown, in two tissues from 14 twin pairs at birth, that twins differ in levels of gene expression on a genome-wide scale (Gordon et al. 2011), most likely in response to epigenetic variability. Furthermore, we subsequently provided direct evidence that DNA methylation can vary considerably within a single locus in multiple tissues from MZ twin pairs collected at birth (Ollikainen et al. 2010). This supports the previously demonstrated genome-scale differences in methylation within MZ and DZ twin pairs in adults (Kaminsky et al. 2009). However, no study has yet focused on genome-scale methylation differences within twins at birth.

    In this study, we used genome-scale DNA methylation profiling to measure the level of epigenetic variation present in three tissues from 22 MZ and 12 DZ twin pairs collected at birth. We then estimated the within-pair variation of methylation profile generally and in the context of specific genomic features, and estimated the contribution of genetic and common and unique environment to DNA methylation profile. We further defined gene pathways and networks subject to epigenetic change in association with birth weight with the aim of investigating a possible epigenetic link with risk for metabolic and cardiovascular disease.

    Previous SectionNext Section

    Results

    Genome-scale analysis of DNA methylation of three tissues from newborn twins

    We used the Illumina Infinium HumanMethylation27 BeadChip array (HM27) platform to profile DNA methylation in cord blood mononuclear cells (CBMCs; 18 MZ, nine DZ pairs), human umbilical vascular endothelial cells (HUVECs, 14 MZ, 10 DZ pairs), and placenta (eight MZ and seven DZ twin pairs) (Table 1). The HM27 platform interrogates 27,578 CpG dinucleotides primarily associated with 14,475 transcription start sites and has a high technical reproducibility (Weisenberger et al. 2008; Bibikova et al. 2009; Rajendram et al. 2011). It contains separate probes to detect methylated and unmethylated sequences, and data from both probes are used to calculate a β-value between 0 and 1 (equivalent to 0%–100% methylation) (Bibikova et al. 2009). Initial quality control led to the removal of two twin pairs from HUVECs, one twin pair from CBMCs, and one twin pair from placenta (Table 1). We chose a highly conservative P-value probe cutoff of 0.001, lower than has previously been reported, to minimize the level of variability attributable to technical factors. After removing probes on the sex chromosomes and probes that had a detection P-value > 0.001, 19,350 probes for HUVECs, 19,204 for CBMCs, and 26,353 for placenta remained for subsequent analysis.

    View this table:
    Table 1.

    Twin pair characteristics

    Twin pairs generally show a similar DNA methylation profile

    In order to visualize the overall relationship between DNA methylation profiles of individuals, both within and between pairs, unsupervised clustering was applied to sample data using all HM27 probes that passed quality-control measures for each tissue separately (Supplemental Figs. S1–S3). Interestingly, twins within the same pair did not always cluster together, and the proportion of within-pair clustering varied considerably in a tissue-dependent manner. For example, only 29% of pairs clustered in HUVECS, whereas intrapair similarity predominated in CBMCS and placenta, with 58% and 71% of individual pairs clustering together respectively (Supplemental Table S1). In all three tissues, a greater proportion of MZ pairs clustered together relative to DZ pairs (Supplemental Table S1). In combination, these data provide prima facie evidence for both tissue-specific and genetic factors in determining neonatal epigenetic profile.

    Identification of factors contributing to DNA methylation profile

    To quantify within-pair relationships both for measures of discordance and similarity, we calculated Euclidean distance (ED) and Pearson's correlation coefficient (Fig. 1; Supplemental Table S2). Both ED and Pearson's correlation coefficient were plotted using logit-transformed β-values (i.e., M-values) with mean-subtracted transformed values used for the latter (Fig. 1). In all instances, MZ pairs showed a greater median within-pair similarity and lower median within-pair discordance than DZ pairs, confirming a role for underlying genetic factors in contributing to neonatal epigenetic profile. These data also show that a proportion of unrelated individuals have a higher degree of similarity in overall DNA methylation profile than some co-twins, highlighting the likely role of stochastic/nonshared environmental factors in determining the epigenetic profile. Surprisingly, when the effects of chorionicity were tested (within MZ pairs only), dichorionic (DC) pairs were found to be generally more similar (less discordant) epigenetically than monochorionic (MC) pairs in both CBMCs (eight MC, nine DC) and HUVECs (eight MC, five DC) (Fig. 1). Insufficient numbers precluded a similar examination in relation to the placental methylation profile. In addition, there were no sex-specific significant differences in discordance or similarity for all tissues (data not shown).

    Figure 1.
    View larger version:
    Figure 1.

    Relationship between within-pair methylation discordance/correlation, zygosity, and chorionicity. Box-and-whisker plots of within-pair methylation discordance (Euclidean distance) and Pearson's correlation coefficient of mean-corrected values in HUVECs, CBMCs, and placenta from MZ and DZ twins, same-sex unrelated (UR) individuals, and MC and DC MZ twins. Numbers of pairs within each category are shown above each graph.

    Estimation of variance components of DNA methylation profile

    Using methylation within our neonatal twin tissues as a variable phenotype, we estimated the narrow sense heritability (h2, the proportion of phenotypic variance due to additive genetic factors) and common (intrauterine) environmental variance (c2) for all HM27 probe-associated CpGs within our data sets using the least squares estimation approach (Visscher 2004). The distribution of the h2 estimates obtained for each probe in the three tissues was overlaid against the expected null distribution with the variance equal to the expected sampling variance (Visscher 2004) and a mean of zero (Fig. 2). Mean heritability across all probes was 0.12 (±0.0017, p = 0.0024) for CBMCs, 0.07 (±0.0014, p = 0.009) for HUVECs, and 0.05 (±0.0016, p = 0.017) for placenta with calculated P-values from empirical null distribution of Graphic determined by a permutation analysis, suggesting a pattern of greater estimated heritability than that expected by chance. Because the distribution of c2 did not differ from a random distribution (median = 0), we conclude that shared environment does not contribute significantly to methylation variation within our twins in utero and that the remaining variance in methylation is due to the sum of unique intrauterine environment and stochastic factors encountered by the tissues in each twin. An investigation of probes with the highest (top 5%) heritability estimates (Supplemental Table S3) revealed 961 probes in CBMCs with h2 values ranging from 0.48 to 0.94; 968 probes in HUVECs with h2 = 0.37–0.95, and 1304 probes in placenta with h2 = 0.49–0.97. In addition, only 3%–10% of highly heritable probes were shared between two tissues (28 between placenta and HUVECs, 109 between HUVECs, and CBMCs and 58 between CBMCs and placenta). Only three probes were highly heritable in all three tissues (cg15052901/SLC24A4, h2 = 0.49–0.62; cg14217157/WHSC2, h2 = 0.52–0.63; cg01593886/COL1A1, h2 = 0.51–0.72). Ontology analysis of genes linked to highly heritable probes revealed enrichment of genes involved in differentiation and development for CBMCs and HUVECs, metabolism, and biosynthesis in HUVECs and signaling in placenta (Supplemental Table S4).

    Figure 2.
    View larger version:
    Figure 2.

    Analysis of variance components of DNA methylation in CBMCs, HUVECs, and placenta. Data from all probes were used to plot histograms of heritability (h2) and common (intrauterine) environmental variance (c2). Distributions of h2 were also compared with the random distribution (dotted lines).

    Within-pair discordance varies with genomic location

    Because the annotation of the HM27 array focuses on CpG islands and gene promoters (Bibikova et al. 2009), we examined how within-pair methylation discordance, measured using median within-pair standard deviation, varies in relation to these landmarks. Previous studies in unrelated individuals (Bock et al. 2008) and twins (Kaminsky et al. 2009; Sandoval et al. 2011) have suggested that such regions are more refractory to methylation variation than non-island CpGs. To test this in our data, we compared median within-pair methylation discordance for CpG islands, CpG island “shores” (sequences up to 2 kb from CpG islands), CpG island “shelves” (sequences within 2 kb and 4 kb of CpG islands), and CpGs >4 kb from CpG islands (Fig. 3; Sandoval et al. 2011). In agreement with previous studies (Doi et al. 2009; Irizarry et al. 2009), we found that mean absolute methylation levels in our samples increased as a function of distance from CpG islands (data not shown). We also found that median within-pair methylation discordance increased with increasing distance from CpG islands (up to 4 kb) in all tissues for both MZ and DZ pairs, with no evidence for a further increase at distances >4 kb.

    Figure 3.
    View larger version:
    Figure 3.

    Relationship between methylation discordance to location within CpG islands, shores, and shelves. Median within-pair methylation discordance is plotted as box-and-whisker plots against probes depending on location in relation to CpG islands. Data are plotted for CpG islands, CpG island shores (0–2 kb from islands), CpG island shelves (2–4 kb from islands), and “open sea” probes (>4 kb from CpG islands).

    Relationship between within-pair methylation discordance and age: no evidence for genome-scale epigenetic drift

    We have presented evidence that the nonshared intrauterine environment contributes to methylation discordance at birth for both MZ and DZ twins. However, little is known about how nonshared environment can influence epigenetic discordance postnatally. We and others have shown using array analysis that within-twin pair discordance in the gene expression profile increases as a function of age (Fraga et al. 2005; Gordon et al. 2011). Additionally, limited evidence from low-resolution analysis also suggests that within-pair DNA methylation discordance increases with age (a phenomenon termed “epigenetic drift”) (Fraga et al. 2005). However, a recent Infinium HM27 array analysis of saliva from adult twins did not find evidence for such drift (Bocklandt et al. 2011). We plotted within-pair methylation discordance using Euclidean distance for blood-derived data from twins from birth to 73 yr of age from our data and 94 MZ pairs and 17 DZ pairs from Infinium HM27 data sets from a previously published data set (Rakyan et al. 2010), with additional sets of unpublished data (Fig. 4; Table 2). We found no evidence for epigenetic drift throughout the life course in either MZ or DZ pairs.

