| Interpreting anonymous DNA samples from mass disasters--probabilistic forensic inference using genetic markers. | |
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MedLine Citation:
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PMID: 16873485 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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MOTIVATION: The problem of identifying victims in a mass disaster using DNA fingerprints involves a scale of computation that requires efficient and accurate algorithms. In a typical scenario there are hundreds of samples taken from remains that must be matched to the pedigrees of the alleged victim's surviving relatives. Moreover the samples are often degraded due to heat and exposure. To develop a competent method for this type of forensic inference problem, the complicated quality issues of DNA typing need to be handled appropriately, the matches between every sample and every family must be considered, and the confidence of matches need to be provided. RESULTS: We present a unified probabilistic framework that efficiently clusters samples, conservatively eliminates implausible sample-pedigree pairings, and handles both degraded samples (missing values) and experimental errors in producing and/or reading a genotype. We present a method that confidently exclude forensically unambiguous sample-family matches from the large hypothesis space of candidate matches, based on posterior probabilistic inference. Due to the high confidentiality of disaster DNA data, simulation experiments are commonly performed and used here for validation. Our framework is shown to be robust to these errors at levels typical in real applications. Furthermore, the flexibility in the probabilistic models makes it possible to extend this framework to include other biological factors such as interdependent markers, mitochondrial sequences, and blood type. AVAILABILITY: The software and data sets are available from the authors upon request. |
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Authors:
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Tien-Ho Lin; Eugene W Myers; Eric P Xing |
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Publication Detail:
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Type: Evaluation Studies; Journal Article |
Journal Detail:
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Title: Bioinformatics (Oxford, England) Volume: 22 ISSN: 1367-4811 ISO Abbreviation: Bioinformatics Publication Date: 2006 Jul |
Date Detail:
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Created Date: 2006-07-28 Completed Date: 2006-10-05 Revised Date: 2009-11-04 |
Medline Journal Info:
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Nlm Unique ID: 9808944 Medline TA: Bioinformatics Country: England |
Other Details:
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Languages: eng Pagination: e298-306 Citation Subset: IM |
Affiliation:
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School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computer Simulation DNA / analysis*, genetics* DNA Fingerprinting / methods* Disasters* Forensic Medicine / methods Genetic Markers / genetics* Humans Models, Genetic* Models, Statistical Sequence Alignment / methods Sequence Analysis, DNA / methods* Software |
| Chemical | |
Reg. No./Substance:
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0/Genetic Markers; 9007-49-2/DNA |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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