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Causality-enriched epigenetic age uncouples damage and adaptation

Nature Aging volume 4, pages 231–246 (2024)Cite this article

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Abstract

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge—clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

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Fig. 1: EWMR on various aging-related phenotypes.
Fig. 2: CpG sites causal to aging are enriched in specific genetic regulatory regions.
Fig. 3: Existing epigenetic clocks are not enriched with CpG sites causal to aging.
Fig. 4: Integration of causal information and age-associated differential methylation to separate protective and damaging epigenetic changes.
Fig. 5: Construction and application of causality-enriched epigenetic clocks.
Fig. 6: Causality-enriched epigenetic clocks can better capture aging-related effects.

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Data availability

All analyses in this study were conducted using publicly available data. The datasets used (Table 1) include longevity GWAS summary statistics (https://www.longevitygenomics.org/downloads/), parental lifespan GWAS summary statistics (https://datashare.ed.ac.uk/handle/10283/3209/), healthspan GWAS summary statistics (https://www.gwasarchive.org/), frailty index GWAS summary statistics (https://figshare.com/articles/dataset/Genome-Wide_Association_Study_of_the_Frailty_Index_-_Atkins_et_al_2019/9204998/), epigenetic age acceleration GWAS summary statistics (https://datashare.ed.ac.uk/handle/10283/3645/) and Gene Expression Omnibus datasets (GSE107143, GSE127985, GSE192918, GSE193795, GSE210245, GSE51954, GSE94876, GSE98056, GSE101673, GSE78773, GSE90117, GSE79257 and GSE42865). Any other data generated in this study upon which conclusions are based are available in Supplementary Tables 1–3. Source data are provided with this paper.

Code availability

MR analyses were conducted using the R packages TwoSampleMR version 0.5.6 (https://mrcieu.github.io/TwoSampleMR/) and MendelianRandomization version 0.7.0 (https://cran.r-project.org/web/packages/MendelianRandomization/index.html). Genetic correlation analysis was performed using LDSC software v1.0.1 (https://github.com/bulik/ldsc/). Mediation analysis was performed using smr-ivw v1.0 (https://github.com/masadler/smrivw/). Colocalization analysis was performed using PWCoCo v1.0 (https://github.com/jwr-git/pwcoco/). The elastic net model was trained using glmnet v4.1 (https://cran.r-project.org/web/packages/glmnet/index.html). Custom code used is available in the Supplementary Information. Algorithms of CausAge, DamAge and AdaptAge are available in the Supplementary Information, as well as ClockBase (https://www.clockbase.org/) and the bio-learn Python package (https://bio-learn.github.io/)62,63.

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Acknowledgements

We thank the DNA Methylation Consortium (GoDMC) for releasing the summary statistics of meQTLs. We also thank C. Kerepesi, M. Mariotti, D. L. McCartney and R. E. Marioni for their help and advice during the initial stages of this study. We thank B. Manning, D. A. Sinclair, S. Sunyaev and A. Zhavoronkov for advising. We especially thank Y. Fang for the artwork design. This study is supported by the National Institute on Aging, Impetus grants and the Michael Antonov Foundation. The FHS is funded by National Institutes of Health contracts N01-HC-25195 and HHSN268201500001I. The US Department of Veterans Affairs (VA) NAS is supported by the National Institute of Environmental Health Sciences (NIEHS; R01ES015172 and R01ES021733) as well as by the Cooperative Studies Program/ERIC, US Department of Veterans Affairs, and is a research component of the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC). Additional support to the VA NAS was provided by the US Department of Agriculture, Agricultural Research Service (contract 53-K06-510).

Author information

Authors and Affiliations

  1. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Kejun Ying, Hanna Liu, Andrei E. Tarkhov, Mahdi Moqri & Vadim N. Gladyshev

  2. T. H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Kejun Ying

  3. Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA

    Hanna Liu

  4. Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA

    Hanna Liu

  5. University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland

    Marie C. Sadler & Zoltán Kutalik

  6. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

    Marie C. Sadler & Zoltán Kutalik

  7. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Marie C. Sadler & Zoltán Kutalik

  8. Altos Labs, San Diego, CA, USA

    Ake T. Lu & Steve Horvath

  9. Departments of Human Genetics and Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA

    Ake T. Lu & Steve Horvath

  10. Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA

    Mahdi Moqri

  11. Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China

    Xia Shen

  12. State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China

    Xia Shen

  13. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Xia Shen

  14. Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK

    Xia Shen

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Contributions

K.Y. initiated the study and performed data collection and analyses. V.N.G. supervised this research and provided funding. H.L., M.C.S. and A.T. were involved in data analysis. A.T.L., M.M. and S.H. contributed to data interpretation. Z.K. and X.S. assisted in methodology refinement and statistical analysis. All authors contributed to writing and revising the manuscript and approved the final version for publication.

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Correspondence to Vadim N. Gladyshev.

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K.Y. and V.N.G. are inventors on a patent application related to the research reported.

