![handle is hein.journals/injlegame132 and id is 1 raw text is: Int J Legal Med (2018) 132:1-11
DOI 10.1007/s00414-017-1636-0
OR[GINL2 \RTI[CL
DNA methylation in ELOVL2 and Clorfl32 correctly predicted
chronological age of individuals from three disease groups
M. Sp6lnicka1 E. Pospiech2'3 B. Peplonska4 R. Zbiec-Piekarska1 Z. Makowska1-
A. Pietal J. Karlowska-Pik5 B. Ziemkiewicz5 M. Wezyk4 P. Gasperowicz6 -
T. Bednarczuk7 M. Barcikowska4 C. Zekanowski4 R. Ploski6 Wojciech Branickil'3
Received: 19 December 2016 /Accepted: 4 July 2017 /Published online: 19 July 2017
C The Author(s) 2017. This article is an open access publication
Abstract Improving accuracy of the available predictive
DNA methods is important for their wider use in routine fo-
rensic work. Information on age in the process of identifica-
tion of an unknown individual may provide important hints
that can speed up the process of investigation. DNA methyl-
ation 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 includ-
ing diseases. We analyzed DNA methylation profile in five
markers from five different genes (ELOVL2, Clorfl32,
KLF 14, FHL2, and TRIM59) used for forensic age prediction
in three groups of individuals with diagnosed medical condi-
tions. 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 pa-
tients. In the Graves' disease patients, altered DNA methyla-
tion profile and modified age prediction accuracy were noted
for TRIM59 and FHL2 with aberrant hypermethylation ob-
served for the former and aberrant hypomethylation for the
latter. Our work emphasizes high utility of the ELOVL2 and
Clorfl32 markers for prediction of chronological age in fo-
rensics by showing unchanged prediction accuracy in individ-
uals affected by three diseases. The study also demonstrates
that artificial neural networks could be a convenient alterna-
tive for the forensic predictive DNA analyses.
Keywords DNA methylation Chronological age-
Alzheimer's disease Graves' disease Neural networks
Prediction accuracy
Wojciech Branicki
wojciech.branicki@uj.edu.pl
Introduction
Central Forensic Laboratory of the Police, Aleje Ujazdowskie 7,
00-583 Warsaw, Poland
2 Department of Genetics and Evolution, Institute of Zoology of the
Jagiellonian University, Gronostajowa 9, 30-387 Krakow, Poland
a Malopolska Centre of Biotechnology of the Jagiellonian University,
Gronostajowa Gronostajowa st. 7A, 30-387 Krakow, Poland
4 Laboratory of Neurogenetics, Department of Neurodegenerative
Disorders, Mossakowski Medical Research Center, Polish Academy
of Sciences, 02-106 Warszawa, 5 Pawinskiego St, Warsaw, Poland
s Faculty of Mathematics and Computer Science, Nicolaus Copernicus
University, Chopina 12/18, 87-100 Torun, Poland
6 Department of Medical Genetics, Centre for Biostructure, Medical
University of Warsaw, Pawinskiego 3c, 02-106 Warsaw, Poland
7 Department of Internal Medicine and Endocrinology, Medical
University of Warsaw, Banacha la, 02-097 Warsaw, Poland
Forensic intelligence through DNA analysis is now achievable
when searching for an unknown individual in criminal, iden-
tification, or security cases. Wider use of the predictive DNA
analysis methods in forensic investigations depends largely on
their accuracy. Currently available predictive tests include bio-
geographic ancestry, pigmentation, hair loss and hair shape,
extreme stature, facial morphology, and age [1]. All these
methods are still being revised by using more advanced math-
ematical approaches, detailed studies of molecular mecha-
nisms involved in phenotype determination and selection of
additional predictors [2-6].
Age prediction has an important place in predictive DNA
analysis. Informative itself, it can also increase prediction ac-
curacy of several progressive physical appearance traits.
' Springer
:ssMark](ViewImageLocal?handle=hein.journals/injlegame132&div=1&collection=&method=preview&ext=.png&size=3)
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