The effect of the uncertainty in the number of contributors to mixed DNA profiles on profile interpretation

Uncertainty in the number of contributor estimation methods applied to a Y-STR profile

Maximum allele count (MAC) and total allele count (TAC) methods are widely used for estimating the number of contributors (NoC) of autosomal short tandem repeat (STR) profile in many forensic laboratories. In this study, we applied NoC estimation methods to mixed Y-STR profiles and evaluated its uncertainty and performance. For the MAC method, as recent Y-STR typing kits involve single- and multi-copy loci, we defined "MAC-single" for use across only single-copy loci and "MAC-multi" for use across only multi-copy loci. We generated a dataset containing 120,000 Y-STR profiles for a one to six-person mixture in silico based on previously reported haplotype frequencies of 27 Y-STR loci in Yfiler Plus for the U.S. population (reported by NIST) and the Henan Han population. The dataset was randomly split into a training set and a test set. The training set was used to construct a TAC distribution (TAC curve), whereas the test set was used to calculate the performance metrics (accuracy, precision, recall, and F1-score). In addition, the effect of the upper limit of NoC considered for estimation on overall accuracy was evaluated. The overall accuracies of MAC-single, MAC-multi, and TAC methods when the upper limit of NoC was set to six-person were 0.7920, 0.4329, and 0.7877 for the U.S. population and 0.8207, 0.4609, and 0.8385 for the Henan Han population. Our results suggest that the MAC-single and TAC methods can estimate the NoC for mixed Y-STR profiles with high levels of accuracy.

A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation

Several fully continuous probabilistic genotyping software (PGS) use Markov chain Monte Carlo algorithms (MCMC) to assign weights to different proposed genotype combinations at a locus. Replicate interpretations of the same profile in these software are expected not to produce identical weights and likelihood ratio (LR) values due to the Monte Carlo aspect. This paper reports a detailed precision study under reproducibility conditions conducted as a collaborative exercise across the National Institute of Standards and Technology (NIST), Federal Bureau of Investigation (FBI), and Institute of Environmental Science and Research (ESR). Replicate interpretations generated across the three laboratories used the same input files, software version, and settings but different random number seed and different computers. This work demonstrates that using different computers to analyze replicate interpretations does not contribute to any variations in LR values. The study quantifies the magnitude of differences in the assigned LRs that is only due to run-to-run MCMC variability and addresses the potential explanations for the observed differences.

A tale of two PG systems: A comparison of the two most widely used continuous probabilistic genotyping systems in the United States

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.

Using simulated microhaplotype genotyping data to evaluate the value of machine learning algorithms for inferring DNA mixture contributor numbers

Inferring the number of contributors (NoC) is a crucial step in interpreting DNA mixtures, as it directly affects the accuracy of the likelihood ratio calculation and the assessment of evidence strength. However, obtaining the correct NoC in complex DNA mixtures remains challenging due to the high degree of allele sharing and dropout. This study aimed to analyze the impact of allele sharing and dropout on NoC inference in complex DNA mixtures when using microhaplotypes (MH). The effectiveness and value of highly polymorphic MH for NoC inference in complex DNA mixtures were evaluated through comparing the performance of three NoC inference methods, including maximum allele count (MAC) method, maximum likelihood estimation (MLE) method, and random forest classification (RFC) algorithm. In this study, we selected the top 100 most polymorphic MH from the Southern Han Chinese (CHS) population, and simulated over 40 million complex DNA mixture profiles with the NoC ranging from 2 to 8. These profiles involve unrelated individuals (RM type) and related pairs of individuals, including parent-offspring pairs (PO type), full-sibling pairs (FS type), and second-degree kinship pairs (SE type). Our results indicated that how the number of detected alleles in DNA mixture profiles varied with the markers' polymorphism, kinship's involvement, NoC, and dropout settings. Across different types of DNA mixtures, the MAC and MLE methods performed best in the RM type, followed by SE, FS, and PO types, while RFC models showed the best performance in the PO type, followed by RM, SE, and FS types. The recall of all three methods for NoC inference were decreased as the NoC and dropout levels increased. Furthermore, the MLE method performed better at low NoC, whereas RFC models excelled at high NoC and/or high dropout levels, regardless of the availability of a priori information about related pairs of individuals in DNA mixtures. However, the RFC models which considered the aforementioned priori information and were trained specifically on each type of DNA mixture profiles, outperformed RFC_ALL model that did not consider such information. Finally, we provided recommendations for model building when applying machine learning algorithms to NoC inference.

