Epigenetic association studies have been carried out to test the hypothesis that environmental perturbations trigger cellular reprogramming, with downstream effects on cellular function and phenotypes. There have now been numerous studies of the potential molecular mediators of epigenetic changes by epigenome-wide association studies (EWAS). However, a challenge for the field is the interpretation of the results obtained. We describe a second-generation EWAS approach, which focuses on the possible cellular models of epigenetic perturbations, studied by rigorous analysis and interpretation of genomic data. Thus refocused, epigenetics research aligns with the field of functional genomics to provide insights into environmental and genetic influences on phenotypic variation in humans.
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Feinberg, A. P. & Vogelstein, B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 301, 89–92 (1983).
Gama-Sosa, M. A. et al. The 5-methylcytosine content of DNA from human tumors. Nucleic Acids Res. 11, 6883–6894 (1983).
Greger, V., Passarge, E., Höpping, W., Messmer, E. & Horsthemke, B. Epigenetic changes may contribute to the formation and spontaneous regression of retinoblastoma. Hum. Genet. 83, 155–158 (1989).
Bird, A. DNA methylation patterns and epigenetic memory. Genes Dev. 16, 6–21 (2002).
Riggs, A. D. X inactivation, differentiation, and DNA methylation. Cytogenet. Cell Genet. 14, 9–25 (1975).
Holliday, R. A new theory of carcinogenesis. Br. J. Cancer 40, 513–522 (1979).
Michels, K. B. et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat. Methods 10, 949–955 (2013).
Birney, E., Smith, G. D. & Greally, J. M. Epigenome-wide association studies and the interpretation of disease-omics. PLOS Genet. 12, e1006105 (2016).
Rakyan, V. K., Down, T. A., Balding, D. J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).
Heijmans, B. T. & Mill, J. Commentary: the seven plagues of epigenetic epidemiology. Int. J. Epidemiol. 41, 74–78 (2012).
Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).
Chen, Y. et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8, 203–209 (2013).
Kraft, P., Zeggini, E. & Ioannidis, J. P. A. Replication in genome-wide association studies. Stat. Sci. 24, 561–573 (2009).
Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).
Jaffe, A. E. & Irizarry, R. A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 15, R31 (2014).
Bell, J. T. et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLOS Genet. 8, e1002629 (2012).
Grundberg, E. et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am. J. Hum. Genet. 93, 876–890 (2013).
Bell, J. T. et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).
Gertz, J. et al. Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLOS Genet. 7, e1002228 (2011).
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).
Cheung, W. A. et al. Functional variation in allelic methylomes underscores a strong genetic contribution and reveals novel epigenetic alterations in the human epigenome. Genome Biol. 18, 50 (2017).
Banovich, N. E. et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLOS Genet. 10, e1004663 (2014).
Gutierrez-Arcelus, M. et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife 2, e00523 (2013).
Zhang, D. et al. Genetic control of individual differences in gene-specific methylation in human brain. Am. J. Hum. Genet. 86, 411–419 (2010).
Richmond, R. C. et al. DNA methylation and BMI: investigating identified methylation sites at HIF3A in a causal framework. Diabetes 65, 1231–1244 (2016).
Wahl, S. et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541, 81–86 (2017).
Dekkers, K. F. et al. Blood lipids influence DNA methylation in circulating cells. Genome Biol. 17, 138 (2016).
Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. & Henikoff, S. Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nat. Genet. 39, 61–69 (2007).
Ball, M. P. et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol. 27, 361–368 (2009).
Pisco, A. O., d'Herouel, A. F. & Huang, S. Conceptual confusion: the case of epigenetics. Preprint at bioRxiv http://biorxiv.org/content/early/2016/05/12/053009 (2016).
Haig, D. Commentary: the epidemiology of epigenetics. Int. J. Epidemiol. 41, 13–16 (2012).
Gillman, M. W. et al. Meeting report on the 3rd International Congress on Developmental Origins of Health and Disease (DOHaD). Pediatr. Res. 61, 625–629 (2007).
Nanney, D. L. Epigenetic control systems. Proc. Natl Acad. Sci. USA 44, 712–717 (1958).
Wu, H., Hauser, R., Krawetz, S. A. & Pilsner, J. R. Environmental susceptibility of the sperm epigenome during windows of male germ cell development. Curr. Environ. Health Rep. 2, 356–366 (2015).
Marsit, C. J. Influence of environmental exposure on human epigenetic regulation. J. Exp. Biol. 218, 71–79 (2015).
Perera, F. & Herbstman, J. Prenatal environmental exposures, epigenetics, and disease. Reprod. Toxicol. 31, 363–373 (2011).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).
Zong, J., Yao, X., Yin, J., Zhang, D. & Ma, H. Evolution of the RNA-dependent RNA polymerase (RdRP) genes: duplications and possible losses before and after the divergence of major eukaryotic groups. Gene 447, 29–39 (2009).
Holoch, D. & Moazed, D. RNA-mediated epigenetic regulation of gene expression. Nat. Rev. Genet. 16, 71–84 (2015).
