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Somatic mutation as an explanation for epigenetic aging

Nature Aging volume 5, pages 709–719 (2025)Cite this article

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Abstract

DNA methylation marks have recently been used to build models known as epigenetic clocks, which predict calendar age. As methylation of cytosine promotes C-to-T mutations, we hypothesized that the methylation changes observed with age should reflect the accrual of somatic mutations, and the two should yield analogous aging estimates. In an analysis of multimodal data from 9,331 human individuals, we found that CpG mutations indeed coincide with changes in methylation, not only at the mutated site but with pervasive remodeling of the methylome out to ±10 kilobases. This one-to-many mapping allows mutation-based predictions of age that agree with epigenetic clocks, including which individuals are aging more rapidly or slowly than expected. Moreover, genomic loci where mutations accumulate with age also tend to have methylation patterns that are especially predictive of age. These results suggest a close coupling between the accumulation of sporadic somatic mutations and the widespread changes in methylation observed over the course of life.

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Fig. 1: Frequency and methylation status of CpG mutation events.
Fig. 2: Association of mutations with regional methylation patterns.
Fig. 3: Magnitude and extent of methylation changes near somatic mutations.
Fig. 4: Association among mutation age, methylation age and chronological age.

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Data availability

All data analyzed were from The Cancer Genome Atlas Pan-Can cohort34,35,36 (http://xena.ucsc.edu/) and the Pan-Cancer Analysis of Whole Genomes48 (https://xenabrowser.net/datapages/?hub=https://pcawg.xenahubs.net:443). Data can be accessed from the provided links and are described further in the respective publications (https://doi.org/10.1038/ng.2764, https://doi.org/10.1038/s41586-020-1969-6)35,37. Data to replicate the figures in this manuscript can be found on figshare (‘Somatic mutation as an explanation for epigenetic aging (Koch et al. 2024)’, https://figshare.com/projects/Somatic_mutation_as_an_explanation_for_epigenetic_aging_Koch_et_al_2024_/224232)75. The panel of normal and gnomAD resources used for filtering the somatic mutation calls can be accessed by downloading Mutect2 (https://gatk.broadinstitute.org/hc/en-us/articles/360037593851-Mutect2). A file containing Illumina 450k array CpG locations and characteristics can be accessed on the Illumina website (https://webdata.illumina.com/downloads/productfiles/humanmethylation450/humanmethylation450_15017482_v1-2.csv). The hg19 genome annotation can be accessed through the University of California, Santa Cruz, website (https://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/cpgIslandExt.txt.gz).

Code availability

All custom algorithms and analysis code are in the GitHub repository at https://github.com/zanekoch/MutationsAndMethylationAging/.

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Acknowledgements

This study was funded by the National Institutes of Health under awards U54 CA274502 (T.I.), P41 GM103504 (T.I.) and R01AG059416 (S.C.). S.C. and D.E. also receive support from The Sequoia Center for Research on Aging, California Pacific Medical Center Research Institute.

Author information

Authors and Affiliations

  1. Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA, USA

    Zane Koch, Adam Li & Trey Ideker

  2. California Pacific Medical Center Research Institute, San Francisco, CA, USA

    Daniel S. Evans & Steven Cummings

  3. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Daniel S. Evans & Steven Cummings

  4. Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    Trey Ideker

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  1. Zane Koch
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  2. Adam Li
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  3. Daniel S. Evans
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  4. Steven Cummings
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Contributions

Z.K. designed the study, carried out the primary data analyses and wrote the manuscript. A.L. and D.S.E. assisted with data analysis and study design considerations. T.I. and S.C. designed the study and wrote the manuscript.

Corresponding authors

Correspondence to Steven Cummings or Trey Ideker.

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Competing interests

T.I. is a cofounder of Serinus and Data4Cure, is on their scientific advisory boards and has an equity interest in both companies. T.I. is on the scientific advisory board of IDEAYA Biosciences and has an equity interest. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. The other authors declare no competing interests.

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Nature Aging thanks Wolfgang Wagner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Links among CpG mutations, methylome remodeling, and aging.

a) Various mutational processes affect the genome. Here, we show that some of these mutations associate with an aberrant DNA methylation pattern at both the mutated site and at numerous neighboring CpGs. b) An individual’s DNA mutation profile and DNA methylation profile make similar predictions of their calendar age and rate of aging. Panel a created with BioRender.com.

