A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles. (November 2016)

• Record Type: Journal Article
• Authors: Bleka, Øyvind; Benschop, Corina C.G.; Storvik, Geir; Gill, Peter
• Is Part Of: Forensic science international. Volume 25(2016:Nov.)
• Page Start: 85
• Page End: 96
• Publication Date: 2016-11
• Subjects: NGM STR DNA -- Comparison study -- Weight-of-evidence -- Deconvolution; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2016.07.016
• Languages: English
• ISSNs: 1872-4973

Abstract

Abstract : Highlights: Characteristics of EuroForMix and LRmix Studio were compared for LR calculations. Characteristics of EuroForMix and LoCIM-tool were compared for deconvolution. A total of 59 complex two–three person mixtures were evaluated. EuroForMix most efficient for major/minor mixtures where the minor was evidential. EuroForMix most efficient to determine genotype of major contributor. Abstract: The investigation of the performance of models to interpret complex DNA profiles is best undertaken using real DNA profiles. Here we used a data set to reflect the variety typically encountered in real casework. The "crime-stains" were constructed from known individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted from blood, 2–3 person mixtures, degradation/no-degradation, differences in allele sharing, dropout/no dropout, etc. Two siblings were also included in the test-set in order to challenge the systems. Two kinds of analyses were performed, namely tests on whether a person of interest is a contributor based on weight-of-evidence (likelihood ratio) calculations, and deconvolution test to estimate the profile of unknown constituent parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool . When we classified persons of interests into being true contributors or non-contributors, we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels compared to LRmix . In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor. Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool .