title: TrueAllele casework on Virginia DNA mixture evidence: computer and manual interpretation in 72 reported criminal cases.
abstract: Mixtures are a commonly encountered form of biological evidence that contain DNA from two or more contributors. Laboratory analysis of mixtures produces data signals that usually cannot be separated into distinct contributor genotypes. Computer modeling can resolve the genotypes up to probability, reflecting the uncertainty inherent in the data. Human analysts address the problem by simplifying the quantitative data in a threshold process that discards considerable identification information. Elevated stochastic threshold levels potentially discard more information. This study examines three different mixture interpretation methods. In 72 criminal cases, 111 genotype comparisons were made between 92 mixture items and relevant reference samples. TrueAllele computer modeling was done on all the evidence samples, and documented in DNA match reports that were provided as evidence for each case. Threshold-based Combined Probability of Inclusion (CPI) and stochastically modified CPI (mCPI) analyses were performed as well. TrueAllele's identification information in 101 positive matches was used to assess the reliability of its modeling approach. Comparison was made with 81 CPI and 53 mCPI DNA match statistics that were manually derived from the same data. There were statistically significant differences between the DNA interpretation methods. TrueAllele gave an average match statistic of 113 billion, CPI averaged 6.68 million, and mCPI averaged 140. The computer was highly specific, with a false positive rate under 0.005%. The modeling approach was precise, having a factor of two within-group standard deviation. TrueAllele accuracy was indicated by having uniformly distributed match statistics over the data set. The computer could make genotype comparisons that were impossible or impractical using manual methods. TrueAllele computer interpretation of DNA mixture evidence is sensitive, specific, precise, accurate and more informative than manual interpretation alternatives. It can determine DNA match statistics when threshold-based methods cannot. Improved forensic science computation can affect criminal cases by providing reliable scientific evidence.
authors.affiliation: Cybergenetics, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Mark W
authors.initials: MW
authors.lastname: Perlin
authors.affiliation: Cybergenetics, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Kiersten
authors.initials: K
authors.lastname: Dormer
authors.affiliation: Cybergenetics, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Hornyak
authors.affiliation: Department of Forensic Science, Richmond, Virginia, United States of America.
authors.forename: Lisa
authors.initials: L
authors.lastname: Schiermeier-Wood
authors.affiliation: Department of Forensic Science, Richmond, Virginia, United States of America.
authors.forename: Susan
authors.initials: S
authors.lastname: Greenspoon
mesh_terms: D000483:Alleles; D003416:Criminal Law; D004247:DNA; D001330:Electronic Data Processing; D005260:Female; D053803:Forensic Genetics / Q000295:instrumentation / Q000331:legislation & jurisprudence / Q000379:methods*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D014768:Virginia
chemical_list: D004247:DNA