ArticleForensic science, medicine, and pathology2025
Reanalyzing DNA mixture: a evaluation of EuroForMix for deconvolution and weight-of-evidence computing.
Article in Forensic science, medicine, and pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Forensic genetics faces significant challenges in the evolving landscape of DNA mixture analysis. This paper highlights the complexities associated with stochastic effects and artifacts in degraded or low-quantity samples and explores the primary objectives of DNA mixture analysis, namely deconvolution and weight of evidence quantification. The study examines the efficacy of the software tool EuroForMix (EFM) in interpreting complex mixtures. Genetic profiles from two forensic cases processed in 2022 by the Brazilian National Institute of Criminalistics' DNA Forensic Laboratory were reanalyzed using EFM v.3.4.0, focusing on deconvoluting DNA mixtures and quantifying the weight of evidence. Results were compared with previous analyses conducted using a laboratory-validated spreadsheet, LRmix Studio (for computing LR), and GeneMapperTM ID-X (for deconvoluting mixtures). EFM demonstrated high efficiency in both deconvolution and weight-of-evidence quantification, showing improved LR values for various profiles compared to previous analyses. In the reanalyzed cases, weight of evidence calculations using EFM produced values comparable to those obtained with the laboratory-validated spreadsheet and superior LR values compared to LRmix Studio. The comparison of deconvoluted profiles using EFM and GeneMapperTM ID-X revealed mostly consistent results for the major contributor genotype, with EFM yielding equal or better outcomes in most profiles. Thus, EFM shows potential as a tool for DNA mixture analysis, including both LR computation and deconvolution. Despite these encouraging results, it is recommended that each forensic laboratory develop DNA mixture interpretation protocols that consider internal validation.
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