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Extended data

Extended Data Fig. 1 Genetic correlation between 9 lifespan-related phenotypes.

Genetic correlations were calculated using LDSC regression. Areas of the squares represent absolute values of corresponding genetic correlations. Genetic correlations that could not be estimated are shown as blanks. Two-sided test was performed. P values are corrected using Bonferroni correction for the number of tests, * P nominal < 0.05, ** P adjusted < 0.05, *** P adjusted < 0.01.

Extended Data Fig. 2 Relationship between meSNPs and causal CpGs.

Forest plot shows enrichment of meSNP (n = 161860 sites) among causal CpGs. Error bar shows the 95% confidential interval. P-value of significant results is annotated (top). Two-sided Fisher’s exact test was used to calculate the enrichment P-value. P-values were corrected using FDR for multiple comparison. Scatter plot shows Pearson’s correlation between the effect of a single CpG site estimated by MR and a single meSNP (bottom). Correlation coefficient and P-value are annotated at the top.

Extended Data Fig. 3 Relationship between estimated causal effects and evolutionary conservation.

Box plot shows the distribution of conservation scores in causal and non-causal CpG sites. Conservation scores were obtained by Learning Evidence of Conservation from Integrated Functional genomic annotations (LECIF), phastCons, and phyloP. Scatter plot shows the relationship between the absolute adjusted effect size and conservation score. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. From left to right, the exact P-value for the top three panels are 1.23e-0, 0.00392, 0.00213.

Extended Data Fig. 4 Enrichment analysis.

a. Bar plot shows enrichment of causal CpG sites in genomic annotations. Y-axis shows -log10(FDR) based on Fisher’s exact test, signed by log2(Odds Ratio). Causal CpG sites identified for different traits are annotated with different colors. Two dotted horizontal lines show the FDR threshold of 0.05. b. Enrichment of causal CpG sites for Aging-GIP1 with positive or negative effect size against transcription-factor-binding sites. Each horizontal bar represents an enriched term. The X-axis shows the -log10(P-value), signed by log2 (Odds ratio). The top 10 enriched terms that passed the FDR threshold of 0.05 for each direction are annotated.

Extended Data Fig. 5 Enrichment of causal CpG sites in EWAS hits.

Enrichment of putative causal CpG sites for 12 aging-related traits against EWAS hits. Each horizontal bar represents an enriched term. The X-axis shows the -log10(P-value), signed by log2 (Odds ratio). The top 10 enriched terms that passed the FDR threshold of 0.05 for each direction are annotated. Two-sided Fisher’s exact test was used to calculate the enrichment P-value. P-values were corrected using FDR for multiple comparison.

Extended Data Fig. 6 Distribution of the effect ratio of strongest meQTL and age (per year).

Scatter plot shows the top 50 age-related CpG sites. The dotted line annotates the mean and median values of the ratio.

Extended Data Fig. 7 Relationship between MR-estimated causal effects (X-axis) and age-related methylation change (Y-axis) for each significant causal CpGs identified in aging-related phenotypes.

The color scheme highlights the expected impact of age-related methylation change on aging. Error bars show the standard error of b. The size reflects the PP-H4. Only CpG sites with adjusted P-values < 0.05 after corrected for multiple comparison and relative PP-H4 > 0.7 are plotted. The Pearson’s correlation coefficient and P-value based on two-sided test are annotated at the top.

Extended Data Fig. 8 Enrichment of causal CpG sites among CpG sites that show age-related changes.

Error bar indicates the 95% confidence interval. The center indicate the estimated log2 odds ratio (N site used for each traits are shown in Fig. 1c). Bar plot shows the signed -log10(P-value) of Spearman’s correlation between age-related change and causal effect size. The orange dotted line shows the threshold of P < 0.05.

Extended Data Fig. 9 Relationship between DamAge acceleration (X-axis) and AdaptAge acceleration (Y-axis) in the test set.

The age acceleration term is age-adjusted by regressing out the chronological age. Pearson’s correlation coefficient and P value based on two-sided test are annotated.

Extended Data Fig. 10 Residual plot in the test set.

These plots show the residuals of the three clocks in the test set across age range. R-square and MSE are reported. Relationship between the number of sites in the clock model and Tau. X-axis shows the choice of causality factor Tau, and the Y-axis shows the number of CpG sites selected in elastic net models.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Cohort Information.

Reporting Summary

Supplementary Table 1

Statistical source data for Extended Data Fig. 1.

Supplementary Code 1

The custom R code used for EWMR analysis.

Supplementary Table 2

Statistical source data for Data Fig. 1, which contains the output putative causal CpG sites from EWMR analysis.

Supplementary Table 3

The model weight for CausAge, DamAge and AdaptAge.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

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Ying, K., Liu, H., Tarkhov, A.E. et al. Causality-enriched epigenetic age uncouples damage and adaptation. Nat Aging 4, 231–246 (2024). https://doi.org/10.1038/s43587-023-00557-0

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