1
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Greenspoon SA, Schiermeier-Wood L, Jenkins BC. A tale of two PG systems: A comparison of the two most widely used continuous probabilistic genotyping systems in the United States. J Forensic Sci 2024. [PMID: 38899548 DOI: 10.1111/1556-4029.15571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/23/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024]
Abstract
The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two-, three-, and four-person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non-supportive, or inconclusive) for ~91% of contributor-specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor-specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low-template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non-supportive) and 12 were either inconclusive versus non-supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab-defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low-template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low-template, degraded contributors, and the deviation is generally predictable.
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2
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Dror IE. The most consistent finding in forensic science is inconsistency. J Forensic Sci 2023; 68:1851-1855. [PMID: 37658789 DOI: 10.1111/1556-4029.15369] [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: 04/08/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023]
Abstract
The most consistent finding in many forensic science domains is inconsistency (i.e., lack of reliability, reproducibility, repeatability, and replicability). The lack of consistency is a major problem, both from a scientific and a criminal justice point of view. Examining forensic conclusion data, from across many forensic domains, highlights the underlying cognitive issues and offers a better understanding of the issues and challenges. Such insights enable the development of ways to minimize these inconsistencies and move forward. The aim is to highlight the problem, so that it can be minimized and the reliability of forensic science evidence can be improved.
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Affiliation(s)
- Itiel E Dror
- Cognitive Consultants International (CCI-HQ), London, UK
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3
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Adamowicz MS, Rambo TN, Clarke JL. Internal Validation of MaSTR™ Probabilistic Genotyping Software for the Interpretation of 2–5 Person Mixed DNA Profiles. Genes (Basel) 2022; 13:genes13081429. [PMID: 36011340 PMCID: PMC9408203 DOI: 10.3390/genes13081429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/07/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Mixed human deoxyribonucleic acid (DNA) samples present one of the most challenging pieces of evidence that a forensic analyst can encounter. When multiple contributors, stochastic amplification, and allele drop-out further complicate the mixture profile, interpretation by hand becomes unreliable and statistical analysis problematic. Probabilistic genotyping software has provided a tool to address complex mixture interpretation and provide likelihood ratios for defined sets of propositions. The MaSTR™ software is a fully continuous probabilistic system that considers a wide range of STR profile data to provide likelihood ratios on DNA mixtures. Mixtures with two to five contributors and a range of component ratios and allele peak heights were created to test the validity of MaSTR™ with data similar to real casework. Over 280 different mixed DNA profiles were used to perform more than 2600 analyses using different sets of propositions and numbers of contributors. The results of the analyses demonstrated that MaSTR™ provided accurate and precise statistical data on DNA mixtures with up to five contributors, including minor contributors with stochastic amplification effects. Tests for both Type I and Type II errors were performed. The findings in this study support that MaSTR™ is a robust tool that meets the current standards for probabilistic genotyping.
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4
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Quantification of forensic genetic evidence: Comparison of results obtained by qualitative and quantitative software for real casework samples. Forensic Sci Int Genet 2022; 59:102715. [DOI: 10.1016/j.fsigen.2022.102715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/29/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022]
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5
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Microhaplotype and Y-SNP/STR (MY): A novel MPS-based system for genotype pattern recognition in two-person DNA mixtures. Forensic Sci Int Genet 2022; 59:102705. [DOI: 10.1016/j.fsigen.2022.102705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 03/10/2022] [Accepted: 04/10/2022] [Indexed: 12/13/2022]
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6
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Gill P, Benschop C, Buckleton J, Bleka Ø, Taylor D. A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™. Genes (Basel) 2021; 12:1559. [PMID: 34680954 PMCID: PMC8535381 DOI: 10.3390/genes12101559] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 11/24/2022] Open
Abstract
Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.
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Affiliation(s)
- Peter Gill
- Forensic Genetics Research Group, Department of Forensic Sciences, Oslo University Hospital, 0372 Oslo, Norway;
- Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, 0315 Oslo, Norway
| | - Corina Benschop
- Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands;
| | - John Buckleton
- Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand;
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand
| | - Øyvind Bleka
- Forensic Genetics Research Group, Department of Forensic Sciences, Oslo University Hospital, 0372 Oslo, Norway;
| | - Duncan Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia;
- School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
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7
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Riman S, Iyer H, Vallone PM. Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset. PLoS One 2021; 16:e0256714. [PMID: 34534241 PMCID: PMC8448353 DOI: 10.1371/journal.pone.0256714] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/07/2021] [Indexed: 11/30/2022] Open
Abstract
A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.
