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Cited by 496Epigenetics and DNA MethylationGenetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and Senescence

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

Open access
Zhang, Qian, Vallerga, Costanza L., Walker, Rosie M., Lin, Tian, Henders, Anjali K., Montgomery, Grant W., He, Ji, Fan, Dongsheng, Fowdar, Javed, Kennedy, Martin, Pitcher, Toni, Pearson, John, Halliday, Glenda, Kwok, John B., Hickie, Ian, Lewis, Simon, Anderson, Tim, Silburn, Peter A., Mellick, George D., Harris, Sarah E., Redmond, Paul, Murray, Alison D., Porteous, David J., Haley, Christopher S., Evans, Kathryn L., McIntosh, Andrew M., Yang, Jian, Gratten, Jacob, Marioni, Riccardo E., Wray, Naomi R., Deary, Ian J., McRae, Allan F., Visscher, Peter M.·2019·Genome Medicine
DOI 10.1186/s13073-019-0667-1PMC PMC6708158
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Abstract

BACKGROUND: DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed 'epigenetic clocks'. The deviation of predicted age from the actual age ('age acceleration residual', AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association. METHODS: In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues. RESULTS: We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91-1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79-1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor. CONCLUSIONS: This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.

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Crossref · 10.1186/s13073-019-0667-1

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Fig. 1 The relationship between training sample size and predictor error measured at the square root of the mean squared error (RMSE) in test data sets. Each point represents the RMSE of the test result based on predictors with different sample size and methods. Points with RMSE larger than 15 are excluded. Prediction results from Horvath are marked as black dash line, and the black solid line represents prediction result from Hannum’s age predictor
Fig. 1 The relationship between training sample size and predictor error measured at the square root of the mean squared error (RMSE) in test data sets. Each point represents the RMSE of the test result based on predictors with different sample size and methods. Points with RMSE larger than 15 are excluded. Prediction results from Horvath are marked as black dash line, and the black solid line represents prediction result from Hannum’s age predictorCC BY · PubMed Central
Fig. 2 Relationship between the training sample size and the test statistics (t test) from the association between age acceleration residual (AAR) and mortality. Each point represents the test statistic from the survival analysis based on the predicted ages from predictors with different training sample sizes
Fig. 2 Relationship between the training sample size and the test statistics (t test) from the association between age acceleration residual (AAR) and mortality. Each point represents the test statistic from the survival analysis based on the predicted ages from predictors with different training sample sizesCC BY · PubMed Central
Fig. 3 The change of odds ratio from the enrichment test with the increase of training sample size (excluding LBC1936). The enrichment test examines whether AAR-associated CpG sites are enriched in probes with cellular heterogeneity
Fig. 3 The change of odds ratio from the enrichment test with the increase of training sample size (excluding LBC1936). The enrichment test examines whether AAR-associated CpG sites are enriched in probes with cellular heterogeneityCC BY · PubMed Central
Fig. 4 Comparison of prediction performance (a correlation and b root mean square error) between the predictor from this study (based on Elastic Net) and Horvath’s age predictor in non-blood samples
Fig. 4 Comparison of prediction performance (a correlation and b root mean square error) between the predictor from this study (based on Elastic Net) and Horvath’s age predictor in non-blood samplesCC BY · PubMed Central

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  1. Therneau TM. A package for survival analysis in S. 2.38 edition; 2015.

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Metadata

Authors
Zhang, Q., Vallerga, C. L., Walker, R. M., Lin, T., Henders, A. K., Montgomery, G. W., He, J., Fan, D., Fowdar, J., Kennedy, M., Pitcher, T., Pearson, J., Halliday, G., Kwok, J. B., Hickie, I., Lewis, S., Anderson, T., Silburn, P. A., Mellick, G. D., Harris, S. E., Redmond, P., Murray, A. D., Porteous, D. J., Haley, C. S., Evans, K. L., McIntosh, A. M., Yang, J., Gratten, J., Marioni, R. E., Wray, N. R., Deary, I. J., McRae, A. F., & Visscher, P. M.
Affiliations
  • The University of Queensland (AU)
  • Institute of Genetics and Cancer (GB)
  • Peking University (CN)
  • Griffith University (AU)
  • University of Otago (NZ)
  • New Zealand Brain Research Institute (NZ)
  • The University of Sydney (AU)
  • University of Edinburgh (GB)
  • University of Aberdeen (GB)
  • Royal Edinburgh Hospital (GB)
Corresponding author
Visscher, Peter M.
ORCID
Venue
Genome Medicine
Publisher
Springer Science and Business Media LLC
Published
2019-08-23
Volume
11
Issue
1
Pages
54-54
ISSN
1756-994X (ISSN-L)
DOI
10.1186/s13073-019-0667-1
PMC
PMC6708158
OpenAlex
W2969410982
Cited by
487
References
42
Citations by year
2026: 81 · 2025: 117 · 2024: 102 · 2023: 47 · 2022: 52 · 2021: 54 · 2020: 30 · 2019: 4
FWCI
11.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Aberdeen University Research Archive (Aberdeen University) (submittedVersion, cc-by)
  • Griffith Research Online (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc)
Funders
  • Australian National Health and Medical Research Council (1078037, 1078901, 1103418, 1107258, 1127440 and 1113400)
  • Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6)
  • Scottish Funding Council (HR03006)
  • Medical Research Council UK and the Wellcome Trust (104036/Z/14/Z)
  • Australian Research Council (DP160102400)
  • Alzheimer’s Research UK Major Project Grant (RUK-PG2017B-10)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
Keywords
epigenetic clocks, DNA methylation, biological aging, mortality, training sample size, saliva, elastic net, age prediction
MeSH terms
Aging, Humans, Organ Specificity, Saliva, Reproducibility of Results, Proportional Hazards Models, DNA Methylation, Genetic Predisposition to Disease, Epigenesis, Genetic, Genome-Wide Association Study, Epigenomics

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