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Forensic DNA Mixture Interpretation and Probabilistic Genotyping
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Forensic DNA Mixture Interpretation and Probabilistic Genotyping

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 8090

Special Issue Editor

Dr. Michael Coble

E-Mail Website
Guest Editor
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
Interests: DNA mixture interpretation; probabilistic methods; haploid marker systems; forensic testing

Special Issue Information

Dear Colleagues, 

The interpretation of evidence from mixed DNA profiles represents one of the greatest challenges to the forensic DNA analyst. Recent advances in probabilistic genotyping software have provided a paradigm shift for the interpretation of low-level complex evidence by laboratories. Despite the wide acceptance of probabilistic genotyping software within the scientific community, there are challenges to the admissibility of evidence interpreted by these methods. This Special Issue will give an overview of the challenges of DNA mixture interpretation, probabilistic methods of interpretation, and the statistical reporting from software in the form of the likelihood ratio. Recent advances in software development, software validation, and the foundational validity of probabilistic methods of interpretation will be presented. The Special Issue intends to provide the scientific and legal community with a body of knowledge to mitigate the misunderstandings of these methods. Potential topics for contributions include, but are not limited to, the following:

  • Review of the applications of probabilistic genotyping (PG);
  • Demonstration of software fit for purpose;
  • Review of the methods to establish scientific suitability;
  • Review of DNA mixture interpretation;
  • Implementation of PG;
  • The meaning of LRs;
  • Review of the admissibility of PG.

Dr. Michael Coble
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • DNA mixture interpretation
  • forensic DNA testing
  • probabilistic genotyping software
  • likelihood ratios

Published Papers (4 papers)