Kaslow, D. C. & Migeon, B. R. DNA methylation stabilizes X chromosome inactivation in eutherians but not in marsupials: evidence for multistep maintenance of mammalian X dosage compensation. Proc. Natl Acad. Sci. USA 84, 6210–6214 (1987).
Sharp, A. J. et al. DNA methylation profiles of human active and inactive X chromosomes. Genome Res. 21, 1592–1600 (2011).
Li, E., Beard, C. & Jaenisch, R. Role for DNA methylation in genomic imprinting. Nature 366, 362–365 (1993).
Smith, N. C. & Matthews, J. M. Mechanisms of DNA-binding specificity and functional gene regulation by transcription factors. Curr. Opin. Struct. Biol. 38, 68–74 (2016).
Lelli, K. M., Slattery, M. & Mann, R. S. Disentangling the many layers of eukaryotic transcriptional regulation. Annu. Rev. Genet. 46, 43–68 (2012).
Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007).
Cheng, Y. et al. Principles of regulatory information conservation between mouse and human. Nature 515, 371–375 (2014).
Savic, D. et al. CETCh-seq: CRISPR epitope tagging ChIP-seq of DNA-binding proteins. Genome Res. 25, 1581–1589 (2015).
Feldmann, A. et al. Transcription factor occupancy can mediate active turnover of DNA methylation at regulatory regions. PLOS Genet. 9, e1003994 (2013).
Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).
Domcke, S. et al. Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528, 575–579 (2015).
Li, X. et al. A maternal-zygotic effect gene, Zfp57, maintains both maternal and paternal imprints. Dev. Cell 15, 547–557 (2008).
Donohoe, M. E., Zhang, L.-F., Xu, N., Shi, Y. & Lee, J. T. Identification of a Ctcf cofactor, Yy1, for the X chromosome binary switch. Mol. Cell 25, 43–56 (2007).
Wang, J. et al. Imprinted X inactivation maintained by a mouse Polycomb group gene. Nat. Genet. 28, 371–375 (2001).
Schuettengruber, B., Chourrout, D., Vervoort, M., Leblanc, B. & Cavalli, G. Genome regulation by Polycomb and trithorax proteins. Cell 128, 735–745 (2007).
Grimaud, C., Nègre, N. & Cavalli, G. From genetics to epigenetics: the tale of Polycomb group and trithorax group genes. Chromosome Res. 14, 363–375 (2006).
Davis, F. P. & Eddy, S. R. Transcription factors that convert adult cell identity are differentially Polycomb repressed. PLOS ONE 8, e63407 (2013).
Orsi, G. A. et al. High-resolution mapping defines the cooperative architecture of Polycomb response elements. Genome Res. 24, 809–820 (2014).
Frey, F. et al. Molecular basis of PRC1 targeting to Polycomb response elements by PhoRC. Genes Dev. 30, 1116–1127 (2016).
Sipos, L., Kozma, G., Molnár, E. & Bender, W. In situ dissection of a Polycomb response element in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 104, 12416–12421 (2007).
Kozma, G., Bender, W. & Sipos, L. Replacement of a Drosophila Polycomb response element core, and in situ analysis of its DNA motifs. Mol. Genet. Genomics 279, 595–603 (2008).
Busturia, A. et al. The MCP silencer of the Drosophila Abd-B gene requires both Pleiohomeotic and GAGA factor for the maintenance of repression. Development 128, 2163–2173 (2001).
Di Croce, L. & Helin, K. Transcriptional regulation by Polycomb group proteins. Nat. Struct. Mol. Biol. 20, 1147–1155 (2013).
Blackledge, N. P., Rose, N. R. & Klose, R. J. Targeting Polycomb systems to regulate gene expression: modifications to a complex story. Nat. Rev. Mol. Cell Biol. 16, 643–649 (2015).
Francis, N. J., Follmer, N. E., Simon, M. D., Aghia, G. & Butler, J. D. Polycomb proteins remain bound to chromatin and DNA during DNA replication in vitro. Cell 137, 110–122 (2009).
Anvar, Z. et al. ZFP57 recognizes multiple and closely spaced sequence motif variants to maintain repressive epigenetic marks in mouse embryonic stem cells. Nucleic Acids Res. 44, 1118–1132 (2016).
Guo, A. M., Sun, K., Su, X., Wang, H. & Sun, H. YY1TargetDB: an integral information resource for Yin Yang 1 target loci. Database (Oxford) 2013, bat007 (2013).
Waddington, C. H. The Strategy of the Genes: a Discussion of Some Aspects of Theoretical Biology (Allen & Unwin, 1957).
Chen, F. et al. Prenatal retinoid deficiency leads to airway hyperresponsiveness in adult mice. J. Clin. Invest. 124, 801–811 (2014).
Foong, R. E. et al. The effects of in utero vitamin D deficiency on airway smooth muscle mass and lung function. Am. J. Respir. Cell. Mol. Biol. 53, 664–675 (2015).
