DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: Combining quantitative data for greater identification information
Abstract Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced stat...
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Veröffentlicht in: | Science & justice 2013-06, Vol.53 (2), p.103-114 |
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description | Abstract Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells (‘binomial sampling’) from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function. As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50 ± 35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. Finally, we discuss the implications of this work for routine forensic practice. |
doi_str_mv | 10.1016/j.scijus.2012.04.004 |
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Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells (‘binomial sampling’) from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function. As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50 ± 35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. Finally, we discuss the implications of this work for routine forensic practice.</description><identifier>ISSN: 1355-0306</identifier><identifier>EISSN: 1876-4452</identifier><identifier>DOI: 10.1016/j.scijus.2012.04.004</identifier><identifier>PMID: 23601717</identifier><language>eng</language><publisher>England: Elsevier Ireland Ltd</publisher><subject>Binomial sampling ; Cells ; Coculture Techniques ; Deoxyribonucleic acid ; Dissection ; DNA ; DNA Fingerprinting - methods ; DNA mixtures ; Forensic sciences ; Genotype ; Genotype & phenotype ; Humans ; Joint likelihood ; Lasers ; LCM ; Likelihood Functions ; Microsatellite Repeats ; Pathology ; Polymerase Chain Reaction ; Probabilistic genotype ; Software ; TrueAllele® system</subject><ispartof>Science & justice, 2013-06, Vol.53 (2), p.103-114</ispartof><rights>Forensic Science Society</rights><rights>2012 Forensic Science Society</rights><rights>Copyright © 2012 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.</rights><rights>Copyright Forensic Science Society Jun 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c445t-47b474773da216a472af29cd53ce40a1472d54b257c4e5c47c460e3433907b83</citedby><cites>FETCH-LOGICAL-c445t-47b474773da216a472af29cd53ce40a1472d54b257c4e5c47c460e3433907b83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.scijus.2012.04.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23601717$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ballantyne, Jack</creatorcontrib><creatorcontrib>Hanson, Erin K</creatorcontrib><creatorcontrib>Perlin, Mark W</creatorcontrib><title>DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: Combining quantitative data for greater identification information</title><title>Science & justice</title><addtitle>Sci Justice</addtitle><description>Abstract Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells (‘binomial sampling’) from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function. As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50 ± 35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. Finally, we discuss the implications of this work for routine forensic practice.</description><subject>Binomial sampling</subject><subject>Cells</subject><subject>Coculture Techniques</subject><subject>Deoxyribonucleic acid</subject><subject>Dissection</subject><subject>DNA</subject><subject>DNA Fingerprinting - methods</subject><subject>DNA mixtures</subject><subject>Forensic sciences</subject><subject>Genotype</subject><subject>Genotype & phenotype</subject><subject>Humans</subject><subject>Joint likelihood</subject><subject>Lasers</subject><subject>LCM</subject><subject>Likelihood Functions</subject><subject>Microsatellite Repeats</subject><subject>Pathology</subject><subject>Polymerase Chain Reaction</subject><subject>Probabilistic genotype</subject><subject>Software</subject><subject>TrueAllele® system</subject><issn>1355-0306</issn><issn>1876-4452</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFUk1v1DAQjRCIlsI_QMgSFy5Zxh-JsxyQquVTquBA75bjTFZenDi1nar5afw7nN0CUi9cPJbnvTeeeVMULylsKND67WETjT3MccOAsg2IDYB4VJzTRtalEBV7nO-8qkrgUJ8Vz2I8AFSS1vC0OGO8BiqpPC9-ffh2SQZ7l-aAZI-jT8tkxz1pFzIF3-rWOhuTNcT4YZoTBmLHfE4Bk07Wj8T3pLWjH6x2bimjHiaHHXE6ZqjR06rbEYPOkclPszuS4juy80OmrZVuZj0mu6rdIul00qT3gewD6mO1DnO2t-ZUzY45ORzvz4snvXYRX9zHi-L608fr3Zfy6vvnr7vLq9LkKaRSyFZIISXvNKO1FpLpnm1NV3GDAjTND10lWlZJI7AyIocakAvOtyDbhl8Ub06yeRw3M8akBhvXfvSIfo6Kct7w7ZZWLENfP4Ae_BzG_LmMElXTNJytguKEMsHHGLBXU7CDDouioFZn1UGdnFWrswqEys5m2qt78bkdsPtL-mNlBrw_ATAP49ZiWFVwNNjZgCapztv_VXgoYFy2yGj3ExeM_3pRMXPUj3W71uWiDABq4Pw3odjQFw</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>Ballantyne, Jack</creator><creator>Hanson, Erin K</creator><creator>Perlin, Mark W</creator><general>Elsevier Ireland Ltd</general><general>Forensic Science Society</general><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>7X8</scope></search><sort><creationdate>20130601</creationdate><title>DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: Combining quantitative data for greater identification information</title><author>Ballantyne, Jack ; Hanson, Erin K ; Perlin, Mark W</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c445t-47b474773da216a472af29cd53ce40a1472d54b257c4e5c47c460e3433907b83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Binomial sampling</topic><topic>Cells</topic><topic>Coculture Techniques</topic><topic>Deoxyribonucleic acid</topic><topic>Dissection</topic><topic>DNA</topic><topic>DNA Fingerprinting - methods</topic><topic>DNA mixtures</topic><topic>Forensic sciences</topic><topic>Genotype</topic><topic>Genotype & phenotype</topic><topic>Humans</topic><topic>Joint likelihood</topic><topic>Lasers</topic><topic>LCM</topic><topic>Likelihood Functions</topic><topic>Microsatellite Repeats</topic><topic>Pathology</topic><topic>Polymerase Chain Reaction</topic><topic>Probabilistic genotype</topic><topic>Software</topic><topic>TrueAllele® system</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ballantyne, Jack</creatorcontrib><creatorcontrib>Hanson, Erin K</creatorcontrib><creatorcontrib>Perlin, Mark W</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Science & justice</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ballantyne, Jack</au><au>Hanson, Erin K</au><au>Perlin, Mark W</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: Combining quantitative data for greater identification information</atitle><jtitle>Science & justice</jtitle><addtitle>Sci Justice</addtitle><date>2013-06-01</date><risdate>2013</risdate><volume>53</volume><issue>2</issue><spage>103</spage><epage>114</epage><pages>103-114</pages><issn>1355-0306</issn><eissn>1876-4452</eissn><abstract>Abstract Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells (‘binomial sampling’) from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function. As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50 ± 35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. 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subjects | Binomial sampling Cells Coculture Techniques Deoxyribonucleic acid Dissection DNA DNA Fingerprinting - methods DNA mixtures Forensic sciences Genotype Genotype & phenotype Humans Joint likelihood Lasers LCM Likelihood Functions Microsatellite Repeats Pathology Polymerase Chain Reaction Probabilistic genotype Software TrueAllele® system |
title | DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: Combining quantitative data for greater identification information |
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