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

• Published in: Forensic science international : genetics Vol. 57; p. 102637
• Main Authors: Aliferi, Anastasia, Sundaram, Sudha, Ballard, David, Freire-Aradas, Ana, Phillips, Christopher, Lareu, Maria Victoria, Court, Denise Syndercombe
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2022
• Subjects: Age prediction; DNA intelligence; DNA methylation; Forensic; Machine learning; DNA methylation; DNA intelligence; Forensic; Age prediction; Machine learning
• ISSN: 1872-4973; 1878-0326
• DOI: 10.1016/j.fsigen.2021.102637

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. •Evaluation of methylation age markers using microarray data and targeted sequencing revealed a set of 11 ‘optimal’ markers.•The prediction model showed high prediction accuracy in both a UK (MAE = 3.3 years) and Spanish sample cohort (MAE = 3.8 years).•Prediction accuracy improved for under 55-year-olds (MAE = 2.6), with 81% predicting with an error of less than 4 years.•The accuracy of DNA methylation quantification and age prediction was retained down to 5 ng of DNA input (~ 1 ng in PCR stage).