    Figure 4.
    View larger version:
    Figure 4.

    Relationship between within-pair methylation discordance with age in blood-derived tissues. Euclidean distance is plotted, as in Methods, for twins from birth to 73 yr of age from our data and 94 MZ pairs and 17 DZ pairs from previously published and unpublished Infinium HM27 data sets (see text and Table 2).

    View this table:
    Table 2.

    Details of the Infinium HumanMethylation27 data sets used for Figure 4

    Identification of highly discordant gene classes

    Despite the overall low median discordance in methylation apparent in all twin pairs (Supplemental Fig. S4), almost all pairs had several HM27 probes with a high level of methylation discordance in excess of ∼20% (Δβ > 0.2). To investigate the potential biological relevance of such probes, we ranked each gene-associated CpG by median within-pair standard deviation, a measure that summarizes the typical discordance between co-twins for that gene, separately for MZ and DZ pairs, for all three tissues (Supplemental Table S5). Using a combination of Gene Ontology (Supplemental Table S6) and pathway analysis (Supplemental Table S7), we found that genes associated with development and morphogenesis were over-represented in MZ and DZ pairs from all three tissues, closely followed by genes involved in response to environment and the cell cycle/cell division. In contrast to our previous analysis of gene expression (Gordon et al. 2011), we found no evidence that imprinted differentially methylated regions (DMRs) (Choufani et al. 2011) and housekeeping genes are significantly variably methylated within MZ twin pairs (p > 0.14 and p > 0.37 for all tissues, respectively).

    Identification of discordantly methylated genes associated with birth weight

    We are particularly interested in identifying epigenetic variation in genes potentially associated with birth weight because of the numerous previous studies linking low birth weight with later development of cardiovascular, metabolic, and other complex diseases (Barker 1990; Morley and Dwyer 2005; Burdge et al. 2007; Gluckman et al. 2007). We have previously identified genes whose expression levels correlate with birth weight in a subset of tissues from newborn twins and shown that these genes are enriched for functions and pathways associated with metabolism, growth, and cardiovascular disease (Gordon et al. 2011). In the present study, we performed a similar regression analysis of methylation M-values on birth weight, using a statistical model that estimates association based on within-pair variation of both DNA methylation and birth weight. Methylation of a small number of genes (seven in DZ CBMCs, one in MZ HUVECs) was significantly associated with birth weight after adjustment for multiple testing (adjusted P-value < 0.1) (Supplemental Table S8) and included genes with links to metabolism, growth, and cardiovascular disease. For example, APOLD1 (apolipoprotein L domain containing 1), identified in HUVECs and EDG1 (sphingosine-1-phosphate receptor 1), identified in CBMCs, both regulate vascular function in mice and humans (Liu et al. 2000; Regard et al. 2004; Simonsen et al. 2010; Gordon et al. 2011). Even though no genes in placenta reached significance after multiple testing, polymorphisms in the genes ranked 1 and 2, HLA-B (major histocompatibility complex class 1B) and SCD (stearoyl-CoA desaturase), have been associated with low birth weight and metabolic disease, respectively, in humans (Warensjo et al. 2007; Sampath and Ntambi 2008; Capittini et al. 2009; Shin et al. 2010).

    To investigate pathways and processes that may be subject to epigenetic variation in association with birth weight, genes from the above regression analysis were ranked by adjusted P-values for birth weight and analyzed using Gene Ontology (Supplemental Table S9) and pathway analysis (Supplemental Table S10). An over-representation of genes associated with metabolism and biosynthesis was found for all three tissues for both MZ and DZ pairs, genes associated with cardiovascular function/disease were over-represented in all three tissues from MZ pairs and HUVECs from DZ pairs, and genes associated with growth and proliferation were over-represented in only CBMCs for both MZ and DZ pairs. To test for an association of specific classes of genes previously linked to birth weight and metabolic function, we performed gene set test analysis on a list of 167 imprinted DMRs (Choufani et al. 2011), 247 cardiovascular-associated genes (http://www.ucl.ac.uk/silva/cardiovasculargeneontology) (Lovering et al. 2008), and 41 genes previously linked with obesity by genome-wide association studies (Thorleifsson et al. 2009; Willer et al. 2009; Heid et al. 2010; Speliotes et al. 2010). No evidence of association of imprinted or cardiovascular-associated genes with birth weight was found in any tissue from MZ or DZ twins (p > 0.15 and p > 0.2, respectively), whereas some evidence for a moderate relationship between obesity-associated genes and birth weight was found in HUVECs from DZ (p = 0.01) and MZ pairs (p = 0.11), but not in other tissues (p > 0.56).

    To further validate the observed associations, we performed locus-specific DNA methylation analysis on three genes using the Sequenom EpiTYPER platform, which we have previously found to be accurate and reproducible (Novakovic et al. 2010, 2011; Ollikainen et al. 2010). Methylation assays were designed to interrogate specific CpG sites (plus adjacent sites) measured by the HM27 platform, with measurements performed using the same sample set. Absolute DNA methylation was highly correlated across the two platforms (Spearman's correlation coefficient 0.485–0.947) (Supplemental Fig. S5) and validated the relationship between within-pair methylation discordance and birth weight discordance (Supplemental Figs. S6–S8). This analysis also highlighted that the observed association of birth weight with methylation level is regional rather than localized to a specific CpG site.

    Previous SectionNext Section

    Discussion

    Despite the growing awareness of the importance of interindividual epigenetic variation in modulating the risk associated with human disease, it remains largely unclear as to when and how such variation arises in humans. In this study, we capitalized on the strengths of a twin study design to present the first evidence for widespread, genome-scale epigenetic discordance between genetically identical humans at birth. These findings highlight the importance of the intrauterine environment in determining the neonatal DNA methylation profile and reveal the presence of tissue-specific variability in response to such factors.

    Nonshared environmental/stochastic factors predominate in determining neonatal DNA methylation

    Unsupervised clustering and pair-specific analysis of discordance and correlation revealed compelling evidence for a limited genetic contribution to the neonatal methylation variability, supported by analysis of variance components of DNA methylation, which produced mean levels of heritability of 0.05–0.12 depending on tissue (Fig. 2). However, the HM27 platform we used for this analysis contains probes associated with gene promoters and CpG islands, and previous studies have demonstrated that genomic regions showing a high heritability of DNA methylation are under-represented in CpG islands (Gertz et al. 2011). As such, our heritability estimates are potentially conservative and may not be representative of the genome as a whole. Additionally, it is important to remember that epigenetic heritability estimates will not only be population-specific, but also cell-, tissue-, time-, and locus-specific. These will also be largely dependent on the sensitivity, resolution, and coverage of the specific epigenetic assay used for measurement.

    The suggestion that the DNA methylation profile is only minimally influenced by genetic variation agrees with a previous array-based, genome-scale study of DNA methylation in buccal cell DNA from teenage twins (Kaminsky et al. 2009) and with an analysis of DNA methylation at 1760 CpG sites in CD4+ lymphocytes from adult twins (Gervin et al. 2011). One study that measured DNA methylation at ∼1500 CpG sites in whole blood from 43 MZ and 43 DZ twin pairs found that 23% of all CpG sites displayed significant heritability of methylation level (Boks et al. 2009). Although low power makes comparison difficult, the range of heritability within the top 5% of probes in the Boks and colleagues study (0.62–0.94) is similar to that found for the top 5% of probes for CBMCs in our study (0.48–0.94). Studies of allele-specific methylation (ASM) have also found evidence for a high heritability of DNA methylation at a subset of genomic loci, with proportions varying with the method of analysis and the tissue examined (Kerkel et al. 2008; Boks et al. 2009; Zhang et al. 2009, 2010; Meaburn et al. 2010; Schalkwyk et al. 2010; Shoemaker et al. 2010; Gertz et al. 2011).

    Surprisingly, we found little evidence for an effect of common environment on the overall DNA methylation profile at birth using variance component analysis. This does not rule out the possibility that a minority of genes are influenced by a common environment. Indeed, previous genome-scale studies of DNA methylation have found a range of probes significantly associated with maternal environmental, from 0.6% (Breton et al. 2009) and 1.1% (Fryer et al. 2011) to 23% (Katari et al. 2009). Because the accuracy of the estimation of effect size depends on study power, further investigation in larger numbers of twins is needed (Visscher 2004).

    Given the small genetic effect and lack of evidence for widespread common environmental effects observed in our study, the largest residual variance component contributing to overall DNA methylation profile represents cumulative nonshared (individual intrauterine) environment and stochastic factors. There is prior evidence that nonshared environmental factors can influence phenotype (Bergvall and Cnattingius 2008; Plomin 2011; Torche and Echevarria 2011). Such factors in twin pregnancies include discordant placental weight, discordant placental umbilical cord insertion (both resulting in differential fetal blood supply), or differential exposure to infection (Stromswold 2006; Antoniou et al. 2011). Further evidence that factors specific to each twin can influence methylation comes from our finding that genes involved in response to environment are discordantly methylated (this study) or expressed (Gordon et al. 2011) within twin pairs.

    Our finding that MZ twins sharing a single placenta (MC) were more discordant for methylation profile than MZ twins with their own placenta (DC) is counterintuitive but similar to that reported previously (Kaminsky et al. 2009). It was suggested that the earlier embryo splitting associated with DC pairs potentially reflects epigenetically divergent cell populations in the pre-splitting blastomere (Kaminsky et al. 2009), although there is no direct evidence supporting this hypothesis. An alternative explanation could be that MC twins are more likely to experience competition for resource allocation, with or without associated vascular connections that in extreme cases can lead to a large flow of blood in one direction known as twin-to-twin transfusion syndrome (TTTS). Although each of these scenarios is possible, it is worth noting that we have shown previously that the effect of chorionicity on DNA methylation can be tissue-dependent (Ollikainen et al. 2010; Novakovic et al. 2011). Clearly, larger studies are needed to resolve this issue.

