NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): Variation observed and lessons learned

An inter-laboratory comparison of probabilistic genotyping parameters and evaluation of performance on DNA mixtures from different laboratories

Probabilistic genotyping (PG) is becoming the preferred standard for evidence interpretation, amongst forensic DNA laboratories, especially those in the United States. Various groups have expressed concern about reliability of PG systems, especially for mixtures beyond two contributors. Studies involving interlaboratory testing of known mixtures have been identified as ways to evaluate the reliability of PG systems. Reliability means different things in different contexts. However, it suffices here to think about it as a mixture of precision and accuracy. We might also consider whether a system is prone to producing misleading results - for example large likelihood ratios (LRs) when the POI is truly not a contributor, or small LRs when the POI is a truly a contributor. In this paper we show that the PG system STRmix™ is relatively unaffected by differences in parameter settings. That is, a DNA mixture that is analyzed in different laboratories using STRmix™ will result in different LRs, but less than 0.05% of these LRs would result in a different, or misleading conclusion as long as the LR is greater than 50. For the purposes of this study, we define LRs assigned using different parameters for the same mixtures as similar if the LR of the true POI is greater than the LRs generated for 99.9% of the general population. These findings are based on an interlaboratory study involving eight laboratories that provided twenty known DNA mixtures of two to four contributors and their individual laboratory STRmix™ parameters. The eight sets of laboratory parameters included differences in STR kits and PCR cycles as well as the peak, stutter, and locus specific amplification efficiency variances.

Indirect DNA Transfer and Forensic Implications: A Literature Review

Progress in DNA profiling techniques has made it possible to detect even the minimum amount of DNA at a crime scene (i.e., a complete DNA profile can be produced using as little as 100 pg of DNA, equivalent to only 15-20 human cells), leading to new defense strategies. While the evidence of a DNA trace is seldom challenged in court by a defendant's legal team, concerns are often raised about how the DNA was transferred to the location of the crime. This review aims to provide an up-to-date overview of the experimental work carried out focusing on indirect DNA transfer, analyzing each selected paper, the experimental method, the sampling technique, the extraction protocol, and the main results. Scopus and Web of Science databases were used as the search engines, including 49 papers. Based on the results of this review, one of the factors that influence secondary transfer is the amount of DNA shed by different individuals. Another factor is the type and duration of contact between individuals or objects (generally, more intimate or prolonged contact results in more DNA transfer). A third factor is the nature and quality of the DNA source. However, there are exceptions and variations depending on individual characteristics and environmental conditions. Considering that secondary transfer depends on multiple factors that interact with each other in unpredictable ways, it should be considered a complex and dynamic phenomenon that can affect forensic investigation in various ways, for example, placing a subject at a crime scene who has never been there. Correct methods and protocols are required to detect and prevent secondary transfer from compromising forensic evidence, as well as the correct interpretation through Bayesian networks. In this context, the definition of well-designed experimental studies combined with the use of new forensic techniques could improve our knowledge in this challenging field, reinforcing the value of DNA evidence in criminal trials.

The most consistent finding in forensic science is inconsistency

The most consistent finding in many forensic science domains is inconsistency (i.e., lack of reliability, reproducibility, repeatability, and replicability). The lack of consistency is a major problem, both from a scientific and a criminal justice point of view. Examining forensic conclusion data, from across many forensic domains, highlights the underlying cognitive issues and offers a better understanding of the issues and challenges. Such insights enable the development of ways to minimize these inconsistencies and move forward. The aim is to highlight the problem, so that it can be minimized and the reliability of forensic science evidence can be improved.

Decreased accuracy of forensic DNA mixture analysis for groups with lower genetic diversity

Forensic investigation of DNA samples from multiple contributors has become commonplace. These complex analyses use statistical frameworks accounting for multiple levels of uncertainty in allelic contributions from different individuals, particularly for samples containing few molecules of DNA. These methods have been thoroughly tested along some axes of variation, but less attention has been paid to accuracy across human genetic variation. Here, we quantify the accuracy of DNA mixture analysis over 244 human groups. We find higher false inclusion rates for mixtures with more contributors, and for groups with lower genetic diversity. Even for two-contributor mixtures where one contributor is known and the reference group is correctly specified, false inclusion rates are 1e-5 or higher for 56 out of 244 groups. This means that, depending on multiple testing, some false inclusions may be expected. These false positives could be lessened with more selective and conservative use of DNA mixture analysis.

