TrueAllele® Genotype Identification on DNA Mixtures Containing up to Five Unknown Contributors |
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| Authors: | Mark W Perlin PhD MD Jennifer M Hornyak MS Garett Sugimoto MS Kevin WP Miller PhD |
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| Affiliation: | 1. Cybergenetics, Pittsburgh, PA;2. Kern Regional Crime Laboratory, Bakersfield, CA |
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| Abstract: | Computer methods have been developed for mathematically interpreting mixed and low‐template DNA. The genotype modeling approach computationally separates out the contributors to a mixture, with uncertainty represented through probability. Comparison of inferred genotypes calculates a likelihood ratio (LR), which measures identification information. This study statistically examined the genotype modeling performance of Cybergenetics TrueAllele® computer system. High‐ and low‐template DNA mixtures of known randomized composition containing 2, 3, 4, and 5 contributors were tested. Sensitivity, specificity, and reproducibility were established through LR quantification in each of these eight groups. Covariance analysis found LR behavior to be relatively invariant to DNA amount or contributor number. Analysis of variance found that consistent solutions were produced, once a sufficient number of contributors were considered. This study demonstrates the reliability of TrueAllele interpretation on complex DNA mixtures of representative casework composition. The results can help predict an information outcome for a DNA mixture analysis. |
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| Keywords: | forensic science DNA mixture genotype modeling validation study likelihood ratio probabilistic genotyping |
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