    Extensive evidence exists for a role of stochastic influence in determining DNA methylation and other epigenetic marks during early development (for review, see Whitelaw et al. 2010). An elegant example of this was reported by Feinberg and colleagues, who demonstrated that the genome-wide DNA methylation profile in the livers of newborn inbred mice raised in identical environments was hypervariable at certain loci, termed variably methylated regions (VMRs) (Feinberg and Irizarry 2011). VMRs were enriched in genes involved in development and morphogenesis both in mice and unrelated humans (Feinberg et al. 2010), and such genes are also discordantly methylated within our twin pairs (Supplemental Tables S9, S10). Because early development is associated with rapid cell division, this could explain our finding that genes involved in the cell cycle are discordantly methylated within twins, which was also found in adult twins (Kaminsky et al. 2009). We also found that genes associated with response to environment are hypervariable within MZ and DZ twin pairs in multiple tissues (Supplemental Tables S9, S10), which agrees with previous studies of expression discordance within MZ twin pairs (Sharma et al. 2005; Choi and Kim 2007).

    Evidence for a tissue-specific effect on heritability of methylation profile

    We found no compelling evidence for a common set of highly heritable DNA methylation variants across different tissues, supporting similar findings in a study of ASM in multiple human cell lines (Shoemaker et al. 2010) and recent data using gene expression as a phenotypic outcome (Powell et al. 2012). This contrasts with a subset of recently described ASM events described in a single individual that appear to be shared across kidney and skeletal muscle (Gertz et al. 2011). Such tissue-specific differences in the heritability of DNA methylation (and expression) may arise due to the possibility that different CpGs or combinations of CpGs may be involved in control of the expression of the same gene in different tissues, in association with the biological function of specific cells (Altschuler and Wu 2010).

    Genomic regions with high within-pair methylation discordance; constraint at CpG islands?

    We found that in all tissues and in all twins, within-pair differences in DNA methylation increased with increasing distance from CpG islands (Fig. 3). This agrees with a similar finding for highly variably methylated regions (VMRs) between unrelated individuals (Zhang et al. 2010) and with data showing that interindividual differences in methylation are lowest in unmethylated CpG islands and highest in methylated regions of the genome (Bock et al. 2008). Furthermore, our data have shown that such regions are hypervariable irrespective of zygosity, in support of our findings of only a minor genetic effect in determining the overall DNA methylation profile. In combination, these data suggest that methylation levels are more constrained in CpG-dense genomic regions, possibly because of a stabilizing influence from neighboring CpGs (Bock et al. 2008).

    What function does variable methylation serve?

    Because MZ twins share the same mother and genotype, factors present with the residual variance component “nonshared environment” are most likely driving variation here (see above). Although the relative proportions of nonshared (intrauterine) environment versus stochastic influences on the neonatal epigenome remain to be demonstrated, Feinberg and colleagues have proposed a model in which a genetically inherited propensity to stochastic variability in DNA methylation has evolved to increase fitness in a varying environment (Feinberg and Irizarry 2011). Furthermore, a combination of influences have been proposed to explain the phenomenon of metastable epialleles, which are variably expressed due to epigenetic modifications that are established in a stochastic manner during early development, and that are also environmentally labile (for review, see Bernal and Jirtle 2010).

    DNA methylation in relation to low birth weight: A mechanistic link with complex disease in later life

    We found that the genes whose methylation was tightly associated with birth weight were enriched for functions and pathways associated with growth, metabolism, and cardiovascular disease (Supplemental Tables S9, S10). A subset of these was confirmed by locus-specific DNA methylation analysis that revealed up to 60% methylation discordance between heaviest versus lightest twins (Supplemental Figs. S6–S8). Taking into account our previous data from expression arrays, we speculate that DNA methylation and expression levels of key genes associated with cardiovascular and metabolic function can be set in utero to confer elevated risk for disease in later life and that this setting is linked with low birth weight.

    Lack of evidence for genome-wide epigenetic drift throughout postnatal life

    Our data support the idea that a combination of stochastic and nonshared intrauterine environment can generate a net within-pair difference in DNA genome-wide epigenomic profiles at birth. But do such factors influence the epigenome after birth in a similar way? The cumulative effects of environmental and stochastic variation on changing epigenetic profile (known as “epigenetic drift”) were first described by a study that examined both genome-wide and locus-specific DNA methylation variation in a small number of young and middle-aged MZ twins, which found a greater within-pair discordance in the latter (Fraga et al. 2005). Our cross-sectional analysis of genome-scale data from HM27 arrays from blood-derived DNA from twins from birth to >70 yr of age does not support a generalized age-related epigenetic drift at the genome-scale level (Fig. 4) and agree with other recent data from saliva using the same platform (Bocklandt et al. 2011). These discrepancies are most likely due to differences in methodology, sample size, and genomic regions the genome studied. Despite our lack of compelling evidence for epigenetic drift over the life course, others have shown that DNA methylation can vary over time, using cross-sectional (Boks et al. 2009; Christensen et al. 2009; Rakyan et al. 2010) or longitudinal (Bjornsson et al. 2008; Feinberg et al. 2010) approaches. Further studies are needed that focus on both epigenetic change and epigenetic discordance within twin pairs over time, ideally addressing the suggestion that epigenetic drift could be driven by differences in environment (Fraga et al. 2005).

    Strengths and weaknesses of this study

    In focusing on newborn twins, we have isolated effects accumulated solely during intrauterine life. This is a unique time during the life span in which individuals share the same maternal environment, thus minimizing differences in shared environment. This has also enabled us to use traditional modeling of variance components to estimate the relative influence of genetic, common, and nonshared environment throughout the genome. Because only one other study has measured chorionicity in twins (Kaminsky et al. 2009), this has enabled us to investigate the effects of sharing or having separate placentas on the methylome. Studying twins at birth has also enabled us to identify a possible epigenetic mechanism linking low birth weight and risk for complex disease in later life. This has implications for future minimization of the poor health outcomes associated with low birth weight, through reversing the associated epigenetic changes. Technologically, we have used a reproducible and accurate method of genome-scale measurement of DNA methylation, which compares well to and is more quantifiable than immunoprecipitation or enzyme-based methods (Bock et al. 2010). We have also looked at multiple tissues, which has enabled us to determine whether methylation variation occurs in a tissue-specific manner. Twin studies sometimes attract the criticism that they may not be applicable to the rest of the population. However, apart from sharing a placenta, and a relatively smaller birth size and lower gestational age, twins have similar health outcomes to singletons (Morley et al. 2003; Morley and Dwyer 2005). Greater than 95% of twins and all singletons have their own amniotic sac and therefore would have similar issues of nonshared environment, albeit with different magnitudes of variation in placenta weight, cord placement, and cord thickness (Antoniou et al. 2011).

    The main weakness of this and similar studies (e.g., Boks et al. 2009) is the relatively small sample size. However, our primary focus is not on specific genes, but on groups of genes with shared ontology, and on compound measurements of similarities and differences between the methylomes of twins. We have also focused on gene promoters and CpG islands, which constitute a relatively small proportion of the genome, and acknowledge that some of our findings may not be reflected in the rest of the genome.

    In our calculations of within-pair discordance and heritability, it is possible that genetic differences within DZ pairs result in differing probe-hybridization efficiencies within such pairs. However, our unpublished studies have shown that such sequence variants do not affect the estimation of DNA methylation at the CpG associated with each probe, essentially because any changes to hybridization kinetics will affect the methylation-specific probes and the non-methylation-specific probes in the same manner. But what of the effect of SNPs at CpG sites assayed by Infinium HM27 probes? Of a small number of probes for which this is the case (Chen et al. 2011), only one appeared in our list of ∼1000 genes with a high heritability (data not shown).

    In summary, our study uses biological samples from twins at birth and contributes to the understanding of prenatal human development and the factors by which it is influenced. It is essential to understand these factors in healthy individuals in order to compare with states of disease and disease predisposition.

    Previous SectionNext Section

    Methods

    Subjects and tissues

    Sample collection from twins at the time of delivery was performed with appropriate human ethics clearances from the Royal Women's Hospital (06/21), Mercy Hospital for Women (R06/30), and Monash Medical Center (06117C), Melbourne. The twin pairs chosen for methylation array analysis are shown in Table 1. They shared a similar sex ratio, gestational age, and birth weight to the full group of 250 pairs. For CBMCs, we studied 18 MZ and nine DZ pairs; from HUVECs, 14 MZ and 10 DZ pairs; and from placenta, eight MZ and seven DZ pairs. HUVECs and CBMCs were examined in combination for 19 pairs, and all three tissues were profiled in seven pairs (Table 1).

    Sample preparation and DNA extraction

    CBMCs were purified using density gradient centrifugation, CD31-positive HUVECS were isolated using collagenase and magnetic sorting, and full-thickness placental samples were isolated as described previously (Novakovic et al. 2010; Gordon et al. 2011). DNA was extracted using phenol:chloroform as described previously (Novakovic et al. 2010).

    Infinium methylation analysis

    DNA samples were processed using the MethylEasy Xceed bisulphite conversion kit (Human Genetic Signatures), according to the manufacturer's instructions. Genome-wide DNA methylation analysis was performed by the Australian Genome Research Facility (Melbourne, Australia) or ServiceXS (Leiden, The Netherlands). Infinium HM27 BeadChip arrays (Illumina) were hybridized and scanned as per the manufacturer's instructions. Raw data were exported from BeadStudio (Illumina). All statistical analysis was performed in R (version 2.12) (R_Development_Core_Team 2009) using packages from the Bioconductor project (Gentleman et al. 2004) and in-house scripts. Data quality was confirmed using arrayQualityMetrics (Kauffmann et al. 2009). Probes on the X and Y chromosomes were removed from further analysis to eliminate sex-specific differences in methylation. The lumi package, which is specifically designed for Illumina data, was used to calculate the log2 ratio for methylated probe intensity to unmethylated probe intensity, the M-value (Du et al. 2008, 2011). These values underwent background correction and normalization using lumi. Possible batch effects from samples processed at different times were compensated for with “color adjustment” from lumi. Any probe within a sample with a highly conservative detection P-value of 0.001 or greater was excluded from further analysis. For correlation coefficients, M-values with batch effects removed were used to calculate a mean M-value for each probe. This was then subtracted across all of the samples to create a mean-corrected set of M-values. The correlation coefficients for each pair for MZ, DZ, MZMC, and MZDC were then calculated from these values. Correlation coefficients were also calculated only within each array, for every unrelated individual (UR) comparison, i.e., pairwise comparison of unrelated individuals. Euclidean distances were also calculated using M-values from all probes.

