DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing. (May 2017)

• Record Type: Journal Article
• Authors: Vidaki, Athina; Ballard, David; Aliferi, Anastasia; Miller, Thomas H.; Barron, Leon P.; Syndercombe Court, Denise
• Is Part Of: Forensic science international. Volume 28(2017)
• Page Start: 225
• Page End: 236
• Publication Date: 2017-05
• Subjects: Forensic epigenetics -- Chronological age prediction -- DNA methylation -- Next generation sequencing -- Artificial neural networks; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2017.02.009
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: Blood DNA methylation profiles of 1156 individuals were assessed for age correlation. Stepwise regression identified 23 age-associated CpG sites in DNA from blood. A machine learning model based on 16 markers predicted age with a mean error of 3.8 years. The model predicted age successfully for twins and 'diseased' individuals. A new NGS-based method was combined with machine learning for age prediction. Abstract: The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2–90 years) analysed with Illumina's genome-wide methylation platforms (27 K/450 K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R 2 = 0.92, mean absolute error (MAE) = 4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R 2 = 0.96) with a MAE = 3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE = 7.1 years) and a cohort of 1011 disease state individuals (MAE = 7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R 2 = 0.96) with a MAE = 3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq ® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE = 7.5 years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.