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

Qian Zhang; Costanza L Vallerga; Rosie M Walker; Tian Lin; Anjali K Henders; Grant W Montgomery; Ji He; Dongsheng Fan; Javed Fowdar; Martin Kennedy; Toni Pitcher; John Pearson; Glenda Halliday; John B Kwok; Ian Hickie; Simon Lewis; Tim Anderson; Peter A Silburn; George D Mellick; Sarah E Harris; Paul Redmond; Alison D Murray; David J Porteous; Christopher S Haley; Kathryn L Evans; Andrew M McIntosh; Jian Yang; Jacob Gratten; Riccardo E Marioni; Naomi R Wray; Ian J Deary; Allan F McRae; Peter M Visscher

doi:10.1186/s13073-019-0667-1

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

Qian Zhang, Costanza L Vallerga, Rosie M Walker, Tian Lin, Anjali K Henders, Grant W Montgomery, Ji He, Dongsheng Fan, Javed Fowdar, Martin Kennedy, Toni Pitcher, John Pearson, Glenda Halliday, John B Kwok, Ian Hickie, Simon Lewis, Tim Anderson, Peter A Silburn, George D Mellick, Sarah E HarrisPaul Redmond, Alison D Murray, David J Porteous, Christopher S Haley, Kathryn L Evans, Andrew M McIntosh, Jian Yang, Jacob Gratten, Riccardo E Marioni, Naomi R Wray, Ian J Deary, Allan F McRae, Peter M Visscher (Corresponding Author)

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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.

Original language	English
Article number	54
Journal	Genome Research
Volume	11
DOIs	https://doi.org/10.1186/s13073-019-0667-1
Publication status	Published - 23 Aug 2019

Bibliographical note

This research was supported by the Australian Research Council (DP160102400), the Australian National Health and Medical Research Council (1078037, 1078901, 1103418, 1107258, 1127440 and 1113400), and the Sylvia & Charles Viertel Charitable Foundation. Riccardo Marioni was supported by Alzheimer’s Research UK Major Project Grant [ARUK-PG2017B-10]. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping and DNA methylation profiling of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z).

Keywords

DNA methylation
age prediction
epigenetic clock
Epigenetic clock
Ageing
Mortality
Age prediction
BIOMARKERS
COHORT PROFILE
DNA METHYLATION AGE

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Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated
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Cite this

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., ... Visscher, P. M. (2019). Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Research, 11, Article 54. https://doi.org/10.1186/s13073-019-0667-1

Zhang, Q, Vallerga, CL, Walker, RM, Lin, T, Henders, AK, Montgomery, GW, He, J, Fan, D, Fowdar, J, Kennedy, M, Pitcher, T, Pearson, J, Halliday, G, Kwok, JB, Hickie, I, Lewis, S, Anderson, T, Silburn, PA, Mellick, GD, Harris, SE, Redmond, P, Murray, AD, Porteous, DJ, Haley, CS, Evans, KL, McIntosh, AM, Yang, J, Gratten, J, Marioni, RE, Wray, NR, Deary, IJ, McRae, AF & Visscher, PM 2019, 'Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing', Genome Research, vol. 11, 54. https://doi.org/10.1186/s13073-019-0667-1

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title = "Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing",

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.",

keywords = "DNA methylation, age prediction, epigenetic clock, Epigenetic clock, Ageing, Mortality, Age prediction, BIOMARKERS, COHORT PROFILE, DNA METHYLATION AGE",

author = "Qian Zhang and Vallerga, {Costanza L} and Walker, {Rosie M} and Tian Lin and Henders, {Anjali K} and Montgomery, {Grant W} and Ji He and Dongsheng Fan and Javed Fowdar and Martin Kennedy and Toni Pitcher and John Pearson and Glenda Halliday and Kwok, {John B} and Ian Hickie and Simon Lewis and Tim Anderson and Silburn, {Peter A} and Mellick, {George D} and Harris, {Sarah E} and Paul Redmond and Murray, {Alison D} and Porteous, {David J} and Haley, {Christopher S} and Evans, {Kathryn L} and McIntosh, {Andrew M} and Jian Yang and Jacob Gratten and Marioni, {Riccardo E} and Wray, {Naomi R} and Deary, {Ian J} and McRae, {Allan F} and Visscher, {Peter M}",

note = "This research was supported by the Australian Research Council (DP160102400), the Australian National Health and Medical Research Council (1078037, 1078901, 1103418, 1107258, 1127440 and 1113400), and the Sylvia & Charles Viertel Charitable Foundation. Riccardo Marioni was supported by Alzheimer{\textquoteright}s Research UK Major Project Grant [ARUK-PG2017B-10]. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping and DNA methylation profiling of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z). ",

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doi = "10.1186/s13073-019-0667-1",

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T1 - Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

AU - Zhang, Qian

AU - Vallerga, Costanza L

AU - Walker, Rosie M

AU - Lin, Tian

AU - Henders, Anjali K

AU - Montgomery, Grant W

AU - He, Ji

AU - Fan, Dongsheng

AU - Fowdar, Javed

AU - Kennedy, Martin

AU - Pitcher, Toni

AU - Pearson, John

AU - Halliday, Glenda

AU - Kwok, John B

AU - Hickie, Ian

AU - Lewis, Simon

AU - Anderson, Tim

AU - Silburn, Peter A

AU - Mellick, George D

AU - Harris, Sarah E

AU - Redmond, Paul

AU - Murray, Alison D

AU - Porteous, David J

AU - Haley, Christopher S

AU - Evans, Kathryn L

AU - McIntosh, Andrew M

AU - Yang, Jian

AU - Gratten, Jacob

AU - Marioni, Riccardo E

AU - Wray, Naomi R

AU - Deary, Ian J

AU - McRae, Allan F

AU - Visscher, Peter M

N1 - This research was supported by the Australian Research Council (DP160102400), the Australian National Health and Medical Research Council (1078037, 1078901, 1103418, 1107258, 1127440 and 1113400), and the Sylvia & Charles Viertel Charitable Foundation. Riccardo Marioni was supported by Alzheimer’s Research UK Major Project Grant [ARUK-PG2017B-10]. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping and DNA methylation profiling of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z).

PY - 2019/8/23

Y1 - 2019/8/23

N2 - 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.

AB - 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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KW - age prediction

KW - epigenetic clock

KW - Epigenetic clock

KW - Ageing

KW - Mortality

KW - Age prediction

KW - BIOMARKERS

KW - COHORT PROFILE

KW - DNA METHYLATION AGE

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

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Alison Murray

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

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Bibliographical note

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Alison Murray

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