Probabilistic genotyping software: An overview

Advancements in differentiation between sperm cells and epithelial cells for efficient forensic DNA analysis in sexual assault cases

Most of the sexual assault casework samples are of mixed sources. Forensic DNA laboratories are always in the requirement of a precise technique for the efficient separation of sperm and non-sperm DNA from mixed samples. Since the introduction of the differential extraction technique in 1985, it has seen significant advancements in the form of either chemicals used or modification of incubation times. Several automated and semi-automated techniques have also adopted the fundamentals of conventional differential extraction techniques. However, lengthy incubation, several manual steps, and carryover over non-sperm material in sperm fraction are some of the major limitations of this technique. Advanced cell separation techniques have shown huge promise in separating sperm cells from a mixture based on their size, shape, composition, and membrane structure and antigens present on sperm membranes. Such advanced techniques such as DEParray, ADE, FACS, LCM, HOT and their respective pros and cons have been discussed in this article. As current-day forensic techniques should be as per the line of Olympic slogan i.e., faster, higher, stronger, the advanced cell separation techniques show a huge potential to be implemented in the casework samples.

Ultrasensitive sequencing of STR markers utilizing unique molecular identifiers and the SiMSen-Seq method

Massively parallel sequencing (MPS) is increasingly applied in forensic short tandem repeat (STR) analysis. The presence of stutter artefacts and other PCR or sequencing errors in the MPS-STR data partly limits the detection of low DNA amounts, e.g., in complex mixtures. Unique molecular identifiers (UMIs) have been applied in several scientific fields to reduce noise in sequencing. UMIs consist of a stretch of random nucleotides, a unique barcode for each starting DNA molecule, that is incorporated in the DNA template using either ligation or PCR. The barcode is used to generate consensus reads, thus removing errors. The SiMSen-Seq (Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing) method relies on PCR-based introduction of UMIs and includes a sophisticated hairpin design to reduce unspecific primer binding as well as PCR protocol adjustments to further optimize the reaction. In this study, SiMSen-Seq is applied to develop a proof-of-concept seven STR multiplex for MPS library preparation and an associated bioinformatics pipeline. Additionally, machine learning (ML) models were evaluated to further improve UMI allele calling. Overall, the seven STR multiplex resulted in complete detection and concordant alleles for 47 single-source samples at 1 ng input DNA as well as for low-template samples at 62.5 pg input DNA. For twelve challenging mixtures with minor contributions of 10 pg to 150 pg and ratios of 1-15% relative to the major donor, 99.2% of the expected alleles were detected by applying the UMIs in combination with an ML filter. The main impact of UMIs was a substantially lowered number of artefacts as well as reduced stutter ratios, which were generally below 5% of the parental allele. In conclusion, UMI-based STR sequencing opens new means for improved analysis of challenging crime scene samples including complex mixtures.

An overview of autosomal STRs and identity SNPs in a Norwegian population using massively parallel sequencing

In recent years, probabilistic genotyping software has been adapted for the analysis of massively parallel sequencing (MPS) forensic data. Likelihood ratios (LR) are based on allele frequencies selected from populations of interest. This study provides an outline of sequence-based (SB) allele frequencies for autosomal short tandem repeats (aSTRs) and identity single nucleotide polymorphisms (iSNPs) in 371 individuals from Southern Norway. 27 aSTRs and 94 iSNPs were previously analysed with the ForenSeq™ DNA Signature Prep Kit (Verogen). The number of alleles with frequencies less than 0.05 for sequenced-based alleles was 4.6 times higher than for length-based alleles. Consistent with previous studies, it was observed that sequence-based data (both with and without flanks) exhibited higher allele diversity compared to length-based (LB) data; random match probabilities were lower for SB alleles confirming their advantage to discriminate between individuals. Two alleles in markers D22S1045 and Penta D were observed with SNPs in the 3´ flanking region, which have not been reported before. Also, a novel SNP with a minor allele frequency (MAF) of 0.001, was found in marker TH01. The impact of the sample size on minor allele frequency (MAF) values was studied in 88 iSNPs from Southern Norway (n = 371). The findings were then compared to a larger Norwegian population dataset (n = 15,769). The results showed that the smaller Southern Norway dataset provided similar results, and it was a representative sample. Population structure was analyzed for regions within Southern Norway; FST estimates for aSTR and iSNPs did not indicate any genetic structure. Finally, we investigated the genetic differences between Southern Norway and two other populations: Northern Norway and Denmark. Allele frequencies between these populations were compared, and we found no significant frequency differences (p-values > 0.0001). We also calculated the pairwise FST values per marker and comparisons between Southern and Northern Norway showed small differences. In contrast, the comparisons between Southern Norway and Denmark showed higher FST values for some markers, possibly driven by distinct alleles that were present in only one of the populations. In summary, we propose that allele frequencies from each population considered in this study could be used interchangeably to calculate genotype probabilities.

