DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

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 su...

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Veröffentlicht in:Forensic science international : genetics 2017-05, Vol.28, p.225-236
Hauptverfasser: Vidaki, Athina, Ballard, David, Aliferi, Anastasia, Miller, Thomas H, Barron, Leon P, Syndercombe Court, Denise
Format: Artikel
Sprache:eng
Schlagworte:
Adult
Aged
Aging - genetics
Artificial neural networks
Chronological age prediction
CpG Islands - genetics
DNA - blood
DNA Methylation
Epigenomics
Forensic epigenetics
Forensic Genetics
High-Throughput Nucleotide Sequencing
Humans
Machine Learning
Middle Aged
Neural Networks, Computer
Next generation sequencing
Pathology
Research Paper
Saliva - chemistry
Twins, Monozygotic - genetics
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Beschreibung
Zusammenfassung: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.
ISSN:1872-4973
1878-0326
1878-0326
DOI:10.1016/j.fsigen.2017.02.009