Validating TrueAllele DNA Mixture Interpretation

• NCJ Number: 236872
• Journal: Journal of Forensic Sciences Volume: 56 Issue: 6 Dated: November 2011 Pages: 1430-1447
• Author(s): Mark W. Perlin, M.D., Ph.D.; Matthew M. Legler, B.S.; Cara E. Spencer, M.S.; Jessica L. Smith, M.S.; William P. Allan, M.S.; Jamie L. Belrose, M.S.; Barry W. Duceman, Ph.D.
• Date Published: November 2011
• Length: 18 pages
• Publication Format: Article
• Publication Type: Report (Study/Research)
• Language: English
• Country: United States of America
• Note: Presented at the 62nd Annual Meeting of the American Academy for Forensic Sciences, February 22-27, 2010, in Seattle, WA.

Annotation

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 DNA mixture data.

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 base ten logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, the authors 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. The paper 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. (Published Abstract)