Improved individual identification in DNA mixtures of unrelated or related contributors through massively parallel sequencing

DNA mixtures are a common sample type in forensic genetics, and we typically assume that contributors to the mixture are unrelated when calculating the likelihood ratio (LR). However, scenarios involving mixtures with related contributors, such as in family murder or incest cases, can also be encountered. Compared to the mixtures with unrelated contributors, the kinship within the mixture would bring additional challenges for the inference of the number of contributors (NOC) and the construction of probabilistic genotyping models. To evaluate the influence of potential kinship on the individual identification of the person of interest (POI), we conducted simulations of two-person (2 P) and three-person (3 P) DNA mixtures containing unrelated or related contributors (parent-child, full-sibling, and uncle-nephew) at different mixing ratios (for 2 P: 1:1, 4:1, 9:1, and 19:1; for 3 P: 1:1:1, 2:1:1, 5:4:1, and 10:5:1), and performed massively parallel sequencing (MPS) using MGIEasy Signature Identification Library Prep Kit on MGI platform. In addition, in silico simulations of mixtures with unrelated and related contributors were also performed. In this study, we evaluated 1): the MPS performance; 2) the influence of multiple genetic markers on determining the presence of related contributors and inferring the NOC within the mixture; 3) the probability distribution of MAC (maximum allele count) and TAC (total allele count) based on in silico mixture profiles; 4) trends in LR values with and without considering kinship in mixtures with related and unrelated contributors; 5) trends in LR values with length- and sequence-based STR genotypes. Results indicated that multiple numbers and types of genetic markers positively influenced kinship and NOC inference in a mixture. The LR values of POI were strongly dependent on the mixing ratio. Non- and correct-kinship hypotheses essentially did not affect the individual identification of the major POI; the correct kinship hypothesis yielded more conservative LR values; the incorrect kinship hypothesis did not necessarily lead to the failure of POI individual identification. However, it is noteworthy that these considerations could lead to uncertain outcomes in the identification of minor contributors. Compared to length-based STR genotyping, using sequence-based STR genotype increases the individual identification power of the POI, concurrently improving the accuracy of mixing ratio inference using EuroForMix. In conclusion, the MGIEasy Signature Identification Library Prep kit demonstrated robust individual identification power, which is a viable MPS panel for forensic DNA mixture interpretations, whether involving unrelated or related contributors.

On forensic likelihood ratios from low-coverage sequencing

With advances in sequencing technology, forensic workers can access genetic information from increasingly challenging samples. A recently published computational approach, IBDGem , analyzes sequencing reads, including from low-coverage samples, in order to arrive at likelihood ratios for tests of identity. Here, we show that likelihood ratios produced by IBDGem test a null hypothesis different from the traditional one used in a forensic genetics context. In particular, rather than testing the hypothesis that the sample comes from a person unrelated to the person of interest, IBDGem tests the hypothesis that the sample comes from an individual who is included in the reference sample used to run the method. This null hypothesis is not generally of forensic interest, because the defense hypothesis is not that the evidence comes from an individual included in a reference panel. Further, it does not take into account genetic variation outside the reference panel, and as a result, the computed likelihood ratios can be much larger than likelihood ratios computed for the standard forensic null hypothesis, often by many orders of magnitude, thus potentially creating an impression of stronger evidence for identity than is warranted. We lay out this result and illustrate it with examples, giving suggestions for directions that might lead to likelihood ratios that have the traditional interpretation.

Mixture detection with Demixtify

The de facto genetic markers of forensics are short tandem repeats (STRs). There are many analytical tools designed to work with STRs, including techniques for analyzing and assessing DNA mixtures. In contrast, the nascent field of forensic genetic genealogy often relies on biallelic single nucleotide polymorphisms (SNPs). Tools designed for the forensic assessment of SNPs are somewhat lacking, especially for DNA mixtures. In this paper we introduce Demixtify, a program that detects DNA mixtures using biallelic SNPs. Demixtify is quite powerful; highly imbalanced mixtures can be detected (≤1:99, considering in silico and in vitro mixtures) when coverage is ample. Demixtify can also detect mixtures in low coverage (∼1×) samples (when the mixture is relatively balanced). Demixtify includes an empirical estimator of sequence error that is specific to the markers assayed, making it especially relevant to the forensic community. Orthogonal techniques are also developed to characterize in vitro mixtures, as well as samples thought to be single source, and the results of these approaches serve to validate the techniques presented.

