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

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Article 02 May 2022 DISENTANGLING AGE-DEPENDENT DNA METHYLATION: DETERMINISTIC, STOCHASTIC, AND NONLINEAR Article Open access 28 April 2021 UNIVERSAL DNA METHYLATION AGE ACROSS MAMMALIAN

TISSUES Article Open access 10 August 2023 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

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references 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 * Program in Bioinformatics

and Systems Biology, University of California, San Diego, La Jolla, CA, USA Zane Koch, Adam Li & Trey Ideker * California Pacific Medical Center Research Institute, San Francisco, CA,

USA Daniel S. Evans & Steven Cummings * Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA Daniel S. Evans & Steven

Cummings * Department of Medicine, University of California, San Diego, La Jolla, CA, USA Trey Ideker Authors * Zane Koch View author publications You can also search for this author

inPubMed Google Scholar * Adam Li View author publications You can also search for this author inPubMed Google Scholar * Daniel S. Evans View author publications You can also search for this

author inPubMed Google Scholar * Steven Cummings View author publications You can also search for this author inPubMed Google Scholar * Trey Ideker View author publications You can also

search for this author inPubMed Google Scholar 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. ETHICS

DECLARATIONS 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. PEER REVIEW PEER REVIEW INFORMATION _Nature Aging_ thanks Wolfgang Wagner and the other, anonymous,

reviewer(s) for their contribution to the peer review of this work. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published

maps and institutional affiliations. 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). SUPPLEMENTARY INFORMATION REPORTING

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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 Download citation * Received: 08 December 2023 * Accepted: 12 December 2024 * Published: 13 January 2025 * Issue Date: April 2025 * DOI:

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