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Cited by 1,025Epigenetics and DNA MethylationGenetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and Senescence

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DNA methylation aging clocks: challenges and recommendations

Open access
Bell, Christopher G., Lowe, Robert, Adams, Peter D., Baccarelli, Andrea A., Beck, Stephan, Bell, Jordana T., Christensen, Brock C., Gladyshev, Vadim N., Heijmans, Bastiaan T., Horvath, Steve, Ideker, Trey, Issa, Jean-Pierre J., Kelsey, Karl T., Marioni, Riccardo E., Reik, Wolf, Relton, Caroline L., Schalkwyk, Leonard C., Teschendorff, Andrew E., Wagner, Wolfgang, Zhang, Kang, Rakyan, Vardhman K.·2019·Genome Biology
DOI 10.1186/s13059-019-1824-yPMC PMC6876109
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Abstract

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

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Crossref · 10.1186/s13059-019-1824-y

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Figures

Fig. 1 a Chronological age estimation error. With increasing training sample size, improved measurement of chronological age is expected, even using current array data (adapted from Zhang et al. [46]). y-axis: root mean square error (RMSE) of the predicted age. b DNA methylation clocks contain both chronological and biological information. The relative proportions of each will depend on the CpG probes employed in the construction of the clock. Therefore, there are multiple clocks that can be deconvoluted from aging-related epigenetic changes. Moving forward, more precise chronological (forensic age clock) and biological clocks, specific for particular diseases, informative of health or disease state need to be defined and separated. c Epigenetic age trajectory. Epigenetic age is not linear over the life course. Chronological age in years (x-axis) and epigenetic age in years (y-axis)
Fig. 1 a Chronological age estimation error. With increasing training sample size, improved measurement of chronological age is expected, even using current array data (adapted from Zhang et al. [46]). y-axis: root mean square error (RMSE) of the predicted age. b DNA methylation clocks contain both chronological and biological information. The relative proportions of each will depend on the CpG probes employed in the construction of the clock. Therefore, there are multiple clocks that can be deconvoluted from aging-related epigenetic changes. Moving forward, more precise chronological (forensic age clock) and biological clocks, specific for particular diseases, informative of health or disease state need to be defined and separated. c Epigenetic age trajectory. Epigenetic age is not linear over the life course. Chronological age in years (x-axis) and epigenetic age in years (y-axis)CC BY · PubMed Central
Fig. 2 All clock probes are strongly biased to reside within active functional loci. This is due to their construction from promoter-focused arrays. Overlap of CpGs from four DNA methylation clocks with the six Core Encode Combined Chromatin Segmentation tracks [130] from ENCODE Analysis Data at UCSC. a Horvath clock [24]. b Hannum et al. clock [23]. c PhenoAge clock [43]. d epiTOC clock [31]. Location is assessed for overlap with the seven functional categories: PF (promoter flanking—light red), TSS (transcription start site and promoter region—red), CTCF (blue), WE (weak enhancer—yellow), E (enhancer—gold), T (transcribed region—green), and R (repressed—grey), from any of the six Core Encode cell types (Gm12878, H1hesc, Helas3, Hepg2, Huvec, K562). This percentage overlap is shown on the y-axis and is compared with the percentage overlap for all ~28 × 106 CpGs in the human genome on the x-axis. Calculated via bedtools [131]. The size of the circle is proportional to the entire genome space for each functional category (~10(genome size proportion)). e-h Direct overlap comparison for four DNA methylation clocks (Horvath clock, Hannum et al. clock, PhenoAge clock, epiTOC clock) as well as