Validating TrueAllele® DNA Mixture Interpretation

:  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...

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Veröffentlicht in:Journal of forensic sciences 2011-11, Vol.56 (6), p.1430-1447
Hauptverfasser: Perlin, Mark W., Legler, Matthew M., Spencer, Cara E., Smith, Jessica L., Allan, William P., Belrose, Jamie L., Duceman, Barry W.
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container_end_page 1447
container_issue 6
container_start_page 1430
container_title Journal of forensic sciences
container_volume 56
creator Perlin, Mark W.
Legler, Matthew M.
Spencer, Cara E.
Smith, Jessica L.
Allan, William P.
Belrose, Jamie L.
Duceman, Barry W.
description :  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.
doi_str_mv 10.1111/j.1556-4029.2011.01859.x
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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. 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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.</description><subject>Alleles</subject><subject>Bayes Theorem</subject><subject>Bayesian model</subject><subject>Case depth</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA - genetics</subject><subject>DNA Fingerprinting</subject><subject>DNA fingerprints</subject><subject>DNA mixture interpretation</subject><subject>expert system</subject><subject>Expert systems</subject><subject>forensic science</subject><subject>Forensic sciences</subject><subject>Genotype</subject><subject>Genotype &amp; phenotype</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Likelihood Functions</subject><subject>likelihood ratio</subject><subject>MCMC computation</subject><subject>Microsatellite Repeats</subject><subject>Qualitative research</subject><subject>quantitative data</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>STR analysis</subject><subject>validation study</subject><issn>0022-1198</issn><issn>1556-4029</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkM9u1DAQh62qqF0Kr4AieugpYcaO_x04rFq2FJWtKi3laHkTp8o2m2ztRGxfiofgyXC6ZQ9ISMzFI_n7zYw-QhKEDGN9WGXIuUhzoDqjgJgBKq6z7QGZ7D8OyQSA0hRRq2PyOoQVAAgUeESOKSoqc64mhN7Zpi5tX7f3ycIPbto0rnG_fiYX82nytd72g3fJVds7v_Guj1zXviGvKtsE9_blPSHfZp8W55_T65vLq_PpdVpwKnXKl6VU2kFJBVohBACnyAUuOahS5sA1R2aLoqygtIKWUvKKVpRSkEJZxtkJOdvN3fjucXChN-s6FK5pbOu6IRgNKHKGTEfy_V_kqht8G4-LEFOayWfo9F8QZSpepnM1LlU7qvBdCN5VZuPrtfVPBsGM7s3KjIrNqNiM7s2ze7ON0XcvC4bl2pX74B_ZEfi4A37UjXv678Hmy-xm7GI-3eXr0LvtPm_9gxGSSW6-zy_NjN7O89s7bhbsN1VEng4</recordid><startdate>201111</startdate><enddate>201111</enddate><creator>Perlin, Mark W.</creator><creator>Legler, Matthew M.</creator><creator>Spencer, Cara E.</creator><creator>Smith, Jessica L.</creator><creator>Allan, William P.</creator><creator>Belrose, Jamie L.</creator><creator>Duceman, Barry W.</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K7.</scope><scope>7X8</scope></search><sort><creationdate>201111</creationdate><title>Validating TrueAllele® DNA Mixture Interpretation</title><author>Perlin, Mark W. ; Legler, Matthew M. ; Spencer, Cara E. ; Smith, Jessica L. ; Allan, William P. ; Belrose, Jamie L. ; Duceman, Barry W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5279-5bd789e0d261a66600521561b508d74059513accdf0da62d775f2f2220768a353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Alleles</topic><topic>Bayes Theorem</topic><topic>Bayesian model</topic><topic>Case depth</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA - genetics</topic><topic>DNA Fingerprinting</topic><topic>DNA fingerprints</topic><topic>DNA mixture interpretation</topic><topic>expert system</topic><topic>Expert systems</topic><topic>forensic science</topic><topic>Forensic sciences</topic><topic>Genotype</topic><topic>Genotype &amp; phenotype</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Likelihood Functions</topic><topic>likelihood ratio</topic><topic>MCMC computation</topic><topic>Microsatellite Repeats</topic><topic>Qualitative research</topic><topic>quantitative data</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>STR analysis</topic><topic>validation study</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Perlin, Mark W.</creatorcontrib><creatorcontrib>Legler, Matthew M.</creatorcontrib><creatorcontrib>Spencer, Cara E.</creatorcontrib><creatorcontrib>Smith, Jessica L.</creatorcontrib><creatorcontrib>Allan, William P.</creatorcontrib><creatorcontrib>Belrose, Jamie L.</creatorcontrib><creatorcontrib>Duceman, Barry W.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Criminal Justice (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of forensic sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Perlin, Mark W.</au><au>Legler, Matthew M.</au><au>Spencer, Cara E.</au><au>Smith, Jessica L.</au><au>Allan, William P.</au><au>Belrose, Jamie L.</au><au>Duceman, Barry W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Validating TrueAllele® DNA Mixture Interpretation</atitle><jtitle>Journal of forensic sciences</jtitle><addtitle>J Forensic Sci</addtitle><date>2011-11</date><risdate>2011</risdate><volume>56</volume><issue>6</issue><spage>1430</spage><epage>1447</epage><pages>1430-1447</pages><issn>0022-1198</issn><eissn>1556-4029</eissn><coden>JFSCAS</coden><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. 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subjects Alleles
Bayes Theorem
Bayesian model
Case depth
Deoxyribonucleic acid
DNA
DNA - genetics
DNA Fingerprinting
DNA fingerprints
DNA mixture interpretation
expert system
Expert systems
forensic science
Forensic sciences
Genotype
Genotype & phenotype
Humans
Identification methods
Likelihood Functions
likelihood ratio
MCMC computation
Microsatellite Repeats
Qualitative research
quantitative data
Software
Statistical analysis
STR analysis
validation study
title Validating TrueAllele® DNA Mixture Interpretation
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