Evaluating genotyping-in-thousands by sequencing as a genetic monitoring tool for a climate sentinel mammal using non-invasive and archival samples

Genetic tools for wildlife monitoring can provide valuable information on spatiotemporal population trends and connectivity, particularly in systems experiencing rapid environmental change. Multiplexed targeted amplicon sequencing techniques, such as genotyping-in-thousands by sequencing (GT-seq), can provide cost-effective approaches for collecting genetic data from low-quality and quantity DNA samples, making them potentially useful for long-term wildlife monitoring using non-invasive and archival samples. Here, we developed a GT-seq panel as a potential monitoring tool for the American pika (Ochotona princeps) and evaluated its performance when applied to traditional, non-invasive, and archival samples, respectively. Specifically, we optimized a GT-seq panel (307 single nucleotide polymorphisms (SNPs)) that included neutral, sex-associated, and putatively adaptive SNPs using contemporary tissue samples (n = 77) from the Northern Rocky Mountains lineage of American pikas. The panel demonstrated high genotyping success (94.7%), low genotyping error (0.001%), and excellent performance identifying individuals, sex, relatedness, and population structure. We subsequently applied the GT-seq panel to archival tissue (n = 17) and contemporary fecal pellet samples (n = 129) collected within the Canadian Rocky Mountains to evaluate its effectiveness. Although the panel demonstrated high efficacy with archival tissue samples (90.5% genotyping success, 0.0% genotyping error), this was not the case for the fecal pellet samples (79.7% genotyping success, 28.4% genotyping error) likely due to the exceptionally low quality/quantity of recovered DNA using the approaches implemented. Overall, our study reinforced GT-seq as an effective tool using contemporary and archival tissue samples, providing future opportunities for temporal applications using historical specimens. Our results further highlight the need for additional optimization of sample and genetic data collection techniques prior to broader-scale implementation of a non-invasive genetic monitoring tool for American pikas.

A New Tool for Probabilistic Assessment of MPS Data Associated with mtDNA Mixtures

Mitochondrial (mt) DNA plays an important role in the fields of forensic and clinical genetics, molecular anthropology, and population genetics, with mixture interpretation being of particular interest in medical and forensic genetics. The high copy number, haploid state (only a single haplotype contributed per individual), high mutation rate, and well-known phylogeny of mtDNA, makes it an attractive marker for mixture deconvolution in damaged and low quantity samples of all types. Given the desire to deconvolute mtDNA mixtures, the goals of this study were to (1) create a new software, MixtureAceMT™, to deconvolute mtDNA mixtures by assessing and combining two existing software tools, MixtureAce™ and Mixemt, (2) create a dataset of in-silico MPS mixtures from whole mitogenome haplotypes representing a diverse set of population groups, and consisting of two and three contributors at different dilution ratios, and (3) since amplicon targeted sequencing is desirable, and is a commonly used approach in forensic laboratories, create biological mixture data associated with two amplification kits: PowerSeq™ Whole Genome Mito (Promega™, Madison, WI, USA) and Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific by AB™, Waltham, MA, USA) to further validate the software for use in forensic laboratories. MixtureAceMT™ provides a user-friendly interface while reducing confounding features such as NUMTs and noise, reducing traditionally prohibitive processing times. The new software was able to detect the correct contributing haplogroups and closely estimate contributor proportions in sequencing data generated from small amplicons for mixtures with minor contributions of ≥5%. A challenge of mixture deconvolution using small amplicon sequencing is the potential generation of spurious haplogroups resulting from private mutations that differ from Phylotree. MixtureAceMT™ was able to resolve these additional haplogroups by including known haplotype/s in the evaluation. In addition, for some samples, the inclusion of known haplotypes was also able to resolve trace contributors (minor contribution 1-2%), which remain challenging to resolve even with deep sequencing.

Effect existence of aging on stutter ratio evaluated via Bayesian inference

The stochastic behavior of the stutter ratio (SR) in capillary electrophoresis-based DNA typing is currently described and predicted using statistical models in forensic genetics. Clarifying this behavior can help obtain more objective and robust evidence to the court in terms of mixture interpretation. This study aimed to investigate the effect existence of aging on SR via a Bayesian framework. Nail scrapings and clippings were collected from 68 healthy individuals with informed consent. Samples were classified by age-class: young group (0-16 years; n = 36) and older-adult group (>61 years; n = 32). Then, they were compared in terms of their SRs for each simple repeat locus included in GlobalFiler Kit. Bayesian modeling was performed with lognormal distribution model, which implemented multiple linear regression, allele and age-class as explanatory variables. For all simple repeat loci, the median of the posterior distribution of the age-class parameter was a positive value. For CSF1PO and D7S820, the 95% credible interval of the posterior distribution did not include 0. Our data suggested that aging slightly increases the SR. These findings might help elucidate the stochastic behavior of SR.

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.

Addressing uncertain assumptions in DNA evidence evaluation

Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant's profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.

Carrying out common DNA donor analysis using DBLR™ on two or five-cell mini-mixture subsamples for improved discrimination power in complex DNA mixtures

Probabilistic genotyping systems are able to analyse complex mixed DNA profiles and show good power to discriminate contributors from non-contributors. However, the abilities of the statistical analyses are still unavoidably bound by the quality of information being analysed. If a profile has a high number of contributors, or a contributor that is present in trace amounts, then the amount of information about those individuals in the DNA profile is limited. Recent work has shown the ability to gain better resolution of the genotypes of contributors to complex profiles using cell subsampling. This is the process of taking many sets of a limited number of cells and individually profiling each set. These 'mini-mixtures' can provide greater information about the genotypes of underlying contributors. In our work we take the resulting profiles from multiple subsamplings of complex DNA profiles in equal amounts and show how testing for, and then assuming, a common DNA donor can further improve the ability to resolve the genotypes of contributors. Using direct cell sub-sampling and statistical analysis software DBLR™, we were able to recover single source profiles of uploadable quality from five out of the six contributors of an equally proportioned mixture. Through the analysis of mixtures in this work we provide a template for carrying out common donor analysis for maximum effect.

A lights-out forensic DNA analysis workflow for no-suspect crime

An automated system of DNA profile processing (termed a 'lights-out' workflow) was trialled for no-suspect cases over a three-month period at Forensic Science SA (FSSA). The lights-out workflow utilised automated DNA profile reading using the neural network reading feature in FaSTR™ DNA with no analytical threshold. The profile information from FaSTR™ DNA was then processed in STRmix™ using a top-down analysis and automatically compared to a de-identified South Australian searchable DNA database. Computer scripts were used to generate link reports and upload reports and these were compared to the links and uploads that were obtained for the cases during their standard processing within the laboratory. The results of the lights-out workflow was an increase in both uploads and links compared to the standard workflow, with minimal adventitious links or erroneous uploads. Overall, the proof-of-concept study shows the potential for using automated DNA profile reading and top-down analysis to improve workflow efficiency in a no-suspect workflow.

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.

From crime scene to courtroom: A review of the current bioanalytical evidence workflows used in rape and sexual assault investigations in the United Kingdom

Sexual assault casework requires the collaboration of multiple agency staff to formalise an investigative pipeline running from crime scene to court. While the same could be said of many other forensic investigations, few require the additional support of health care staff and the combined forensic involvement of body-fluid examiners, DNA experts and analytical chemists. The sheer amount of collaborative effort between agencies is laid out through a detailed examination of the investigative workflow from crime scene to courtroom with each step in the pipelines detailed and discussed. Beginning with a review of sexual assault legislation in the United Kingdom this article details how sexual assault investigations are initiated by police and supported by sexual assault referral centre (SARC) staff who are often the first responders providing primary healthcare and patient support to victims while simultaneously collecting and assessing forensic evidence. Detailing the myriad of evidential material that can be documented and collected at the SARC, the review identifies and categorises key forensic tests to first detect and identify body-fluids recovered from evidence through to the secondary analysis of DNA to help identify the suspect. This review also focusses on the collection and analysis of biological material used to support the allegation that the sexual activity was non-consensual and provides a breakdown of common marks and trauma as well as a review of common analytical methods used to infer Drug Facilitated Sexual Assault (DFSA). The culmination of the investigative pipeline is discussed by reviewing the Rape and Serious Sexual Assault (RASSO) workflow used by the Crown Prosecution Service before providing our thoughts on the future of forensic analysis and possible changes to the described workflows.

Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives

In recent years, new studies based on artificial intelligence (AI) have been conducted in the forensic field, posing new challenges and demonstrating the advantages and disadvantages of using AI methodologies to solve forensic well-known problems. Specifically, AI technology has tried to overcome the human subjective bias limitations of the traditional approach of the forensic sciences, which include sex prediction and age estimation from morphometric measurements in forensic anthropology or evaluating the third molar stage of development in forensic odontology. Likewise, AI has been studied as an assisting tool in forensic pathology for a quick and easy identification of the taxonomy of diatoms. The present systematic review follows the PRISMA 2020 statements and aims to explore an emerging topic that has been poorly analyzed in the forensic literature. Benefits, limitations, and forensic implications concerning AI are therefore highlighted, by providing an extensive critical review of its current applications on forensic sciences as well as its future directions. Results are divided into 5 subsections which included forensic anthropology, forensic odontology, forensic pathology, forensic genetics, and other forensic branches. The discussion offers a useful instrument to investigate the potential benefits of AI in the forensic fields as well as to point out the existing open questions and issues concerning its application on real-life scenarios. Procedural notes and technical aspects are also provided to the readers.

Magnetic bead-based separation of sperm cells from semen-vaginal fluid mixed stains using an anti-ACRBP antibody

Forensic DNA analysis of semen-vaginal fluid mixed stains is essential and necessary in sexual assault cases. Here, we used a magnetic bead conjugated acrosin binding protein (ACRBP) antibody to separate and enrich sperm cells from mixed stains. Previously, western blotting indicated that ACRBP was specifically expressed in sperm cells, but not in female blood and epithelial cells, while immunofluorescence data showed ACRBP was localized to the acrosome in sperm cells. In our study, sperm were separated from mixed samples at three sperm cell/female buccal epithelial cell ratios (10³:10³; 10³:10⁴; and 10³:10⁵) using a magnetic bead conjugated ACRBP antibody. Subsequently, 23 autosomal short tandem repeat (STR) loci were amplified using the Huaxia™ Platinum PCR Amplification System and genotyped using capillary electrophoresis. The genotyping success rate for STR loci was 90% when the sperm to female buccal epithelial cell ratio was > 1:100 in mixed samples. Our results suggest that the magnetic bead conjugated ACRBP antibody is effective for isolating sperm cells in sexual assault cases.

Einsatz vollkontinuierlicher Modelle zur biostatistischen Bewertung forensischer DNA-analytischer Befunde

Die biostatistische Bewertung DNA-analytischer Befunde unterstützt Gerichte bei der Einschätzung des Beweiswertes einer Spur. In der Praxis werden dabei zunehmend Spuren mit minimaler DNA-Menge und möglichen „Drop-in“- und „Drop-out“-Ereignissen sowie komplexe Mischspuren analysiert. Solche Spuren sind mit einer klassischen „binären“ Berechnung biostatistisch häufig nicht oder nur eingeschränkt bewertbar.

Die Entwicklung vollkontinuierlicher Modelle (VKM) macht eine Vielzahl dieser bisher nicht berechenbaren Spuren einer biostatistischen Bewertung zugänglich. Dabei werden nahezu sämtliche verfügbaren Informationen einer DNA-Spur in die Berechnung einbezogen. Während diese probabilistischen Verfahren international bereits vielfach zum Einsatz kommen, liegen hierzu im deutschsprachigen Raum nur wenige Erfahrungen vor.

Um Funktionsweise, Möglichkeiten und Grenzen von VKM-Berechnungen zu erfassen, wurden Mischspuren bekannter Zusammensetzung mit 4 aktuell verfügbaren VKM-Programmen vergleichend analysiert. Bei der Auswertung wurden zentrale Aspekte betrachtet, wie beispielsweise die Konkordanz von Berechnungsergebnissen, der Einfluss von Drop-in- und Drop-out-Ereignissen auf die berechneten vollkontinuierlichen LR-Werte (LRfc) sowie die Ableitung recherchefähiger DNA-Profile mithilfe wahrscheinlichkeitsbasierter Prognosen (Deconvolution).

Die im Rahmen dieser Arbeit gewonnenen Erfahrungen bilden, zusammen mit weiteren bereits international publizierten Studien, eine Basis für Empfehlungen zum Einsatz von VKM-basierter Software bei der biostatistischen Bewertung DNA-analytischer Befunde.

