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Foox J, Bezdan D, Vijay P, Getz K, Ratanachai K, Davis JW, Booher K, Yang X, Meydan C, Mason CE. Epigenetic Forensics for Suspect Identification and Age Prediction. Forensic Genom 2021; 1:83-86. [PMID: 34806083 PMCID: PMC8596498 DOI: 10.1089/forensic.2021.0005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Background: Genetic testing at crime scenes is an instrumental molecular technique to identify or eliminate suspects, as well as to overturn wrongful convictions. Yet, genotyping alone cannot reveal the age of a sample, which could help advance the utility of crime scene samples for suspect identification. The distribution of cytosine methylation within a DNA sample can be leveraged to determine the epigenetic age of someone's blood. Methodology: We sought to demonstrate the ability of DNA methylation markers to accurately discern the age of blood spots from an actual crime scene, a "mock" crime scene, and also from a tube of blood stored in ethylenediaminetetraacetic acid for >20 years. This was achieved by quantifying methylation within known age-associated genetic loci across each DNA sample. We observed a strong linear coefficient (0.91) and high overall correlation (R 2 = 0.963) between the known age of a sample and the predicted age. Conclusion: We show that novel methods for targeted methylation and low-input whole-genome bisulfite sequencing can enable a novel and improved forensic profile of a crime scene that discerns not only who was present at the crime, but also their age. Finally, we use this model to discern the age and provenance of a blood sample that was used in a criminal investigation.
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Affiliation(s)
- Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Daniela Bezdan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Priyanka Vijay
- Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Kylie Getz
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Kamolwat Ratanachai
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Justin W. Davis
- AbbVie, Inc., Department of Statistics, North Chicago, Illinois, USA
| | - Keith Booher
- Zymo Research, Epigenetics Division, Irvine, California, USA
| | - Xiaojing Yang
- Zymo Research, Epigenetics Division, Irvine, California, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Christopher E. Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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