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A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

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A. Freire-Aradas, E. Pośpiech, A. Aliferi, L. Girón-Santamaría, A. Mosquera-Miguel, A. Pisarek, A. Ambroa-Conde, C. Phillips, M. A. Casares de Cal, A. Gómez-Tato, M. Spólnicka, A. Woźniak, J. Álvarez-Dios, D. Ballard, D. Syndercombe Court, W. Branicki, Ángel Carracedo, M. V. Lareu

Original languageEnglish
Article number932
JournalFrontiers in Genetics
Volume11
DOIs
Published19 Aug 2020

King's Authors

Abstract

Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual’s lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of ±3–4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER®, pyrosequencing, MiSeq, and SNaPshotTM. The DNA methylation levels detected for CpG sites from ELOVL2, FHL2, and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER®, pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshotTM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.

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