Complex DNA mixture analysis in a forensic context: Evaluating the probative value using a likelihood ratio model |
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| Institution: | 1. Department of Human Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands;2. National Institute of Public Health, Department of Forensic Biology, P.O. Box 4404 Nydalen, 0403 Oslo, Norway;3. National Institute of Public Health, Department of Forensic Medicine, P.O. Box 4950 Nydalen, 0424 Oslo, Norway;1. UCL Genetics Institute, Darwin Building, Gower Street, London WC1E 6BT, UK;2. Orchid Cellmark Ltd., Abingdon Business Park, Blacklands Way, Abingdon OX14 1YX, UK;1. National Institute of Standards and Technology, Applied Genetics Group, Gaithersburg, MD, USA;2. ESR, Private Bag 92021, Auckland 1142, New Zealand;3. National Institute of Standards and Technology, Statistical Engineering Division (Guest Researcher), Gaithersburg, MD, USA;4. National Institute of Standards and Technology, Special Programs Office, Gaithersburg, MD, USA;5. Norwegian University of Life Sciences, Oslo, Norway;6. Institute for Medical Statistics, Informatics, and Epidemiology, University Bonn, Germany;7. Norwegian Institute of Public Health, Oslo, Norway;8. University of Oslo, Oslo, Norway;9. State University of Rio de Janeiro (UERJ), Rio de Janeiro, Brazil;10. IPATIMUP, Institute of Molecular Pathology and Immunology of the University of Porto, Portugal;11. Instituto de Investigação e Inovação em Saúde, University of Porto, Portugal;12. National Institute of Standards and Technology, Software and Systems Division, Gaithersburg, MD, USA;13. Institute of Medical Informatics and Statistics, Christian-Albrechts University of Kiel, Germany;14. Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark;15. Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria;p. Forensic Science Program, The Pennsylvania State University, PA, USA;q. Institute for Research and Innovation in Health (I3S), University of Porto, Porto, Portugal;r. Centre of Mathematics of the University of Porto, Porto, Portugal;s. Institute of Legal Medicine, Faculty of Medicine, University of Cologne, Germany;t. National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA;u. Department of Forensic Genetics, Institute of Legal Medicine and Forensic Sciences, Charité—Universitätsmedizin, Berlin, Germany;v. John Jay College of Criminal Justice, New York, USA;1. ESR, Private Bag 92021, Auckland 1142, New Zealand;2. University of Auckland, Department of Statistics, Private Bag 92019, Auckland 1142, New Zealand;3. Principal Forensic Services Ltd, United Kingdom;4. Forensic Science South Australia, 21 Divett Place, SA 5000, Australia;5. School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia;1. Department of Forensic Biology, Norwegian Institute of Public Health, Oslo, Norway;2. Department of Mathematics, University of Oslo, Oslo, Norway;3. Division of Biological Traces, Netherlands Forensic Institute, The Hague, The Netherlands;4. Department of Forensic Medicine, University of Oslo, Oslo, Norway |
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| Abstract: | The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors.The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a ‘gold standard’ LR, to the LR that would be obtained in casework. The ‘gold standard’ LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The ‘casework LR’ was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim.We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework. |
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| Keywords: | High order mixtures Likelihood ratios LRmix Drop-out Drop-in Robustness |
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