Longitudinal prediction of DNA methylation to forecast epigenetic outcomes
File(s)
Published version
Author(s)
Leroy, Arthur
Teh, Ai Ling
Dondelinger, Frank
Alvarez, Mauricio A
Wang, Dennis
Type
Journal Article
Abstract
Background:
Epigenetic changes in early life play an important role in the development of health conditions in children. Longitudinally measuring and forecasting changes in DNA methylation can reveal patterns of ageing and disease progression, but biosamples may not always be available.
Methods:
We introduce a probabilistic machine learning framework based on multi-mean Gaussian processes, accounting for individual and gene correlations across time to forecast the methylation status of an individual into the future. Predicted methylation values were used to compute future epigenetic age and compared to chronological age.
Findings:
We show that this method can simultaneously predict methylation status at multiple genomic sites in children (age 5–7) using methylation data from earlier ages (0–4). Less than 10% difference between observed and predicted methylation values is found in approximately 95% of methylation sites. We show that predicted methylation profiles can be used to estimate other molecular phenotypes, such as epigenetic age, at any timepoint and enable association tests with health outcomes measured at the same timepoint.
Interpretation:
Limited longitudinal profiling of DNA methylation coupled with machine learning enables forecasting of epigenetic ageing and future health outcomes.
Funding:
Wellcome Trust, Singapore National Research Foundation (NRF), Singapore National Medical Research Council (NMRC), Agency for Science, Technology and Research (A∗STAR), UK Academy of Medical Sciences and the UK Engineering and Physical Sciences Research Council (EPSRC).
Epigenetic changes in early life play an important role in the development of health conditions in children. Longitudinally measuring and forecasting changes in DNA methylation can reveal patterns of ageing and disease progression, but biosamples may not always be available.
Methods:
We introduce a probabilistic machine learning framework based on multi-mean Gaussian processes, accounting for individual and gene correlations across time to forecast the methylation status of an individual into the future. Predicted methylation values were used to compute future epigenetic age and compared to chronological age.
Findings:
We show that this method can simultaneously predict methylation status at multiple genomic sites in children (age 5–7) using methylation data from earlier ages (0–4). Less than 10% difference between observed and predicted methylation values is found in approximately 95% of methylation sites. We show that predicted methylation profiles can be used to estimate other molecular phenotypes, such as epigenetic age, at any timepoint and enable association tests with health outcomes measured at the same timepoint.
Interpretation:
Limited longitudinal profiling of DNA methylation coupled with machine learning enables forecasting of epigenetic ageing and future health outcomes.
Funding:
Wellcome Trust, Singapore National Research Foundation (NRF), Singapore National Medical Research Council (NMRC), Agency for Science, Technology and Research (A∗STAR), UK Academy of Medical Sciences and the UK Engineering and Physical Sciences Research Council (EPSRC).
Date Issued
2025-05-01
Date Acceptance
2025-04-03
Citation
EBioMedicine, 2025, 115
ISSN
2352-3964
Publisher
Elsevier
Journal / Book Title
EBioMedicine
Volume
115
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
License URL
Subjects
DNA methylation
Epigenetic age
Longitudinal data
Machine learning
Publication Status
Published
Article Number
ARTN 105709
Date Publish Online
2025-04-22