Y-STR mixture deconvolution by single-cell analysis

Since Y-STR typing only amplifies male Y chromosomal DNA, it can simplify the interpretation of some DNA mixtures that contain female DNA. However, if there are multiple male contributors, mixed Y-STR DNA profiles will often be obtained. Y-STR mixture analysis cases are particularly challenging though as, currently, there are no validated probabilistic genotyping (PG) software solutions commercially available to aid in their interpretation. One approach to fully deconvoluting these challenging mixtures into their individual donors is to conduct single-cell genotyping by isolating individual cells from a mixture prior to conducting DNA typing. In this work, a physical micromanipulation technique involving a tungsten needle and direct PCR with decreased reaction volume and increased cycle number was applied to equimolar 2- and 3-person buccal cell male DNA mixtures and a mock touch DNA case scenario involving the consecutive firing of a handgun by two males. A consensus DNA profiling approach was then utilized to obtain YFiler™ Plus Y-STR haplotypes. Buccal cells were used to optimize and test the direct single-cell subsampling approach, and 2-3 person male buccal cell mixtures were fully deconvoluted into their individual donor Y-STR haplotypes. Single-cell (or agglomerated cell clump) subsampling from the gun's trigger recovered single-source Y-STR profiles from both individuals who fired the gun, the owner, and the other unrelated male. Only the non-owner's DNA was found in the cells recovered from the handle. In summary, direct single-cell subsampling as described represents a potential simple way to analyze and interpret Y-STR mixtures.

LT-RPA: An Isothermal DNA Amplification Approach for Improved Microsatellite Genotyping and Microsatellite Instability Detection

Microsatellites are short tandem repeats of one to six nucleotides that are highly polymorphic and extensively used as genetic markers in numerous biomedical applications, including the detection of microsatellite instability (MSI) in cancer. The standard analytical method for microsatellite analysis relies on PCR amplification followed by capillary electrophoresis or, more recently, next-generation sequencing (NGS). However, their amplification during PCR generates undesirable frameshift products known as stutter peaks caused by polymerase slippage, complicating data analysis and interpretation, while very few alternative methods for microsatellite amplification have been developed to reduce the formation of these artifacts. In this context, the recently developed low-temperature recombinase polymerase amplification (LT-RPA) is an isothermal DNA amplification method at low temperature (32 °C) that drastically reduces and sometimes completely abolishes the formation of stutter peaks. LT-RPA greatly simplifies the genotyping of microsatellites and improves the detection of MSI in cancer. In this chapter, we describe in detail all the experimental steps necessary for the development of LT-RPA simplex and multiplex assays for microsatellite genotyping and MSI detection, including the design, optimization, and validation of the assays combined with capillary electrophoresis or NGS.

Developmental validation of STRmix™ NGS, a probabilistic genotyping tool for the interpretation of autosomal STRs from forensic profiles generated using NGS

We describe the developmental validation of the probabilistic genotyping software - STRmix™ NGS - developed for the interpretation of forensic DNA profiles containing autosomal STRs generated using next generation sequencing (NGS) also known as massively parallel sequencing (MPS) technologies. Developmental validation was carried out in accordance with the Scientific Working Group on DNA Analysis Methods (SWGDAM) Guidelines for the Validation of Probabilistic Genotyping Systems and the International Society for Forensic Genetics (ISFG) recommendations and included sensitivity and specificity testing, accuracy, precision, and the interpretation of case-types samples. The results of developmental validation demonstrate the appropriateness of the software for the interpretation of profiles developed using NGS technology.

A comparison of likelihood ratios with and without assuming relatedness for DNA mixtures interpreted using a continuous model

When evaluating support for the contribution of a person of interest (POI) to a mixed DNA sample, it is generally assumed that the mixture contributors are unrelated to the POI and to each other. In practice, there may be situations where this assumption is violated, for instance if two mixture contributors are siblings. The effect on the likelihood ratio of (in)correctly assuming relatedness between mixture contributors has previously been investigated using simulation studies based on simplified models ignoring peak heights. We revisit this problem using a simulation study that applies peak height models both in the simulation and mixture interpretation part of the study. Specifically, we sample sets of mixtures comprising both related and unrelated contributors and evaluate support for the contribution of the mixture donors as well as unrelated persons with and without incorporating an assumption of relatedness. The results show, consistent with earlier studies, that including a correct assumption of relatedness increases the capacity of the probabilistic genotyping system to distinguish between mixture donors and unrelated persons. Any effect of the relatedness is found to depend strongly on the mixture ratio. We further show that the results do not change materially when a sub-population correction is applied. Finally, we suggest and discuss a likelihood ratio approach that considers relatedness between mixture contributors using a prior probability.

