1
|
Marsico F, Sibilla G, Escobar MS, Chernomoretz A. The Missing Person problem through the lens of information theory. Forensic Sci Int Genet 2024; 70:103025. [PMID: 38382248 DOI: 10.1016/j.fsigen.2024.103025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/30/2024] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
Missing person cases typically require a genetic kinship test to determine the relationship between an unidentified individual and the relatives of the missing person. When not enough genetic evidence has been collected the lack of statistical power of these tests might lead to unreliable results. This is particularly true when just a few distant relatives are available for genotyping. In this contribution, we considered a Bayesian network approach for kinship testing and proposed several information theoretic metrics in order to quantitatively evaluate the information content of pedigrees. We show how these statistics are related to the widely used likelihood ratio values and could be employed to efficiently prioritize family members in order to optimize the statistical power in missing person problems. Our methodology seamlessly integrates with Bayesian modeling approaches, like the GENis platform that we have recently developed for high-throughput missing person identification tasks. Furthermore, our approach can also be easily incorporated into Elston-Stewart forensic frameworks. To facilitate the application of our methodology, we have developed the forensIT package, freely available on CRAN repository, which implements all the methodologies described in our manuscript.
Collapse
Affiliation(s)
- Franco Marsico
- GENis development team, Argentina; IDEPI-UNPAZ, Argentina; Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Argentina
| | - Gustavo Sibilla
- GENis development team, Argentina; Fundación Sadosky, Argentina
| | | | - Ariel Chernomoretz
- GENis development team, Argentina; Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, Argentina; CONICET - Universidad de Buenos Aires, Instituto de Física Interdisciplinaria y Aplicada (INFINA), Argentina; Fundación Instituto Leloir, Buenos Aires, C1405 BWE, Argentina.
| |
Collapse
|
2
|
Interpretation of DNA data within the context of UK forensic science - investigation. Emerg Top Life Sci 2021; 5:395-404. [PMID: 34151948 PMCID: PMC8457768 DOI: 10.1042/etls20210165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 05/08/2021] [Accepted: 06/01/2021] [Indexed: 11/17/2022]
Abstract
This article is the second part of a review of the interpretation of DNA data in forensic science. The first part describes the evaluation of autosomal profile for criminal trials where an evidential weight is assigned to the profile of a person of interest (POI) and a crime-scene profile. This part describes the state of the art and future advances in the interpretation of forensic DNA data for providing intelligence information during an investigation. Forensic DNA is crucial in the investigative phase of an undetected crime where a POI needs to be identified. A sample taken from a crime scene is profiled using a range of forensic DNA tests. This review covers investigation using autosomal profiles including searching national and international crime and reference DNA databases. Other investigative methodologies described are kinship analysis; familial searching; Y chromosome (Y-STR) and mitochondrial (mtDNA) profiles; appearance prediction and geographic ancestry; forensic genetic genealogy; and body identification. For completeness, the evaluation of Y-STRs, mtDNA and kinship analysis are briefly described. Taken together, parts I and II, cover the range of interpretation of DNA data in a forensic context.
Collapse
|
3
|
Kruijver M, Taylor D, Bright JA. Evaluating DNA evidence possibly involving multiple (mixed) samples, common donors and related contributors. Forensic Sci Int Genet 2021; 54:102532. [PMID: 34130043 DOI: 10.1016/j.fsigen.2021.102532] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 11/18/2022]
Abstract
Forensic DNA profiling is used in various circumstances to evaluate support for two competing propositions with the assignment of a likelihood ratio. Many software implementations exist that tackle a range of inference problems spanning identification and relationship testing. We propose a flexible likelihood ratio framework that caters to inference problems in forensic genetics. The framework allows for investigation of the degree of support for the contribution of multiple persons to multiple samples allowing for persons to be related according to a pedigree, including inbred relationships. We explain how a number of routine as well as more complex problems can be treated within this framework.
