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.
Graphical abstract
Graphical Abstract
Keywords
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Article info
Publication history
Published online: June 08, 2016
Accepted:
June 6,
2016
Received in revised form:
June 3,
2016
Received:
April 25,
2016
Identification
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© 2016 Elsevier Ireland Ltd. All rights reserved.