    Differential methylation analyses were performed using the limma package (Smyth 2005), which is designed for the analysis of microarray data. Array quality weights were estimated to allow for the possibility of variable DNA quality between the samples (Ritchie et al. 2006), and these weights were incorporated in all differential methylation analyses. To study discordance between co-twins, a linear model was fitted to the M-values for each gene with twin-pair as a predictive factor, equivalent to a one-way ANOVA analysis in which variability is broken up into between-twin and within-twin components. This analysis yielded a within-twin standard deviation for each CpG across all twin pairs, which was taken to summarize the level of discordance between co-twins for that gene. To examine the relationship between birth weight and DNA methylation, genewise linear models were fitted with twin-pair as a factor and log-weight as a covariate. The linear models also included a correction for possible batch effects. The association of birth weight on each gene was assessed using moderated t-statistics (Smyth 2004). Genes were judged to be differentially methylated if the false discovery rate was under 0.1 after adjustment for multiple testing using the Benjamini and Hochberg algorithm (Hochberg and Benjamini 1990).

    Limma was also used to conduct several gene set tests. These tests use a Wilcoxon rank-sum test to assess whether a particular set of genes tends to be more highly ranked according to some given criterion than the remaining genes (Michaud et al. 2008). To test whether certain functional categories of genes tend to be more discordant than others, genes were ranked by within-pair standard deviation. This approach was applied to 45 imprinted genes and to 568 housekeeping genes (Eisenberg and Levanon 2003). To test whether certain functional groups of genes are associated with birth weight, genes were ranked by moderated t-statistic when testing for birth weight as a covariate. This approach was applied to a list of 247 cardiovascular-associated genes from the Cardiovascular Gene Ontology Annotation Initiative (Lovering et al. 2008).

    Estimating variance components of DNA methylation

    Using genetic (twin) models, we are able decompose the observed variance in methylation levels into its additive genetic and common environmental components (Neale and Cardon 1992). Additive genetic variance (A) denotes the variance resulting from the sum of allelic effects throughout the genome, whereas common environmental variance (C) relates to the environmental influences shared within twin pairs. The remaining variance (E) is nonshared (individual) environmental effects and includes error terms. We can estimate A, C, and E for each methylation probe based on the resemblance between MZ twins and DZ twins, who share, on average, 50% of segregating loci. For each methylation probe, we calculate the intraclass correlation between MZ (rMZ) and DZ (rDZ) twin pairs using a least-squares estimator (Visscher 2004). Variance components A, C, and E are calculated as follows: A = 2(rMZ − rDZ); C = rMZ − A; E = 1 − rMZ. Heritability (h2) is calculated as h2 = A/P, where P is the sum of A, C, and E (observed phenotypic variance).

    Locus-specific methylation analysis

    Sequenom MassARRAY EpiTYPING was performed as previously described (Ollikainen et al. 2010; Novakovic et al. 2011). The primers are listed in Supplemental Table S11. In brief, amplification was performed after bisulfite conversion of genomic DNA with the MethylEasy Xceed bisulphite conversion kit (Human Genetic Signatures). All PCR amplifications and downstream processing were performed at least in duplicate, and the mean methylation level at specific CpG sites was determined. Raw data obtained from MassArray EpiTYPING were cleaned systematically using an R-script to remove samples that failed to generate data for >70% of CpG sites tested. Also, technical replicates showing ≥5% absolute difference from the median value of the technical replicates were removed, and only samples with at least two successful technical replicates were analyzed. Samples were compared across each analyzable CpG site in the amplicon, as well as the mean across the whole amplicon.

    Gene Ontology and pathway analysis

    Gene Ontology (GO) analyses were conducted using GOrilla software (Eden et al. 2009), with the default options and searching all ontologies. This program identifies enriched GO terms from ranked gene lists using all ranked genes, an approach that is analogous to the limma Wilcoxon gene set tests. Genes were ranked for GOrilla analyses using the same ranking statistics as described above for the gene sets tests. Pathway analysis was performed using Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems). The functional analysis identified the biological functions and/or diseases that were most significant for each of the data sets. IPA was used to identify enriched canonical pathways, gene networks, and functional classes. Genes corresponding to all Infinium probes that passed QC were used as a reference set.

    Public microarray data analysis

    Illumina HumanMethylation27 BeadChip twin data were obtained from various sources (see Table 2). The data sets used were restricted to blood and, wherever possible, phenotypically normal twins. X- and Y-chromosome probes were removed from the analysis, and correlation coefficients and Euclidean distances were calculated from all probes as described above (Fig. 4).

    Previous SectionNext Section

    Data access

    The data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE36642.

    Previous SectionNext Section

    Acknowledgments

    We thank the following people for kindly sharing their unpublished data: Robert Lyle and Kristina Gervin, Department of Medical Genetics, Oslo University Hospital and University of Oslo, Norway; Jennifer Harris, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway; Jordana Bell and Pei-Chien Tsai, Department of Twin Research and Genetic Epidemiology, King's College and St. Thomas' Hospital, London, England; and Jonathan Mill and Chloe Wong, King's College, London, England. We also thank John Carlin for his contributions to establishing the PETS cohort and for biostatistical support; obstetricians Mark Umstad, Royal Women's Hospital, Melbourne; Euan Wallace, Monash Medical Centre, Melbourne; and Mark Permezel, Mercy Hospital for Women, Melbourne for their contributions to establishing the PETS cohort and access to study participants; Sarah Healy, Tina Vaiano, Nicole Brooks, Jennifer Foord, Sheila Holland, Anne Krastev, Siva Illancheran, and Joanne Mockler for recruitment and sample collection; and Technical Officer Anna Czajko, Study Coordinator Geraldine McIlroy, and all mothers and twins that participated in this study. Finally, we are grateful to all of the families at the participating SFARI Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, E. Wijsman). We also appreciate the access to phenotypic data on SFARI Base. Approved researchers can obtain the SSC population data set described in this study by applying at https://base.sfari.org. This work was supported by grants from the Australian National Health and Medical Research Council (grant numbers 437015 and 607358 to J.C. and R.S.), the Bonnie Babes Foundation (grant number BBF20704 to E.J.), the Sigrid Juselius Foundation (to M.O.), the Academy of Finland (to M.O.), the Finnish Cultural Foundation (to M.O.), the Financial Markets Foundation for Children (grant no. 032-2007), and by the Victorian Government's Operational Infrastructure Support Program.

    Previous SectionNext Section

    Footnotes

    • Received December 16, 2011.
    • Accepted April 26, 2012.

    This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.