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019-2022

This review paper covers the forensic-relevant literature in biological sciences from 2019 to 2022 as a part of the 20th INTERPOL International Forensic Science Managers Symposium. Topics reviewed include rapid DNA testing, using law enforcement DNA databases plus investigative genetic genealogy DNA databases along with privacy/ethical issues, forensic biology and body fluid identification, DNA extraction and typing methods, mixture interpretation involving probabilistic genotyping software (PGS), DNA transfer and activity-level evaluations, next-generation sequencing (NGS), DNA phenotyping, lineage markers (Y-chromosome, mitochondrial DNA, X-chromosome), new markers and approaches (microhaplotypes, proteomics, and microbial DNA), kinship analysis and human identification with disaster victim identification (DVI), and non-human DNA testing including wildlife forensics. Available books and review articles are summarized as well as 70 guidance documents to assist in quality control that were published in the past three years by various groups within the United States and around the world.

Gemeinsame Empfehlungen der Projektgruppe „Biostatistische DNA-Berechnungen“ und der Spurenkommission zur biostatistischen Bewertung forensischer DNA-analytischer Befunde mit vollkontinuierlichen Modellen (VKM)

Die biostatistische Bewertung DNA-analytischer Befunde unterstützt Gerichte bei der Einschätzung des Beweiswertes hinsichtlich einer möglichen Spurenbeteiligung durch eine zu betrachtende Person (engl. „Person Of Interest“; POI). Um die Vergleichbarkeit derartiger Berechnungen auf Grundlage etablierter wissenschaftlicher Standards zu gewährleisten, wurden bereits in der Vergangenheit entsprechende Empfehlungen im nationalen Konsens formuliert.

Mit Einführung sog. vollkontinuierlicher Modelle (VKM) für die probabilistische Genotypisierung, die u. a. die Signalintensitäten eines Elektropherogramms berücksichtigen, wurde eine Ergänzung zu den damaligen Empfehlungen erforderlich. VKM erlauben eine biostatistische Bewertung von Spuren mit möglichen Drop-in- und Drop-out-Ereignissen und wahrscheinlichkeitsbasierte Prognosen der zu einer Mischspur beitragenden Genotypen („Deconvolution“).

Die vorliegende Veröffentlichung enthält Empfehlungen zum Einsatz VKM-basierter Software und zur Berichterstattung vollkontinuierlicher LR-Werte (engl. „Fully Continuous Likelihood Ratios“; LRfc). Sie empfiehlt bei schwierig zu interpretierenden Befunden eine VKM-Berechnung zur Bewertung einer Spurenlegerschaft. Die VKM-Berechnung ersetzt die bisher in Ausnahmefällen als hinnehmbar erachtete Vorgehensweise einer binären Berechnung unter Ausklammern einzelner Merkmalssysteme. Der Einsatz von VKM erfordert eine umfassende Anwenderschulung sowie eine Validierung und Verifizierung gemäß den Vorgaben der Programmanbieter. Mit der Empfehlung von LRfc-Schwellenwerten soll eine sichere, vergleichbare Anwendung von VKM gewährleistet werden.

A multipurpose panel of microhaplotypes for use with STR markers in casework

A small panel of highly informative loci that can be genotyped on the same equipment as the standard CODIS short tandem repeat (STR) markers has strong potential for application in forensic casework. Single nucleotide polymorphisms (SNPs) can be typed by a couple of methods on capillary electrophoresis (CE) machines and on sequencers, but the amount of information relative to the laboratory effort has hindered use of SNPs in actual casework. Insertion-deletion markers (InDels) suffer from similar problems. Microhaplotypes (MHs) are much more informative per locus but have similar technical difficulties unless they are typed by massively parallel sequencing (MPS). As forensic labs are acquiring sequencing machines, MHs become more likely to be used in casework, especially if multiplexed with STRs. Here we present the details of a multipurpose panel of 24 MHs with the highest effective number of alleles (Ae) from previous work. An augmented STR panel of 24 loci (20 CODIS markers plus four commonly typed STRs) is also considered. The Ae and ancestry informativeness (In) distributions of these two datasets are compared. The MH panel is shown to have better individualization and population distinction than the augmented CODIS STRs. We note that the 24 MHs should be better for mixture analyses than the STRs. Finally, we suggest that a commercial kit including both the standard CODIS markers and this set of 24 MH would greatly improve the discrimination power over that of current commercial assays.

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.

