Journal Article10.1111/1556-4029.15424
Commentary on: Hahn M, Anslinger K, Eckert M, Fimmers R, Grethe S, Hohoff C, et al. [Joint recommendations of the project group “Biostatistical <scp>DNA</scp> Calculations” and the Trace Commission on the Biostatistical Evaluation of Forensic <scp>DNA</scp> Analytical Findings with Fully Continuous Models (<scp>FCM</scp>)]. Rechtsmedizin (Berl). 2023; 33(1):3–12. doi: 10.1007/s00194‐022‐00599‐5
Charles E.H. Berger,Maarten Kruijver,Tacha Hicks,Christophe Champod,Duncan Taylor,John Buckleton +5 more
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TL;DR: The article discusses the joint recommendations of the project group "Biostatistical DNA Calculations" and the Trace Commission on the Biostatistical Evaluation of Forensic DNA Analytical Findings with Fully Continuous Models (FCM). The recommendations aim to improve the accuracy and reliability of forensic DNA analysis.
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Abstract: Journal of Forensic SciencesEarly View LETTER TO THE EDITOR Commentary on: Hahn M, Anslinger K, Eckert M, Fimmers R, Grethe S, Hohoff C, et al. [Joint recommendations of the project group "Biostatistical DNA Calculations" and the Trace Commission on the Biostatistical Evaluation of Forensic DNA Analytical Findings with Fully Continuous Models (FCM)]. Rechtsmedizin (Berl). 2023; 33(1):3–12. doi: 10.1007/s00194-022-00599-5 Charles E. H. Berger PhD, Charles E. H. Berger PhD Netherlands Forensic Institute, The Hague, The Netherlands Institute of Criminal Law and Criminology, Leiden University, Leiden, The NetherlandsSearch for more papers by this authorMaarten Kruijver PhD, Maarten Kruijver PhD Institute of Environmental Science and Research Limited, Auckland, New ZealandSearch for more papers by this authorTacha Hicks PhD, Tacha Hicks PhD Forensic Genetics Unit, University Center of Legal Medicine, Lausanne—Geneva, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland Fondation pour la Formation Continue Universitaire Lausannoise (UNIL-EPFL) & School of Criminal Justice, Lausanne, SwitzerlandSearch for more papers by this authorChristophe Champod PhD, Christophe Champod PhD orcid.org/0000-0002-4035-2698 Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice, University of Lausanne, Lausanne, SwitzerlandSearch for more papers by this authorDuncan Taylor PhD, Duncan Taylor PhD Forensic Science SA, Adelaide, South Australia, Australia School of Biological Sciences, Flinders University, Adelaide, South Australia, AustraliaSearch for more papers by this authorJohn Buckleton DSc, Corresponding Author John Buckleton DSc [email protected] Institute of Environmental Science and Research Limited, Auckland, New Zealand Department of Statistics, University of Auckland, Auckland, New Zealand Correspondence John Buckleton, Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand. Email: [email protected]Search for more papers by this author Charles E. H. Berger PhD, Charles E. H. Berger PhD Netherlands Forensic Institute, The Hague, The Netherlands Institute of Criminal Law and Criminology, Leiden University, Leiden, The NetherlandsSearch for more papers by this authorMaarten Kruijver PhD, Maarten Kruijver PhD Institute of Environmental Science and Research Limited, Auckland, New ZealandSearch for more papers by this authorTacha Hicks PhD, Tacha Hicks PhD Forensic Genetics Unit, University Center of Legal Medicine, Lausanne—Geneva, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland Fondation pour la Formation Continue Universitaire Lausannoise (UNIL-EPFL) & School of Criminal Justice, Lausanne, SwitzerlandSearch for more papers by this authorChristophe Champod PhD, Christophe Champod PhD orcid.org/0000-0002-4035-2698 Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice, University of Lausanne, Lausanne, SwitzerlandSearch for more papers by this authorDuncan Taylor PhD, Duncan Taylor PhD Forensic Science SA, Adelaide, South Australia, Australia School of Biological Sciences, Flinders University, Adelaide, South Australia, AustraliaSearch for more papers by this authorJohn Buckleton DSc, Corresponding Author John Buckleton DSc [email protected] Institute of Environmental Science and Research Limited, Auckland, New Zealand Department of Statistics, University of Auckland, Auckland, New Zealand Correspondence John Buckleton, Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand. Email: [email protected]Search for more papers by this author First published: 20 November 2023 https://doi.org/10.1111/1556-4029.15424 See Original Article here. See Authors' response here. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat REFERENCES 1Hahn M, Anslinger K, Eckert M, Fimmers R, Grethe S, Hohoff C, et al. Gemeinsame Empfehlungen der Projektgruppe "Biostatistische DNA-Berechnungen" und der Spurenkommission zur biostatistischen Bewertung forensischer DNA-analytischer Befunde mit vollkontinuierlichen Modellen (VKM) [Joint recommendations of the project group "biostatistical DNA calculations" and the trace commission on the biostatistical evaluation of forensic DNA analytical findings with fully continuous models (FCM)]. Rechtsmedizin (Berl). Rechtsmedizin. 2023; 33(1): 3–12. https://doi.org/10.1007/s00194-022-00599-5 10.1007/s00194-022-00599-5 Google Scholar 2Templin M, Zimmermann P, Kranz S, Eckert M, Leuker C, Razbin S, et al. Einsatz vollkontinuierlicher modelle zur biostatistischen bewertung forensischer DNA-analytischer befunde [Use of fully continuous models for the biostatistical evaluation of forensic DNA analysis findings]. Dent Rec. 2023; 33(1): 13–29. https://doi.org/10.1007/s00194-022-00600-1 10.1007/s00194?022?00600?1 Google Scholar 3Riman S, Iyer H, Vallone PM. Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset. PLoS One. 2021; 16(9):e0256714. https://doi.org/10.1371/journal.pone.0256714 10.1371/journal.pone.0256714 CASPubMedWeb of Science®Google Scholar 4Costa C, Figueiredo C, Amorim A, Costa S, Ferreira PM, Pinto N. Quantification of forensic genetic evidence: comparison of results obtained by qualitative and quantitative software for real casework samples. Forensic Sci Int Genetics. 2022; 59:102715. https://doi.org/10.1016/j.fsigen.2022.102715 10.1016/j.fsigen.2022.102715 CASPubMedWeb of Science®Google Scholar 5Buckleton J, Bright J-A, Taylor D, Wivell R, Bleka Ø, Gill P, et al. Re: Riman et al. Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt Dataset. Forensic Sci Int Genetics. 2022; 59:102709. https://doi.org/10.1016/j.fsigen.2022.102709 10.1016/j.fsigen.2022.102709 CASPubMedWeb of Science®Google Scholar 6Susik M, Sbalzarini IF. Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark. Forensic Sci Int Genetics. 2023; 64:102840. https://doi.org/10.1016/j.fsigen.2023.102840 10.1016/j.fsigen.2023.102840 CASPubMedWeb of Science®Google Scholar 7Cheng K, Bleka O, Gill P, Curran J, Bright J-A, Taylor D, et al. A comparison of likelihood ratios obtained from EuroForMix and STRmix™. J Forensic Sci. 2021; 66(6): 2138–2155. https://doi.org/10.1111/1556-4029.14886 10.1111/1556-4029.14886 CASPubMedWeb of Science®Google Scholar 8Berger CEH, Slooten K. The LR does not exist. Sci Justice. 2016; 56(5): 388–391. https://doi.org/10.1016/j.scijus.2016.06.005 10.1016/j.scijus.2016.06.005 PubMedWeb of Science®Google Scholar 9Ramos D, Gonzalez-Rodriguez J. Reliable support: measuring calibration of likelihood ratios. Forensic Sci Int. 2013; 230(1–3): 156–169. https://doi.org/10.1016/j.forsciint.2013.04.014 10.1016/j.forsciint.2013.04.014 PubMedWeb of Science®Google Scholar 10Ramos D. On the calibration of likelihood ratios. 