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

• Published in: Forensic science international : genetics Vol. 28; pp. 225 - 236
• Main Authors: Vidaki, Athina, Ballard, David, Aliferi, Anastasia, Miller, Thomas H, Barron, Leon P, Syndercombe Court, Denise
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2017; Elsevier
• Subjects: 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; DNA methylation; Chronological age prediction; Forensic epigenetics; Next generation sequencing; Artificial neural networks
• ISSN: 1872-4973; 1878-0326
• DOI: 10.1016/j.fsigen.2017.02.009

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.