1
|
Bleka Ø, Prieto L, Gill P. EFMrep: An extension of EuroForMix for improved combination of STR DNA mixture profiles. Forensic Sci Int Genet 2022; 61:102771. [DOI: 10.1016/j.fsigen.2022.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/19/2022] [Accepted: 08/25/2022] [Indexed: 11/04/2022]
|
2
|
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.
Collapse
|
3
|
Bille T, Coble MD, Bright JA. Exploring the advantages of amplifying the entire extract versus splitting the extract and interpreting replicates using a continuous model of interpretation. AUST J FORENSIC SCI 2021. [DOI: 10.1080/00450618.2021.1882568] [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]
Affiliation(s)
- Todd Bille
- United States Bureau of Alcohol, Tobacco, Firearms, and Explosives, National Laboratory Center, Beltsville, MD, USA
| | - Michael D. Coble
- Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Jo-Anne Bright
- Forensic Business Group, Institute of Environmental Science and Research Limited, Auckland, New Zealand
| |
Collapse
|
4
|
Slooten K. The analogy between DNA kinship and DNA mixture evaluation, with applications for the interpretation of likelihood ratios produced by possibly imperfect models. Forensic Sci Int Genet 2020; 52:102449. [PMID: 33517022 DOI: 10.1016/j.fsigen.2020.102449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/19/2020] [Accepted: 12/01/2020] [Indexed: 12/22/2022]
Abstract
Two main applications of forensic DNA analysis are the investigation of possible relatedness and the investigation whether a person left DNA in a trace. Both of these are usually carried out by the calculation of likelihood ratios. In the kinship case, it is standard to let the likelihood ratio express the support in favour of the investigated relatedness versus no relatedness, and in the investigation of traces, one by default compares the hypothesis that the person of interest contributed DNA, versus that he is unrelated to any of the actual contributors. In both cases however, we can also view the probabilistic procedure as an inference of the profile of the person we look for: in other words, in both cases we carry out probabilistic genotyping. In this article we use this general analogy to develop various more specific analogies between kinship and mixture likelihood ratios. These analogies help to understand the concepts that play a role, and also to understand the importance of the statistical modeling needed for DNA mixtures. In this article, we apply our findings to consider what we can and cannot conclude from a likelihood ratio in favour of contribution to a mixed DNA profile, if that is computed by a model whose specifics are not entirely known to us, or where we do not know whether they provide a good description of the stochastic effects involved in the generation of DNA trace profiles. We show that, if unrelated individuals are adequately modeled, we can give bounds on how often LR's coming from certain types of black box models may arise, both for persons who are actual contributors and who are unrelated. In particular we show that no model, provided it satisfies basic requirements, can overestimate the evidence found for actual contributors both often and strongly.
Collapse
Affiliation(s)
- Klaas Slooten
- Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands; VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.
| |
Collapse
|
5
|
Duke KR, Myers SP. Systematic evaluation of STRmix™ performance on degraded DNA profile data. Forensic Sci Int Genet 2019; 44:102174. [PMID: 31707114 DOI: 10.1016/j.fsigen.2019.102174] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 08/28/2019] [Accepted: 10/04/2019] [Indexed: 12/22/2022]
Abstract
This study examined the DNA degradation modeling capacity of STRmix™, a widely implemented DNA interpretation software program. As a part of the CAL DOJ STRmix™ v2.4 validation, a large volume of STR profile data was generated from intact template DNA exposed to DNase I for a series of increasing time intervals. The resulting degraded profile data was analyzed with STRmix™ v2.4, and the efficacy of the analysis was assessed, both in terms of how the degradation modeling parameter values from the STRmix™ analysis compared to ground truth values, and how the weight-of-evidence statistics calculated for degraded profiles compared to those calculated for corresponding intact profiles. An additional set of differentially degraded mixture data was generated in silico to further challenge the STRmix™ degradation model, as well as to determine the extent to which end-user adjustment of the model's application can assist in resolving analysis problems that arise when high levels of degradation are observed in a profile. This work demonstrates that the degradation model in STRmix™ is capable of addressing a wide range of degraded STR profile data. The assessment expands the range of samples that have been rigorously examined using probabilistic genotyping approaches, as called for by forensic advisory bodies such as the United States President's Council of Advisors on Science and Technology.
