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Making sense of the ageing methylome

Abstract

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.

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Fig. 1: Linear models classify age-associated changes in DNAm.
Fig. 2: Entropy measures chaos in the ageing methylome.
Fig. 3: Correlation networks reveal connectivity in the ageing methylome.
Fig. 4: DMPs and VMPs reflect primary and secondary ageing processes.
Fig. 5: Proposed mechanisms of epigenetic ageing.

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Authors and Affiliations

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Contributions

K.S. and S.V. researched the literature and wrote the manuscript. All authors provided substantial contributions to discussions of the content and reviewed and/or edited the manuscript before submission.

Corresponding authors

Correspondence to Nir Eynon or Sarah Voisin.

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Competing interests

S.H. is a founder of the non-profit Epigenetic Clock Development Foundation, which plans to license several patents from his employer UC Regents. These patents list S.H. as inventor. All other authors declare no competing interests.

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Nature Reviews Genetics thanks Mario F. Fraga who co-reviewed with Raúl F. Pérez, Sara Hägg, and David Sinclair who co-reviewed with Patrick T. Griffin for their contribution to the peer review of this work.

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Related links

blockFinder: https://bioconductor.org/packages/release/bioc/html/minfi.html

bumphunter: https://bioconductor.org/packages/release/bioc/html/bumphunter.html

comb-p: https://github.com/brentp/combined-pvalues

DiffVar: https://bioconductor.org/packages/release/bioc/html/missMethyl.html

DMRcate: https://bioconductor.org/packages/release/bioc/html/DMRcate.html

glmnet: https://CRAN.R-project.org/package=glmnet

limma: https://bioconductor.org/packages/release/bioc/html/limma.html

Probe Lasso: https://www.bioconductor.org/packages/release/bioc/html/ChAMP.html

TAME trial: https://www.afar.org/tame-trial

WGCNA: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/

Glossary

CpGs

(Cytosine–guanine dinucleotides). Regions of DNA whereby a cytosine resides alongside a guanine to form a CpG.

Whole-genome bisulfite sequencing

A sequencing technology to survey and quantify DNA methylation (DNAm) at the single-base resolution on a genome-wide scale.

Reduced representation bisulfite sequencing

A sequencing technology to quantify DNA methylation (DNAm) at the single-base resolution at specific regions of interest, such as promoters and repeat regions.

Epigenome-wide association studies

(EWAS). The genome-wide investigations of the association of epigenetic marks, such as DNA methylation (DNAm), and a disease or trait of interest.

Methylation fraction

For a given DNA methylation (DNAm) locus in the human genome, the proportion of DNA strands that are methylated relative to the total number of DNA strands (0–100%), in a given population of cells.

Heteroscedastic

The variance of the residuals of a variable is non-constant or unequal across a range of values.

Breusch–Pagan test

A statistical test for heteroscedasticity of the errors in a linear regression model. It works by determining whether the errors of the response variable (such as DNA methylation (DNAm)) are dependent on the independent variable with continuous values (such as age).

White test

A statistical test for heteroscedasticity of the errors in a regression model. Unlike the Breusch–Pagan test, the White test can be used to identify both linear and non-linear forms of heteroscedasticity.

Bartlett’s test

A statistical test for heteroscedasticity between two discrete groups. This test assumes normality for each group and is thus sensitive to departures from normality.

Levene’s test

A statistical test for heteroscedasticity that compares deviations from the mean between groups.

Brown–Forsythe test

A statistical test for heteroscedasticity that compares deviations from the median between groups.

R package

A collection of functions, code, documentation and data bundled into a standardized format that can be downloaded and installed by R users.

Singular value decomposition

(SVD). A technique used to reduce the dimensionality of a data matrix. This is useful for identifying sources of variation.

Homoscedastic

The variance of the residuals of a variable is constant across a range of values.

GrimAge clock

An epigenetic clock that is a predictor of both lifespan and healthspan. DNA methylation (DNAm) GrimAge is the output of an epigenetic clock, which utilizes a machine learning algorithm trained against a linear combination of chronological age, sex, DNAm-based surrogate biomarkers for seven plasma proteins and smoking pack-years.

TruAge clock

A direct-to-consumer epigenetic age test built using a machine learning algorithm trained to predict chronological age of a saliva sample from a limited number of cytosine–guanine dinucleotides (CpGs) in CpG islands (CGIs) and promoter regions.

Polycomb repressive complexes

(PRCs). Multiprotein complexes of Polycomb group proteins. PRCs modify epigenetic marks to control gene repression and are involved in regulating developmental genes in a multitude of cell types, including embryonic and adult stem cells.

CpG islands

(CGIs). Regions of the genome containing a high frequency of cytosine–guanine dinucleotide (CpG) repeats.

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Seale, K., Horvath, S., Teschendorff, A. et al. Making sense of the ageing methylome. Nat Rev Genet 23, 585–605 (2022). https://doi.org/10.1038/s41576-022-00477-6

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