DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups
PBN-AR
Instytucja
Instytut Medycyny Doświadczalnej i Klinicznej im. Mirosława Mossakowskiego Polskiej Akademii Nauk
Informacje podstawowe
Główny język publikacji
en
Czasopismo
International Journal of Legal Medicine (45pkt w roku publikacji)
ISSN
0937-9827
EISSN
1437-1596
Wydawca
SPRINGER
DOI
URL
Rok publikacji
2018
Numer zeszytu
1
Strony od-do
1-11
Numer tomu
132
Identyfikator DOI
Liczba arkuszy
1.18
Słowa kluczowe
en
DNA methylation
Chronological age
Alzheimer's disease
Graves' disease
Neural networks
Prediction accuracy
Streszczenia
Język
en
Treść
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.
Cechy publikacji
discipline:Medycyna
discipline:Medicine
Original article
Original article presents the results of original research or experiment.
Oryginalny artykuł naukowy
Oryginalny artykuł naukowy przedstawia rezultaty oryginalnych badań naukowych lub eksperymentu.
Inne
System-identifier
PBN-R:848389
CrossrefMetadata from Crossref logo
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