The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Improved age determination of blood and teeth samples using a selected set of DNA methylation markers
Open accessAbstract
Age estimation from DNA methylation markers has seen an exponential growth of interest, not in the least from forensic scientists. The current published assays, however, can still be improved by lowering the number of markers in the assay and by providing more accurate models to predict chronological age. From the published literature we selected 4 age-associated genes (ASPA, PDE4C, ELOVL2, and EDARADD) and determined CpG methylation levels from 206 blood samples of both deceased and living individuals (age range: 0-91 years). This data was subsequently used to compare prediction accuracy with both linear and non-linear regression models. A quadratic regression model in which the methylation levels of ELOVL2 were squared showed the highest accuracy with a Mean Absolute Deviation (MAD) between chronological age and predicted age of 3.75 years and an adjusted R(2) of 0.95. No difference in accuracy was observed for samples obtained either from living and deceased individuals or between the 2 genders. In addition, 29 teeth from different individuals (age range: 19-70 years) were analyzed using the same set of markers resulting in a MAD of 4.86 years and an adjusted R(2) of 0.74. Cross validation of the results obtained from blood samples demonstrated the robustness and reproducibility of the assay. In conclusion, the set of 4 CpG DNA methylation markers is capable of producing highly accurate age predictions for blood samples from deceased and living individuals.
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Cites
DNA methylation age of human tissues and cell types
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Genome-wide methylation profiles reveal quantitative views of human aging rates
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Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer
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Aging of blood can be tracked by DNA methylation changes at just three CpG sites
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Methylation of ELOVL 2 gene as a new epigenetic marker of age
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Cited by
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Horvath, S., & Raj, K. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0004-3 · Cited by 3,095
Gasparoni, G., Bultmann, S., Lutsik, P., Kraus, T. F. J., Sordon, S., Vlcek, J., Dietinger, V., Steinmaurer, M., Haider, M., Mulholland, C. B., et al. (2018) · Epigenetics & Chromatin · doi:10.1186/s13072-018-0211-3 · Cited by 229
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Spólnicka, M., Pośpiech, E., Pepłońska, B., Zbieć-Piekarska, R., Makowska, Ż., Pięta, A., Karłowska-Pik, J., Ziemkiewicz, B., Wężyk, M., Gasperowicz, P., et al. (2018) · International Journal of Legal Medicine · doi:10.1007/s00414-017-1636-0 · Cited by 105
Impact of Methods on the Measurement of mRNA Turnover
Wada, T., & Becskei, A. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18122723 · Cited by 66
Metadata
- Authors
- Bekaert, B., Kamalandua, A., Zapico, S. C., Van de Voorde, W., & Decorte, R.
- Venue
- Epigenetics
- Publisher
- Informa UK Limited
- Published
- 2015-08-17
- Volume
- 10
- Issue
- 10
- Pages
- 922-930
- ISSN
- 1559-2294 (ISSN-L)
- 1559-2308
- OpenAlex
- W1692222554
- Cited by
- 276
- References
- 28
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- bronze
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