The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

John S. Buckleton, Jo Anne Bright, Simone Gittelson, Tamyra R. Moretti, Anthony J. Onorato, Frederick R. Bieber, Bruce Budowle, Duncan A. Taylor

    Research output: Contribution to journalArticlepeer-review

    32 Citations (Scopus)


    Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.

    Original languageEnglish
    Pages (from-to)393-405
    Number of pages13
    JournalJournal of Forensic Sciences
    Issue number2
    Publication statusPublished - Mar 2019


    • DNA
    • forensic science
    • probabilistic genotyping
    • STRmix™
    • validation


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