Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping - PubMed (original) (raw)
Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping
Christoph Bock et al. Nucleic Acids Res. 2008 Jun.
Abstract
Genomic DNA methylation profiles exhibit substantial variation within the human population, with important functional implications for gene regulation. So far little is known about the characteristics and determinants of DNA methylation variation among healthy individuals. We performed bioinformatic analysis of high-resolution methylation profiles from multiple individuals, uncovering complex patterns of inter-individual variation that are strongly correlated with the local DNA sequence. CpG-rich regions exhibit low and relatively similar levels of DNA methylation in all individuals, but the sequential order of the (few) methylated among the (many) unmethylated CpGs differs randomly across individuals. In contrast, CpG-poor regions exhibit substantially elevated levels of inter-individual variation, but also significant conservation of specific DNA methylation patterns between unrelated individuals. This observation has important implications for experimental analysis of DNA methylation, e.g. in the context of epigenome projects. First, DNA methylation mapping at single-CpG resolution is expected to uncover informative DNA methylation patterns for the CpG-poor bulk of the human genome. Second, for CpG-rich regions it will be sufficient to measure average methylation levels rather than assaying every single CpG. We substantiate these conclusions by an in silico benchmarking study of six widely used methods for DNA methylation mapping. Based on our findings, we propose a cost-optimized two-track strategy for mammalian methylome projects.
Figures
Figure 1.
DNA methylation variation among healthy individuals (schematic figure). This figure displays artificial DNA methylation data for two amplicons with two unrelated samples/profiles each, which were designed to illustrate the effect of the three measures of inter-individual variation used in this study. The typical amplicon with high overall methylation (blue profiles, top) has a relatively high pairwise deviation between means (_v_3) and a pairwise deviation between high-resolution profiles (_v_1) that is substantially lower than the deviation between mean and high-resolution profile (_v_2), which is reflected in a substantial correlation between the rising and falling of the DNA methylation profile curves over the length of the amplicon. In contrast, the typical amplicon with low overall methylation (red profiles, bottom) has a low pairwise deviation between means (_v_3) and similar values for pairwise deviation between high-resolution profiles (_v_1) and deviation between mean and high-resolution profile (_v_2), indicating that the fluctuations in the profiles are not inter-individually conserved and presumably random.
Figure 2.
Effect of average amplicon methylation (left) and overlap with bona fide CpG islands (right) on inter-individual variation of DNA methylation. This figure shows the means of the three measures of DNA methylation variation as bar plots. In the left panel, values are reported separately for the top-25% most unmethylated amplicons with an average amplicon methylation of <11.5% (this threshold is motivated in the Materials and Methods section) and for the remaining 75% of amplicons. In the right panel, distinction is made between amplicons that overlap with a bona fide CpG island (4) and those that do not. In both cases, error bars represent 95% confidence intervals under the assumption of normal distribution and the _P_-values in the legends are based on two-sample, two-sided, _t_-tests between the group means for each measure.
Figure 3.
Benchmarking results for experimental mapping of DNA methylation. This figure displays the results of in silico benchmarking of different DNA methylation mapping methods for all amplicons. The _y_-axis shows _v_method values for all experimental methods included in this study (A1–F9, described in Table 1) and for seven negative controls, which are based on guessing rules rather than on experimental data (G1–G7, described in Table 1). The standard boxplot format is used (boxes show center quartiles, whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box) and outliers are hidden.
Similar articles
- CpG island mapping by epigenome prediction.
Bock C, Walter J, Paulsen M, Lengauer T. Bock C, et al. PLoS Comput Biol. 2007 Jun;3(6):e110. doi: 10.1371/journal.pcbi.0030110. Epub 2007 May 2. PLoS Comput Biol. 2007. PMID: 17559301 Free PMC article. - Mapping of Variable DNA Methylation Across Multiple Cell Types Defines a Dynamic Regulatory Landscape of the Human Genome.
