TrueAllele Casework on Virginia DNA Mixture Evidence: Computer and Manual Interpretation in 72 Reported Criminal Cases
Identifieur interne : 000A66 ( Main/Exploration ); précédent : 000A65; suivant : 000A67TrueAllele Casework on Virginia DNA Mixture Evidence: Computer and Manual Interpretation in 72 Reported Criminal Cases
Auteurs : Mark W. Perlin [États-Unis] ; Kiersten Dormer [États-Unis] ; Jennifer Hornyak [États-Unis] ; Lisa Schiermeier-Wood [États-Unis] ; Susan Greenspoon [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- législation et jurisprudence : Génétique légale.
- ADN, Allèles, Droit pénal, Femelle, Génétique légale, Humains, Modèles théoriques, Mâle, Traitement automatique des données, Virginie.
English descriptors
- KwdEn :
- MESH :
- chemical : DNA.
- geographic : Virginia.
- instrumentation : Forensic Genetics.
- legislation & jurisprudence : Forensic Genetics.
- methods : Forensic Genetics.
- Alleles, Automatic Data Processing, Criminal Law, Female, Humans, Male, Models, Theoretical.
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.
Url:
DOI: 10.1371/journal.pone.0092837
PubMed: 24667531
PubMed Central: 3965478
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>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.</p>
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<author><name sortKey="Biega, La" uniqKey="Biega L">LA Biega</name>
</author>
<author><name sortKey="Duceman, Bw" uniqKey="Duceman B">BW Duceman</name>
</author>
</analytic>
</biblStruct>
<biblStruct></biblStruct>
<biblStruct></biblStruct>
<biblStruct><analytic><author><name sortKey="Koehler, Jj" uniqKey="Koehler J">JJ Koehler</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Gill, P" uniqKey="Gill P">P Gill</name>
</author>
<author><name sortKey="Brenner, Ch" uniqKey="Brenner C">CH Brenner</name>
</author>
<author><name sortKey="Buckleton, Js" uniqKey="Buckleton J">JS Buckleton</name>
</author>
<author><name sortKey="Carracedo, A" uniqKey="Carracedo A">A Carracedo</name>
</author>
<author><name sortKey="Krawczak, M" uniqKey="Krawczak M">M Krawczak</name>
</author>
</analytic>
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<biblStruct></biblStruct>
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</div1>
</back>
</TEI>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Pennsylvanie</li>
<li>Virginie</li>
</region>
</list>
<tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Perlin, Mark W" sort="Perlin, Mark W" uniqKey="Perlin M" first="Mark W." last="Perlin">Mark W. Perlin</name>
</region>
<name sortKey="Dormer, Kiersten" sort="Dormer, Kiersten" uniqKey="Dormer K" first="Kiersten" last="Dormer">Kiersten Dormer</name>
<name sortKey="Greenspoon, Susan" sort="Greenspoon, Susan" uniqKey="Greenspoon S" first="Susan" last="Greenspoon">Susan Greenspoon</name>
<name sortKey="Hornyak, Jennifer" sort="Hornyak, Jennifer" uniqKey="Hornyak J" first="Jennifer" last="Hornyak">Jennifer Hornyak</name>
<name sortKey="Schiermeier Wood, Lisa" sort="Schiermeier Wood, Lisa" uniqKey="Schiermeier Wood L" first="Lisa" last="Schiermeier-Wood">Lisa Schiermeier-Wood</name>
</country>
</tree>
</affiliations>
</record>
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