DNAmix 2021: Laboratory policies, procedures, and casework scenarios summary and dataset

DNAmix 2021 is a large-scale study conducted to evaluate the extent of consistency and variation among forensic laboratories in the interpretation of DNA mixtures, and to assess the effects of various potential sources of variability. This study utilized a multi-phasic approach designed to collect information about participating laboratories, laboratory policies, and their standard operating procedures (SOPs). It also characterizes the degree of variation in assessments of suitability and number of contributors as well as in comparisons and statistical analyses of DNA mixture profiles. This paper specifically details the study design and the data collected in the first two phases of the study: the Policies & Procedures (P&P) Questionnaire and the Casework Scenarios Questionnaire (CSQ). We report on the variation in policies and SOPs for 86 forensic laboratories-including information about their DNA workflows, systems, and type of statistics reported. We also provide details regarding various case-scenario specific decisions and the nature of mixture casework for 83 forensic laboratories. The data discussed in this article provide insight into the state of the field for forensic DNA mixture interpretation policies and SOPs at the time of the study (2021-2022).

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.

Evidentiary evaluation of single cells renders highly informative forensic comparisons across multifarious admixtures

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.

Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution

MOTIVATION

Analysing mixed DNA profiles is a common task in forensic genetics. Due to the complexity of the data, such analysis is often performed using Markov Chain Monte Carlo (MCMC)-based genotyping algorithms. These trade off precision against execution time. When default settings (including default chain lengths) are used, as large as a 10-fold changes in inferred log-likelihood ratios (LR) are observed when the software is run twice on the same case. So far, this uncertainty has been attributed to the stochasticity of MCMC algorithms. Since LRs translate directly to strength of the evidence in a criminal trial, forensic laboratories desire LR with small run-to-run variability.

RESULTS

We present the use of a Hamiltonian Monte Carlo (HMC) algorithm that reduces run-to-run variability in forensic DNA mixture deconvolution by around an order of magnitude without increased runtime. We achieve this by enforcing strict convergence criteria. We show that the choice of convergence metric strongly influences precision. We validate our method by reproducing previously published results for benchmark DNA mixtures (MIX05, MIX13, and ProvedIt). We also present a complete software implementation of our algorithm that is able to leverage GPU acceleration for the inference process. In the benchmark mixtures, on consumer-grade hardware, the runtime is less than 7 min for 3 contributors, less than 35 min for 4 contributors, and less than an hour for 5 contributors with one known contributor.

A practical tool for information management in forensic decisions: Using Linear Sequential Unmasking-Expanded (LSU-E) in casework

Forensic analysts often receive information from a multitude of sources. Empirical work clearly demonstrates that biasing information can affect analysts' decisions, and that the order in which task-relevant information is received impacts human cognition and decision-making. Linear Sequential Unmasking (LSU; Dror et al., 2015) and LSU-Expanded (LSU-E; Dror & Kukucka, 2021) are examples of research-based procedural frameworks to guide laboratories' and analysts' consideration and evaluation of case information. These frameworks identify parameters—such as objectivity, relevance, and biasing power—to prioritize and optimally sequence information for forensic analyses. Moreover, the LSU-E framework can be practically incorporated into any forensic discipline to improve decision quality by increasing the repeatability, reproducibility, and transparency of forensic analysts’ decisions, as well as reduce bias. Future implementation of LSU and LSU-E in actual forensic casework can be facilitated by concrete guidance. We present here a practical worksheet designed to bridge the gap between research and practice by facilitating the implementation of LSU-E.

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Contextual information can influence how forensic analysts perceive, interpret, and evaluate evidence.

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Proper information sequencing can reduce bias and improve the repeatability and reproducibility of forensic decisions.