Collapse
Affiliation(s)
- Kyle R Duke
- California Department of Justice Bureau of Forensic Services Jan Bashinski DNA Laboratory, 1001 W Cutting Boulevard, Richmond, CA, 94804, United States.
| | - Steven P Myers
- California Department of Justice Bureau of Forensic Services Jan Bashinski DNA Laboratory, 1001 W Cutting Boulevard, Richmond, CA, 94804, United States
| |
Collapse
|
6
|
Bright JA, Jones Dukes M, Pugh SN, Evett IW, Buckleton JS. Applying calibration to LRs produced by a DNA interpretation software. AUST J FORENSIC SCI 2019. [DOI: 10.1080/00450618.2019.1682668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Jo-Anne Bright
- Institute of Environmental Science and Research Limited, Auckland, New Zealand
| | | | | | - I. W. Evett
- Principal Forensic Services Ltd., Bromley, UK
| | - J. S. Buckleton
- Institute of Environmental Science and Research Limited, Auckland, New Zealand
| |
Collapse
|
7
|
An assessment of the performance of the probabilistic genotyping software EuroForMix: Trends in likelihood ratios and analysis of Type I & II errors. Forensic Sci Int Genet 2019; 42:31-38. [DOI: 10.1016/j.fsigen.2019.06.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 01/25/2023]
|
8
|
STRmix™ put to the test: 300 000 non-contributor profiles compared to four-contributor DNA mixtures and the impact of replicates. Forensic Sci Int Genet 2019; 41:24-31. [DOI: 10.1016/j.fsigen.2019.03.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 03/19/2019] [Accepted: 03/20/2019] [Indexed: 12/24/2022]
|
9
|
Alfonse LE, Garrett AD, Lun DS, Duffy KR, Grgicak CM. A large-scale dataset of single and mixed-source short tandem repeat profiles to inform human identification strategies: PROVEDIt. Forensic Sci Int Genet 2018; 32:62-70. [DOI: 10.1016/j.fsigen.2017.10.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 09/07/2017] [Accepted: 10/20/2017] [Indexed: 01/15/2023]
|
10
|
Manabe S, Morimoto C, Hamano Y, Fujimoto S, Tamaki K. Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model. PLoS One 2017; 12:e0188183. [PMID: 29149210 PMCID: PMC5693437 DOI: 10.1371/journal.pone.0188183] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 11/02/2017] [Indexed: 02/01/2023] Open
Abstract
In criminal investigations, forensic scientists need to evaluate DNA mixtures. The estimation of the number of contributors and evaluation of the contribution of a person of interest (POI) from these samples are challenging. In this study, we developed a new open-source software “Kongoh” for interpreting DNA mixture based on a quantitative continuous model. The model uses quantitative information of peak heights in the DNA profile and considers the effect of artifacts and allelic drop-out. By using this software, the likelihoods of 1–4 persons’ contributions are calculated, and the most optimal number of contributors is automatically determined; this differs from other open-source software. Therefore, we can eliminate the need to manually determine the number of contributors before the analysis. Kongoh also considers allele- or locus-specific effects of biological parameters based on the experimental data. We then validated Kongoh by calculating the likelihood ratio (LR) of a POI’s contribution in true contributors and non-contributors by using 2–4 person mixtures analyzed through a 15 short tandem repeat typing system. Most LR values obtained from Kongoh during true-contributor testing strongly supported the POI’s contribution even for small amounts or degraded DNA samples. Kongoh correctly rejected a false hypothesis in the non-contributor testing, generated reproducible LR values, and demonstrated higher accuracy of the estimated number of contributors than another software based on the quantitative continuous model. Therefore, Kongoh is useful in accurately interpreting DNA evidence like mixtures and small amounts or degraded DNA samples.
