Journal Article
Research Support, Non-U.S. Gov't
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Epigenetic discrimination of identical twins from blood under the forensic scenario.

Monozygotic (MZ) twins share the same STR profile, demonstrating a practical problem in forensic casework. DNA methylation has provided a suitable resource for MZ twin differentiation; however, studies addressing the forensic feasibility are lacking. Here, we investigated epigenetic MZ twin differentiation from blood under the forensic scenario comprising i) the discovery of candidate markers in reference-type blood DNA via genome-wide analysis, ii) the technical validation of candidate markers in reference-type blood DNA using a suitable targeted method, and iii) the analysis of the validated markers in trace-type DNA. Genome-wide methylation analysis in blood DNA from 10 MZ twin pairs resulted in 19-111 twin-differentially methylated sites (tDMSs) per pair with >0.3 twin-to-twin differences. Considering all top three candidate tDMSs across all pairs in the technical validation based on methylation-specific qPCR, 67.85% generated >0.1 twin-to-twin differences. Of the validated tDMSs, 68.4% showed >0.1 twin-to-twin differences with qPCR in trace-type DNA across 8 pairs. Using an updated marker selection strategy, 8 additional candidate tDMSs were obtained for an example MZ pair, of which 7 showed >0.1 twin-to-twin differences in both reference- and trace-type DNA. Lastly, we introduce a high-resolution melting curve analysis of the entire fragment that can complement the proposed approach. Overall, our study demonstrates the general feasibility of epigenetic twin differentiation in the forensic context and highlights that the number of informative tDMSs in the final trace DNA analysis is crucial, as some candidate markers identified in reference DNA were shown not informative in the trace DNA due to various, including technical, reasons. Future studies will need to address the optimal number of epigenetic markers required for reliable identification of MZ twin individuals including statistical considerations.

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