    Previous Section
     

    References

    1. ↵
      1. Altschuler SJ,
      2. Wu LF
      Altschuler SJ, Wu LF. 2010. Cellular heterogeneity: Do differences make a difference? Cell 141: 559–563.
      CrossRefMedlineGoogle Scholar
    2. ↵
      1. Antoniou EE,
      2. Derom C,
      3. Thiery E,
      4. Fowler T,
      5. Southwood TR,
      6. Zeegers MP
      Antoniou EE, Derom C, Thiery E, Fowler T, Southwood TR, Zeegers MP. 2011. The influence of genetic and environmental factors on the etiology of the human umbilical cord: The East Flanders prospective twin survey. Biol Reprod 85: 137–143.
      Abstract/FREE Full Text
    3. ↵
      1. Barker DJ
      Barker DJ. 1990. The fetal and infant origins of adult disease. BMJ 301: 1111.
      FREE Full Text
    4. ↵
      1. Bell J,
      2. Saffery R
      Bell J, Saffery R. 2012. The value of twins in epigenetic epidemiology. Int J Epidemiol 41: 140–150.
      Abstract/FREE Full Text
    5. ↵
      1. Bennett-Baker PE,
      2. Wilkowski J,
      3. Burke DT
      Bennett-Baker PE, Wilkowski J, Burke DT. 2003. Age-associated activation of epigenetically repressed genes in the mouse. Genetics 165: 2055–2062.
      MedlineGoogle Scholar
    6. ↵
      1. Bergvall N,
      2. Cnattingius S
      Bergvall N, Cnattingius S. 2008. Familial (shared environmental and genetic) factors and the foetal origins of cardiovascular diseases and type 2 diabetes: A review of the literature. J Intern Med 264: 205–223.
      CrossRefMedlineGoogle Scholar
    7. ↵
      1. Bernal AJ,
      2. Jirtle RL
      Bernal AJ, Jirtle RL. 2010. Epigenomic disruption: The effects of early developmental exposures. Birth Defects Res A Clin Mol Teratol 88: 938–944.
      CrossRefMedlineGoogle Scholar
    8. ↵
      1. Bibikova M,
      2. Le J,
      3. Barnes B,
      4. Saedinia-Melnyk S,
      5. Zhou L,
      6. Shen R,
      7. Gunderson KL
      Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, Gunderson KL. 2009. Genome-wide methylation profiling using Infinium assay. Epigenomics 1: 177–200.
      CrossRefMedlineGoogle Scholar
    9. ↵
      1. Bird A
      Bird A. 2007. Perceptions of epigenetics. Nature 447: 396–398.
      CrossRefMedlineGoogle Scholar
    10. ↵
      1. Bjornsson HT,
      2. Sigurdsson MI,
      3. Fallin MD,
      4. Irizarry RA,
      5. Aspelund T,
      6. Cui H,
      7. Yu W,
      8. Rongione MA,
      9. Ekstrom TJ,
      10. Harris TB
      Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, Cui H, Yu W, Rongione MA, Ekstrom TJ, Harris TB, et al. 2008. Intra-individual change over time in DNA methylation with familial clustering. JAMA 299: 2877–2883.
      CrossRefMedlineGoogle Scholar
    11. ↵
      1. Bock C,
      2. Walter J,
      3. Paulsen M,
      4. Lengauer T
      Bock C, Walter J, Paulsen M, Lengauer T. 2008. Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping. Nucleic Acids Res 36: e55. doi: 10.1093/nar/gkn122.
      Abstract/FREE Full Text
    12. ↵
      1. Bock C,
      2. Tomazou EM,
      3. Brinkman AB,
      4. Muller F,
      5. Simmer F,
      6. Gu H,
      7. Jager N,
      8. Gnirke A,
      9. Stunnenberg HG,
      10. Meissner A
      Bock C, Tomazou EM, Brinkman AB, Muller F, Simmer F, Gu H, Jager N, Gnirke A, Stunnenberg HG, Meissner A. 2010. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28: 1106–1114.
      CrossRefMedlineGoogle Scholar
    13. ↵
      1. Bocklandt S,
      2. Lin W,
      3. Sehl ME,
      4. Sanchez FJ,
      5. Sinsheimer JS,
      6. Horvath S,
      7. Vilain E
      Bocklandt S, Lin W, Sehl ME, Sanchez FJ, Sinsheimer JS, Horvath S, Vilain E. 2011. Epigenetic predictor of age. PLoS ONE 6: e14821. doi: 10.1371/journal.pone.0014821.
      CrossRefMedlineGoogle Scholar
    14. ↵
      1. Boks MP,
      2. Derks EM,
      3. Weisenberger DJ,
      4. Strengman E,
      5. Janson E,
      6. Sommer IE,
      7. Kahn RS,
      8. Ophoff RA
      Boks MP, Derks EM, Weisenberger DJ, Strengman E, Janson E, Sommer IE, Kahn RS, Ophoff RA. 2009. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS ONE 4: e6767. doi: 10.1371/journal.pone.0006767.
      CrossRefMedlineGoogle Scholar
    15. ↵
      1. Breton CV,
      2. Byun HM,
      3. Wenten M,
      4. Pan F,
      5. Yang A,
      6. Gilliland FD
      Breton CV, Byun HM, Wenten M, Pan F, Yang A, Gilliland FD. 2009. Prenatal tobacco smoke exposure affects global and gene-specific DNA methylation. Am J Respir Crit Care Med 180: 462–467.
      CrossRefMedlineGoogle Scholar
    16. ↵
      1. Brunner AL,
      2. Johnson DS,
      3. Kim SW,
      4. Valouev A,
      5. Reddy TE,
      6. Neff NF,
      7. Anton E,
      8. Medina C,
      9. Nguyen L,
      10. Chiao E
      Brunner AL, Johnson DS, Kim SW, Valouev A, Reddy TE, Neff NF, Anton E, Medina C, Nguyen L, Chiao E, et al. 2009. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res 19: 1044–1056.
      Abstract/FREE Full Text
    17. ↵
      1. Burdge GC,
      2. Hanson MA,
      3. Slater-Jefferies JL,
      4. Lillycrop KA
      Burdge GC, Hanson MA, Slater-Jefferies JL, Lillycrop KA. 2007. Epigenetic regulation of transcription: A mechanism for inducing variations in phenotype (fetal programming) by differences in nutrition during early life? Br J Nutr 97: 1036–1046.
      CrossRefMedlineGoogle Scholar
    18. ↵
      1. Capittini C,
      2. Pasi A,
      3. Bergamaschi P,
      4. Tinelli C,
      5. De Silvestri A,
      6. Mercati MP,
      7. Badulli C,
      8. Garlaschelli F,
      9. Sbarsi I,
      10. Guarene M
      Capittini C, Pasi A, Bergamaschi P, Tinelli C, De Silvestri A, Mercati MP, Badulli C, Garlaschelli F, Sbarsi I, Guarene M, et al. 2009. HLA haplotypes and birth weight variation: Is your future going to be light or heavy? Tissue Antigens 74: 156–163.
      CrossRefMedlineGoogle Scholar
    19. ↵
      1. Chen YA,
      2. Choufani S,
      3. Ferreira JC,
      4. Grafodatskaya D,
      5. Butcher DT,
      6. Weksberg R
      Chen YA, Choufani S, Ferreira JC, Grafodatskaya D, Butcher DT, Weksberg R. 2011. Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray. Genomics 97: 214–222.
      CrossRefMedlineGoogle Scholar
    20. ↵
      1. Choi JK,
      2. Kim SC
      Choi JK, Kim SC. 2007. Environmental effects on gene expression phenotype have regional biases in the human genome. Genetics 175: 1607–1613.
      CrossRefMedlineGoogle Scholar
    21. ↵
      1. Choufani S,
      2. Shapiro JS,
      3. Susiarjo M,
      4. Butcher DT,
      5. Grafodatskaya D,
      6. Lou Y,
      7. Ferreira JC,
      8. Pinto D,
      9. Scherer SW,
      10. Shaffer LG
      Choufani S, Shapiro JS, Susiarjo M, Butcher DT, Grafodatskaya D, Lou Y, Ferreira JC, Pinto D, Scherer SW, Shaffer LG, et al. 2011. A novel approach identifies new differentially methylated regions (DMRs) associated with imprinted genes. Genome Res 21: 465–476.
      Abstract/FREE Full Text
    22. ↵
      1. Christensen BC,
      2. Houseman EA,
      3. Marsit CJ,
      4. Zheng S,
      5. Wrensch MR,
      6. Wiemels JL,
      7. Nelson HH,
      8. Karagas MR,
      9. Padbury JF,
      10. Bueno R
      Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL, Nelson HH, Karagas MR, Padbury JF, Bueno R, et al. 2009. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet 5: e1000602. doi: 10.1371/journal.pgen.1000602.
      CrossRefMedlineGoogle Scholar
    23. ↵
      1. Doi A,
      2. Park IH,
      3. Wen B,
      4. Murakami P,
      5. Aryee MJ,
      6. Irizarry R,
      7. Herb B,
      8. Ladd-Acosta C,
      9. Rho J,
      10. Loewer S
      Doi A, Park IH, Wen B, Murakami P, Aryee MJ, Irizarry R, Herb B, Ladd-Acosta C, Rho J, Loewer S, et al. 2009. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat Genet 41: 1350–1353.
      CrossRefMedlineGoogle Scholar
    24. ↵
      1. Du P,
      2. Kibbe WA,
      3. Lin SM
      Du P, Kibbe WA, Lin SM. 2008. lumi: A pipeline for processing Illumina microarray. Bioinformatics 24: 1547–1548.
      Abstract/FREE Full Text
    25. ↵
      1. Du P,
      2. Zhang X,
      3. Huang CC,
      4. Jafari N,
      5. Kibbe WA,
      6. Hou L,
      7. Lin SM
      Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, Lin SM. 2011. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11: 587. doi: 10.1186/1471-2105-11-587.
      Google Scholar
    26. ↵
      1. Eden E,
      2. Navon R,
      3. Steinfeld I,
      4. Lipson D,
      5. Yakhini Z
      Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. 2009. GOrilla: A tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10: 48. doi: 10.1186/1471-2105-10-48.
      CrossRefMedlineGoogle Scholar
    27. ↵
      1. Eisenberg E,
      2. Levanon EY
      Eisenberg E, Levanon EY. 2003. Human housekeeping genes are compact. Trends Genet 19: 362–365.
      CrossRefMedlineGoogle Scholar
    28. ↵
      1. Feinberg AP,
      2. Irizarry RA
      Feinberg AP, Irizarry RA. 2011. Evolution in health and medicine Sackler colloquium: Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc Natl Acad Sci (Suppl 1) 107: 1757–1764.
      Google Scholar
    29. ↵
      1. Feinberg AP,
      2. Irizarry RA,
      3. Fradin D,
      4. Aryee MJ,
      5. Murakami P,
      6. Aspelund T,
      7. Eiriksdottir G,
      8. Harris TB,
      9. Launer L,
      10. Gudnason V
      Feinberg AP, Irizarry RA, Fradin D, Aryee MJ, Murakami P, Aspelund T, Eiriksdottir G, Harris TB, Launer L, Gudnason V, et al. 2010. Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci Transl Med 2: 49ra67. doi: 10.1126/scitranslmed.3001262.
      Abstract/FREE Full Text
    30. ↵
      1. Foley DL,
      2. Craig JM,
      3. Morley R,
      4. Olsson CA,
      5. Dwyer T,
      6. Smith K,
      7. Saffery R
      Foley DL, Craig JM, Morley R, Olsson CA, Dwyer T, Smith K, Saffery R. 2009. Prospects for epigenetic epidemiology. Am J Epidemiol 169: 389–400.
      Abstract/FREE Full Text
    31. ↵
      1. Fraga MF,
      2. Ballestar E,
      3. Paz MF,
      4. Ropero S,
      5. Setien F,
      6. Ballestar ML,
      7. Heine-Suner D,
      8. Cigudosa JC,
      9. Urioste M,
      10. Benitez J
      Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, Cigudosa JC, Urioste M, Benitez J, et al. 2005. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci 102: 10604–10609.
      Abstract/FREE Full Text
    32. ↵
      1. Fryer AA,
      2. Emes RD,
      3. Ismail KM,
      4. Haworth KE,
      5. Mein C,
      6. Carroll WD,
      7. Farrell WE
      Fryer AA, Emes RD, Ismail KM, Haworth KE, Mein C, Carroll WD, Farrell WE. 2011. Quantitative, high-resolution epigenetic profiling of CpG loci identifies associations with cord blood plasma homocysteine and birth weight in humans. Epigenetics 6: 86–94.
      CrossRefMedlineGoogle Scholar
    33. ↵
      1. Gartner K,
      2. Baunack E
      Gartner K, Baunack E. 1981. Is the similarity of monozygotic twins due to genetic factors alone? Nature 292: 646–647.
      CrossRefMedlineGoogle Scholar
    34. ↵
      1. Gentleman RC,
      2. Carey VJ,
      3. Bates DM,
      4. Bolstad B,