Collapse
Affiliation(s)
- Maarten Kruijver
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand.
| | - Duncan Taylor
- College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia; Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia
| | - Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand
| |
Collapse
|
4
|
Evaluation of forensic genetics findings given activity level propositions: A review. Forensic Sci Int Genet 2018; 36:34-49. [DOI: 10.1016/j.fsigen.2018.06.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 05/31/2018] [Accepted: 06/01/2018] [Indexed: 12/31/2022]
|
5
|
Taylor D, Biedermann A, Hicks T, Champod C. A template for constructing Bayesian networks in forensic biology cases when considering activity level propositions. Forensic Sci Int Genet 2018; 33:136-146. [DOI: 10.1016/j.fsigen.2017.12.006] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 11/09/2017] [Accepted: 12/11/2017] [Indexed: 11/16/2022]
|
6
|
Maitre M, Kirkbride K, Horder M, Roux C, Beavis A. Current perspectives in the interpretation of gunshot residues in forensic science: A review. Forensic Sci Int 2017; 270:1-11. [DOI: 10.1016/j.forsciint.2016.09.003] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 08/05/2016] [Accepted: 09/06/2016] [Indexed: 11/16/2022]
|
7
|
Taylor D, Hicks T, Champod C. Using sensitivity analyses in Bayesian Networks to highlight the impact of data paucity and direct future analyses: a contribution to the debate on measuring and reporting the precision of likelihood ratios. Sci Justice 2016; 56:402-410. [DOI: 10.1016/j.scijus.2016.06.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
8
|
Simonsson I, Mostad P. Stationary mutation models. Forensic Sci Int Genet 2016; 23:217-225. [DOI: 10.1016/j.fsigen.2016.04.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 03/17/2016] [Accepted: 04/02/2016] [Indexed: 10/22/2022]
|
9
|
Taylor D, Abarno D, Hicks T, Champod C. Evaluating forensic biology results given source level propositions. Forensic Sci Int Genet 2015; 21:54-67. [PMID: 26720813 DOI: 10.1016/j.fsigen.2015.11.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 11/15/2015] [Accepted: 11/23/2015] [Indexed: 11/28/2022]
Abstract
The evaluation of forensic evidence can occur at any level within the hierarchy of propositions depending on the question being asked and the amount and type of information that is taken into account within the evaluation. Commonly DNA evidence is reported given propositions that deal with the sub-source level in the hierarchy, which deals only with the possibility that a nominated individual is a source of DNA in a trace (or contributor to the DNA in the case of a mixed DNA trace). We explore the use of information obtained from examinations, presumptive and discriminating tests for body fluids, DNA concentrations and some case circumstances within a Bayesian network in order to provide assistance to the Courts that have to consider propositions at source level. We use a scenario in which the presence of blood is of interest as an exemplar and consider how DNA profiling results and the potential for laboratory error can be taken into account. We finish with examples of how the results of these reports could be presented in court using either numerical values or verbal descriptions of the results.
Collapse
Affiliation(s)
- Duncan Taylor
- Forensic Science South Australia, 21 Divett Place, Adelaide, SA 5000, Australia; School of Biological Sciences, Flinders University, GPO Box 2100 Adelaide SA, Australia 5001.