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Affiliation(s)
- Sarah Riman
- Applied Genetics Group, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - Hari Iyer
- Statistical Design, Analysis, Modeling Group, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
| | - Peter M. Vallone
- Applied Genetics Group, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
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8
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Statistefix 4.0: A novel probabilistic software tool. Forensic Sci Int Genet 2021; 55:102570. [PMID: 34474323 DOI: 10.1016/j.fsigen.2021.102570] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 01/09/2023]
Abstract
Latest innovations indicate that continuous tools are promising DNA trace assessment methods. In this study, we present the continuous software solution Statistefix 4.0. The software supports DNA experts in deducing DNA profiles for database queries and can help to preselect DNA samples suitable for further processing using advanced probabilistic search engines. The novel tool weights genotype contributions and deduces major contributors from high- and low-quality DNA traces. Peak height, degradation, stutter as well as allelic drop-in/-out events are incorporated in the statistical model. We analyzed reference and casework samples as well as artificially generated mixture samples for software evaluation. The tool offers the completely automated assessment of reference and mixture samples. Deconvolution outcomes of mixtures are compared with EuroForMix, GenoProof Mixture 3 and STRmix™. Data show that Statistefix 4.0 is as successful as analogously tested and implemented software. Deduced DNA profiles from casework samples highlight the potential benefit in routine casework. Statistefix 4.0 is freely available, works with replicates of different autosomal kits and enables bulk sample processing. This inter-laboratory study includes a variety of sample types and indicates a timesaving, robust and easily implemented software that supports DNA analysts in evaluating DNA traces.
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9
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Sheth N, Swaminathan H, Gonzalez AJ, Duffy KR, Grgicak CM. Towards developing forensically relevant single-cell pipelines by incorporating direct-to-PCR extraction: compatibility, signal quality, and allele detection. Int J Legal Med 2021; 135:727-738. [PMID: 33484330 DOI: 10.1007/s00414-021-02503-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 01/04/2021] [Indexed: 12/24/2022]
Abstract
Current analysis of forensic DNA stains relies on the probabilistic interpretation of bulk-processed samples that represent mixed profiles consisting of an unknown number of potentially partial representations of each contributor. Single-cell methods, in contrast, offer a solution to the forensic DNA mixture problem by incorporating a step that separates cells before extraction. A forensically relevant single-cell pipeline relies on efficient direct-to-PCR extractions that are compatible with standard downstream forensic reagents. Here we demonstrate the feasibility of implementing single-cell pipelines into the forensic process by exploring four metrics of electropherogram (EPG) signal quality-i.e., allele detection rates, peak heights, peak height ratios, and peak height balance across low- to high-molecular-weight short tandem repeat (STR) markers-obtained with four direct-to-PCR extraction treatments and a common post-PCR laboratory procedure. Each treatment was used to extract DNA from 102 single buccal cells, whereupon the amplification reagents were immediately added to the tube and the DNA was amplified/injected using post-PCR conditions known to elicit a limit of detection (LoD) of one DNA molecule. The results show that most cells, regardless of extraction treatment, rendered EPGs with at least a 50% true positive allele detection rate and that allele drop-out was not cell independent. Statistical tests demonstrated that extraction treatments significantly impacted all metrics of EPG quality, where the Arcturus® PicoPure™ extraction method resulted in the lowest median allele drop-out rate, highest median average peak height, highest median average peak height ratio, and least negative median values of EPG sloping for GlobalFiler™ STR loci amplified at half volume. We, therefore, conclude the feasibility of implementing single-cell pipelines for casework purposes and demonstrate that inferential systems assuming cell independence will not be appropriate in the probabilistic interpretation of a collection of single-cell EPGs.
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Affiliation(s)
- Nidhi Sheth
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA
| | - Harish Swaminathan
- Biomedical Forensic Sciences Program, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Amanda J Gonzalez
- Department of Chemistry, Rutgers University, 315 Penn Street R306C, Camden, NJ, 08102, USA
| | - Ken R Duffy
- Hamilton Institute, Maynooth University, Maynooth, Ireland
| | - Catherine M Grgicak
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
- Biomedical Forensic Sciences Program, Boston University School of Medicine, Boston, MA, 02118, USA.
