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. 2023 Mar 11;24(6):5381.
doi: 10.3390/ijms24065381.

Forensic Age Estimation through a DNA Methylation-Based Age Prediction Model in the Italian Population: A Pilot Study

Martina Onofri  1 , Arianna Delicati  2 , Beatrice Marcante  2 , Luigi Carlini  1 , Federica Alessandrini  3 , Pamela Tozzo  2 , Eugenia Carnevali  1
Affiliations

Forensic Age Estimation through a DNA Methylation-Based Age Prediction Model in the Italian Population: A Pilot Study

Martina Onofri et al. Int J Mol Sci. .

Abstract

DNA methylation is one of the epigenetic marks which has been studied intensively in recent years for age predicting purposes in the forensic area. In order to integrate age prediction into routine forensic workflow, the purpose of this study was to standardize and optimize a DNA methylation-based protocol tailored to the Italian context. A previously published protocol and age-predictive method was implemented for the analysis of 84 blood samples originating from Central Italy. The study here presented is based on the Single Base Extension method, considering five genes: ELOVL2, FHL2, KLF14, C1orf132, now identified as MIR29B2C, and TRIM59. The precise and specific steps consist of DNA extraction and quantification, bisulfite conversion, amplification of converted DNA, first purification, single base extension, second purification, capillary electrophoresis, and analysis of the results to train and test the tool. The prediction error obtained, expressed as mean absolute deviation, showed a value of 3.12 years in the training set and 3.01 years in the test set. Given that population-based differences in DNA methylation patterns have been previously reported in the literature, it would be useful to further improve the study implementing additional samples representative of the entire Italian population.

Keywords: DNA methylation; SNaPshot; age estimation; epigenetics; forensic genetics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Modulation of DNA Methylation Clocks.
Figure 2
Figure 2
Experimental workflow.
Figure 3
Figure 3
Scatterplots displaying the correlation of DNA methylation levels at each of the five loci studied with chronological age for all three datasets.
Figure 4
Figure 4
Scatterplots displaying the correlation between the age predicted using the tool here described and chronological age, for training, test, and combined datasets.
Figure 5
Figure 5
Correlation of MAD and chronological age in the training, test, and combined sets in the 4 markers model. Samples are sorted in ascending order of age.
See this image and copyright information in PMC

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