2011. Accessed from: http://arantxa.ii.uam.es/~dramos/files/2011_02_08_WIC_Ramos_calibrtionLRValues_v2.pdf. Accessed 24 Oct 2023. Google Scholar 11Ramos D, Meuwly D, Haraksim R, Berger CEH. Validation of forensic automatic likelihood ratio methods. In: D Banks, K Kafadar, D Kaye, M Tackett, editors. Handbook of forensic statistics. Boca Raton, FL: Chapman & Hall/CRC; 2020. p. 143–164. 10.1201/9780367527709-7 Google Scholar 12Robertson B, Vignaux GA, Berger CEH. Interpreting evidence: evaluating forensic science in the courtroom. 2nd ed. Hoboken, NJ: John Wiley & Sons, Ltd; 2016. 10.1002/9781118492475 Google Scholar 13Buckleton JS, Kruijver M, Curran J, Bright J-A. Calibration of STRmix LRs following the method of Hannig et al. Wellington, New Zealand: Institute of Environmental Science and Research; 2020. https://doi.org/10.26091/ESRNZ.12324011.v1 Google Scholar 14Bright J-A, Jones Dukes M, Pugh SN, Evett IW, Buckleton JS. Applying calibration to LRs produced by a DNA interpretation software. Aust J Forensic Sci. 2019; 53(2): 147–153. https://doi.org/10.1080/00450618.2019.1682668 10.1080/00450618.2019.1682668 Web of Science®Google Scholar 15Stoney DA. What made us ever think we could individualize using statistics? J Forensic Sci Soc. 1991; 31(2): 197–199. https://doi.org/10.1016/s0015-7368(91)7368(91)73138-1 10.1016/S0015-7368(91)73138-1 CASPubMedWeb of Science®Google Scholar 16Champod C, Biedermann A. Overview and meaning of identification/individualization. In: MM Houck, editor. Encyclopedia of forensic sciences. 3rd ed. Oxford, U.K.: Elsevier Ltd.; 2023. p. 53–62. 10.1016/B978-0-12-823677-2.00152-5 Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
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Citations
‘Low’ LRs obtained from DNA mixtures: On calibration and discrimination performance of probabilistic genotyping software
Moya McCarthy-Allen,Øyvind Bleka,Rolf Ypma,Peter Gill,Corina C.G. Benschop +4 more
TL;DR: This study evaluates the calibration and discriminatory performance of probabilistic genotyping software, assessing its validity through metrics such as Empirical Cross-Entropy plots, PAV plots, and log likelihood ratio cost, in addition to traditional discriminatory power measures.
References
The LR does not exist
TL;DR: Given evidence of a shared feature of a trace and an accused, this framework is applied to assign an evidential value to this correspondence, and it is demonstrated the LR is given by what the authors know about the proportion rather than by the unknown proportion itself.
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Applying calibration to LRs produced by a DNA interpretation software
TL;DR: An approximate correspondence of the posterior probability, as assigned from the LR and the prior odds, with the observed rate of true donors for this dataset is observed.
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Re: Riman et al. Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
John Buckleton,Jo-Anne Bright,Duncan Taylor,R Wivell,Øyvind Bleka,Peter Gill,Corina C.G. Benschop,Bruce Budowle,Michael D. Coble +8 more
- 01 Apr 2022
TL;DR: The editors, this paper , have published a survey of the state of the art in bioinformatics, biology, and computer science, and biology, including: http://www.
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Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark
Ann Cartwright
- 01 May 2023
TL;DR: In this paper , an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling (Susik et al., 2022) was conducted and compared with two state-of-the-art software products: STRmix™ v2.6 and Euroformix v3.4.
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