- Department of Chemistry, Rutgers University, 315 Penn Street R306C, Camden, NJ, 08102, USA.
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10
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Dror IE. Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias. Anal Chem 2020; 92:7998-8004. [PMID: 32508089 DOI: 10.1021/acs.analchem.0c00704] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Fallacies about the nature of biases have shadowed a proper cognitive understanding of biases and their sources, which in turn lead to ways that minimize their impact. Six such fallacies are presented: it is an ethical issue, only applies to "bad apples", experts are impartial and immune, technology eliminates bias, blind spot, and the illusion of control. Then, eight sources of bias are discussed and conceptualized within three categories: (A) factors that relate to the specific case and analysis, which include the data, reference materials, and contextual information, (B) factors that relate to the specific person doing the analysis, which include past experience base rates, organizational factors, education and training, and personal factors, and lastly, (C) cognitive architecture and human nature that impacts all of us. These factors can impact what the data are (e.g., how data are sampled and collected, or what is considered as noise and therefore disregarded), the actual results (e.g., decisions on testing strategies, how analysis is conducted, and when to stop testing), and the conclusions (e.g., interpretation of the results). The paper concludes with specific measures that can minimize these biases.
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Affiliation(s)
- Itiel E Dror
- University College London (UCL), London WC1H 9EZ, United Kingdom
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11
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Dror IE. The Error in "Error Rate": Why Error Rates Are So Needed, Yet So Elusive. J Forensic Sci 2020; 65:1034-1039. [PMID: 32315087 DOI: 10.1111/1556-4029.14435] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 03/22/2020] [Accepted: 03/24/2020] [Indexed: 12/14/2022]
Abstract
Establishing error rates is crucial for knowing how well one is performing, determining whether improvement is needed, measuring whether interventions are effective, as well as for providing transparency. However, the flurry of activities in establishing error rates for the forensic sciences has largely overlooked some fundamental issues that make error rates a problematic construct and limit the ability to obtain a meaningful error rate. These include knowing the ground truth, establishing appropriate databases, determining what counts as an error, characterizing what is an acceptable error rate, ecological validity, and transparency within the adversarial legal system. Without addressing these practical and theoretical challenges, the very notion of a meaningful error rate is limited.
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Affiliation(s)
- Itiel E Dror
- UCL Centre for the Forensic Sciences, University College London, London, U.K.,Cognitive Consultants Internationals (CCI-HQ), London, U.K
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12
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Butler JM, Willis S. Interpol review of forensic biology and forensic DNA typing 2016-2019. Forensic Sci Int Synerg 2020; 2:352-367. [PMID: 33385135 PMCID: PMC7770417 DOI: 10.1016/j.fsisyn.2019.12.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 12/10/2019] [Indexed: 12/23/2022]
Abstract
This review paper covers the forensic-relevant literature in biological sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.
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13
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Hicks T, Biedermann A, Taroni F, Champod C. Problematic reporting in DNA cases: the need for accredited formats and certified reporting competence. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2019. [DOI: 10.1016/j.fsigss.2019.09.079] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Buckleton JS, Bright JA, Ciecko A, Kruijver M, Mallinder B, Magee A, Malsom S, Moretti T, Weitz S, Bille T, Noël S, Oefelein RH, Peck B, Kalafut T, Taylor DA. Response to: Commentary on: Bright et al. (2018) Internal validation of STRmix™ - A multi laboratory response to PCAST, Forensic Science International: Genetics, 34: 11-24. Forensic Sci Int Genet 2019; 44:102198. [PMID: 31710898 DOI: 10.1016/j.fsigen.2019.102198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/28/2019] [Accepted: 10/30/2019] [Indexed: 10/25/2022]
Affiliation(s)
- John S Buckleton
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142, New Zealand; University of Auckland, Department of Statistics, Auckland, New Zealand
| | - Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142, New Zealand.
