DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups.

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DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups.

Int J Legal Med. 2017 Jul 19;:

Authors: Spólnicka M, Pośpiech E, Pepłońska B, Zbieć-Piekarska R, Makowska Ż, Pięta A, Karłowska-Pik J, Ziemkiewicz B, Wężyk M, Gasperowicz P, Bednarczuk T, Barcikowska M, Żekanowski C, Płoski R, Branicki W

Abstract
Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for forensic age prediction in three groups of individuals with diagnosed medical conditions. The obtained results showed that the selected age-related CpG sites have unchanged age prediction capacity in the group of late onset Alzheimer’s disease patients. Aberrant hypermethylation and decreased prediction accuracy were found for TRIM59 and KLF14 markers in the group of early onset Alzheimer’s disease suggesting accelerated aging of patients. In the Graves’ disease patients, altered DNA methylation profile and modified age prediction accuracy were noted for TRIM59 and FHL2 with aberrant hypermethylation observed for the former and aberrant hypomethylation for the latter. Our work emphasizes high utility of the ELOVL2 and C1orf132 markers for prediction of chronological age in forensics by showing unchanged prediction accuracy in individuals affected by three diseases. The study also demonstrates that artificial neural networks could be a convenient alternative for the forensic predictive DNA analyses.

PMID: 28725932 [PubMed – as supplied by publisher]

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