Single cell genomics applications in forensic science: Current state and future directions

Standard methods of mixture analysis involve subjecting a dried crime scene sample to a "bulk" DNA extraction method such that the resulting isolate compromises a homogenized DNA mixture from the individual donors. If, however, instead of bulk DNA extraction, a sufficient number of individual cells from the mixed stain are subsampled prior to genetic analysis then it should be possible to recover highly probative single source, non-mixed scDNA profiles from each of the donors. This approach can detect low DNA level minor donors to a mixture that otherwise would not be identified using standard methods and can also resolve rare mixtures comprising first degree relatives and thereby also prevent the false inclusion of non-donor relatives. This literature landscape review and associated commentary reports on the history and increasing interest in current and potential future applications of scDNA in forensic genomics, and critically evaluates opportunities and impediments to further progress.

Variation in assessments of suitability and number of contributors for DNA mixtures

The interpretation of a DNA mixture (a sample that contains DNA from two or more people) depends on a laboratory/analyst's assessment of the suitability of the sample for comparison/analysis, and an assessment of the number of contributors (NoC) present in the sample. In this study, 134 participants from 67 forensic laboratories provided a total of 2272 assessments of 29 DNA mixtures (provided as electropherograms). The laboratories' responses were evaluated in terms of the variability of suitability assessments, and the accuracy and variability of NoC assessments. Policies and procedures related to suitability and NoC varied notably among labs. We observed notable variation in whether labs would assess a given mixture as suitable or not, predominantly due to differences in lab policies: if two labs following their standard operating procedures (SOPs) were given the same mixture, they agreed on whether the mixture was suitable for comparison 66% of the time. Differences in suitability assessments have a direct effect on variability in interpretations among labs, since mixtures assessed as not suitable would not result in reported interpretations. For labs following their SOPs, 79% of assessments of NoC were correct. When two different labs provided NoC responses, 63% of the time both labs were correct, and 7% of the time both labs were incorrect. Incorrect NoC assessments have been shown to affect statistical analyses in some cases, but do not necessarily imply inaccurate interpretations or conclusions. Most incorrect NoC estimates were overestimates, which previous research has shown have less of an effect on likelihood ratios than underestimates.

ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model

Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.

Internal Validation of MaSTR™ Probabilistic Genotyping Software for the Interpretation of 2–5 Person Mixed DNA Profiles

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.

Allele frequencies and minor contributor match statistic convergence using simulated population replicates

Probabilistic genotyping permits a comparison of forensic evidence given hypotheses regarding the origin of observed short tandem repeat alleles in a mixed DNA profile. Using the publicly available R package forensim, it has been proposed that mixtures with non-contributors from low genetic diversity populations are more likely to be mistakenly identified as contributors to a mixture than non-contributors from high genetic diversity populations. We hypothesized that these observations are attributed to the unique distribution of alleles in the reference population and may not generalize to other samplings of the same population. We used forensim to simulate 200 US populations (50 each of self-reported African-American, Asian-American, European-American, and Hispanic descent). We compared likelihood ratios for 2400 mixtures to those derived from published data and identified stark differences. A minimum of ten population replicates were required to reduce observed differences relative to published data. Deviations from Hardy-Weinberg equilibrium and allele frequency distributions suggest that simulated populations should be sufficiently evaluated for expectations of population genetic parameters prior to use in DNA mixture modeling experiments. Overall, our findings support the utility of forensim and further describe its suitability to model population genetic parameters but suggest that a single population replicate (directly ascertained or simulated) may be insufficient to make conclusions about a given DNA mixture.