| | - Damien Abarno
- Forensic Science South Australia, 21 Divett Place, Adelaide, SA 5000, Australia; School of Biological Sciences, Flinders University, GPO Box 2100 Adelaide SA, Australia 5001
| | - Tacha Hicks
- School of Criminal Justice, University of Lausanne & Fondation pour la formation continue universitaire lausannoise, Lausanne, Dorigny, Switzerland
| | - Christophe Champod
- School of Criminal Justice, University of Lausanne, Lausanne, Dorigny, Switzerland
| |
Collapse
|
10
|
Improved maximum likelihood reconstruction of complex multi-generational pedigrees. Theor Popul Biol 2014; 97:11-9. [DOI: 10.1016/j.tpb.2014.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2014] [Revised: 07/11/2014] [Accepted: 07/16/2014] [Indexed: 11/17/2022]
|
11
|
Egeland T, Dørum G, Vigeland MD, Sheehan NA. Mixtures with relatives: A pedigree perspective. Forensic Sci Int Genet 2014; 10:49-54. [DOI: 10.1016/j.fsigen.2014.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2013] [Revised: 01/13/2014] [Accepted: 01/22/2014] [Indexed: 10/25/2022]
|
12
|
Gittelson S, Biedermann A, Bozza S, Taroni F. Decision analysis for the genotype designation in low-template-DNA profiles. Forensic Sci Int Genet 2014; 9:118-33. [DOI: 10.1016/j.fsigen.2013.11.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Revised: 11/18/2013] [Accepted: 11/26/2013] [Indexed: 11/16/2022]
|
13
|
Object-oriented Bayesian networks for evaluating DIP–STR profiling results from unbalanced DNA mixtures. Forensic Sci Int Genet 2014; 8:159-69. [DOI: 10.1016/j.fsigen.2013.09.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2012] [Revised: 07/24/2013] [Accepted: 09/06/2013] [Indexed: 11/22/2022]
|
14
|
Marella D, Vicard P. Object-Oriented Bayesian Networks for Modeling the Respondent Measurement Error. COMMUN STAT-THEOR M 2013. [DOI: 10.1080/03610926.2011.630769] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
15
|
Corradi F, Ricciardi F. Evaluation of kinship identification systems based on short tandem repeat DNA profiles. J R Stat Soc Ser C Appl Stat 2013. [DOI: 10.1111/rssc.12017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
16
|
Biedermann A, Garbolino P, Taroni F. The subjectivist interpretation of probability and the problem of individualisation in forensic science. Sci Justice 2013; 53:192-200. [DOI: 10.1016/j.scijus.2013.01.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2012] [Revised: 12/19/2012] [Accepted: 01/07/2013] [Indexed: 10/27/2022]
|
17
|
Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method. Forensic Sci Int Genet 2012; 6:689-96. [DOI: 10.1016/j.fsigen.2012.03.006] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2011] [Revised: 03/21/2012] [Accepted: 03/26/2012] [Indexed: 11/15/2022]
|
18
|
Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks. Forensic Sci Int 2012; 223:217-27. [DOI: 10.1016/j.forsciint.2012.09.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 07/18/2012] [Accepted: 09/05/2012] [Indexed: 11/19/2022]
|
19
|
Learning about Bayesian networks for forensic interpretation: an example based on the 'the problem of multiple propositions'. Sci Justice 2012; 52:191-8. [PMID: 22841144 DOI: 10.1016/j.scijus.2012.05.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2011] [Revised: 05/07/2012] [Accepted: 05/08/2012] [Indexed: 11/20/2022]
Abstract
Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.
Collapse
|
20
|
Gittelson S, Biedermann A, Bozza S, Taroni F. Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem*. J Forensic Sci 2012; 57:1199-216. [DOI: 10.1111/j.1556-4029.2012.02127.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
21
|
Bayesian networks for evaluating forensic DNA profiling evidence: A review and guide to literature. Forensic Sci Int Genet 2012; 6:147-57. [DOI: 10.1016/j.fsigen.2011.06.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2011] [Revised: 06/07/2011] [Accepted: 06/24/2011] [Indexed: 11/17/2022]
|
22
|
van Dongen C, Slooten K, Slagter M, Burgers W, Wiegerinck W. Bonaparte: Application of new software for missing persons program. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2011. [DOI: 10.1016/j.fsigss.2011.08.059] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
23
|
Response to: DNA identification by pedigree likelihood ratio accommodating population substructure and mutations. INVESTIGATIVE GENETICS 2011; 2:7. [PMID: 21439065 PMCID: PMC3070664 DOI: 10.1186/2041-2223-2-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2011] [Accepted: 03/25/2011] [Indexed: 11/22/2022]
Abstract
Mutation models are important in many areas of genetics including forensics. This letter criticizes the model of the paper 'DNA identification by pedigree likelihood ratio accommodating population substructure and mutations' by Ge et al. (2010). Furthermore, we argue that the paper in some cases misrepresents previously published papers. Please see related letter: http://www.investigativegenetics.com/content/2/1/8.
