Improved age determination of blood and teeth samples using a selected set of DNA methylation markers

Bram Bekaert, Aubeline Kamalandua, Sara C. Zapico, Wim Van De Voorde, Ronny Decorte

Research output: Contribution to journalArticlepeer-review

167 Scopus citations


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 R2 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 R2 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.

Original languageEnglish (US)
Pages (from-to)922-930
Number of pages9
Issue number10
StatePublished - 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Cancer Research


  • CpG marker
  • DNA methylation
  • DNA-based age prediction
  • Quadratic regression modeling


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