Search

In the absence of a suspect the forensic aim is investigative, and the focus is one of discerning what genotypes best explain the evidence. In traditional systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor's contribution and, as a result, possibly overlooked or inefficient investigative leads. Recent developments in single-cell analysis offer a way forward, by producing data capable of discriminating genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster's content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes' rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least 1-α. This is the credible genotype set and is used to inform database search criteria. Within this work we demonstrate the salience of single-cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most probable genotype was the true genotype for 97% of the loci when the number of cells in a cluster was at least two. Since efficient investigative leads will be borne by posterior mass distributions that are narrow and concentrated at the true genotype, we report that, for this test set, 47,900 (86%) loci returned only one credible genotype and of these 47,551 (99%) were the true genotype. When determining the LR for true contributors, 91% of the clusters rendered LR>10 18 , showing the potential of single-cell data to positively affect investigative reporting., Competing Interests: Declaration of Competing Interest Catherine M. Grgicak, Desmond S. Lun and Ken R. Duffy are authors of US Patent Application for SYSTEMS AND METHODS FOR AUTOMATED ANALYSES OF A BIOLOGICAL SAMPLE Patent Application (Application #20220270712)., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The consistency between DNA evidence and person(s) of interest (PoI) is summarized by a likelihood ratio (LR): the probability of the data given the PoI contributed divided by the probability given they did not. It is often the case that there are several PoI who may have individually or jointly contributed to the stain. If there is more than one PoI, or the number of contributors (NoC) cannot easily be determined, then several sets of hypotheses are needed, requiring significant resources to complete the interpretation. Recent technological developments in laboratory systems offer a way forward, by enabling production of single cell data. Though single-cell data may be procured by next generation sequencing or capillary electrophoresis workflows, in this work we focus our attention on assessing the consistency between PoIs and a collection of single cell electropherograms (scEPGs) from diploid cells - i.e., leukocytes and epithelial cells. Specifically, we introduce a framework that: I) clusters scEPGs into collections, each originating from one genetic source; II) for each PoI, determines a LR for each cluster of scEPGs; and III) by averaging the likelihood ratios for each PoI across all clusters provides a whole-sample weight of evidence summary. By using Model Based Clustering (MBC) in step I) and an algorithm, named EESCIt for Evidentiary Evaluation of Single Cells, that computes single-cell LRs in step II), we show that 99% of the comparisons rendered log LR values > 0 for true contributors, and of these all but one gave log LR > 5, regardless of the number of donors or whether the smallest contributor donated less than 20% of the cells, greatly expanding the collection of cases for which DNA forensics provides informative results., Competing Interests: Competing interest Catherine M. Grgicak, Desmond S. Lun and Ken R. Duffy are authors to US Patent Application for SYSTEMS AND METHODS FOR AUTOMATED ANALYSES OF A BIOLOGICAL SAMPLE Patent Application (Application #20220270712)., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Interpreting forensic DNA signal is arduous since the total intensity is a cacophony of signal from noise, artifact, and allele from an unknown number of contributors (NOC). An alternate to traditional bulk-processing pipelines is a single-cell one, where the sample is collected, and each cell is sequestered resulting in n single-source, single-cell EPGs (scEPG) that must be interpreted using applicable strategies. As with all forensic DNA interpretation strategies, high quality electropherograms are required; thus, to enhance the credibility of single-cell forensics, it is necessary to produce an efficient direct-to-PCR treatment that is compatible with prevailing downstream laboratory processes. We incorporated the semi-automated micro-fluidic DEPArray™ technology into the single-cell laboratory and optimized its implementation by testing the effects of four laboratory treatments on single-cell profiles. We focused on testing effects of phosphate buffer saline (PBS) since it is an important reagent that mitigates cell rupture but is also a PCR inhibitor. Specifically, we explored the effect of decreasing PBS concentrations on five electropherogram-quality metrics from 241 leukocytes: profile drop-out, allele drop-out, allele peak heights, peak height ratios, and scEPG sloping. In an effort to improve reagent use, we also assessed two concentrations of proteinase K. The results indicate that decreasing PBS concentrations to 0.5X or 0.25X improves scEPG quality, while modest modifications to proteinase K concentrations did not significantly impact it. We, therefore, conclude that a lower than recommended proteinase K concentration coupled with a lower than recommended PBS concentration results in enhanced scEPGs within the semi-automated single-cell pipeline., (© 2021 American Academy of Forensic Sciences.)

Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpretation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational tools that make point estimates on the Number of Contributors (NOC) to a sample have been developed, as have Bayesian methods that evaluate an A Posteriori Probability (APP) distribution on the NOC. In cases where an overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the evidence is evaluated with an inadequate set of propositions. In the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the NOC given an electropherogram, by reporting on an implementation where the user can add assumed contributors. NOCIt is a continuous system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele frequencies in a reference population. When conditioned on a known contributor, we found that the mode of the APP distribution can shift to one greater when compared with the circumstance where no known contributor is assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture. In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution and defense hypotheses, we show that this computation must not only use coincident models, but also coincident contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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.

Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signal from forensic stains is often garnered from low-copy, high-contributor-number samples and is frequently obfuscated by allele sharing, allele drop-out, stutter and noise. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous, probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution's and defense's hypotheses have been developed. In the current paper, we validate a new version of the NOCIt inference platform that determines an A Posteriori Probability (APP) distribution of the number of contributors given an electropherogram. NOCIt is a continuous inference system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise and allelic drop-out while taking into account allele frequencies in a reference population. We established the algorithm's performance by conducting tests on samples that were representative of types often encountered in practice. In total, we tested NOCIt's performance on 815 degraded, UV-damaged, inhibited, differentially degraded, or uncompromised DNA mixture samples containing up to 5 contributors. We found that the model makes accurate, repeatable and reliable inferences about the NOCs and significantly outperformed methods that rely on signal filtering. By leveraging recent theoretical results of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establish the NOC can be treated as a nuisance variable, we demonstrated that when NOCIt's APP is used in conjunction with a downstream likelihood ratio (LR) inference system that employs the same probabilistic model, a full evaluation across multiple contributor numbers is rendered. This work, therefore, illustrates the power of modern probabilistic systems to report holistic and interpretable weights-of-evidence to the trier-of-fact without assigning a specified number of contributors or filtering signal., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Continuous mixture interpretation methods that employ probabilistic genotyping to compute the Likelihood Ratio (LR) utilize more information than threshold-based systems. The continuous interpretation schemes described in the literature, however, do not all use the same underlying probabilistic model and standards outlining which probabilistic models may or may not be implemented into casework do not exist; thus, it is the individual forensic laboratory or expert that decides which model and corresponding software program to implement. For countries, such as the United States, with an adversarial legal system, one can envision a scenario where two probabilistic models are used to present the weight of evidence, and two LRs are presented by two experts. Conversely, if no independent review of the evidence is requested, one expert using one model may present one LR as there is no standard or guideline requiring the uncertainty in the LR estimate be presented. The choice of model determines the underlying probability calculation, and changes to it can result in non-negligible differences in the reported LR or corresponding verbal categorization presented to the trier-of-fact. In this paper, we study the impact of model differences on the LR and on the corresponding verbal expression computed using four variants of a continuous mixture interpretation method. The four models were tested five times each on 101, 1-, 2- and 3-person experimental samples with known contributors. For each sample, LRs were computed using the known contributor as the person of interest. In all four models, intra-model variability increased with an increase in the number of contributors and with a decrease in the contributor's template mass. Inter-model variability in the associated verbal expression of the LR was observed in 32 of the 195 LRs used for comparison. Moreover, in 11 of these profiles there was a change from LR > 1 to LR < 1. These results indicate that modifications to existing continuous models do have the potential to significantly impact the final statistic, justifying the continuation of broad-based, large-scale, independent studies to quantify the limits of reliability and variability of existing forensically relevant systems., Competing Interests: The authors have declared that no competing interests exist.

DNA-based human identity testing is conducted by comparison of PCR-amplified polymorphic Short Tandem Repeat (STR) motifs from a known source with the STR profiles obtained from uncertain sources. Samples such as those found at crime scenes often result in signal that is a composite of incomplete STR profiles from an unknown number of unknown contributors, making interpretation an arduous task. To facilitate advancement in STR interpretation challenges we provide over 25,000 multiplex STR profiles produced from one to five known individuals at target levels ranging from one to 160 copies of DNA. The data, generated under 144 laboratory conditions, are classified by total copy number and contributor proportions. For the 70% of samples that were synthetically compromised, we report the level of DNA damage using quantitative and end-point PCR. In addition, we characterize the complexity of the signal by exploring the number of detected alleles in each profile., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Samples containing low-copy numbers of DNA are routinely encountered in casework. The signal acquired from these sample types can be difficult to interpret as they do not always contain all of the genotypic information from each contributor, where the loss of genetic information is associated with sampling and detection effects. The present work focuses on developing a validation scheme to aid in mitigating the effects of the latter. We establish a scheme designed to simultaneously improve signal resolution and detection rates without costly large-scale experimental validation studies by applying a combined simulation and experimental based approach. Specifically, we parameterize an in silico DNA pipeline with experimental data acquired from the laboratory and use this to evaluate multifarious scenarios in a cost-effective manner. Metrics such as signal 1copy -to-noise resolution, false positive and false negative signal detection rates are used to select tenable laboratory parameters that result in high-fidelity signal in the single-copy regime. We demonstrate that the metrics acquired from simulation are consistent with experimental data obtained from two capillary electrophoresis platforms and various injection parameters. Once good resolution is obtained, analytical thresholds can be determined using detection error tradeoff analysis, if necessary. Decreasing the limit of detection of the forensic process to one copy of DNA is a powerful mechanism by which to increase the information content on minor components of a mixture, which is particularly important for probabilistic system inference. If the forensic pipeline is engineered such that high-fidelity electropherogram signal is obtained, then the likelihood ratio (LR) of a true contributor increases and the probability that the LR of a randomly chosen person is greater than one decreases. This is, potentially, the first step towards standardization of the analytical pipeline across operational laboratories., (Copyright © 2017 Elsevier B.V. All rights reserved.)