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Research

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16 pages, 2248 KiB  
Article
An Investigation into Compound Likelihood Ratios for Forensic DNA Mixtures
by Richard Wivell, Hannah Kelly, Jason Kokoszka, Jace Daniels, Laura Dickson, John Buckleton and Jo-Anne Bright
Genes 2023, 14(3), 714; https://doi.org/10.3390/genes14030714 - 14 Mar 2023
Cited by 1 | Viewed by 1862
Abstract
Simple propositions are defined as those with one POI and the remaining contributors unknown under Hp and all unknown contributors under Ha. Conditional propositions are defined as those with one POI, one or more assumed contributors, and the remaining contributors [...] Read more.
Simple propositions are defined as those with one POI and the remaining contributors unknown under Hp and all unknown contributors under Ha. Conditional propositions are defined as those with one POI, one or more assumed contributors, and the remaining contributors (if any) unknown under Hp, and the assumed contributor(s) and N unknown contributors under Ha. In this study, compound propositions are those with multiple POI and the remaining contributors unknown under Hp and all unknown contributors under Ha. We study the performance of these three proposition sets on thirty-two samples (two laboratories × four NOCs × four mixtures) consisting of four mixtures, each with N = 2, N = 3, N = 4, and N = 5 contributors using the probabilistic genotyping software, STRmix™. In this study, it was found that conditional propositions have a much higher ability to differentiate true from false donors than simple propositions. Compound propositions can misstate the weight of evidence given the propositions strongly in either direction. Full article
(This article belongs to the Special Issue Forensic DNA Mixture Interpretation and Probabilistic Genotyping)
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16 pages, 2909 KiB  
Article
Improving the Utilization of STRmix™ Variance Parameters as Semi-Quantitative Profile Modeling Metrics
by Kyle Duke, Steven Myers, Daniela Cuenca and Jeanette Wallin
Genes 2023, 14(1), 102; https://doi.org/10.3390/genes14010102 - 29 Dec 2022
Viewed by 1781
Abstract
Distributions of the variance parameter values developed during the validation process. Comparisons of these prior distributions to the run-specific average are one measure used by analysts to assess the reliability of a STRmix deconvolution. This study examined the behavior of three different STRmix [...] Read more.
Distributions of the variance parameter values developed during the validation process. Comparisons of these prior distributions to the run-specific average are one measure used by analysts to assess the reliability of a STRmix deconvolution. This study examined the behavior of three different STRmix variance parameters under standard amplification and interpretation conditions, as well as under a variety of challenging conditions, with the goal of making comparisons to the prior distributions more practical and meaningful. Using information found in STRmix v2.8 Interpretation Reports, we plotted the log10 of each variance parameter against the log10 of the template amount of the highest-level contributor (Tc) for a large set of mixture data amplified under standard conditions. We observed nonlinear trends in these plots, which we regressed to fourth-order polynomials, and used the regression data to establish typical ranges for the variance parameters over the Tc range. We then compared the typical variance parameter ranges to log10(variance parameter) v log10(Tc) plots for mixtures amplified and interpreted under a variety of challenging conditions. We observed several distinct patterns to variance parameter shifts in the challenged data interpretations in comparison to the unchallenged data interpretations, as well as distinct shifts in the unchallenged variance parameters away from their prior gamma distribution modes over specific ranges of Tc. These findings suggest that employing empirically determined working ranges for variance parameters may be an improved means of detecting whether aberrations in the interpretation were meaningful enough to trigger greater scrutiny of the electropherogram and genotype interpretation. Full article
(This article belongs to the Special Issue Forensic DNA Mixture Interpretation and Probabilistic Genotyping)
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18 pages, 5086 KiB  
Article
Compound and Conditioned Likelihood Ratio Behavior within a Probabilistic Genotyping Context
by Kyle Duke, Daniela Cuenca, Steven Myers and Jeanette Wallin
Genes 2022, 13(11), 2031; https://doi.org/10.3390/genes13112031 - 04 Nov 2022
Cited by 4 | Viewed by 1585
Abstract
In cases where multiple questioned individuals are separately supported as contributors to a mixed DNA profile, guidance documents recommend performing a comparison to see if there is support for their joint contribution. Anecdotal observations suggest the summed log of the individual likelihood ratios [...] Read more.
In cases where multiple questioned individuals are separately supported as contributors to a mixed DNA profile, guidance documents recommend performing a comparison to see if there is support for their joint contribution. Anecdotal observations suggest the summed log of the individual likelihood ratios (LR), termed the simple LR product, should be roughly equivalent to or less than the log(LR) for the joint likelihood ratio, termed the compound LR. To assist casework analysts in evaluating statistical weights applied to a case at hand, this study assessed how consistently compound LRs conform to an additive behavior when compared to the simple LR product counterparts. Two-, three-, and four-person DNA mixture data, of various mixture proportions and DNA inputs, were interpreted by STRmix® version 2.8 Probabilistic Genotyping Software. Relative magnitudes of LR increases were found to be dependent on both template level and mixture composition. The distribution of log(LR) differences between all compound/simple LR comparisons was ~−2.7 to ~28.3. This level of information gain was similar to that for compound LR comparisons, with and without interpretation conditioning (~−3.2 to ~27.7). In both scenarios, the probability density peaked at approximately 0.5, indicating the information gain from constrained genotype combinations has a comparable impact on the outcome of LR calculations whether the restriction is applied before or after interpretation. Full article
(This article belongs to the Special Issue Forensic DNA Mixture Interpretation and Probabilistic Genotyping)
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Other

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5 pages, 168 KiB  
Brief Report
Study of CTS DNA Proficiency Tests with Regard to DNA Mixture Interpretation: A NIST Scientific Foundation Review
by Todd Bille, Michael D. Coble, Tim Kalafut and John Buckleton
Genes 2022, 13(11), 2171; https://doi.org/10.3390/genes13112171 - 21 Nov 2022
Cited by 1 | Viewed by 1738
Abstract
The National Institute of Standards and Technology has released a document entitled DNA Mixture Interpretation: A NIST Scientific Foundation Review for public comment. This has become known as the Draft NIST Foundation Review. It contains the statement: “Across these 69 data sets, there [...] Read more.
The National Institute of Standards and Technology has released a document entitled DNA Mixture Interpretation: A NIST Scientific Foundation Review for public comment. This has become known as the Draft NIST Foundation Review. It contains the statement: “Across these 69 data sets, there were 80 false negatives and 18 false positives reported from 110,408 possible responses (27,602 participants × two evidence items × two reference items). In the past five years, the number of participants using PGS has grown.” We examine a set of proficiency test results to determine if these NIST statements could be justified. The summary reports for each relevant forensic biology test (Forensic Biology, Semen, and Mixture) in the years 2018–2021 were reviewed. Data were also provided to us by CTS upon our request. None of the false positives or negatives could be attributed to the mixture interpretation strategy and certainly not to the use of PGS. Full article
(This article belongs to the Special Issue Forensic DNA Mixture Interpretation and Probabilistic Genotyping)
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