Literature DB >> 32453457

A New Robust Epigenetic Model for Forensic Age Prediction.

Alberto Montesanto1, Patrizia D'Aquila1, Vincenzo Lagani2,3, Ersilia Paparazzo1, Silvana Geracitano1, Laura Formentini4, Robertina Giacconi4, Maurizio Cardelli4, Mauro Provinciali4, Dina Bellizzi1, Giuseppe Passarino1.   

Abstract

Forensic DNA phenotyping refers to an emerging field of forensic sciences aimed at the prediction of externally visible characteristics of unknown sample donors directly from biological materials. The aging process significantly affects most of the above characteristics making the development of a reliable method of age prediction very important. Today, the so-called "epigenetic clocks" represent the most accurate models for age prediction. Since they are technically not achievable in a typical forensic laboratory, forensic DNA technology has triggered efforts toward the simplification of these models. The present study aimed to build an epigenetic clock using a set of methylation markers of five different genes in a sample of the Italian population of different ages covering the whole span of adult life. In a sample of 330 subjects, 42 selected markers were analyzed with a machine learning approach for building a prediction model for age prediction. A ridge linear regression model including eight of the proposed markers was identified as the best performing model across a plethora of candidates. This model was tested on an independent sample of 83 subjects providing a median error of 4.5 years. In the present study, an epigenetic model for age prediction was validated in a sample of the Italian population. However, its applicability to advanced ages still represents the main limitation in forensic caseworks.
© 2020 American Academy of Forensic Sciences.

Keywords:  zzm321990ELOVL2zzm321990; FDP; age prediction; automated machine learning; epigenetic clock; externally visible characteristics; methylation

Mesh:

Substances:

Year:  2020        PMID: 32453457     DOI: 10.1111/1556-4029.14460

Source DB:  PubMed          Journal:  J Forensic Sci        ISSN: 0022-1198            Impact factor:   1.832


  2 in total

1.  Just Add Data: automated predictive modeling for knowledge discovery and feature selection.

Authors:  Ioannis Tsamardinos; Paulos Charonyktakis; Georgios Papoutsoglou; Giorgos Borboudakis; Kleanthi Lakiotaki; Jean Claude Zenklusen; Hartmut Juhl; Ekaterini Chatzaki; Vincenzo Lagani
Journal:  NPJ Precis Oncol       Date:  2022-06-16

Review 2.  Common Ground between Biological Rhythms and Forensics.

Authors:  Klara Janjić; Christoph Reisinger; Fabian Kanz
Journal:  Biology (Basel)       Date:  2022-07-18
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.