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Validating TrueAllele® interpretation of DNA mixtures containing up to ten unknown contributors
Bauer, D.W., Butt, N., Hornyak, J.M., and Perlin, M.W. Validating TrueAllele® interpretation of DNA mixtures containing up to ten unknown contributors. Journal of Forensic Sciences, 65(2):380-398, 2020.
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.