Collapse
|
24
|
Chakraborty R, Ge J, Budowle B. Response to: DNA identification by pedigree likelihood ratio accommodating population substructure and mutations- authors' reply. INVESTIGATIVE GENETICS 2011; 2:8. [PMID: 21439066 PMCID: PMC3080307 DOI: 10.1186/2041-2223-2-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2011] [Accepted: 03/25/2011] [Indexed: 11/23/2022]
Affiliation(s)
- Ranajit Chakraborty
- Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, USA
- Institute of Investigative Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, USA
| | - Jianye Ge
- Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, USA
- Institute of Investigative Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, USA
| | - Bruce Budowle
- Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, USA
- Institute of Investigative Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, USA
| |
Collapse
|
25
|
|
26
|
Implementing statistical learning methods through Bayesian networks (Part 2): Bayesian evaluations for results of black toner analyses in forensic document examination. Forensic Sci Int 2011; 204:58-66. [DOI: 10.1016/j.forsciint.2010.05.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2009] [Revised: 04/21/2010] [Accepted: 05/04/2010] [Indexed: 11/19/2022]
|
27
|
|
28
|
Hong YL, Lee HJ, Lee JW. Interpreting Mixtures Using Allele Peak Areas. KOREAN JOURNAL OF APPLIED STATISTICS 2010. [DOI: 10.5351/kjas.2010.23.1.113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
29
|
Rho SY. Technical Improvements of the Projection of Household Health Care Expenditure. KOREAN JOURNAL OF APPLIED STATISTICS 2010. [DOI: 10.5351/kjas.2010.23.1.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
30
|
Bayesian networks for victim identification on the basis of DNA profiles. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2009. [DOI: 10.1016/j.fsigss.2009.08.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
31
|
Implementing statistical learning methods through Bayesian networks. Part 1: A guide to Bayesian parameter estimation using forensic science data. Forensic Sci Int 2009; 193:63-71. [DOI: 10.1016/j.forsciint.2009.09.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2008] [Revised: 07/10/2009] [Accepted: 09/14/2009] [Indexed: 11/24/2022]
|
32
|
Dobosz M, Bocci C, Bonuglia M, Grasso C, Merigioli S, Russo A, De iuliis P. Probabilistic Expert Systems for Forensic Inference from DNA Markers in Horses: Applications to Confirm Genealogies with Lack of Genetic Data. J Hered 2009; 101:240-5. [DOI: 10.1093/jhered/esp090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
33
|
Affiliation(s)
- James M. Curran
- Department of Statistics, University of Auckland, Auckland, New Zealand
| |
Collapse
|
34
|
Cowell RG. Efficient maximum likelihood pedigree reconstruction. Theor Popul Biol 2009; 76:285-91. [PMID: 19781561 DOI: 10.1016/j.tpb.2009.09.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2009] [Revised: 09/10/2009] [Accepted: 09/16/2009] [Indexed: 11/24/2022]
Abstract
A simple and efficient algorithm is presented for finding a maximum likelihood pedigree using microsatellite (STR) genotype information on a complete sample of related individuals. The computational complexity of the algorithm is at worst (O(n(3)2(n))), where n is the number of individuals. Thus it is possible to exhaustively search the space of all pedigrees of up to thirty individuals for one that maximizes the likelihood. A priori age and sex information can be used if available, but is not essential. The algorithm is applied in a simulation study, and to some real data on humans.