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Linear Sequential Unmasking–Expanded (LSU-E) prioritizes objective, relevant, and non-suggestive information.

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To close the gap between research and practice, we developed a worksheet to help laboratories and analysts implement LSU-E.

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This worksheet aims to optimize information sequencing and promote transparency in forensic decisions.

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.

Towards developing forensically relevant single-cell pipelines by incorporating direct-to-PCR extraction: compatibility, signal quality, and allele detection

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.

Expert Reliability in Legal Proceedings: "Eeny, Meeny, Miny, Moe, With Which Expert Should We Go?"

Between Expert Reliability refers to the extent to which different experts examining identical evidence make the same observations and reach the same conclusions. Some areas of expert decision making have been shown to entail questions with relatively low Between Expert Reliability, but the disagreement between experts is not always communicated to the legal actors forming decisions on the basis of the expert evidence. In this paper, we discuss the issues of Between Expert Reliability in legal proceedings, using forensic age estimations as a case study. Across national as well international jurisdictions, there is large variation in which experts are hired to conduct age estimations as well as the methods they use. Simultaneously, age estimations can be fully decisive for outcomes e.g. in asylum law and criminal law. Using datasets obtained from the Swedish legal context, we identify that radiologists and odontologists examining knees or teeth images to estimate age seem to disagree within their own disciplines (radiologist 1 v. radiologist 2 or odontologist 1 v. odontologist 2) as well as across different disciplines (radiologist v. odontologist) relatively often. This may have large implications e.g. in cases where only one expert from the respective field is involved. The paper discusses appropriate ways for legal actors to deal with the possibility of lacking Between Expert Reliability. This is indeed a challenging task provided that legal actors are legal experts but not necessarily scientific experts.

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.

The STRidER Report on Two Years of Quality Control of Autosomal STR Population Datasets

STRidER, the STRs for Identity ENFSI Reference Database, is a curated, freely publicly available online allele frequency database, quality control (QC) and software platform for autosomal Short Tandem Repeats (STRs) developed under the endorsement of the International Society for Forensic Genetics. Continuous updates comprise additional STR loci and populations in the frequency database and many further STR-related aspects. One significant innovation is the autosomal STR data QC provided prior to publication of datasets. Such scrutiny was lacking previously, leaving QC to authors, reviewers and editors, which led to an unacceptably high error rate in scientific papers. The results from scrutinizing 184 STR datasets containing >177,000 individual genotypes submitted in the first two years of STRidER QC since 2017 revealed that about two-thirds of the STR datasets were either being withdrawn by the authors after initial feedback or rejected based on a conservative error rate. Almost no error-free submissions were received, which clearly shows that centralized QC and data curation are essential to maintain the high-quality standard required in forensic genetics. While many errors had minor impact on the resulting allele frequencies, multiple error categories were commonly found within single datasets. Several datasets contained serious flaws. We discuss the factors that caused the errors to draw the attention to redundant pitfalls and thus contribute to better quality of autosomal STR datasets and allele frequency reports.

Forensic Source Conclusions

Abstract. Forensic science evidence may be the most powerful weapon in the arsenal of law enforcement officials and prosecutors. As such, it is important to be aware of factors that threaten the validity of forensic source conclusions (e.g., “This bullet was fired from that gun”). This paper identifies 20 threats to the validity of forensic source conclusions including threats that spring from (a) the evidentiary samples themselves, (b) forensic examiners and the procedures they use, (c) features of the law, and (d) cognitive limitations of jurors and others who must weigh forensic science source claims.

Cognitive and Human Factors in Expert Decision Making: Six Fallacies and the Eight Sources of Bias

Fallacies about the nature of biases have shadowed a proper cognitive understanding of biases and their sources, which in turn lead to ways that minimize their impact. Six such fallacies are presented: it is an ethical issue, only applies to "bad apples", experts are impartial and immune, technology eliminates bias, blind spot, and the illusion of control. Then, eight sources of bias are discussed and conceptualized within three categories: (A) factors that relate to the specific case and analysis, which include the data, reference materials, and contextual information, (B) factors that relate to the specific person doing the analysis, which include past experience base rates, organizational factors, education and training, and personal factors, and lastly, (C) cognitive architecture and human nature that impacts all of us. These factors can impact what the data are (e.g., how data are sampled and collected, or what is considered as noise and therefore disregarded), the actual results (e.g., decisions on testing strategies, how analysis is conducted, and when to stop testing), and the conclusions (e.g., interpretation of the results). The paper concludes with specific measures that can minimize these biases.