Illumina array CpGs (27k, 450k, EPIC) and all genomic CpGs (far right bar) with: e the Combined Segmentation track for blood-derived tissue (GM12878) [130]. Functional segments are delineated as PF (promoter flanking), TSS (transcription start site and promoter region), CTCF, WE (weak enhancer), E (enhancer), T (transcribed region), and R (repressed). NC, not covered CpGs in this Combined Segmentation overlap; f Gencode [132] Exon and Transcripts; g UCSC [133]-defined CpG islands and shore regions (+/−2 kb); h Major repeat classes (UCSC RepeatMasker [133]), including DNA repeat elements (DNA_repeats), long interspersed nuclear elements (LINE), low complexity repeats and other rare repeat classes, long terminal repeat elements (LTR), simple repeats (microsatellites aka short tandem repeats), and short interspersed nuclear elements (SINE), of which ~63% are Alu elements
Fig. 2 All clock probes are strongly biased to reside within active functional loci. This is due to their construction from promoter-focused arrays. Overlap of CpGs from four DNA methylation clocks with the six Core Encode Combined Chromatin Segmentation tracks [130] from ENCODE Analysis Data at UCSC. a Horvath clock [24]. b Hannum et al. clock [23]. c PhenoAge clock [43]. d epiTOC clock [31]. Location is assessed for overlap with the seven functional categories: PF (promoter flanking—light red), TSS (transcription start site and promoter region—red), CTCF (blue), WE (weak enhancer—yellow), E (enhancer—gold), T (transcribed region—green), and R (repressed—grey), from any of the six Core Encode cell types (Gm12878, H1hesc, Helas3, Hepg2, Huvec, K562). This percentage overlap is shown on the y-axis and is compared with the percentage overlap for all ~28 × 106 CpGs in the human genome on the x-axis. Calculated via bedtools [131]. The size of the circle is proportional to the entire genome space for each functional category (~10(genome size proportion)). e-h Direct overlap comparison for four DNA methylation clocks (Horvath clock, Hannum et al. clock, PhenoAge clock, epiTOC clock) as well as Illumina array CpGs (27k, 450k, EPIC) and all genomic CpGs (far right bar) with: e the Combined Segmentation track for blood-derived tissue (GM12878) [130]. Functional segments are delineated as PF (promoter flanking), TSS (transcription start site and promoter region), CTCF, WE (weak enhancer), E (enhancer), T (transcribed region), and R (repressed). NC, not covered CpGs in this Combined Segmentation overlap; f Gencode [132] Exon and Transcripts; g UCSC [133]-defined CpG islands and shore regions (+/−2 kb); h Major repeat classes (UCSC RepeatMasker [133]), including DNA repeat elements (DNA_repeats), long interspersed nuclear elements (LINE), low complexity repeats and other rare repeat classes, long terminal repeat elements (LTR), simple repeats (microsatellites aka short tandem repeats), and short interspersed nuclear elements (SINE), of which ~63% are Alu elementsCC BY · PubMed Central
Fig. 3 Single-cell analysis. Distinct cell variation in aging epigenetic clock changes may exist that would not be apparent in bulk comparison. Black and white squares represent methylated and unmethylated loci, respectively. Each row represents a single cell’s epigenome (represented as haploid for simplicity) with increased variability present in individual 2
Fig. 3 Single-cell analysis. Distinct cell variation in aging epigenetic clock changes may exist that would not be apparent in bulk comparison. Black and white squares represent methylated and unmethylated loci, respectively. Each row represents a single cell’s epigenome (represented as haploid for simplicity) with increased variability present in individual 2CC BY · PubMed Central

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Research works printed but not linked: 2

  1. He W, Goodkind D, Kowal P. An aging world: 2015; 2016.
  2. Horvath S. Epigenetic clocks: from molecular pathways to anti-aging interventions. In: Epigenomics of Common Disease 2017. Hinxton, Cambridge; 2017. p. S73.