Collapse
Affiliation(s)
- Robert G Cowell
- Faculty of Actuarial Science and Insurance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UK.
| |
Collapse
|
35
|
Biedermann A, Bozza S, Taroni F. Probabilistic evidential assessment of gunshot residue particle evidence (Part I): likelihood ratio calculation and case pre-assessment using Bayesian networks. Forensic Sci Int 2009; 191:24-35. [PMID: 19592185 DOI: 10.1016/j.forsciint.2009.06.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2008] [Revised: 05/23/2009] [Accepted: 06/05/2009] [Indexed: 10/20/2022]
Abstract
Well developed experimental procedures currently exist for retrieving and analyzing particle evidence from hands of individuals suspected of being associated with the discharge of a firearm. Although analytical approaches (e.g. automated Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDS) microanalysis) allow the determination of the presence of elements typically found in gunshot residue (GSR) particles, such analyses provide no information about a given particle's actual source. Possible origins for which scientists may need to account for are a primary exposure to the discharge of a firearm or a secondary transfer due to a contaminated environment. In order to approach such sources of uncertainty in the context of evidential assessment, this paper studies the construction and practical implementation of graphical probability models (i.e. Bayesian networks). These can assist forensic scientists in making the issue tractable within a probabilistic perspective. The proposed models focus on likelihood ratio calculations at various levels of detail as well as case pre-assessment.
Collapse
Affiliation(s)
- A Biedermann
- The University of Lausanne, Ecole des Sciences Criminelles, Institut de Police Scientifique, 1015 Lausanne-Dorigny, Switzerland.
| | | | | |
Collapse
|
36
|
Green PJ, Mortera J. Sensitivity of inferences in forensic genetics to assumptions about founding genes. Ann Appl Stat 2009. [DOI: 10.1214/09-aoas235] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
37
|
Abstract
This study extends the current use of Bayesian networks by incorporating the effects of allelic dependencies in paternity calculations. The use of object-oriented networks greatly simplify the process of building and interpreting forensic identification models, allowing researchers to solve new, more complex problems. We explore two paternity examples: the most common scenario where DNA evidence is available from the alleged father, the mother and the child; a more complex casewhere DNA is not available from the alleged father, but is available from the alleged father's brother. Object-oriented networks are built, using HUGIN, for each example which incorporate the effects of allelic dependence caused by evolutionary relatedness.
Collapse
Affiliation(s)
- Amanda B Hepler
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, United Kingdom.
| | | |
Collapse
|
38
|
Validation of software for calculating the likelihood ratio for parentage and kinship. Forensic Sci Int Genet 2008; 3:112-8. [PMID: 19215880 DOI: 10.1016/j.fsigen.2008.11.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2008] [Revised: 09/19/2008] [Accepted: 11/17/2008] [Indexed: 11/23/2022]
Abstract
Although the likelihood ratio is a well-known statistical technique, commercial off-the-shelf (COTS) software products for its calculation are not sufficiently validated to suit general requirements for the competence of testing and calibration laboratories (EN/ISO/IEC 17025:2005 norm) per se. The software in question can be considered critical as it directly weighs the forensic evidence allowing judges to decide on guilt or innocence or to identify person or kin (i.e.: in mass fatalities). For these reasons, accredited laboratories shall validate likelihood ratio software in accordance with the above norm. To validate software for calculating the likelihood ratio in parentage/kinship scenarios I assessed available vendors, chose two programs (Paternity Index and familias) for testing, and finally validated them using tests derived from elaboration of the available guidelines for the field of forensics, biomedicine, and software engineering. MS Excel calculation using known likelihood ratio formulas or peer-reviewed results of difficult paternity cases were used as a reference. Using seven testing cases, it was found that both programs satisfied the requirements for basic paternity cases. However, only a combination of two software programs fulfills the criteria needed for our purpose in the whole spectrum of functions under validation with the exceptions of providing algebraic formulas in cases of mutation and/or silent allele.