      5. Dettling M,
      6. Dudoit S,
      7. Ellis B,
      8. Gautier L,
      9. Ge Y,
      10. Gentry J
      Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. 2004. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 5: R80.
      CrossRefMedlineGoogle Scholar
    35. ↵
      1. Gertz J,
      2. Varley KE,
      3. Reddy TE,
      4. Bowling KM,
      5. Pauli F,
      6. Parker SL,
      7. Kucera KS,
      8. Willard HF,
      9. Myers RM
      Gertz J, Varley KE, Reddy TE, Bowling KM, Pauli F, Parker SL, Kucera KS, Willard HF, Myers RM. 2011. Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet 7: e1002228. doi: 10.1371/journal.pgen.1002228.
      CrossRefMedlineGoogle Scholar
    36. ↵
      1. Gervin K,
      2. Hammero M,
      3. Akselsen HE,
      4. Moe R,
      5. Nygard H,
      6. Brandt I,
      7. Gjessing HK,
      8. Harris JR,
      9. Undlien DE,
      10. Lyle R
      Gervin K, Hammero M, Akselsen HE, Moe R, Nygard H, Brandt I, Gjessing HK, Harris JR, Undlien DE, Lyle R. 2011. Extensive variation and low heritability of DNA methylation identified in a twin study. Genome Res 21: 1813–1821.
      Abstract/FREE Full Text
    37. ↵
      1. Gluckman PD,
      2. Hanson MA,
      3. Beedle AS
      Gluckman PD, Hanson MA, Beedle AS. 2007. Early life events and their consequences for later disease: A life history and evolutionary perspective. Am J Hum Biol 19: 1–19.
      CrossRefMedlineGoogle Scholar
    38. ↵
      1. Gluckman PD,
      2. Hanson MA,
      3. Buklijas T
      Gluckman PD, Hanson MA, Buklijas T. 2010. A conceptual framework for the developmental origins of health and disease. J Dev Orig Health Dis 1: 6–18.
      CrossRefMedlineGoogle Scholar
    39. ↵
      1. Gordon L,
      2. Joo JH,
      3. Andronikos R,
      4. Ollikainen M,
      5. Wallace EM,
      6. Umstad MP,
      7. Permezel M,
      8. Oshlack A,
      9. Morley R,
      10. Carlin JB
      Gordon L, Joo JH, Andronikos R, Ollikainen M, Wallace EM, Umstad MP, Permezel M, Oshlack A, Morley R, Carlin JB, et al. 2011. Expression discordance of monozygotic twins at birth: Effect of intrauterine environment and a possible mechanism for fetal programming. Epigenetics 6: 579–592.
      CrossRefMedlineGoogle Scholar
    40. ↵
      1. Heid IM,
      2. Jackson AU,
      3. Randall JC,
      4. Winkler TW,
      5. Qi L,
      6. Steinthorsdottir V,
      7. Thorleifsson G,
      8. Zillikens MC,
      9. Speliotes EK,
      10. Magi R
      Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, Thorleifsson G, Zillikens MC, Speliotes EK, Magi R, et al. 2010. Meta-analysis identifies 13 new loci associated with waist–hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42: 949–960.
      CrossRefMedlineGoogle Scholar
    41. ↵
      1. Heijmans BT,
      2. Kremer D,
      3. Tobi EW,
      4. Boomsma DI,
      5. Slagboom PE
      Heijmans BT, Kremer D, Tobi EW, Boomsma DI, Slagboom PE. 2007. Heritable rather than age-related environmental and stochastic factors dominate variation in DNA methylation of the human IGF2/H19 locus. Hum Mol Genet 16: 547–554.
      Abstract/FREE Full Text
    42. ↵
      1. Hochberg Y,
      2. Benjamini Y
      Hochberg Y, Benjamini Y. 1990. More powerful procedures for multiple significance testing. Stat Med 9: 811–818.
      CrossRefMedlineGoogle Scholar
    43. ↵
      1. Huang K,
      2. Fan G
      Huang K, Fan G. 2010. DNA methylation in cell differentiation and reprogramming: An emerging systematic view. Regen Med 5: 531–544.
      CrossRefMedlineGoogle Scholar
    44. ↵
      1. Irizarry RA,
      2. Ladd-Acosta C,
      3. Wen B,
      4. Wu Z,
      5. Montano C,
      6. Onyango P,
      7. Cui H,
      8. Gabo K,
      9. Rongione M,
      10. Webster M
      Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, et al. 2009. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41: 178–186.
      CrossRefMedlineGoogle Scholar
    45. ↵
      1. Javierre BM,
      2. Fernandez AF,
      3. Richter J,
      4. Al-Shahrour F,
      5. Martin-Subero JI,
      6. Rodriguez-Ubreva J,
      7. Berdasco M,
      8. Fraga MF,
      9. O'Hanlon TP,
      10. Rider LG
      Javierre BM, Fernandez AF, Richter J, Al-Shahrour F, Martin-Subero JI, Rodriguez-Ubreva J, Berdasco M, Fraga MF, O'Hanlon TP, Rider LG, et al. 2010. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome Res 20: 170–179.
      Abstract/FREE Full Text
    46. ↵
      1. Kaminsky ZA,
      2. Tang T,
      3. Wang SC,
      4. Ptak C,
      5. Oh GH,
      6. Wong AH,
      7. Feldcamp LA,
      8. Virtanen C,
      9. Halfvarson J,
      10. Tysk C
      Kaminsky ZA, Tang T, Wang SC, Ptak C, Oh GH, Wong AH, Feldcamp LA, Virtanen C, Halfvarson J, Tysk C, et al. 2009. DNA methylation profiles in monozygotic and dizygotic twins. Nat Genet 41: 240–245.
      CrossRefMedlineGoogle Scholar
    47. ↵
      1. Katari S,
      2. Turan N,
      3. Bibikova M,
      4. Erinle O,
      5. Chalian R,
      6. Foster M,
      7. Gaughan JP,
      8. Coutifaris C,
      9. Sapienza C
      Katari S, Turan N, Bibikova M, Erinle O, Chalian R, Foster M, Gaughan JP, Coutifaris C, Sapienza C. 2009. DNA methylation and gene expression differences in children conceived in vitro or in vivo. Hum Mol Genet 18: 3769–3778.
      Abstract/FREE Full Text
    48. ↵
      1. Kauffmann A,
      2. Gentleman R,
      3. Huber W
      Kauffmann A, Gentleman R, Huber W. 2009. arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics 25: 415–416.
      Abstract/FREE Full Text
    49. ↵
      1. Kerkel K,
      2. Spadola A,
      3. Yuan E,
      4. Kosek J,
      5. Jiang L,
      6. Hod E,
      7. Li K,
      8. Murty VV,
      9. Schupf N,
      10. Vilain E
      Kerkel K, Spadola A, Yuan E, Kosek J, Jiang L, Hod E, Li K, Murty VV, Schupf N, Vilain E, et al. 2008. Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet 40: 904–908.
      CrossRefMedlineGoogle Scholar
    50. ↵
      1. Kuratomi G,
      2. Iwamoto K,
      3. Bundo M,
      4. Kusumi I,
      5. Kato N,
      6. Iwata N,
      7. Ozaki N,
      8. Kato T
      Kuratomi G, Iwamoto K, Bundo M, Kusumi I, Kato N, Iwata N, Ozaki N, Kato T. 2008. Aberrant DNA methylation associated with bipolar disorder identified from discordant monozygotic twins. Mol Psychiatry 13: 429–441.
      CrossRefMedlineGoogle Scholar
    51. ↵
      1. Liu Y,
      2. Wada R,
      3. Yamashita T,
      4. Mi Y,
      5. Deng CX,
      6. Hobson JP,
      7. Rosenfeldt HM,
      8. Nava VE,
      9. Chae SS,
      10. Lee MJ
      Liu Y, Wada R, Yamashita T, Mi Y, Deng CX, Hobson JP, Rosenfeldt HM, Nava VE, Chae SS, Lee MJ, et al. 2000. Edg-1, the G protein-coupled receptor for sphingosine-1-phosphate, is essential for vascular maturation. J Clin Invest 106: 951–961.
      CrossRefMedlineGoogle Scholar
    52. ↵
      1. Lovering RC,
      2. Dimmer E,
      3. Khodiyar VK,
      4. Barrell DG,
      5. Scambler P,
      6. Hubank M,
      7. Apweiler R,
      8. Talmud PJ
      Lovering RC, Dimmer E, Khodiyar VK, Barrell DG, Scambler P, Hubank M, Apweiler R, Talmud PJ. 2008. Cardiovascular GO annotation initiative year 1 report: Why cardiovascular GO? Proteomics 8: 1950–1953.
      CrossRefMedlineGoogle Scholar
    53. ↵
      1. Martin GM
      Martin GM. 2005. Epigenetic drift in aging identical twins. Proc Natl Acad Sci 102: 10413–10414.
      FREE Full Text
    54. ↵
      1. Meaburn EL,
      2. Schalkwyk LC,
      3. Mill J
      Meaburn EL, Schalkwyk LC, Mill J. 2010. Allele-specific methylation in the human genome: Implications for genetic studies of complex disease. Epigenetics 5: 578–582.
      CrossRefMedlineGoogle Scholar
    55. ↵
      1. Michaud J,
      2. Simpson KM,
      3. Escher R,
      4. Buchet-Poyau K,
      5. Beissbarth T,
      6. Carmichael C,
      7. Ritchie ME,
      8. Schutz F,
      9. Cannon P,
      10. Liu M
      Michaud J, Simpson KM, Escher R, Buchet-Poyau K, Beissbarth T, Carmichael C, Ritchie ME, Schutz F, Cannon P, Liu M, et al. 2008. Integrative analysis of RUNX1 downstream pathways and target genes. BMC Genomics 9: 363. doi: 10.1186/1471-2164-9-363.
      CrossRefMedlineGoogle Scholar
    56. ↵
      1. Morley R,
      2. Dwyer T
      Morley R, Dwyer T. 2005. Studies of twins: What can they tell us about the fetal origins of adult disease? Paediatr Perinat Epidemiol (Suppl 1) 19: 2–7.
      MedlineGoogle Scholar
    57. ↵
      1. Morley R,
      2. Dwyer T,
      3. Carlin JB
      Morley R, Dwyer T, Carlin JB. 2003. Studies of twins: Can they shed light on the fetal origins of adult disease hypothesis? Twin Res 6: 520–525.
      CrossRefMedlineGoogle Scholar
    58. ↵
      1. Neale MC,
      2. Cardon LR
      Neale MC, Cardon LR. 1992. Methodology for genetic studies of twins and families. Kluwer Academic Publishers, Dordrecht.
      Google Scholar
    59. ↵
      1. Novakovic B,
      2. Wong NC,
      3. Sibson M,
      4. Ng HK,
      5. Morley R,
      6. Manuelpillai U,
      7. Down T,
      8. Rakyan VK,
      9. Beck S,
      10. Hiendleder S
      Novakovic B, Wong NC, Sibson M, Ng HK, Morley R, Manuelpillai U, Down T, Rakyan VK, Beck S, Hiendleder S, et al. 2010. DNA methylation-mediated down-regulation of DNA methyltransferase-1 (DNMT1) is coincident with, but not essential for, global hypomethylation in human placenta. J Biol Chem 285: 9583–9593.
      Abstract/FREE Full Text
    60. ↵
      1. Novakovic B,
      2. Gordon L,
      3. Wong NC,
      4. Moffett A,
      5. Manuelpillai U,
      6. Craig JM,
      7. Sharkey A,
      8. Saffery R
      Novakovic B, Gordon L, Wong NC, Moffett A, Manuelpillai U, Craig JM, Sharkey A, Saffery R. 2011. Wide ranging DNA methylation differences of primary trophoblast cell populations and derived-cell lines: Implications and opportunities for understanding trophoblast function. Mol Hum Reprod 17: 344–353.
      Abstract/FREE Full Text
    61. ↵
      1. Ollikainen M,
      2. Smith KR,
      3. Joo EJ,
      4. Ng HK,
      5. Andronikos R,
      6. Novakovic B,
      7. Abdul Aziz NK,
      8. Carlin JB,
      9. Morley R,
      10. Saffery R
      Ollikainen M, Smith KR, Joo EJ, Ng HK, Andronikos R, Novakovic B, Abdul Aziz NK, Carlin JB, Morley R, Saffery R, et al. 2010. DNA methylation analysis of multiple tissues from newborn twins reveals both genetic and intrauterine components to variation in the human neonatal epigenome. Hum Mol Genet 19: 4176–4188.
      Abstract/FREE Full Text
    62. ↵
      1. Ozanne SE,
      2. Constancia M
      Ozanne SE, Constancia M. 2007. Mechanisms of disease: The developmental origins of disease and the role of the epigenotype. Nat Clin Pract Endocrinol Metab 3: 539–546.
      CrossRefMedlineGoogle Scholar
    63. ↵
      1. Plomin R
      Plomin R. 2011. Commentary: Why are children in the same family so different? Non-shared environment three decades later. Int J Epidemiol 40: 582–592.
      FREE Full Text
    64. ↵
      1. Powell JE,
      2. Henders AK,
      3. McRae AF,
      4. Wright MJ,
      5. Martin NG,
      6. Dermitzakis ET,
      7. Montgomery GW,