Development and validation of Kongoh ver. 3.0.1: Open-source software for DNA mixture interpretation in the GlobalFiler system based on a quantitative continuous model

Probabilistic genotyping software based on continuous models is effective for interpreting DNA profiles derived from DNA mixtures and small DNA samples. In this study, we updated our previously developed Kongoh software (to ver. 3.0.1) to interpret DNA profiles typed using the GlobalFiler™ PCR Amplification Kit. Recently, highly sensitive typing systems such as the GlobalFiler system have facilitated the detection of forward, double-back, and minus 2-nt stutters; therefore, we implemented statistical models for these stutters in Kongoh. In addition, we validated the new version of Kongoh using 2-4-person mixtures and DNA profiles with degradation in the GlobalFiler system. The likelihood ratios (LRs) for true contributors and non-contributors were well separated as the information increased (i.e., larger peak height and fewer contributors), and these LRs tended to neutrality as the information decreased. These trends were observed even in profiles with DNA degradation. The LR values were highly reproducible, and the accuracy of the calculation was also confirmed. Therefore, Kongoh ver. 3.0.1 is useful for interpreting DNA mixtures and degraded DNA samples in the GlobalFiler system.

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

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.

Revisiting single cell analysis in forensic science

Forensic science has yet to take full advantage of single cell analysis. Its greatest benefit is the ability to alleviate the challenges associated with DNA mixture analysis, which remains a significant hurdle in forensic science. Many of the factors that cause complexity in mixture interpretation are absent in single cell analyses—multiple contributors, varied levels of contribution, and allele masking. This study revisits single cell analyses in the context of forensic identification, introducing previously unseen depth to the characterization of data generated from single cells using a novel pipeline that includes recovery of single cells using the DEPArray NxT and amplification using the PowerPlex Fusion 6c kit with varied PCR cycles (29, 30, and 31). The resulting allelic signal was assessed using analytical thresholds of 10, 100, and 150RFU. The mean peak heights across the sample sets generally increased as cycle number increased, 75.0 ± 85.3, 147.1 ± 172.6, and 226.1 ± 298.2 RFU, for 29, 30, and 31 cycles, respectively. The average proportion of allele/locus dropout was most significantly impacted by changes in the detection threshold, whereas increases in PCR cycle number had less impact. Overall data quality improved notably when increasing PCR from 29 to 30 cycles, less improvement and more volatility was introduced at 31 cycles. The average random match probabilities for the 29, 30, and 31 cycle sets at 150RFU are 1 in 2.4 × 10¹⁸ ± 1.46 × 10¹⁹, 1 in 1.49 × 10²⁵ ± 5.8 × 10²⁵, and 1 in 1.83 × 10²⁴ ± 8.09 × 10²⁴, respectively. This demonstrates the current power of single cell analysis in removing the need for complex mixture analysis.

Application of a probabilistic genotyping software to MPS mixture STR data is supported by similar trends in LRs compared with CE data

The interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out, for instance from sample degradation. Mixture interpretation can be improved by increasing the number of STRs and/or loci with a higher discriminatory power. Both capillary electrophoresis (CE, 6-dye) and massively parallel sequencing (MPS) provide a platform for analysing relatively large numbers of autosomal STRs. In addition, MPS enables distinguishing between sequence variants, resulting in enlarged discriminatory power. Also, MPS allows for small amplicon sizes for all loci as spacing is not an issue, which is beneficial with degraded DNA. Altogether, MPS has the potential to increase the weights of evidence for true contributors to (complex) DNA profiles. In this study, likelihood ratio (LR) calculations were performed using STR profiles obtained with two different MPS systems and analysed using different settings: 1) MPS PowerSeq™ Auto System profiles analysed using FDSTools equipped with optimized settings such as noise correction, 2) ForenSeq™ DNA Signature Prep Kit profiles analysed using the default settings in the Universal Analysis Software (UAS), and 3) ForenSeq™ DNA Signature Prep Kit profiles analysed using FDSTools empirically adapted to cope with one-directional reads and provisional, basic settings. The LR calculations used genotyping data for two- to four-person mixtures varying for mixture proportion, level of drop-out and allele sharing and were generated with the continuous model EuroForMix. The LR results for the over 2000 sets of propositions were affected by the variation for the number of markers and analysis settings used in the three approaches. Nevertheless, trends for true and non-contributors, effects of replicates, assigned number of contributors, and model validation results were comparable for the three MPS approaches and alike the trends known for CE data. Based on this analogy, we regard the probabilistic interpretation of MPS STR data fit for forensic DNA casework. In addition, guidelines were derived on when to apply LR calculations to MPS autosomal STR data and report the corresponding results.

Micromanipulation of single cells and fingerprints for forensic identification

Crime scene samples often include biological stains, handled items, or worn clothes and may contain cells from various donors. Applying routine sample collection methods by using a portion of a biological stain or swabbing the entire suspected touched area of the evidence followed by DNA extraction often leads to DNA mixtures. Some mixtures can be addressed with sophisticated interpretation protocols and probabilistic genotyping software resulting in DNA profiles of their contributors. However, many samples remain unresolved, providing no investigative information. Samples with many contributors are often the most challenging samples in forensic biology. Examples include gang rape situations or where the perpetrator's DNA is present in traces among the overwhelming amounts of the victim's DNA. If this is the only available evidence in a case, it is of paramount importance to generate usable information. An alternative approach, to address biological mixtures, could be the collection of individual cells directly from the evidence and testing them separately. This method could prevent cells from being inadvertently blended during the extraction process, thus resulting in DNA mixtures. In this study, multiple tools coupled with adhesive microcarriers to collect single cells were evaluated. These were tested on epithelial (buccal) and sperm cells, as well as on touched items. Single cells were successfully collected but fingerprints were swabbed in their entirety to account for the extracellular DNA of these samples and the poor DNA quality of shed skin flakes. Furthermore, micromanipulation devices, such as the P.A.L.M.® and the Axio Zoom.V16 operated manually or with a robotic arm aureka®, were compared for their effectiveness in collecting cells. The P.A.L.M.® was suitable for single cell isolation when smeared on membrane slides. Manual or robotic manipulations, by utilizing the Axio Zoom.V16, have wider applications as they can be used to isolate cells from various substrates such as glass or membrane slides, tapes, or directly from the evidence. Manipulations using the Axio Zoom.V16, either with the robotic arm aureka® or manually, generated similar outcomes which were significantly better than the outcomes by using the P.A.L.M.®. Robotic manipulations using the aureka® produced more consistent results, but operating the aureka® required training and often needed re-calibrations. This made the process of cell manipulations slower than when manually operated. Our preferred method was the manual manipulations as it was fast, cost effective, required little training, but relied on a steady hand of the technician.

Estimating the number of contributors to a DNA profile using decision trees

The interpretation of DNA profiles typically starts with an assessment of the number of contributors. In the last two decades, several methods have been proposed to assist with this assessment. We describe a relatively simple method using decision trees, that is fast to run and fully transparent to a forensic analyst. We use mixtures from the publicly available PROVEDIt dataset to demonstrate the performance of the method. We show that the performance of the method crucially depends on the performance of filters for stutter and other artefacts. We compare the performance of the decision tree method with other published methods for the same dataset.

When evaluating DNA evidence within a likelihood ratio framework, should the propositions be exhaustive?

