The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity. Issue 2 (21st August 2018)

• Record Type: Journal Article
• Authors: Buckleton, John S.; Bright, Jo‐Anne; Gittelson, Simone; Moretti, Tamyra R.; Onorato, Anthony J.; Bieber, Frederick R.; Budowle, Bruce; Taylor, Duncan A.
• Is Part Of: Journal of forensic sciences. Volume 64:Issue 2(2019)
• Page Start: 393
• Page End: 405
• Publication Date: 2018-08-21
• Subjects: forensic science -- DNA -- probabilistic genotyping -- validation -- STRmix™; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.13898
• Languages: English
• ISSNs: 0022-1198

Abstract

Abstract: 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.