Collapse
|
39
|
Wolańska-Nowak P, Branicki W, Parys-Proszek A, Kupiec T. Examples of combining genetic evidence—Bayesian network approach. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2008. [DOI: 10.1016/j.fsigss.2007.10.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
40
|
Lauritzen SL, Mazumder A. Informativeness of genetic markers for forensic inference––An information theoretic approach. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2008. [DOI: 10.1016/j.fsigss.2007.10.144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
41
|
|
42
|
Biedermann A, Bozza S, Taroni F. Decision theoretic properties of forensic identification: underlying logic and argumentative implications. Forensic Sci Int 2008; 177:120-32. [PMID: 18187279 DOI: 10.1016/j.forsciint.2007.11.008] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2007] [Accepted: 11/13/2007] [Indexed: 11/29/2022]
Abstract
The field of forensic science has profited from recent advances in the elicitation of various kinds probabilistic data. These provide the basis for implementing probabilistic inference procedures (e.g., in terms of likelihood ratios) that address the task of discriminating among competing target propositions. There is ongoing discussion, however, whether forensic identification, that is, a conclusion that associates a potential source (such as an individual or object) with a given item of scientific evidence (e.g., a biological stain or a tool mark), can, if ever, be based on purely probabilistic argument. With regard to this issue, the present paper proposes to analyze the process of forensic identification from a decision theoretic point of view. Existing probabilistic inference procedures are used therein as an integral part. The idea underlying the proposed analyses is that inference and decision are connected in the sense that the former is the point of departure for the latter. As such the approach forms a coordinated whole, that is a framework also known in the context as 'full Bayesian (decision) approach'. This study points out that, as a logical extension to purely probabilistic reasoning, a decision theoretic conceptualization of forensic identification allows the content and structure of arguments to be examined from a reasonably distinct perspective and common fallacious interpretations to be avoided.
Collapse
Affiliation(s)
- A Biedermann
- The University of Lausanne, Ecole des Sciences Criminelles, Institut de Police Scientifique, le Batochime, 1015 Lausanne-Dorigny, Switzerland.
| | | | | |
Collapse
|
43
|
Vicard P, Dawid AP, Mortera J, Lauritzen SL. Estimating mutation rates from paternity casework. Forensic Sci Int Genet 2007; 2:9-18. [PMID: 19083784 DOI: 10.1016/j.fsigen.2007.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2007] [Accepted: 07/19/2007] [Indexed: 10/22/2022]
Abstract
We present a statistical methodology for making inferences about mutation rates from paternity casework. This takes account of a number of sources of potential bias, including hidden mutation, incomplete family triplets, uncertain paternity status and differing maternal and paternal mutation rates, while allowing a wide variety of mutation models. An object-oriented Bayesian network is used to facilitate computation of the likelihood function for the mutation parameters. This can process either full or summary genotypic information, both from complete putative father-mother-child triplets and from defective cases where only the child and one of its parents are observed. We use a dataset from paternity casework to illustrate the effects on inferences about mutation parameters of various types of biases and the mutation model assumed. In particular, we show that there can be relevant information in cases of unconfirmed paternity, and that excluding these, as has generally been done, can lead to biased conclusions.
Collapse
Affiliation(s)
- P Vicard
- Dipartimento di Economia, Università Roma Tre, Via Silvio D'Amico 77, Roma 00145, Italy.
| | | | | | | |
Collapse
|
44
|
DNA-testing for immigration cases: The risk of erroneous conclusions. Forensic Sci Int 2007; 172:144-9. [DOI: 10.1016/j.forsciint.2006.12.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2006] [Revised: 11/27/2006] [Accepted: 12/03/2006] [Indexed: 11/19/2022]
|
45
|
Ge J, Wang T, Birdwell JD, Chakraborty R. Further remarks on: "Paternity analysis in special fatherless cases without direct testing of alleged father" [FSI 146S (2004) S159-S161] and remarks on it [FSI 163 (2006) 158-160]. Forensic Sci Int 2007; 172:e6-8. [PMID: 17714899 DOI: 10.1016/j.forsciint.2007.07.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2007] [Accepted: 07/04/2007] [Indexed: 11/18/2022]
|
46
|
Baio G, Corradi F. Handling manipulated evidence. Forensic Sci Int 2007; 169:181-7. [PMID: 17029861 DOI: 10.1016/j.forsciint.2006.08.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2005] [Revised: 07/20/2006] [Accepted: 08/25/2006] [Indexed: 10/24/2022]
Abstract
Bayesian Networks have been advocated as useful tools to describe the relations of dependence/independence among random variables and relevant hypotheses in a crime case. Moreover, they have been applied to help the investigator structure the problem and evaluate the impact of the observed evidence, typically with respect to the hypothesis of guilt of a suspect. In this paper we describe a model to handle the possibility that one or more pieces of evidence have been manipulated in order to mislead the investigations. This method is based on causal inference models, although it is developed in a different, specific framework.
