Key Points
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Computationally integrated chromatin state maps define 'epigenomes' and provide a snapshot of the functional state of the genome. Comparisons of reference epigenomes across different tissues, developmental stages, disease states and environmental treatments show that enhancer elements are the most variable, whereas transcription start sites (TSSs) and repressive regions vary the least.
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Human population genetic studies of chromatin state maps involving up to five histone modifications show that the most variable regions correspond to enhancer states. A relatively small proportion of variable regions are associated with genetic variation, in which individual single-nucleotide polymorphisms (SNPs) can act as histone quantitative trait loci (hQTL) and affect multiple levels of local chromatin organization as well as expression levels of proximal or distal genes, most likely through a combination of differential transcription factor binding and chromatin looping.
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In humans, DNA methylation variation within and between populations is depleted in CpG islands and enriched in active chromatin states such as enhancers (weak and active) and active TSSs. Variation in DNA methylation correlates, locally, with variability in other epigenetic marks.
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In humans, array-based heritability estimates of DNA methylation variation at single CpG sites is about 0.2, and only a small proportion of CpGs can be associated with methylation QTL (meQTL). The majority of meQTL are strictly local and seem to involve mutations that impair DNA methylation itself or that disrupt transcription factor binding. The impact of genetic variation in DNA methylation is probably biased downward as current array technologies undersample distal enhancer elements.
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In plants, many cis-meQTL seem to be due to SNPs tagging structural variants such as transposable element (TE) insertions that spread DNA methylation into flanking regions or facilitate siRNA silencing of downstream homologous sequences. Trans-acting meQTL are prevalent and often involve variants in chromatin control genes. As a result, trans-acting meQTL can affect methylation levels at the genome-wide scale, and some evidence indicates that such effects can be adaptive.
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In plants, the interpretation of detected cis associations is complicated by the fact that alternative DNA methylation states (epialleles) can be inherited across generations, so that cis association may reflect linkage disequilibrium rather than active genetic regulation. Conversely, epialleles can also become disassociated from their underlying sequence haplotypes through high epimutation rates, and thus contribute to population epigenomic variation independently of DNA sequence variants.
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Scaling up current studies to include more epigenetic marks, cell types and individuals promises to provide deeper insights into the heritable basis underlying population epigenomic variation and will clarify its implications for biomedical, agricultural and evolutionary research.
Abstract
The field of epigenomics has rapidly progressed from the study of individual reference epigenomes to surveying epigenomic variation in populations. Recent studies in a number of species, from yeast to humans, have begun to dissect the cis- and trans-regulatory genetic mechanisms that shape patterns of population epigenomic variation at the level of single epigenetic marks, as well as at the level of integrated chromatin state maps. We show that this information is paving the way towards a more complete understanding of the heritable basis underlying population epigenomic variation. We also highlight important conceptual challenges when interpreting results from these genetic studies, particularly in plants, in which epigenomic variation can be determined both by genetic and epigenetic inheritance.
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Acknowledgements
F.J. acknowledges support from the Technical University of Munich–Institute for Advanced Study, funded by the German Excellence Initiative and the European Union Seventh Framework Programme under grant agreement #291763. M.C-T. was supported by the Netherlands Organization for Scientific Research and by a University of Groningen Rosalind Franklin Fellowship.
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Supplementary information
Supplementary information S1 (table)
Overview of studies that have generated integrated chromatin state maps. (PDF 131 kb)
Supplementary information S2 (figure)
Heatmap showing the fold enrichment of the Illumina 450k array probes with chromatin states from an 18-state model determined by ChromHMM (y-axis) in different cell types from the Roadmap Epigenomics [18] project (x-axis). (PDF 131 kb)
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Glossary
- Chromatin state maps
-
Computationally integrated genome-wide measurements of different epigenetic marks.
- Epigenome
-
The complete set of epigenetic marks at every genomic position in a given cell at a given time.
- Histone quantitative trait loci
-
(hQTL). Genetic loci that contribute to variation in histone modification states and/or levels in cis or trans.
- Expression quantitative trait loci
-
(eQTL). Genetic loci that contribute to variation in expression levels of mRNA in cis or trans.
- CpG islands
-
Genomic regions with a high percentage of CpGs and a large observed-to-expected CG ratio.
- Differentially methylated regions
-
(DMRs). Regions of DNA that have different methylation patterns between samples or individuals.
- Epialleles
-
Alternative chromatin states at a given locus. Typically, they refer to alternative DNA methylation states, although in principle they could also refer to changes in other epigenetic marks.
- Gene body methylation
-
(GBM). Average CG methylation levels over the body of genes.
- Epimutation
-
Heritable stochastic change in chromatin state at a given position or region. In the context of cytosine methylation, epimutations are defined as heritable stochastic changes in the methylation status of a single cytosine or of a region or cluster of cytosines. Such changes do not necessarily imply changes in gene expression.
- Genotype–phenotype map
-
A map that describes the functional connections between genotype and phenotype. The concept of a genotype–phenotype map is widely used as a metaphor for the many ways in which genotypic information influences the phenotype of an organism.
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Taudt, A., Colomé-Tatché, M. & Johannes, F. Genetic sources of population epigenomic variation. Nat Rev Genet 17, 319–332 (2016). https://doi.org/10.1038/nrg.2016.45
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DOI: https://doi.org/10.1038/nrg.2016.45
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