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Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

Research output: Contribution to journalArticlepeer-review

Anastasia Aliferi, Sudha Sundaram, David Ballard, Ana Freire-Aradas, Christopher Phillips, Maria Victoria Lareu, Denise Syndercombe Court

Original languageEnglish
Article number102637
JournalForensic Science International: Genetics
Volume57
Early online date24 Nov 2021
DOIs
Accepted/In press17 Nov 2021
E-pub ahead of print24 Nov 2021
PublishedMar 2022

Bibliographical note

Publisher Copyright: © 2021 Elsevier B.V.

King's Authors

Abstract

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic ‘DNA intelligence’ tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.

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