Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system. (September 2016) - Freire-Aradas, A.; Phillips, C.; Mosquera-Miguel, A.; Girón-Santamaría, L.; Gómez-Tato, A.; Casares de Cal, M.; Álvarez-Dios, J.; Ansede-Bermejo, J.; Torres-Español, M.; Schneider, P.M.; Pośpiech, E.; Branicki, W.; et al.
Graphical abstract: Highlights: Methylation β-values in 3702 samples of 19–101 years analyzed with Illumina's HumanMethylation 450 K assessed for age correlation. 22 genomic regions covering 177 CpG sites studied using the Agena Bioscience EpiTYPER system in 725 Europeans. 7 DNA methylation markers highest age-correlation values and compiled into a new age prediction tool using multivariate quantile regression. Prediction intervals calculated from the model correctly classified 83.70% of an independent group of 52 MZ twins. The age prediction model has been launched as an open-access online tool within the Snipper classification website. Abstract: Individual age estimation has the potential to provide key information that could enhance and extend DNA intelligence tools. Following predictive tests for externally visible characteristics developed in recent years, prediction of age could guide police investigations and improve the assessment of age-related phenotype expression patterns such as hair colour changes and early onset of male pattern baldness. DNA methylation at CpG positions has emerged as the most promising DNA tests to ascertain the individual age of the donor of a biological contact trace. Although different methodologies are available to detect DNA methylation, EpiTYPER technology (Agena Bioscience, formerly Sequenom) provides useful characteristics that can be applied as a discovery tool in localized regions of the genome. In our study, a total of twenty-two candidate genomic regions, selected from the assessment of publically available data from the Illumina HumanMethylation 450 BeadChip, had a total of 177 CpG sites with informative methylation patterns that were subsequently investigated in detail. From the methylation analyses made, a novel age prediction model based on a multivariate quantile regression analysis was built using the seven highest age-correlated loci of ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132 and chr16:85395429. The detected methylation levels in these loci provide a median absolute age prediction error of ±3.07 years and a percentage of prediction error relative to the age of 6.3%. We report the predictive performance of the developed model using cross validation of a carefully age-graded training set of 725 European individuals and a test set of 52 monozygotic twin pairs. The multivariate quantile regression age predictor, using the CpG sites selected in this study, has been placed in the open-access Snipper forensic classification website.