Validation study of the TrueAllele automated data review system

The New York State Convicted Offender DNA Databank is the first U.S. lab to complete an internal validation of the TrueAllele expert data review system. TrueAllele is designed to assess short tandem repeat (STR) DNA data based on several key features such as peak height, shape, area, and position relative to a standard ladder and use this information to make accurate allele calls. The software then prioritizes the allele calls based on several user-defined rules. As a result, the user need only review low-quality data. The validation of this system consisted of an extensive optimization phase and a large concordance phase. During optimization, the rule settings were tailored to minimize the amount of high-quality data viewed by the user. In the concordance phase, a large dataset was typed in parallel with the ABI software Gene Scan and Genotyper (manual review) and TrueAllele (automated review) for comparison of allele calls and sample state assignment. Only one significant difference was discovered out of 2048 samples in the concordance study. In this case, TrueAllele revealed a spike in the profile that was interpreted as a DNA peak by the analyst in Genotyper. TrueAllele was designed to focus the review on poor data and to eliminate the need for complete reanalysis technical review. This validation project proved TrueAllele to be dependable for use at the NYS Convicted Offender DNA Databank.     

Validating TrueAllele® DNA mixture interpretation

Abstract: DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all‐or‐none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two‐person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min = 2.32, max = 10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min = 1.00, max = 11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in‐depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele^® DNA mixture interpretation and establish a significant information improvement over human review.     

New York State TrueAllele® Casework Validation Study

DNA evidence can pose interpretation challenges, particularly with low‐level or mixed samples. It would be desirable to make full use of the quantitative data, consider every genotype possibility, and objectively produce accurate and reproducible DNA match results. Probabilistic genotype computing is designed to achieve these goals. This validation study assessed TrueAllele^® probabilistic computer interpretation on 368 evidence items in 41 test cases and compared the results with human review of the same data. Whenever there was a human result, the computer's genotype was concordant. Further, the computer produced a match statistic on 81 mixture items (for 87 inferred matching genotypes) in the test cases, while human review reported a statistic on 25 of these items (30.9%). Using match statistics to quantify information, probabilistic genotyping was shown to be sensitive, specific, and reproducible. These results demonstrate that objective probabilistic genotyping of biological evidence can reliably preserve DNA identification information.     

STR primer concordance study.

Over 1500 population database samples comprising African Americans, Caucasians, Hispanics, Native Americans, Chamorros and Filipinos were typed using the PowerPlex 16 and the Profiler Plus/COfiler kits. Except for the D8S1179 locus in Chamorros and Filipinos from Guam, there were eight examples in which a typing difference due to allele dropout was observed. At the D8S1179 locus in the population samples from Guam, there were 13 examples of allele dropout observed when using the Profiler Plus kit. The data support that the primers used in the PowerPlex 16, Profiler Plus, and COfiler kits are reliable for typing reference samples that are for use in CODIS. In addition, allele frequency databases have been established for the STR loci Penta D and Penta E. Both loci are highly polymorphic.