      8. Visscher PM
      Powell JE, Henders AK, McRae AF, Wright MJ, Martin NG, Dermitzakis ET, Montgomery GW, Visscher PM. 2012. Genetic control of gene expression in whole blood and lympho blastoid cell lines is largely independent. Genome Res 22: 456–466.
      Abstract/FREE Full Text
    65. ↵
      1. Pritchard C,
      2. Coil D,
      3. Hawley S,
      4. Hsu L,
      5. Nelson PS
      Pritchard C, Coil D, Hawley S, Hsu L, Nelson PS. 2006. The contributions of normal variation and genetic background to mammalian gene expression. Genome Biol 7: R26. doi: 10.1186/gb-2006-7-3-r26.
      CrossRefMedlineGoogle Scholar
    66. ↵
      R Development Core Team. 2009. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
      Google Scholar
    67. ↵
      1. Rajendram R,
      2. Ferreira JC,
      3. Grafodatskaya D,
      4. Choufani S,
      5. Chiang T,
      6. Pu S,
      7. Butcher DT,
      8. Wodak SJ,
      9. Weksberg R
      Rajendram R, Ferreira JC, Grafodatskaya D, Choufani S, Chiang T, Pu S, Butcher DT, Wodak SJ, Weksberg R. 2011. Assessment of methylation level prediction accuracy in methyl-DNA immunoprecipitation and sodium bisulfite based microarray platforms. Epigenetics 6: 410–415.
      CrossRefMedlineGoogle Scholar
    68. ↵
      1. Rakyan VK,
      2. Down TA,
      3. Maslau S,
      4. Andrew T,
      5. Yang TP,
      6. Beyan H,
      7. Whittaker P,
      8. McCann OT,
      9. Finer S,
      10. Valdes AM
      Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM, et al. 2010. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res 20: 434–439.
      Abstract/FREE Full Text
    69. ↵
      1. Rakyan VK,
      2. Beyan H,
      3. Down TA,
      4. Hawa MI,
      5. Maslau S,
      6. Aden D,
      7. Daunay A,
      8. Busato F,
      9. Mein CA,
      10. Manfras B
      Rakyan VK, Beyan H, Down TA, Hawa MI, Maslau S, Aden D, Daunay A, Busato F, Mein CA, Manfras B, et al. 2011a. Identification of type 1 diabetes–associated DNA methylation variable positions that precede disease diagnosis. PLoS Genet 7: e1002300. doi: 10.1371/journal.pgen.1002300.
      CrossRefMedlineGoogle Scholar
    70. ↵
      1. Rakyan VK,
      2. Down TA,
      3. Balding DJ,
      4. Beck S
      Rakyan VK, Down TA, Balding DJ, Beck S. 2011b. Epigenome-wide association studies for common human diseases. Nat Rev Genet 12: 529–541.
      CrossRefMedlineGoogle Scholar
    71. ↵
      1. Regard JB,
      2. Scheek S,
      3. Borbiev T,
      4. Lanahan AA,
      5. Schneider A,
      6. Demetriades AM,
      7. Hiemisch H,
      8. Barnes CA,
      9. Verin AD,
      10. Worley PF
      Regard JB, Scheek S, Borbiev T, Lanahan AA, Schneider A, Demetriades AM, Hiemisch H, Barnes CA, Verin AD, Worley PF. 2004. Verge: A novel vascular early response gene. J Neurosci 24: 4092–4103.
      Abstract/FREE Full Text
    72. ↵
      1. Reik W
      Reik W. 2007. Stability and flexibility of epigenetic gene regulation in mammalian development. Nature 447: 425–432.
      CrossRefMedlineGoogle Scholar
    73. ↵
      1. Ritchie ME,
      2. Diyagama D,
      3. Neilson J,
      4. van Laar R,
      5. Dobrovic A,
      6. Holloway A,
      7. Smyth GK
      Ritchie ME, Diyagama D, Neilson J, van Laar R, Dobrovic A, Holloway A, Smyth GK. 2006. Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics 7: 261. doi: 10.1186/1471-2105-7-261.
      CrossRefMedlineGoogle Scholar
    74. ↵
      1. Saffery R,
      2. Morley R,
      3. Carlin JB,
      4. Joo JH,
      5. Ollikainen M,
      6. Novakovic B,
      7. Andronikos R,
      8. Li X,
      9. Loke YJ,
      10. Carson N
      Saffery R, Morley R, Carlin JB, Joo JH, Ollikainen M, Novakovic B, Andronikos R, Li X, Loke YJ, Carson N, et al. 2012. Cohort profile: The peri/post-natal epigenetic twins study. Int J Epidemiol 41: 55–61.
      FREE Full Text
    75. ↵
      1. Sampath H,
      2. Ntambi JM
      Sampath H, Ntambi JM. 2008. Role of stearoyl-CoA desaturase in human metabolic disease. Future Lipidol 3: 163.
      CrossRefGoogle Scholar
    76. ↵
      1. Sandoval J,
      2. Heyn HA,
      3. Moran S,
      4. Serra-Musach J,
      5. Pujana MA,
      6. Bibikova M,
      7. Esteller M
      Sandoval J, Heyn HA, Moran S, Serra-Musach J, Pujana MA, Bibikova M, Esteller M. 2011. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6: 692–702.
      CrossRefMedlineGoogle Scholar
    77. ↵
      1. Schalkwyk LC,
      2. Meaburn EL,
      3. Smith R,
      4. Dempster EL,
      5. Jeffries AR,
      6. Davies MN,
      7. Plomin R,
      8. Mill J
      Schalkwyk LC, Meaburn EL, Smith R, Dempster EL, Jeffries AR, Davies MN, Plomin R, Mill J. 2010. Allelic skewing of DNA methylation is widespread across the genome. Am J Hum Genet 86: 196–212.
      CrossRefMedlineGoogle Scholar
    78. ↵
      1. Sharma A,
      2. Sharma VK,
      3. Horn-Saban S,
      4. Lancet D,
      5. Ramachandran S,
      6. Brahmachari SK
      Sharma A, Sharma VK, Horn-Saban S, Lancet D, Ramachandran S, Brahmachari SK. 2005. Assessing natural variations in gene expression in humans by comparing with monozygotic twins using microarrays. Physiol Genomics 21: 117–123.
      Abstract/FREE Full Text
    79. ↵
      1. Shin S,
      2. Yoon JH,
      3. Lee HR,
      4. Hwang SM,
      5. Roh EY
      Shin S, Yoon JH, Lee HR, Hwang SM, Roh EY. 2010. Association of HLA-A, -B and -DRB1 genotype with birthweight and CD34+ cell content: Analysis of Korean newborns and their cord blood. Mol Hum Reprod 16: 338–346.
      Abstract/FREE Full Text
    80. ↵
      1. Shoemaker R,
      2. Deng J,
      3. Wang W,
      4. Zhang K
      Shoemaker R, Deng J, Wang W, Zhang K. 2010. Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome. Genome Res 20: 883–889.
      Abstract/FREE Full Text
    81. ↵
      1. Simonsen ML,
      2. Alessio HM,
      3. White P,
      4. Newsom DL,
      5. Hagerman AE
      Simonsen ML, Alessio HM, White P, Newsom DL, Hagerman AE. 2010. Acute physical activity effects on cardiac gene expression. Exp Physiol 95: 1071–1080.
      CrossRefMedlineGoogle Scholar
    82. ↵
      1. Smyth GK
      Smyth GK. 2004. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: 1544–6115.
      CrossRefGoogle Scholar
    83. ↵
      1. Smyth GK
      Smyth GK. 2005. Limma: Linear models for microarray data. In Bioinformatics and computational biology solutions using R and Bioconducto (ed. R Gentleman et al.), pp. 397–420. Springer, New York.
      Google Scholar
    84. ↵
      1. Speliotes EK,
      2. Willer CJ,
      3. Berndt SI,
      4. Monda KL,
      5. Thorleifsson G,
      6. Jackson AU,
      7. Allen HL,
      8. Lindgren CM,
      9. Luan J,
      10. Magi R
      Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Magi R, et al. 2010. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948.
      CrossRefMedlineGoogle Scholar
    85. ↵
      1. Stromswold K
      Stromswold K. 2006. Why aren't identical twins linguistically identical? Genetic, prenatal and postnatal factors. Cognition 101: 333–384.
      CrossRefMedlineGoogle Scholar
    86. ↵
      1. Teschendorff AE,
      2. Menon U,
      3. Gentry-Maharaj A,
      4. Ramus SJ,
      5. Weisenberger DJ,
      6. Shen H,
      7. Campan M,
      8. Noushmehr H,
      9. Bell CG,
      10. Maxwell AP
      Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, et al. 2010. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 20: 440–446.
      Abstract/FREE Full Text
    87. ↵
      1. Thorleifsson G,
      2. Walters GB,
      3. Gudbjartsson DF,
      4. Steinthorsdottir V,
      5. Sulem P,
      6. Helgadottir A,
      7. Styrkarsdottir U,
      8. Gretarsdottir S,
      9. Thorlacius S,
      10. Jonsdottir I
      Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, Styrkarsdottir U, Gretarsdottir S, Thorlacius S, Jonsdottir I, et al. 2009. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 41: 18–24.
      CrossRefMedlineGoogle Scholar
    88. ↵
      1. Torche F,
      2. Echevarria G
      Torche F, Echevarria G. 2011. The effect of birthweight on childhood cognitive development in a middle-income country. Int J Epidemiol 40: 1008–1018.
      Abstract/FREE Full Text
    89. ↵
      1. van Vliet J,
      2. Oates NA,
      3. Whitelaw E
      van Vliet J, Oates NA, Whitelaw E. 2007. Epigenetic mechanisms in the context of complex diseases. Cell Mol Life Sci 64: 1531–1538.
      CrossRefMedlineGoogle Scholar
    90. ↵
      1. Visscher PM
      Visscher PM. 2004. Power of the classical twin design revisited. Twin Res 7: 505–512.
      CrossRefMedlineGoogle Scholar
    91. ↵
      1. Warensjo E,
      2. Ingelsson E,
      3. Lundmark P,
      4. Lannfelt L,
      5. Syvanen AC,
      6. Vessby B,
      7. Riserus U
      Warensjo E, Ingelsson E, Lundmark P, Lannfelt L, Syvanen AC, Vessby B, Riserus U. 2007. Polymorphisms in the SCD1 gene: Associations with body fat distribution and insulin sensitivity. Obesity (Silver Spring) 15: 1732–1740.
      CrossRefMedlineGoogle Scholar
    92. ↵
      1. Weisenberger DJ,
      2. Van Den Berg D,
      3. Pan F,
      4. Berman BP,
      5. Laird PW
      Weisenberger DJ, Van Den Berg D, Pan F, Berman BP, Laird PW. 2008. Comprehensive DNA methylation analysis on the Illumina Infinium Assay Platform. Illumina, San Diego.
      Google Scholar
    93. ↵
      1. Whitelaw NC,
      2. Chong S,
      3. Whitelaw E
      Whitelaw NC, Chong S, Whitelaw E. 2010. Tuning in to noise: Epigenetics and intangible variation. Dev Cell 19: 649–650.
      CrossRefMedlineGoogle Scholar
    94. ↵
      1. Willer CJ,
      2. Speliotes EK,
      3. Loos RJ,
      4. Li S,
      5. Lindgren CM,
      6. Heid IM,
      7. Berndt SI,
      8. Elliott AL,
      9. Jackson AU,
      10. Lamina C
      Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI, Elliott AL, Jackson AU, Lamina C, et al. 2009. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41: 25–34.
      CrossRefMedlineGoogle Scholar
    95. ↵
      1. Wong CC,
      2. Caspi A,
      3. Williams B,
      4. Craig IW,
      5. Houts R,
      6. Ambler A,
      7. Moffitt TE,
      8. Mill J
      Wong CC, Caspi A, Williams B, Craig IW, Houts R, Ambler A, Moffitt TE, Mill J. 2010. A longitudinal study of epigenetic variation in twins. Epigenetics 5: 516–526.
      CrossRefMedlineGoogle Scholar
    96. ↵
      1. Zhang Y,
      2. Rohde C,
      3. Reinhardt R,
      4. Voelcker-Rehage C,
      5. Jeltsch A
      Zhang Y, Rohde C, Reinhardt R, Voelcker-Rehage C, Jeltsch A. 2009. Non-imprinted allele-specific DNA methylation on human autosomes. Genome Biol 10: R138. doi: 10.1186/gb-2009-10-12-r138.
      CrossRefMedlineGoogle Scholar
    97. ↵
      1. Zhang D,
      2. Cheng L,
      3. Badner JA,
      4. Chen C,
      5. Chen Q,
      6. Luo W,
      7. Craig DW,
      8. Redman M,
      9. Gershon ES,
      10. Liu C
      Zhang D, Cheng L, Badner JA, Chen C, Chen Q, Luo W, Craig DW, Redman M, Gershon ES, Liu C. 2010. Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet 86: 411–419.
      CrossRefMedlineGoogle Scholar

