Abstract
Among the different biomarkers of aging based on omics and clinical data, DNA methylation clocks stand apart providing unmatched accuracy in assessing the biological age of both humans and animal models of aging. Here, we discuss robustness of DNA methylation clocks and bounds on their out-of-sample performance and review computational strategies for development of the clocks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexandrov LB et al (2015) Clock-like mutational processes in human somatic cells. Nat Genet 47:1402–1407
de Magalhães JP, Curado J, Church GM (2009) Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 25:875–881
Dollé ME, Snyder WK, Gossen JA, Lohman PH, Vijg J (2000) Distinct spectra of somatic mutations accumulated with age in mouse heart and small intestine. Proc Natl Acad Sci U S A 97:8403–8408
Field AE et al (2018) DNA methylation clocks in aging: categories, causes, and consequences. Mol Cell 71:882–895
Geary RC (1930) The frequency distribution of the quotient of two normal variates. J R Stat Soc 93:442
Hannum G et al (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49:359–367
Harley CB, Futcher AB, Greider CW (1990) Telomeres shorten during ageing of human fibroblasts. Nature 345:458–460
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115
Meissner A et al (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 33:5868–5877
Petkovich DA et al (2017) Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab 25:954–960.e6
Podolskiy DI, Gladyshev VN (2016) Intrinsic versus extrinsic cancer risk factors and aging. Trends Mol Med 22:833–834
Podolskiy DI, Lobanov AV, Kryukov GV, Gladyshev VN (2016) Analysis of cancer genomes reveals basic features of human aging and its role in cancer development. Nat Commun 7:12157
Polanowski AM, Robbins J, Chandler D, Jarman SN (2014) Epigenetic estimation of age in humpback whales. Mol Ecol Resour 14
Stubbs TM et al (2017) Multi-tissue DNA methylation age predictor in mouse. Genome Biol 18:68
Thompson MJ, vonHoldt B, Horvath S, Pellegrini M (2017) An epigenetic aging clock for dogs and wolves. Aging (Albany, NY) 9:1055–1068
Wang T et al (2017) Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol 18:57
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67:301–320
Acknowledgement
Supported by NIH grants.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Podolskiy, D.I., Gladyshev, V.N. (2019). DNA Methylation Markers to Assess Biological Age. In: Moskalev, A. (eds) Biomarkers of Human Aging. Healthy Ageing and Longevity, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-24970-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-24970-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24969-4
Online ISBN: 978-3-030-24970-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)