| | - Anne Ciecko
- Midwest Regional Forensic Laboratory, Andover, Minnesota, United States
| | - Maarten Kruijver
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142, New Zealand
| | | | | | - Simon Malsom
- Key Forensic Services Ltd., UK, Norwich Laboratory, United Kingdom
| | | | - Steven Weitz
- US Bureau of Alcohol, Tobacco, Firearms, Explosives Laboratory (ATF), United States
| | - Todd Bille
- US Bureau of Alcohol, Tobacco, Firearms, Explosives Laboratory (ATF), United States
| | - Sarah Noël
- Laboratoire de Sciences Judiciaires et de Médecine Légale, Direction Biologie/ADN, 1701 Parthenais, Montréal, Québec, H2K 3S7, Canada
| | | | - Brian Peck
- Center of Forensic Science Toronto, Canada
| | | | - Duncan A Taylor
- Forensic Science South Australia, Australia; University of Adelaide, South Australia, Australia
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15
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Murrie DC, Gardner BO, Kelley S, Dror IE. Perceptions and estimates of error rates in forensic science: A survey of forensic analysts. Forensic Sci Int 2019; 302:109887. [PMID: 31404811 DOI: 10.1016/j.forsciint.2019.109887] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 07/04/2019] [Accepted: 07/20/2019] [Indexed: 11/29/2022]
Abstract
Every scientific technique features some error, and legal standards for the admissibility of scientific evidence (e.g., Daubert v. Merrill Dow Pharmaceuticals, Inc., 1993; Kumho Tire Co v. Carmichael, 1999) guide trial courts to consider known error rates. However, recent reviews of forensic science conclude that error rates for some common techniques are not well-documented or even established (e.g., NAS, 2009; PCAST, 2016). Furthermore, many forensic analysts have historically denied the presence of error in their field. Therefore, it is important to establish what forensic scientists actually know or believe about errors rates in their disciplines. We surveyed 183 practicing forensic analysts to examine what they think and estimate about error rates in their various disciplines. Results revealed that analysts perceive all types of errors to be rare, with false positive errors even more rare than false negatives. Likewise, analysts typically reported that they prefer to minimize the risk of false positives over false negatives. Most analysts could not specify where error rates for their discipline were documented or published. Their estimates of error in their fields were widely divergent - with some estimates unrealistically low.
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Affiliation(s)
- Daniel C Murrie
- Institute of Law, Psychiatry, and Public Policy, University of Virginia, United States.
| | - Brett O Gardner
- Institute of Law, Psychiatry, and Public Policy, University of Virginia, United States
| | - Sharon Kelley
- Institute of Law, Psychiatry, and Public Policy, University of Virginia, United States
| | - Itiel E Dror
- Center for the Forensic Sciences, University College London, United Kingdom
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17
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Bright JA, Cheng K, Kerr Z, McGovern C, Kelly H, Moretti TR, Smith MA, Bieber FR, Budowle B, Coble MD, Alghafri R, Allen PS, Barber A, Beamer V, Buettner C, Russell M, Gehrig C, Hicks T, Charak J, Cheong-Wing K, Ciecko A, Davis CT, Donley M, Pedersen N, Gartside B, Granger D, Greer-Ritzheimer M, Reisinger E, Kennedy J, Grammer E, Kaplan M, Hansen D, Larsen HJ, Laureano A, Li C, Lien E, Lindberg E, Kelly C, Mallinder B, Malsom S, Yacovone-Margetts A, McWhorter A, Prajapati SM, Powell T, Shutler G, Stevenson K, Stonehouse AR, Smith L, Murakami J, Halsing E, Wright D, Clark L, Taylor DA, Buckleton J. STRmix™ collaborative exercise on DNA mixture interpretation. Forensic Sci Int Genet 2019; 40:1-8. [PMID: 30665115 DOI: 10.1016/j.fsigen.2019.01.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/23/2018] [Accepted: 01/13/2019] [Indexed: 10/27/2022]
Abstract
An intra and inter-laboratory study using the probabilistic genotyping (PG) software STRmix™ is reported. Two complex mixtures from the PROVEDIt set, analysed on an Applied Biosystems™ 3500 Series Genetic Analyzer, were selected. 174 participants responded. For Sample 1 (low template, in the order of 200 rfu for major contributors) five participants described the comparison as inconclusive with respect to the POI or excluded him. Where LRs were assigned, the point estimates ranging from 2 × 104 to 8 × 106. For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 1028 to 2 × 1029. Where LRs were calculated, the differences between participants can be attributed to (from largest to smallest impact): This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.