    Articles citing this article

    • Genetics and Environment Distinctively Shape the Human Immune Cell Epigenome bioRxiv January 7, 2025 0: 2023.06.29.546792v2-2023.06.29.546792
    • Birth weight discordance, gene expression, and DNA methylation: A review of epigenetic twin studies bioRxiv December 3, 2024 0: 2024.11.28.625968v1-2024.11.28.625968
    • Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life bioRxiv March 7, 2024 0: 2024.03.02.583106v1-2024.03.02.583106
    • PRDM1 DNA-binding zinc finger domain is required for normal limb development and is disrupted in Split Hand/Foot Malformation medRxiv November 21, 2022 0: 2022.11.16.22282191v1-2022.11.16.22282191
    • Tissue- and ethnicity-independent hypervariable DNA methylation states show evidence of establishment in the early human embryo bioRxiv December 21, 2021 0: 2021.12.17.473110v1-2021.12.17.473110
    • Epigenetic Effects of Assisted Reproductive Technology in Human Offspring bioRxiv October 26, 2019 0: 816157v1-816157
    • Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits bioRxiv June 25, 2019 0: 493601v1-493601
    • Integrating brain methylome with GWAS for psychiatric risk gene discovery bioRxiv April 13, 2019 0: 440206v1-440206
    • A survey of DNA methylation polymorphism identifies environmentally responsive co-regulated networks of epigenetic variation in the human genome bioRxiv March 24, 2019 0: 189704v1-189704
    • Identification of 55,000 Replicated DNA Methylation QTL bioRxiv March 23, 2019 0: 166710v1-166710
    • A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data bioRxiv February 26, 2019 0: 019562v3-19562
    • Recent progress towards understanding the role of DNA methylation in human placental development Reproduction July 1, 2016 152: R23-R30
    • Genetic and environmental exposures constrain epigenetic drift over the human life course Genome Res November 1, 2014 24: 1725-1733
    • Validation of DNA Methylation Biomarkers for Diagnosis of Acute Lymphoblastic Leukemia Clinical Chemistry July 1, 2014 60: 995-1003
    • The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes Genome Res July 1, 2014 24: 1064-1074
    • Reprogramming of the human intestinal epigenome by surgical tissue transposition Genome Res April 1, 2014 24: 545-553
    | Table of Contents

    This Article

    1. Published in Advance July 16, 2012, doi: 10.1101/gr.136598.111 Genome Res. 22: 1395-1406 © 2012, Published by Cold Spring Harbor Laboratory Press

    Article Category

    Share

    Preprint Server



    Navigate This Article

    Current Issue

    1. March 2026, 36 (3)
    1. Current Issue
    1. Alert me to new issues of Genome Research