The Error in "Error Rate": Why Error Rates Are So Needed, Yet So Elusive

Establishing error rates is crucial for knowing how well one is performing, determining whether improvement is needed, measuring whether interventions are effective, as well as for providing transparency. However, the flurry of activities in establishing error rates for the forensic sciences has largely overlooked some fundamental issues that make error rates a problematic construct and limit the ability to obtain a meaningful error rate. These include knowing the ground truth, establishing appropriate databases, determining what counts as an error, characterizing what is an acceptable error rate, ecological validity, and transparency within the adversarial legal system. Without addressing these practical and theoretical challenges, the very notion of a meaningful error rate is limited.

Interpol review of forensic biology and forensic DNA typing 2016-2019

This review paper covers the forensic-relevant literature in biological sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

Human Leukocyte Antigen alleles as an aid to STR in complex forensic DNA samples

Human biological samples with multiple contributors remain one of the most challenging aspects of DNA typing within a forensic science context. With the increasing sensitivity of commercially available kits allowing detection of low template DNA, complex mixtures are now a standard component of forensic DNA evidence. Over the years, various methods and techniques have been developed to try to resolve the issue of mixed profiles. However, forensic DNA analysis has relied on the same markers to generate DNA profiles for the past 30 years causing considerable challenges in the deconvolution of complex mixed samples. The future of resolving complicated DNA mixtures may rely on utilising markers that have been previously applied to gene typing of non-forensic relevance. With Massively Parallel Sequencing (MPS), techniques becoming more popular and accessible even epigenetic markers have become a source of interest for forensic scientists. The aim of this review is to consider the potential of alleles from the Human Leukocyte Antigen (HLA) complex as effective forensic markers. While Massively Parallel Sequencing of HLA is routinely used in clinical laboratories in fields such as transplantation, pharmacology or population studies, there have not been any studies testing its suitability for forensic casework samples.

Inter-laboratory study on standardized MPS libraries: evaluation of performance, concordance, and sensitivity using mixtures and degraded DNA

We present results from an inter-laboratory massively parallel sequencing (MPS) study in the framework of the SeqForSTRs project to evaluate forensically relevant parameters, such as performance, concordance, and sensitivity, using a standardized sequencing library including reference material, mixtures, and ancient DNA samples. The standardized library was prepared using the ForenSeq DNA Signature Prep Kit (primer mix A). The library was shared between eight European laboratories located in Austria, France, Germany, The Netherlands, and Sweden to perform MPS on their particular MiSeq FGx sequencers. Despite variation in performance between sequencing runs, all laboratories obtained quality metrics that fell within the manufacturer’s recommended ranges. Furthermore, differences in locus coverage did not inevitably adversely affect heterozygous balance. Inter-laboratory concordance showed 100% concordant genotypes for the included autosomal and Y-STRs, and still, X-STR concordance exceeded 83%. The exclusive reasons for X-STR discordances were drop-outs at DXS10103. Sensitivity experiments demonstrated that correct allele calling varied between sequencing instruments in particular for lower DNA amounts (≤ 125 pg). The analysis of compromised DNA samples showed the drop-out of one sample (FA10013B01A) while for the remaining three degraded DNA samples MPS was able to successfully type ≥ 87% of all aSTRs, ≥ 78% of all Y-STRs, ≥ 68% of all X-STRs, and ≥ 92% of all iSNPs demonstrating that MPS is a promising tool for human identity testing, which in return, has to undergo rigorous in-house validation before it can be implemented into forensic routine casework.

