More Informative DNA Identification: Computer Reinterpretation of Existing Data

R. David and M.W. Perlin, "More informative DNA identification: Computer reinterpretation of existing data", Midwestern Association of Forensic Scientists, Kansas City, MO, 8-Oct-2010.

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Abstract

Forensic analysts work hard producing DNA data from complex biological evidence. They then expend further effort interpreting their data. However, current DNA interpretation guidelines can limit the amount of information that they are permitted to report. As more challenging data (e.g., mixed, low-level, degraded) enters the laboratory workflow, we see scientists working ever harder to extract diminishing identification information.

Computer interpretation using probabilistic genotypes is now allowed under the SWGDAM guidelines (Section 3.2.2). The computer can use validated quantitative models, instead of qualitative thresholds, to interpret and match the DNA evidence. Therefore, a computer assistant enables an analyst to extract all the information present in their data.

A previous study with the New York State Police DNA lab compared quantitative computer and qualitative human interpretation. We assessed the relative identification information derived from the same DNA evidence mixture items, which ranged from simple mixtures to low copy and three contributor challenges. TrueAllele^® computer interpretation preserved (on average) a million times more identification information, as measured by DNA match statistics.

Importantly, the study also showed on a large data set that quantitative computation could interpret evidence that qualitative review could not. The computer yielded a match score almost every item, while human review quantified the match only 30% of the time. The computer assistant was shown to be an informative tool for the forensic analyst.

Society now expects crime labs to process increasing amounts of challenging DNA evidence. Computer interpretation empowers forensic analysts to use all their hard won DNA data to make positive identifications. Our study shows that there is a vast reserve of untapped identification information in existing DNA data that could be usefully reinterpreted by computer.