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

Authors :
Riccardo E. Marioni
Peter A. Silburn
John B.J. Kwok
Ian J. Deary
Simon J.G. Lewis
Dongsheng Fan
Qian Zhang
David J. Porteous
Tian Lin
Alison D. Murray
Ji He
Glenda M. Halliday
Kathryn L. Evans
Sarah E. Harris
Tim J. Anderson
Grant W. Montgomery
John F. Pearson
Toni L. Pitcher
Peter M. Visscher
Chris Haley
Ian B. Hickie
Naomi R. Wray
Andrew M. McIntosh
Javed Fowdar
Anjali K. Henders
George D. Mellick
Costanza L. Vallerga
Allan F. McRae
Martin A. Kennedy
Jian Yang
Paul Redmond
Jacob Gratten
Rosie M. Walker
Source :
Genome Medicine, Vol 11, Iss 1, Pp 1-11 (2019), Genome Medicine, 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 2019, ' Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing ', Genome Medicine, vol. 11, no. 1, 54 . https://doi.org/10.1186/s13073-019-0667-1
Publication Year :
2019
Publisher :
BMC, 2019.

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. Electronic supplementary material The online version of this article (10.1186/s13073-019-0667-1) contains supplementary material, which is available to authorized users.

Details

Language :
English
Volume :
11
Issue :
1
Database :
OpenAIRE
Journal :
Genome Medicine
Accession number :
edsair.doi.dedup.....6e23aea39874f3892565dd03cfa18b71