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Affiliation(s)
- Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand.
| | - Kevin Cheng
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand
| | - Zane Kerr
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand
| | - Catherine McGovern
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand
| | - Hannah Kelly
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand
| | - Tamyra R Moretti
- DNA Support Unit, Federal Bureau of Investigation Laboratory, 2501 Investigation Parkway, Quantico, VA 22135, USA
| | - Michael A Smith
- DNA Support Unit, Federal Bureau of Investigation Laboratory, 2501 Investigation Parkway, Quantico, VA 22135, USA
| | - Frederick R Bieber
- Center for Advanced Molecular Diagnostics, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Bruce Budowle
- Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX, 76107, USA
| | - Michael D Coble
- Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX, 76107, USA
| | - Rashed Alghafri
- General Department of Forensic Sciences and Criminology, Dubai Police G.H.Q., Dubai, United Arab Emirates
| | | | - Amy Barber
- Massachusetts State Police Crime Laboratory, USA
| | | | | | | | - Christian Gehrig
- University Center of Legal Medicine, Lausanne-Geneva (CURML), Switzerland
| | - Tacha Hicks
- School of Criminal Justice, University of Lausanne, Switzerland
| | | | - Kate Cheong-Wing
- Northern Territory Police, Fire and Emergency Services, Australia
| | | | | | | | | | | | - Dominic Granger
- Laboratoire de sciences judiciaires et de médecine légale, Montréal, Canada
| | | | | | | | | | - Marla Kaplan
- Oregon State Police Portland Metro Crime Laboratory, USA
| | | | | | | | | | - Eugene Lien
- New York City Office of Chief Medical Examiner (OCME), USA
| | | | | | | | | | | | | | | | | | | | - Kate Stevenson
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand
| | | | | | | | | | | | | | - Duncan A Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia; School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia
| | - John Buckleton
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand; University of Auckland, Department of Statistics, Auckland, New Zealand
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18
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Coble MD, Bright JA. Probabilistic genotyping software: An overview. Forensic Sci Int Genet 2019; 38:219-224. [PMID: 30458407 DOI: 10.1016/j.fsigen.2018.11.009] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 11/07/2018] [Accepted: 11/07/2018] [Indexed: 01/08/2023]
Abstract
The interpretation of mixed profiles from DNA evidentiary material is one of the more challenging duties of the forensic scientist. Traditionally, analysts have used a "binary" approach to interpretation where inferred genotypes are either included or excluded from the mixture using a stochastic threshold and other biological parameters such as heterozygote balance, mixture ratio, and stutter ratios. As the sensitivity of STR multiplexes and capillary electrophoresis instrumentation improved over the past 25 years, coupled with the change in the type of evidence being submitted for analysis (from high quality and quantity (often single-source) stains to low quality and quantity (often mixed) "touch" samples), the complexity of DNA profile interpretation has equally increased. This review provides a historical perspective on the movement from binary methods of interpretation to probabilistic methods of interpretation. We describe the two approaches to probabilistic genotyping (semi-continuous and fully continuous) and address issues such as validation and court acceptance. Areas of future needs for probabilistic software are discussed.
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Affiliation(s)
- Michael D Coble
- Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107, USA.
| | - Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand
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NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): Variation observed and lessons learned. Forensic Sci Int Genet 2018; 37:81-94. [DOI: 10.1016/j.fsigen.2018.07.024] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/24/2018] [Accepted: 07/31/2018] [Indexed: 11/19/2022]
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Buckleton JS, Bright JA, Gittelson S, Moretti TR, Onorato AJ, Bieber FR, Budowle B, Taylor DA. The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity. J Forensic Sci 2018; 64:393-405. [PMID: 30132900 DOI: 10.1111/1556-4029.13898] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 07/14/2018] [Accepted: 07/17/2018] [Indexed: 01/08/2023]
Abstract
Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.
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Affiliation(s)
- John S Buckleton
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142, New Zealand.,Department of Statistics, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142, New Zealand
| | - Simone Gittelson
- Centre for Forensic Science, University of Technology Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia
| | - Tamyra R Moretti
- DNA Support Unit, Federal Bureau of Investigation Laboratory, 2501 Investigation Parkway, Quantico, VA, 22135
| | - Anthony J Onorato
- DNA Support Unit, Federal Bureau of Investigation Laboratory, 2501 Investigation Parkway, Quantico, VA, 22135
| | - Frederick R Bieber
- Center for Advanced Molecular Diagnostics, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115
| | - Bruce Budowle
- Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX, 76107
| | - Duncan A Taylor
- Forensic Science South Australia, 21 Divett Place, Adelaide, SA, Australia.,Flinders University - School of Biology, Stuart Road, Bedford Park, Adelaide, SA, Australia
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