Extended Data Fig. 2 Supplemental characterization of CpG mutations.

a) The distribution of methylation fraction values of each CpG site in the TCGA and PCAWG datasets separately (TCGA = 273,202 and PCAWG = 326,749 CpG sites) in each sample (TCGA = 8,680 and PCAWG = 651 samples). b) The CpG density (number of CpGs per base pair) in the 50 and 125 base pairs surrounding each of the CpG sites in (a). The central line of the inner boxplot represents the median, the edges of the box the interquartile range (IQR), and the whiskers 1.5-times the IQR. c) Violin plots of the distribution of mean methylation fraction of non-mutated individuals at the same mutated CpG sites as in Fig. 1d (n = 8,037 sites), stratified by CpG mutation type. d) As in (c), but the distribution of CpG density in the 125 bp surrounding each CpG site. e) Pie chart showing the proportion of CpG mutations (n = 467,079 mutations) that result in specific mutated nucleotides. Note that 5’-CpG-3’ sites are palindromic, corresponding to a 3’-GpC-5’ sequence on the opposite strand; thus, mutation of the C residue is equivalent to mutation of the complementary G residue. For simplicity, we refer to all CpG mutations by the status of the C residue. f) Violin plot showing the mean methylation fraction across all PCAWG samples, considering CpG sites where a mutation has occurred in at least one sample (left, n = 1,137 CpG sites), CpG sites where no mutation has occurred in any sample (middle, n = 325,614 CpG sites), and all measured CpG sites (right, n = 326,751). Significant difference of distribution (p ≤ 3.03 × 10–50) is marked with (***) and non-significant (p > 0.05) with (n.s.), based on a two-sided Mann-Whitney test. g) Methylation fraction at the same mutated CpG sites as Fig. 1d (n = 8,037 sites). CpG sites are binned into five groups based on MAF, with violin plots summarizing the distribution of methylation fraction within each group. Vertical bars inside each violin represent the interquartile range. Two-sided p value calculated based on the exact distribution of Pearson’s r modeled as a beta function.

Extended Data Fig. 3 Magnitude of methylation change near somatic mutations by tissue and genomic context.

a) Boxplots of the distribution of ΔMF10kb values for mutated (red) versus random control (n = 260,000, blue) sites for each tissue type separately (n = 813, 144, and 1,643 mutated sites from Pancreas, Brain, and Ovary tissues, respectively). P value shown for a two-sided Mann-Whitney test for a difference in median methylation fraction between the mutated and non-mutated random control loci. P value shown for a two-sided Mann-Whitney test for a difference in median absolute deviation (MAD) of ΔMF10kb between the mutated and non-mutated random control loci. The central line represents the median, the edges of the box the interquartile range (IQR), and the whiskers 1.5-times the IQR. b) A histogram of the median methylation fraction across comparison sites within ±10 kb of mutated (n = 2,600, red) and random control sites (n = 260,000, blue). Mutated sites are the same as Fig. 3b. Random control sites have been selected as before, with the additional criteria of having a methylation profile matched to that of the matched samples at mutated sites (as measured by the median methylation fraction of comparison sites, Methods). P value shown for a two-sided Mann-Whitney test for a difference in median methylation fraction between the mutated and random control loci. c) Probability distribution of ΔMF10kb values for mutated (red) versus random control (blue) sites. Mutated and random sites are the same as (b). P value calculated as in (a). d) Line plot depicting the fold enrichment for mutated over non-mutated random control sites as a function of ΔMF10kb, for the same sites as Fig. 3b. Sites are stratified depending on whether the site is a CpG and/or falls within a CpG island (n = 419 CpG-non-CGI, 21 CpG-CGI, 2,120 non-CpG-non-CGI, and 39 non-CpG-CGI sites). Fold enrichment is the ratio of the probability of observing a given ΔMF10kb for mutated sites versus non-mutated random control sites. ΔMF10kb is divided into equally spaced bins from –0.4 to 0.4. e) Barchart showing the fold-enrichment of mutated sites with the most extreme methylation changes (absolute ΔMF10kb | Z-score | > 1.96, n = 401 mutated sites) in various genomic regions, compared to all other mutated sites (n = 2,199 mutated sites). P values were calculated using a two-sided Fisher exact test. The categories ‘Upstream gene’ and ‘Downstream gene’ refer to variants located within 1 kb of the 5’ transcription start site and the 3’ transcription stop site, respectively, but outside the gene itself. f) As in (e), but comparing the mutated sites with the most extreme gains of methylation (Z-score of ΔMF10kb > 1) to those with the most extreme losses of methylation (Z-score of ΔMF10kb < –1). g) Boxplot of the ΔMF10kb value as a function of the mutated allele frequency (MAF). Same sites and samples as Fig. 3e (n = 3,880 mutated loci. The Pearson correlation is shown for the association of MAF with ΔMF10kb and the absolute value of ΔMF10kb. Two-sided p values were calculated based on the exact distribution of Pearson’s r modeled as a beta function. The central line represents the median, the edges of the box the interquartile range (IQR), the whiskers 1.5-times the IQR, and the points all ΔMF10kb value outside of these ranges.