Collapse
Affiliation(s)
- Sho Manabe
- Department of Forensic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Chie Morimoto
- Department of Forensic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuya Hamano
- Department of Forensic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Forensic Science Laboratory, Kyoto Prefectural Police Headquarters, Kyoto, Japan
| | - Shuntaro Fujimoto
- Department of Forensic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Keiji Tamaki
- Department of Forensic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- * E-mail:
| |
Collapse
|
11
|
Coble M, Buckleton J, Butler J, Egeland T, Fimmers R, Gill P, Gusmão L, Guttman B, Krawczak M, Morling N, Parson W, Pinto N, Schneider P, Sherry S, Willuweit S, Prinz M. DNA Commission of the International Society for Forensic Genetics: Recommendations on the validation of software programs performing biostatistical calculations for forensic genetics applications. Forensic Sci Int Genet 2016; 25:191-197. [DOI: 10.1016/j.fsigen.2016.09.002] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 09/02/2016] [Indexed: 10/21/2022]
|
12
|
Bieber FR, Buckleton JS, Budowle B, Butler JM, Coble MD. Evaluation of forensic DNA mixture evidence: protocol for evaluation, interpretation, and statistical calculations using the combined probability of inclusion. BMC Genet 2016; 17:125. [PMID: 27580588 PMCID: PMC5007818 DOI: 10.1186/s12863-016-0429-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 08/18/2016] [Indexed: 12/02/2022] Open
Abstract
Background The evaluation and interpretation of forensic DNA mixture evidence faces greater interpretational challenges due to increasingly complex mixture evidence. Such challenges include: casework involving low quantity or degraded evidence leading to allele and locus dropout; allele sharing of contributors leading to allele stacking; and differentiation of PCR stutter artifacts from true alleles. There is variation in statistical approaches used to evaluate the strength of the evidence when inclusion of a specific known individual(s) is determined, and the approaches used must be supportable. There are concerns that methods utilized for interpretation of complex forensic DNA mixtures may not be implemented properly in some casework. Similar questions are being raised in a number of U.S. jurisdictions, leading to some confusion about mixture interpretation for current and previous casework. Results Key elements necessary for the interpretation and statistical evaluation of forensic DNA mixtures are described. Given the most common method for statistical evaluation of DNA mixtures in many parts of the world, including the USA, is the Combined Probability of Inclusion/Exclusion (CPI/CPE). Exposition and elucidation of this method and a protocol for use is the focus of this article. Formulae and other supporting materials are provided. Conclusions Guidance and details of a DNA mixture interpretation protocol is provided for application of the CPI/CPE method in the analysis of more complex forensic DNA mixtures. This description, in turn, should help reduce the variability of interpretation with application of this methodology and thereby improve the quality of DNA mixture interpretation throughout the forensic community. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0429-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- 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.
| | - John S Buckleton
- ESR (The Institute of Environmental Science and Research), Private Bag 92021, Auckland, 1142, New Zealand.,Statistical Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Mail Stop 8980, Gaithersburg, MD, 20899, USA
| | - Bruce Budowle
- Department of Molecular and Medical Genetics, Institute of Applied Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX, 76107, USA
| | - John M Butler
- National Institute of Standards and Technology, Special Programs Office, 100 Bureau Drive, Mail Stop 4701, Gaithersburg, MD, 20899, USA
| | - Michael D Coble
- National Institute of Standards and Technology, Applied Genetics Group, 100 Bureau Drive, Mail Stop 8314, Gaithersburg, MD, 20899, USA
| |
Collapse
|
13
|
Gittelson S, Steffen CR, Coble MD. Low-template DNA: A single DNA analysis or two replicates? Forensic Sci Int 2016; 264:139-45. [PMID: 27131143 PMCID: PMC5225751 DOI: 10.1016/j.forsciint.2016.04.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Revised: 03/12/2016] [Accepted: 04/05/2016] [Indexed: 11/28/2022]
Abstract
This study investigates the following two questions: (1) Should the DNA analyst concentrate the DNA extract into a single amplification or should he/she split it up to do two replicates? (2) Given the electropherogram obtained from a first analysis, is it worthwhile for the DNA analyst to invest in obtaining a second replicate? A decision-theoretic approach addresses these questions by quantitatively expressing the expected net gain (ENG) of each DNA analysis of interest. The results indicate that two replicates generally have a greater ENG than a single DNA analysis for DNA quantities capable of producing two replicates having an average allelic peak height as low as 43rfu. This supports the position that two replicates increase the information content with regard to a single analysis.
Collapse
Affiliation(s)
- Simone Gittelson
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, United States.
| | - Carolyn R Steffen
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, United States
| | - Michael D Coble
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, United States
| |
Collapse
|
14
|
Steele CD, Greenhalgh M, Balding DJ. Evaluation of low-template DNA profiles using peak heights. Stat Appl Genet Mol Biol 2016; 15:431-445. [DOI: 10.1515/sagmb-2016-0038] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractIn recent years statistical models for the analysis of complex (low-template and/or mixed) DNA profiles have moved from using only presence/absence information about allelic peaks in an electropherogram, to quantitative use of peak heights. This is challenging because peak heights are very variable and affected by a number of factors. We present a new peak-height model with important novel features, including over- and double-stutter, and a new approach to dropin. Our model is incorporated in open-source
Collapse
|
15
|
Validating multiplexes for use in conjunction with modern interpretation strategies. Forensic Sci Int Genet 2016; 20:6-19. [DOI: 10.1016/j.fsigen.2015.09.011] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2015] [Revised: 09/21/2015] [Accepted: 09/22/2015] [Indexed: 11/18/2022]
|