Collapse
Affiliation(s)
- Gianluca Baio
- University College London, Department of Statistical Science, Gower Street, London WC1E 6BT, UK.
| | | |
Collapse
|
47
|
Sheehan NA, Egeland T. Structured Incorporation of Prior Information in Relationship Identification Problems. Ann Hum Genet 2007; 71:501-18. [PMID: 17233753 DOI: 10.1111/j.1469-1809.2006.00345.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The objective of this paper is to show how various sources of information can be modelled and integrated to address relationship identification problems. Applications come from areas as diverse as evolution and conservation research, genealogical research in human, plant and animal populations, and forensic problems including paternity cases, identification following disasters, family reunions and immigration issues. We propose assigning a prior probability distribution to the sample space of pedigrees, calculating the likelihood based on DNA data using available software and posterior probabilities using Bayes' Theorem. Our emphasis here is on the modelling of this prior information in a formal and consistent manner. We introduce the distinction between local and global prior information, whereby local information usually applies to particular components of the pedigree and global prior information refers to more general features. When it is difficult to decide on a prior distribution, robustness to various choices should be studied. When suitable prior information is not available, a flat prior can be used which will then correspond to a strict likelihood approach. In practice, prior information is often considered for these problems, but in a generally ad hoc manner. This paper offers a consistent alternative. We emphasise that many practical problems can be addressed using freely available software.
Collapse
Affiliation(s)
- N A Sheehan
- Department of Health Sciences, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | | |
Collapse
|
48
|
|
49
|
Vicard P, Dawid AP. Remarks on: “Paternity analysis in special fatherless cases without direct testing of alleged father” [Forensic Science International 146S (2004) S159–S161]. Forensic Sci Int 2006; 163:158-60. [PMID: 16376502 DOI: 10.1016/j.forsciint.2005.11.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2005] [Revised: 11/02/2005] [Accepted: 11/13/2005] [Indexed: 11/16/2022]
|
50
|
Biedermann A, Taroni F. A probabilistic approach to the joint evaluation of firearm evidence and gunshot residues. Forensic Sci Int 2006; 163:18-33. [PMID: 16332419 DOI: 10.1016/j.forsciint.2005.11.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2005] [Revised: 11/01/2005] [Accepted: 11/01/2005] [Indexed: 10/25/2022]
Abstract
The present paper addresses issues that affect both the separate as well as the joint evaluation of firearm evidence (i.e., marks) and gunshot residues (GSR). Mark evidence will be used as a basis to discriminate among barrels through which a bullet in question might have been shot whereas GSR will be used to draw inferences about the distance of firing. Particular attention is drawn to the coherent handling of uncertainties associated with the various parameters considered within each item of evidence. The proposed analysis relies on a probabilistic viewpoint that uses graphical models (i.e., Bayesian networks) as an aid to cope with the complexity induced by the number of variables considered. The paper discusses how an approach based on a probabilistic network environment can be used for the formal analysis and construction of arguments. Emphasis is made on the gain of insight into structural dependencies that may be uncovered when the evaluative process is extended beyond single items of scientific evidence.
Collapse
Affiliation(s)
- A Biedermann
- Ecole des Sciences Criminelles, Institut de Police Scientifique, The University of Lausanne, le Batochime, 1015 Lausanne-Dorigny, Switzerland.
| | | |
Collapse
|