Gu J, Stevens M, Xing X, Li D, Zhang B, Payton JE, Oltz EM, Jarvis JN, Jiang K, Cicero T, Costello JF, Wang T. Gu J, et al. G3 (Bethesda). 2016 Apr 7;6(4):973-86. doi: 10.1534/g3.115.025437. G3 (Bethesda). 2016. PMID: 26888867 Free PMC article. - Resolution of the DNA methylation state of single CpG dyads using in silico strand annealing and WGBS data.
Xu C, Corces VG. Xu C, et al. Nat Protoc. 2019 Jan;14(1):202-216. doi: 10.1038/s41596-018-0090-x. Nat Protoc. 2019. PMID: 30542058 Free PMC article. - Population whole-genome bisulfite sequencing across two tissues highlights the environment as the principal source of human methylome variation.
Busche S, Shao X, Caron M, Kwan T, Allum F, Cheung WA, Ge B, Westfall S, Simon MM; Multiple Tissue Human Expression Resource; Barrett A, Bell JT, McCarthy MI, Deloukas P, Blanchette M, Bourque G, Spector TD, Lathrop M, Pastinen T, Grundberg E. Busche S, et al. Genome Biol. 2015 Dec 23;16:290. doi: 10.1186/s13059-015-0856-1. Genome Biol. 2015. PMID: 26699896 Free PMC article. - Monitoring methylation changes in cancer.
Beier V, Mund C, Hoheisel JD. Beier V, et al. Adv Biochem Eng Biotechnol. 2007;104:1-11. doi: 10.1007/10_024. Adv Biochem Eng Biotechnol. 2007. PMID: 17290816 Review.
Cited by
- DNA methylation patterns in CD4+ T-cells separate psoriasis patients from healthy controls, and skin psoriasis from psoriatic arthritis.
Natoli V, Charras A, Hofmann SR, Northey S, Russ S, Schulze F, McCann L, Abraham S, Hedrich CM. Natoli V, et al. Front Immunol. 2023 Aug 15;14:1245876. doi: 10.3389/fimmu.2023.1245876. eCollection 2023. Front Immunol. 2023. PMID: 37662940 Free PMC article. - MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data.
De Riso G, Sarnataro A, Scala G, Cuomo M, Della Monica R, Amente S, Chiariotti L, Miele G, Cocozza S. De Riso G, et al. NAR Genom Bioinform. 2022 Dec 31;4(4):lqac096. doi: 10.1093/nargab/lqac096. eCollection 2022 Dec. NAR Genom Bioinform. 2022. PMID: 36601577 Free PMC article. - Selection-Corrected Statistical Inference for Region Detection With High-Throughput Assays.
Benjamini Y, Taylor J, Irizarry RA. Benjamini Y, et al. J Am Stat Assoc. 2019;114(527):1351-1365. doi: 10.1080/01621459.2018.1498347. Epub 2018 Nov 13. J Am Stat Assoc. 2019. PMID: 36312875 Free PMC article. - DNA Methylation Patterns in CD8+ T Cells Discern Psoriasis From Psoriatic Arthritis and Correlate With Cutaneous Disease Activity.
Charras A, Garau J, Hofmann SR, Carlsson E, Cereda C, Russ S, Abraham S, Hedrich CM. Charras A, et al. Front Cell Dev Biol. 2021 Oct 21;9:746145. doi: 10.3389/fcell.2021.746145. eCollection 2021. Front Cell Dev Biol. 2021. PMID: 34746142 Free PMC article. - Methylomes in Vegans versus Pescatarians and Nonvegetarians.
Filippov V, Jaceldo-Siegl K, Eroshkin A, Loskutov V, Chen X, Wang C, Duerksen-Hughes PJ. Filippov V, et al. Epigenomes. 2020 Dec;4(4):28. doi: 10.3390/epigenomes4040028. Epub 2020 Dec 11. Epigenomes. 2020. PMID: 33768971 Free PMC article.
References
- Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21. - PubMed
- Weber M, Schübeler D. Genomic patterns of DNA methylation: targets and function of an epigenetic mark. Curr. Opin. Cell Biol. 2007;19:273–280. - PubMed
- Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J. Mol. Biol. 1987;196:261–282. - PubMed
- Reik W. Stability and flexibility of epigenetic gene regulation in mammalian development. Nature. 2007;447:425–432. - PubMed