Extended Data Fig. 4 Mutation-associated methylation change in normal tissues.

a) Probability distribution of ΔMF1kb values for mutated (red) versus random control (blue) sites. Includes n = 463 mutated sites (n = 146 samples) with MAF ≤ 0.15, ≥10 matched individuals (individuals of same tissue type within ± 10 years of age), and ≥1 measured CpG within the window. Random control sites include n = 46,300 non-mutated sites (n = 146 samples, Methods). P value shown for a two-sided Mann-Whitney test for a difference in median absolute deviation (MAD) of ΔMF1kb between the mutated and non-mutated random control loci. b) Line plot depicting the fold enrichment for mutated over non-mutated sites as a function of ΔMF1kb. Fold enrichment is the ratio of the probability of observing a given ΔMF1kb for mutated sites versus the probability of that ΔMF1kb for non-mutated control sites. ΔMF1kb is divided into equally spaced bins from –0.45 to 0.45. c) Absolute ΔMF1kb as the window center is moved away from the mutated site (n = 463, red). This quantity is also shown for non-mutated random control sites (n = 46,300, blue) (Methods). Points indicate the mean value and error bars denote the 95% confidence interval. A significant difference in distribution of absolute ΔMF1kb values (two-sided t-test) is marked (**, p ≤ .01), (*, p ≤ .05). Other comparisons are non-significant (n.s., p > 0.05).

Extended Data Fig. 5 Supplemental age prediction accuracy.

a) Bar plot indicating the correlation of chronological age with the age predictions of mutation clocks (left) or methylation clocks (right). Correlations are shown across all tumor tissues (n = 1,601) and in each of five TCGA tumor tissues individually: LGG (Brain), GBM (Brain-2), SARC (Bone), KIRP (Kidney), and THCA (Thyroid). b) As in (a) but for age predictions using samples from normal (that is non-cancerous) tissues (n = 40 individuals). c) Heatmap indicating the pairwise consistencies (Pearson correlation) among the mutation age in normal tissue, mutation age in tumor tissue, and chronological age. Data shown for n = 22 individuals with mutations measured in both normal and tumor tissues (the same individuals as from panel b with the exception of 11 colon samples and 7 liver samples as these were not available in the tumor samples). d) As in (c), but comparing predictions from methylation clocks. e) Scatter plot of human individuals, showing age predictions from the mutation model versus their chronological age. Shared area denotes the 95% confidence interval of the line of best fit. Includes 40 individuals from four normal tissues (Methods). A two-sided p value was calculated based on the exact distribution of Pearson’s r modeled as a beta function. f) Similar to panel (b) but showing age predictions from the methylation rather than mutation model. g) Violin plots of the methylation age residual versus mutation age residual (Methods). Plots include the same individuals as in panels (b,c). Pearson r refers to the correlation between methylation age residual and mutation age residual, controlling for chronological age (that is, partial correlation, p = 1.76 × 10–3). The central line of the inner boxplot represents the median, the edges of the box the interquartile range (IQR), the whiskers 1.5-times the IQR, and the points all the methylation age residual values. Statistics calculated as in (e).

Extended Data Fig. 6 Performance comparison to previous epigenetic clocks.

a) Pearson r between predicted and chronological age for Hannum, Horvath, and PhenoAge clocks across the same samples as Fig. 4b (n = 1,601). Predictions were done using the subset of features from each clock that existed in our methylation data after quality control (66%, 63%, and 61% of CpG sites from the Hannum, Horvath, and PhenoAge clocks, respectively). The performance of this study’s methylation clock is not shown as it is inherently fit to the TCGA dataset in 5-fold CV. b) Pearson r between predicted and chronological age for Hannum, Horvath, and PhenoAge clocks after re-fitting (Methods). Same samples as (a). The performance of the methylation clock trained in this study (‘This study’) is shown for reference.

Extended Data Fig. 7 Mutation age prediction without whole-genome features.

a) Correlation of chronological versus predicted age, shown for mutation or methylation clocks built without whole-genome features (n = 1,601 individuals). Correlations are shown across all tissues and in each of five TCGA tissues individually: LGG (Brain), GBM (Brain-2), SARC (Bone), KIRP (Kidney), and THCA (Thyroid). b) As in (a) but for age predictions using samples from normal (that is non-cancerous) tissues (n = 40). c) The methylation age residual is plotted versus the mutation age residual, using clocks without whole-genome features (Methods). Violin plots summarize the same samples as in panel (a). Pearson r refers to the correlation between methylation age residual and mutation age residual, controlling for chronological age (that is, partial correlation, p = 6.66 × 10–105). The central line of the inner boxplot represents the median, the edges of the box the interquartile range (IQR), and the whiskers 1.5-times the IQR. A two-sided p value was calculated based on the exact distribution of Pearson’s r modeled as a beta function. d) Similar to (c), but for the samples in (b). The central line of the inner boxplot represents the median, the edges of the box the interquartile range (IQR), the whiskers 1.5-times the IQR, and the points all the methylation age residual values. Statistics calculated as in (c).

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Koch, Z., Li, A., Evans, D.S. et al. Somatic mutation as an explanation for epigenetic aging. Nat Aging 5, 709–719 (2025). https://doi.org/10.1038/s43587-024-00794-x

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