Lelièvre-Pégorier, M. et al. Mild vitamin A deficiency leads to inborn nephron deficit in the rat. Kidney Int. 54, 1455–1462 (1998).
Greally, J. M. & Jacobs, M. N. In vitro and in vivo testing methods of epigenomic endpoints for evaluating endocrine disruptors. ALTEX 30, 445–471 (2013).
Grün, F. et al. Endocrine-disrupting organotin compounds are potent inducers of adipogenesis in vertebrates. Mol. Endocrinol. 20, 2141–2155 (2006).
Kirchner, S., Kieu, T., Chow, C., Casey, S. & Blumberg, B. Prenatal exposure to the environmental obesogen tributyltin predisposes multipotent stem cells to become adipocytes. Mol. Endocrinol. 24, 526–539 (2010).
Woodsworth, D. J., Castellarin, M. & Holt, R. A. Sequence analysis of T cell repertoires in health and disease. Genome Med. 5, 98 (2013).
Houseman, E. A., Molitor, J. & Marsit, C. J. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014).
Zou, J., Lippert, C., Heckerman, D., Aryee, M. & Listgarten, J. Epigenome-wide association studies without the need for cell-type composition. Nat. Methods 11, 309–311 (2014).
Adalsteinsson, B. T. et al. Heterogeneity in white blood cells has potential to confound DNA methylation measurements. PLOS ONE 7, e46705 (2012).
Moris, N., Pina, C. & Arias, A. M. Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet. 17, 693–703 (2016).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
Wijetunga, N. A. et al. The meta-epigenomic structure of purified human stem cell populations is defined at cis-regulatory sequences. Nat. Commun. 5, 5195 (2014).
Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).
Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Zhao, M. et al. Distinct epigenomes in CD4+ T cells of newborns, middle-ages and centenarians. Sci. Rep. 6, 38411 (2016).
Qi, Q. et al. Diversity and clonal selection in the human T cell repertoire. Proc. Natl Acad. Sci. USA 111, 13139–13144 (2014).
Ulahannan, N. & Greally, J. M. Genome-wide assays that identify and quantify modified cytosines in human disease studies. Epigenetics Chromatin 8, 5 (2015).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Relton, C. L. & Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41, 161–176 (2012).
Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).
Stranger, B. E. et al. Genome-wide associations of gene expression variation in humans. PLOS Genet. 1, e78 (2005).
Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004).
Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).
Pai, A. A., Pritchard, J. K. & Gilad, Y. The genetic and mechanistic basis for variation in gene regulation. PLOS Genet. 11, e1004857 (2015).
Lappalainen, T. Functional genomics bridges the gap between quantitative genetics and molecular biology. Genome Res. 25, 1427–1431 (2015).
Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).
Waddington, C. H. The epigenotype. 1942. Int. J. Epidemiol. 41, 10–13 (2012).
Waddington, C. H. Organisers and Genes (Cambridge Univ. Press, 1940).
Li, Y. & Sasaki, H. Genomic imprinting in mammals: its life cycle, molecular mechanisms and reprogramming. Cell Res. 21, 466–473 (2011).
Gendrel, A.-V. & Heard, E. Noncoding RNAs and epigenetic mechanisms during X-chromosome inactivation. Annu. Rev. Cell Dev. Biol. 30, 561–580 (2014).
Bonasio, R., Tu, S. & Reinberg, D. Molecular signals of epigenetic states. Science 330, 612–616 (2010).
The authors thank S. Henikoff for sharing his insights and acknowledge his creation of the useful term epi+genetics, as well as R. Satija for advice in single-cell RNA sequencing data analysis.
The authors declare no competing financial interests.
The maintenance of a cell and its progeny within a differentiation lineage. The term refers to the canal-like structures depicted by Waddington in his epigenetic landscape, which he described not as canals but as creodes, a neologism from biblical Greek words meaning 'necessary' and 'path'.
We define an epigenetic property as that of a cell, mediated by genomic regulators, conferring on the cell the ability to remember a past event.
- Epigenome-wide association studies
(EWAS). Studies of the epigenome for nonrandom association of a difference in organization of a genomic regulator, comparing individuals with a phenotype with individuals lacking the phenotype. The epigenome is itself defined as the genome-wide distribution of transcriptional regulators believed to mediate the memory of past cellular events.
- Genome-wide association study
(GWAS). A study that looks for association between genetic variation and a high-level trait such as disease or biomarker across individuals, typically scanning millions of genetic variants genome-wide for association signals.
A systematic variability of cell fate decisions to create a distinctive repertoire of cells in a tissue.
- Quantitative trait loci
(QTLs). Loci in the genome at which genetic variation is associated with molecular variation across individuals. For example, individuals with a particular single nucleotide variant have altered expression levels of a gene (eQTL), altered DNA methylation (meQTL; also known as mQTL) or altered chromatin state (chromQTL).
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Lappalainen, T., Greally, J. Associating cellular epigenetic models with human phenotypes. Nat Rev Genet 18, 441–451 (2017). https://doi.org/10.1038/nrg.2017.32
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