We seek to develop a rational approach to forming propositions when little information is available from the outset, as this often happens in casework. If propositions used when evaluating evidence are not exhaustive (in the context of the case), then there is a theoretical risk that an LR greater than one may be associated with a proposition in the numerator that - if all meaningful propositions had been considered - would in fact have a lower posterior probability after consideration of the evidence. Ideally, all propositions should be considered. However, with multiple propositions, some terms will be larger than others and for simplification very small terms can be neglected without changing the order of magnitude of the value of the evidence (i.e. LR). Our analysis shows that mathematically a contributor's DNA can be assumed to be present under both prosecution and alternative propositions (H_p and H_a) if there is a reasonable prior probability of their DNA being present and their inclusion is supported by the profile. This is because the terms associated to these sub-propositions will dominate our LR. For example, in the absence of specific information, when considering two persons of interest (POI) as potential contributors to a mixed DNA profile we suggest the assumption of one when examining the presence of the other, after checking that both collectively explain the profile well. This represents more meaningful propositions and allows better discrimination. Slooten and Caliebe have shown that the overall LR is the weighted average of LRs with the same number of contributors (NoC) under both propositions. The weights involve both an assessment of the probability of the crime scene DNA profile and the probability of this NoC given the background information.

A comparative study for the isolation of exogenous trace DNA from fingernails

Often fingernails from a victim or suspect involved in a physical assault, such as murder or sexual assault, are submitted to crime laboratories for DNA testing of foreign/exogenous biological material; however, very few studies have been conducted comparing the effectiveness of different sampling methods on the removal of foreign/exogenous DNA while minimizing the fingernail endogenous DNA. In this study three different sampling methods (swabbing, PBS soak, and PrepFiler® lysis buffer soak) were compared in order to identify one that minimizes the amount of endogenous DNA removed and maximizes the amount of foreign/exogenous male DNA removed. The samples were processed using the Tecan HIDEVO150 robot in order to reduce analyst time and the DNA mixtures were interpreted using the probabilistic genotyping software STRmix™. For each sampling method the quantity of male DNA, the mixture proportions, the number of foreign/exogenous male alleles detected, the amount of DNA degradation, and the discrimination power via the likelihood ratio obtained for the foreign/exogenous male DNA donor were determined and compared. The PrepFiler® lysis buffer soak and swabbing sampling methods appear to be equally effective at removing foreign/exogenous DNA from fingernails; however, the lysis buffer soak sampling method extracts more female endogenous DNA from the fingernail and the female DNA is degraded. Marginally higher likelihood ratios were obtained for the swab samples versus the PrepFiler® lysis buffer soak samples; therefore, it was determined that the swabbing sampling method was the best sampling method for the recovery of foreign exogenous DNA from fingernails while minimizing the amount of endogenous DNA removed.

STRmix™ collaborative exercise on DNA mixture interpretation

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.

Four model variants within a continuous forensic DNA mixture interpretation framework: Effects on evidential inference and reporting

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.

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

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.

Determining the number of contributors to DNA mixtures in the low-template regime: Exploring the impacts of sampling and detection effects

The interpretation of DNA evidence may rely upon the assumption that the forensic short tandem repeat (STR) profile is composed of multiple genotypes, or partial genotypes, originating from n contributors. In cases where the number of contributors (NOC) is in dispute, it may be justifiable to compute likelihood ratios that utilize different NOC parameters in the numerator and denominator, or present different likelihoods separately. Therefore, in this work, we evaluate the impact of allele dropout on estimating the NOC for simulated mixtures with up to six contributors in the presence or absence of a major contributor. These simulations demonstrate that in the presence of dropout, or with the application of an analytical threshold (AT), estimating the NOC using counting methods was unreliable for mixtures containing one or more minor contributors present at low levels. The number of misidentifications was only slightly reduced when we expand the number of STR loci from 16 to 21. In many of the simulations tested herein, the minimum and actual NOC differed by more than two, suggesting that low-template, high-order mixtures with allele counts fewer than six may be originating from as many as four-, five-, or six-persons. Thus, there is justification for the use of differing or multiple assumptions on the NOC when computing the weight of DNA evidence for low-template mixtures, particularly when the peak heights are in the vicinity of the signal threshold or allele counting methods are the mechanism by which the NOC is assessed.