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Concordance study on Y-STRs typing between SeqStudio™ genetic analyzer for HID and MiSeq™ FGx forensic genomics system

Background Massively Parallel Sequencing (MPS) allowed an increased number of information to be retrieved from short tandem repeat (STR) analysis, expanding them not only to the size, as already performed in Capillary Electrophoresis (CE), but also to the sequence. MPS requires constant development...

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Bibliographic Details
Published in:Molecular biology reports 2023-12, Vol.50 (12), p.9779-9789
Main Authors: Soldati, Giulia, Turrina, Stefania, Treccani, Mirko, Saccardo, Chiara, Ausania, Francesco, De Leo, Domenico
Format: Article
Language:English
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Summary:Background Massively Parallel Sequencing (MPS) allowed an increased number of information to be retrieved from short tandem repeat (STR) analysis, expanding them not only to the size, as already performed in Capillary Electrophoresis (CE), but also to the sequence. MPS requires constant development and validation of the analytical parameters to ensure that the genotyping results of STRs correspond to those obtained by CE. Given the increased frequency of usage of Y-STRs as supplementary markers to the autosomal STRs analysis, it is urgent to validate the concordance of the typing results between CE and MPS analyses. Methods and results DNA extracted from 125 saliva samples of unrelated males was genotyped using Yfiler™ Plus PCR Amplification Kit and ForenSeq™ DNA Signature Prep Kit, which were analyzed by SeqStudio™ Genetic Analyzer for HID and MiSeq™ FGx Forensic Genomics System, respectively. For each shared Y-STR, allele designation, number of length- and sequence-based alleles per locus, stutter percentage, and the intra-locus balance of multicopy Y-STRs were screened. Conclusions Although the number of forensic genetics laboratories that are applying the MPS technique in routine analysis is small and does not allow a global assessment of MPS limitations, this comparative study highlights the ability of MPS to produce reliable profiles despite the generation of large amounts of raw data.
ISSN:0301-4851
1573-4978
DOI:10.1007/s11033-023-08808-4