A crisis for the future of forensic science: Lessons from the UK of the importance of epistemology for funding research and development

This study presents analysis of forensic science research funded by UK Research and Innovation (UKRI) research councils (2009–2018), representing 150 projects with a cumulative value of £56.1 m (0.01% of the total UKRI budget over this time period). The findings indicate that dedicated forensic science funding represents only 46.0% of the projects included in the dataset. Research focussed on developing technological outputs represented 69.5% of the total funding (£37.2 m) in comparison to foundational research which represented 19.2% (£10.7 m). Traditional forensic science evidence types such as fingerprints and DNA received 1.3% and 5.1% of the total funding respectively, in comparison to digital and cyber projects which received 25.7%. These data offer insight into the scale of the funding crisis in forensic science in the UK, and the need to increase the resources available, to develop ways of articulating value and to ensure that both technological and foundational research are enabled.

Perceptions and estimates of error rates in forensic science: A survey of forensic analysts

Every scientific technique features some error, and legal standards for the admissibility of scientific evidence (e.g., Daubert v. Merrill Dow Pharmaceuticals, Inc., 1993; Kumho Tire Co v. Carmichael, 1999) guide trial courts to consider known error rates. However, recent reviews of forensic science conclude that error rates for some common techniques are not well-documented or even established (e.g., NAS, 2009; PCAST, 2016). Furthermore, many forensic analysts have historically denied the presence of error in their field. Therefore, it is important to establish what forensic scientists actually know or believe about errors rates in their disciplines. We surveyed 183 practicing forensic analysts to examine what they think and estimate about error rates in their various disciplines. Results revealed that analysts perceive all types of errors to be rare, with false positive errors even more rare than false negatives. Likewise, analysts typically reported that they prefer to minimize the risk of false positives over false negatives. Most analysts could not specify where error rates for their discipline were documented or published. Their estimates of error in their fields were widely divergent - with some estimates unrealistically low.

Optical tweezers as an effective tool for spermatozoa isolation from mixed forensic samples

A single focus optical tweezer is formed when a laser beam is launched through a high numerical aperture immersion objective. This objective focuses the beam down to a diffraction-limited spot, which creates an optical trap where cells suspended in aqueous solutions can be held fixed. Spermatozoa, an often probative cell type in forensic investigations, can be captured inside this optical trap and dragged one by one across millimeter-length distances in order to create a cluster of cells which can be subsequently drawn up into a capillary for collection. Sperm cells are then ejected onto a sterile cover slip, counted, and transferred to a tube for DNA analysis workflow. The objective of this research was to optimize sperm cell collection for maximum DNA yield, and to determine the number of trapped sperm cells necessary to produce a full STR profile. A varying number of sperm cells from both a single-source semen sample and a mock sexual assault sample were isolated utilizing optical tweezers and processed using conventional STR analysis methods. Results demonstrated that approximately 50 trapped spermatozoa were required to obtain a consistently full DNA profile. A complete, single-source DNA profile was also achieved by isolating sperm cells via optical trapping from a mixture of sperm and vaginal epithelial cells. Based on these results, optical tweezers are a viable option for forensic applications such as separation of mixed populations of cells in forensic evidence.

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.

Probabilistic genotyping software: An overview

The interpretation of mixed profiles from DNA evidentiary material is one of the more challenging duties of the forensic scientist. Traditionally, analysts have used a "binary" approach to interpretation where inferred genotypes are either included or excluded from the mixture using a stochastic threshold and other biological parameters such as heterozygote balance, mixture ratio, and stutter ratios. As the sensitivity of STR multiplexes and capillary electrophoresis instrumentation improved over the past 25 years, coupled with the change in the type of evidence being submitted for analysis (from high quality and quantity (often single-source) stains to low quality and quantity (often mixed) "touch" samples), the complexity of DNA profile interpretation has equally increased. This review provides a historical perspective on the movement from binary methods of interpretation to probabilistic methods of interpretation. We describe the two approaches to probabilistic genotyping (semi-continuous and fully continuous) and address issues such as validation and court acceptance. Areas of future needs for probabilistic software are discussed.