Search

Semaan et al. (J Forensic Res, 2020, 11, 453) discuss a mock case "where eight different individuals [P1 through P8 ] could not be excluded in a mixed DNA analysis. Even though … expert DNA mixture analysis software was used." Two of these are the true donors. The LRs reported are incorrect due to the incorrect entry of propositions into LRmix Studio. This forced the software to account for most of the alleles as drop-in, resulting in LRs 60-70 orders of magnitude larger than expected. P1 , P2 , P4 , P5 , and P8 can be manually excluded using peak heights. This has relevance when using LRmix which does not use peak heights. We extend the work using the same two reference genotypes who were the true contributors as Semaan et al. (J Forensic Res, 2020, 11, 453). We simulate three two-donor mixtures with peak heights using these two genotypes and analyze using STRmix™. For the simulated 1:1 mixture, one of the non-donors' LRs supported him being a contributor when no conditioning was used. When considered in combination with any other potential donors (i.e., with conditioning), this non-donor was correctly eliminated. For the 3:1 mixture, all results correctly supported that the non-donors were not contributors. The low-template 4:1 mixture LRs with no conditioning showed support for all eight profiles as donors. However, the results from pair-wise conditioning showed that only the two ground truth donors had LRs supporting that they were contributors to the mixture. We recommend the use of peak heights and conditioning profiles, as this allows better sensitivity and specificity even when the persons share many alleles.

The reporting of a likelihood ratio (LR) calculated from probabilistic genotyping software has become more popular since 2015 and has allowed for the use of more complex mixtures at court. The meaning of "inconclusive" LRs and how to communicate the significance of low LRs at court is now important. We present a method here using the distribution of LRs obtained from nondonors. The nondonor distribution is useful for examining calibration and discrimination for profiles that have produced LRs less than about 104 . In this paper, a range of mixed DNA profiles of varying quantity were constructed and the LR distribution considering the minor contributor for a number of nondonors was compared to the expectation given a calibrated system. It is demonstrated that conditioning genotypes should be used where reasonable given the background information to decrease the rate of nondonor LRs above 1. In all 17 cases examined, the LR for the minor donor was higher than the nondonor LRs, and in 12 of the 17 cases, the 99.9 percentile of the nondonor distribution was lower when appropriate conditioning information was used. The output of the tool is a graph that can show the position of the LR for the person of interest set against the nondonor LR distribution. This may assist communication between scientists and the court.

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.

The interpretation of mixtures containing related individuals can be difficult due to allele sharing between the contributors. Challenges include the assignment of the number of contributors (NoC) to the mixture with the under assignment of NoC resulting in false exclusions of true donors. Non-donating relatives of the true contributors to mixtures of close relatives can result in likelihood ratios supporting their adventitious inclusion within the mixture. We examine the effect of non-donor likelihood ratios on mixtures of first order relatives. Mixtures of full siblings and parent-child were created by mixing the DNA from known family members in vitro, or by in silico simulation. Mixtures were interpreted using the probabilistic genotyping software STRmix™ and likelihood ratios were assigned for the true donors and non-donors who were either further relatives of the true donors or unrelated to the true donors. The two donor balanced mixtures deconvoluted straightforwardly when analysed as NoC = 2 giving approximately the experimental design 1:1 ratio. When analysed as NoC = 3 a very large number of non-donor genotypes produced LRs close to 1 including many instances of adventitious support. The in vitro three donor balanced mixtures proved difficult to assign as NoC = 3 by a blind examination of the profile. It is likely that many of these would be misassigned as NoC = 2. The analysis of the in vitro and in silico mixtures assuming NoC = 3 with no use of a conditioning profile or with the use of a conditioning profile but without informed priors on the mixture proportions (Mx priors) was ineffective. If the profile can be assigned as NoC = 3 then assignment of the Mx priors is straightforward. This analysis gave no false exclusions. Adventitious support did happen for relatives with high allele sharing. Adventitious support was not observed for any unrelated non-donors. The analysis of the three-person mixtures as NoC = 2 produced many false exclusions and fewer instances of adventitious support. The three donor unbalanced mixtures could all be assigned as NoC= 3. Analysis without Mx priors produced an alternate genotype explanation.

We report a large compilation of the internal validations of the probabilistic genotyping software STRmix™. Thirty one laboratories contributed data resulting in 2825 mixtures comprising three to six donors and a wide range of multiplex, equipment, mixture proportions and templates. Previously reported trends in the LR were confirmed including less discriminatory LRs occurring both for donors and non-donors at low template (for the donor in question) and at high contributor number. We were unable to isolate an effect of allelic sharing. Any apparent effect appears to be largely confounded with increased contributor number.

In response to requests from the forensic community, commercial companies are generating larger, more sensitive, and more discriminating STR multiplexes. These multiplexes are now applied to a wider range of samples including complex multi-person mixtures. In parallel there is an overdue reappraisal of profile interpretation methodology. Aspects of this reappraisal include 1. The need for a quantitative understanding of allele and stutter peak heights and their variability, 2. An interest in reassessing the utility of smaller peaks below the often used analytical threshold, 3. A need to understand not just the occurrence of peak drop-in but also the height distribution of such peaks, and 4. A need to understand the limitations of the multiplex-interpretation strategy pair implemented. In this work we present a full scheme for validation of a new multiplex that is suitable for informing modern interpretation practice. We predominantly use GlobalFiler™ as an example multiplex but we suggest that the aspects investigated here are fundamental to introducing any multiplex in the modern interpretation environment.

The interpretation of complex DNA profiles is facilitated by a Bayesian approach. This approach requires the development of a pair of propositions: one aligned to the prosecution case and one to the defense case. This note explores the issue of proposition setting in an adversarial environment by a series of examples. A set of guidelines generalize how to formulate propositions when there is a single person of interest and when there are multiple individuals of interest. Additional explanations cover how to handle multiple defense propositions, relatives, and the transition from subsource level to activity level propositions. The propositions depend on case information and the allegations of each of the parties. The prosecution proposition is usually known. The authors suggest that a sensible proposition is selected for the defense that is consistent with their stance, if available, and consistent with a realistic defense if their position is not known.

Modern probabilistic genotyping (PG) software is capable of modeling stutter as part of the profile weighting statistic. This allows for peaks in stutter positions to be considered as allelic or stutter or both. However, prior to running any sample through a PG calculator, the examiner must first interpret the sample, considering such things as artifacts and number of contributors (NOC or N). Stutter can play a major role both during the assignment of the number of contributors, and the assessment of inclusion and exclusion. If stutter peaks are not filtered when they should be, it can lead to the assignment of an additional contributor, causing N contributors to be assigned as N + 1. If peaks in the stutter position of a major contributor are filtered using a threshold that is too high, true alleles of minor contributors can be lost. Until now, the software used to view the electropherogram stutter filters are based on a locus specific model. Combined stutter peaks occur when a peak could be the result of both back stutter (stutter one repeat shorter than the allele) and forward stutter (stutter one repeat unit larger than the allele). This can challenge existing filters. We present here a novel stutter filter model in the ArmedXpert™ software package that uses a linear model based on allele for back stutter and applies an additive filter for combined stutter. We term this the allele specific stutter model (AM). We compared AM with a traditional model based on locus specific stutter filters (termed LM). This improved stutter model has the benefit of: Instances of over filtering were reduced 78% from 101 for a traditional model (LM) to 22 for the allele specific model (AM) when scored against each other. Instances of under filtering were reduced 80% from 85 (LM) to 17 (AM) when scored against ground truth mixtures.