Validating TrueAllele^® Interpretation of DNA Mixtures Containing up to Ten Unknown Contributors

Abstract

Most DNA evidence is a mixture of two or more people. Cybergenetics TrueAllele® system uses Bayesian computing to separate genotypes from mixture data and compare genotypes to calculate likelihood ratio (LR) match statistics. This validation study examined the reliability of TrueAllele computing on laboratory‐generated DNA mixtures containing up to ten unknown contributors. Using log(LR) match information, the study measured sensitivity, specificity, and reproducibility. These reliability metrics were assessed under different conditions, including varying the number of assumed contributors, statistical sampling duration, and setting known genotypes. The main determiner of match information and variability was how much DNA a person contributed to a mixture. Observed contributor number based on data peaks gave better results than the number known from experimental design. The study found that TrueAllele is a reliable method for analyzing DNA mixtures containing up to ten unknown contributors.

Proposed Framework for Comparison of Continuous Probabilistic Genotyping Systems amongst Different Laboratories

“…The LR will approach a maximum for H1 true as DNA template amount increases and as wi → 0. This is demonstrated by Bauer et al [44] in their Figure 1 (originally in [8]) and defines our next condition: 7. Each laboratory is presented with aliquots of the same dilution series of DNA solutions which then undergo analyses to produce epgs for each solution according to each laboratory's standard practice (according to which the PG system was validated in that laboratory).…”

“…The LR will approach a maximum for H 1 true as DNA template amount increases and as w i → 0. This is demonstrated by Bauer et al [44] in their Figure 1 (originally in [8]) and defines our next condition:…”

“…We now have the eight conditions for an inter-laboratory comparison originally suggested by McNevin et al [35]. Such a comparison should produce the results described by them in their Figure 1 and by Bauer et al [44] in their Figure 1 where the maximised value of the LR corresponding with the plateau in both cases is given by Equation ( 7) for all propositions and Equation ( 9) for H 1 true (Figure 1). We would go so far as to say that Equation (12) defines the "expected" LR range under our eight conditions, in the same way that the reciprocal of the random match probability is the expected LR for a high quality single source profile.…”

“…One way to achieve it is to amplify a dilution series of DNA such that there is a range of DNA template input amounts ranging from below the optimum to above the optimum. This is a general approach when assessing PG systems (e.g., [41,43,44]) and has been previously used to compare amongst them [29]. The LR will approach a maximum for H 1 true as DNA template amount increases and as w i → 0.…”

Probabilistic Genotyping of Single Cell Replicates from Complex DNA Mixtures Recovers Higher Contributor LRs than Standard Analysis

“…DNA mixtures are a commonly encountered source of evidence found at crime scenes and are one of the more difficult sources of biological evidence to interpret due to their inherent complexity [1] as multiple genotype combinations can often explain the STR mixture data. This is particularly troublesome since much DNA evidence is comprised of a mixture originating from multiple contributors including the victim (s) and/or perpetrator(s) to the crime or to others who have no direct relationship to the incident in question [2][3][4]. With the implementation of probabilistic genotyping (PG), mixture deconvolution analysis has been revolutionized as PG is less subjective, more consistent, uses more DNA profile information, and provides greater statistical power resulting in the analysis of mixtures that were previously unresolvable [5,6].…”