Efficient construction of match strength distributions for uncertain multi-locus genotypes

A tale of two PG systems: A comparison of the two most widely used continuous probabilistic genotyping systems in the United States

The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two-, three-, and four-person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non-supportive, or inconclusive) for ~91% of contributor-specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor-specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low-template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non-supportive) and 12 were either inconclusive versus non-supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab-defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low-template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low-template, degraded contributors, and the deviation is generally predictable.

Searching CODIS with binary conversions of STRmix interpretations

The predominant approach to interpreting autosomal STR DNA typing results is through the use of probabilistic genotyping software. The primary output from such software is a list of genotypes and associated genotype weights representing the likelihood of observing the typing results if an individual or set of individuals with that genotype(s) was the donor of the evidence DNA. While such lists are used by probabilistic software to calculate likelihood ratios based upon a pair of propositions regarding specific donors of the DNA, they are not directly amendable to entry into law enforcement databases that use the Combined DNA Index System (CODIS) software. An approach to creating CODIS-compatible profiles from the output of the probabilistic genotyping program STRmix is discussed and assessed for sensitivity and specificity. Combining this information with an assessment of the weight-of-evidence potential contained in the STRmix interpretation, pragmatic guidance was developed regarding the creation and searching of CODIS profiles in a way that balances the detection of investigative leads with the potential burdensome effects on workload.

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

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.