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Metadata

Authors
Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., Ideker, T., Issa, J.-P. J., Kelsey, K. T., Marioni, R. E., Reik, W., Relton, C. L., Schalkwyk, L. C., Teschendorff, A. E., Wagner, W., Zhang, K., & Rakyan, V. K.
Affiliations
  • Queen Mary University of London (GB)
  • Sanford Burnham Prebys Medical Discovery Institute (US)
  • Columbia University (US)
  • University College London (GB)
  • King's College London (GB)
  • Dartmouth College (US)
  • Brigham and Women's Hospital (US)
  • Leiden University Medical Center (NL)
  • University of California, Los Angeles (US)
  • University of California San Diego (US)
  • Temple University (US)
  • Brown University (US)
  • Institute of Genetics and Cancer (GB)
  • Babraham Institute (GB)
  • University of Bristol (GB)
  • University of Essex (GB)
  • Shanghai Institute of Nutrition and Health (CN)
  • RWTH Aachen University (DE)
  • Macau University of Science and Technology (MO)
Corresponding author
  • Bell, Christopher G.
  • Lowe, Robert
  • Adams, Peter D.
  • Baccarelli, Andrea A.
  • Beck, Stephan
  • Bell, Jordana T.
  • Christensen, Brock C.
  • Gladyshev, Vadim N.
  • Heijmans, Bastiaan T.
  • Horvath, Steve
  • Ideker, Trey
  • Issa, Jean-Pierre J.
  • Kelsey, Karl T.
  • Marioni, Riccardo E.
  • Reik, Wolf
  • Relton, Caroline L.
  • Schalkwyk, Leonard C.
  • Teschendorff, Andrew E.
  • Wagner, Wolfgang
  • Zhang, Kang
  • Rakyan, Vardhman K.
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2019-11-25
Volume
20
Issue
1
Pages
249-249
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-019-1824-y
PMC
PMC6876109
OpenAlex
W2990428470
Cited by
1,025
References
224
Citations by year
2026: 123 · 2025: 155 · 2024: 200 · 2023: 164 · 2022: 142 · 2021: 153 · 2020: 80 · 2019: 6
FWCI
8.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, other-oa)
  • Edinburgh Research Explorer (University of Edinburgh) (acceptedVersion, cc-by)
  • Open Access at Essex (University of Essex) (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
  • Bristol Research (University of Bristol) (submittedVersion)
  • Columbia Academic Commons (Columbia University)
Funders
  • National Institute on Aging (AG031862-12, U34AG051425-01)
  • National Cancer Institute
  • Glenn/AFAR
  • National Institutes of Health (R01 ES025225, P30 ES009089, R01 ES027747, CA216265, AG021518, AG047745, AG047200, CA080946, CA214005, CA100632, CA221705, CA207360, CA207110, RO1AI121226, RO1MH113930, RO1MDO1430401)
  • National Institute for Health Research
  • UCLH Biomedical Research Centre (BRC369/CN/SB/101310)
  • Economic and Social Research Council (ES/N000404/1, ESN000498/1)
  • Biotechnology and Biological Sciences Research Council (BB/S020845/1, BB/K010867/1, BB/PO28187, BB/R00675X/1)
  • Nederlandse Hartstichting (2017T075)
  • Joint Programming Initiative A healthy diet for a healthy life (ZonMw 529051021)
  • BBMRI-NL (NWO 184.021.007, NWO 184.033.111)
  • National Institute of Environmental Health Sciences (R01ES014811)
  • AACR (PR0125)
  • Alzheimer’s Research UK (PG2017B-10)
  • Medical Research Council (MC_UU_12013/5, MR/K013807/1, MR/R005176/1)
  • Cancer Research UK (C18281/A19169)
  • National Science Foundation of China (31571359, 31771464)
  • Royal Society Newton Advanced Fellowship (164914)
  • Deutsche Forschungsgemeinschaft (WA 1706/8-1)
  • Bundesministerium für Bildung und Forschung (VIP + Epi-Blood-Count)
  • Interdisciplinary Center for Clinical Research, RWTH Aachen University (O3-3)
  • Deutsche Krebshilfe (TRACK-AML)
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
DNA methylation, epigenetic clocks, biological aging, chronological age estimation, CpG sites, longevity interventions, rejuvenation, biomarkers, forensic age estimation, biological age prediction, single-cell epigenomics
MeSH terms
Aging, Animals, Biological Clocks, Humans, Genome, Human, DNA Methylation, Epigenesis, Genetic, Genome-Wide Association Study

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