State of the Art for Microhaplotypes

In recent years, the number of publications on microhaplotypes has averaged more than a dozen papers annually. Many have contributed to a significant increase in the number of highly polymorphic microhaplotype loci. This increase allows microhaplotypes to be very informative in four main areas of forensic uses of DNA: individualization, ancestry inference, kinship analysis, and mixture deconvolution. The random match Probability (RMP) can be as small as 10−100 for a large panel of microhaplotypes. It is possible to measure the heterozygosity of an MH as the effective number of alleles (Ae). Ae > 7.5 exists for African populations and >4.5 exists for Native American populations for a smaller panel of two dozen selected microhaplotypes. Using STRUCTURE, at least 10 different ancestral clusters can be defined by microhaplotypes. The Ae for a locus is also identical to the Paternity Index (PI), the measure of how informative a locus will be in parentage testing. High Ae loci can also be useful in missing persons cases. Finally, high Ae microhaplotypes allow the near certainty of seeing multiple additional alleles in a mixture of two or more individuals in a DNA sample. In summary, a panel of higher Ae microhaplotypes can outperform the standard CODIS markers.

A tool for simulating single source and mixed DNA profiles

Simulation studies play an important role in the study of probabilistic genotyping systems, as a low cost and fast alternative to in vitro studies. With ongoing calls for further study of the behaviour of probabilistic genotyping systems, there is a continuous need for such studies. In most cases, researchers use simplified models, for example ignoring complexities such as peak height variability due to lack of availability of advanced tools. We fill this void and describe a tool that can simulate DNA profiles in silico for the validation and investigation of probabilistic genotyping software. Contributor genotypes are simulated by randomly sampling alleles from selected allele frequencies. Some or all contributors may be related to a pedigree and the genotypes of non-founders are obtained by random gene dropping. The number of contributors per profile, and ranges for parameters such as DNA template amount and degradation parameters can be configured. Peak height variability is modelled using a lognormal distribution or a gamma distribution. Profile behaviour of simulated profiles is shown to be broadly similar to laboratory generated profiles though the latter shows more variation. Simulation studies do not remove the need for experimental data. The tool has been made available as an R-package named simDNAmixtures.

New Perspectives for Whole Genome Amplification in Forensic STR Analysis

Modern PCR-based analytical techniques have reached sensitivity levels that allow for obtaining complete forensic DNA profiles from even tiny traces containing genomic DNA amounts as small as 125 pg. Yet these techniques have reached their limits when it comes to the analysis of traces such as fingerprints or single cells. One suggestion to overcome these limits has been the usage of whole genome amplification (WGA) methods. These methods aim at increasing the copy number of genomic DNA and by this means generate more template DNA for subsequent analyses. Their application in forensic contexts has so far remained mostly an academic exercise, and results have not shown significant improvements and even have raised additional analytical problems. Until very recently, based on these disappointments, the forensic application of WGA seems to have largely been abandoned. In the meantime, however, novel improved methods are pointing towards a perspective for WGA in specific forensic applications. This review article tries to summarize current knowledge about WGA in forensics and suggests the forensic analysis of single-donor bioparticles and of single cells as promising applications.

Exploring likelihood ratios assigned for siblings of the true mixture contributor as an alternate contributor

Relatives tend to have more DNA in common than unrelated people. The closer the biological relationship, the higher the chance of alleles being identical by descent between the individuals. Therefore, when considering a mixed DNA profile, close relatives of the true contributor may not always be excluded as a possible contributor to a mixture due to allele sharing. In these situations, it might be more appropriate under the alternate proposition to consider that the DNA could have originated from a relative of the person of interest rather than an unrelated individual. The probabilistic genotyping software STRmix™ automatically provides LRs considering close biological relatives as alternate sources of the DNA. In this paper, we investigate the support for siblings of the true contributor to a mixture (who are not present in the mixture themselves). We interpret the mixtures and assign LRs using STRmix™ and investigate whether the resulting LRs could be used to indicate whether the true contributor could be a sibling of the POI. Most siblings will have one or more alleles that are not observed in the mixture profile. Support for siblings to have contributed can only occur when allelic dropout is a possibility at the loci where the siblings have alleles that are not observed in the profile. In these data, that was only observed in components with assigned template of 588 rfu or less.

Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable artificial intelligence (XAI) to help users understand why specific predictions are made. Where previous attempts at explainability for NOC estimation have relied upon using simpler, more understandable models that achieve lower accuracy, we use techniques that can be applied to any machine learning model. Our explanations incorporate SHAP values and counterfactual examples for each prediction into a single visualization. Existing methods for generating counterfactuals focus on uncorrelated features. This makes them inappropriate for the highly correlated features derived from STR data for NOC estimation, as these techniques simulate combinations of features that could not have resulted from an STR profile. For this reason, we have constructed a new counterfactual method, Realistic Counterfactuals (ReCo), which generates realistic counterfactual explanations for correlated data. We show that ReCo outperforms state-of-the-art methods on traditional metrics, as well as on a novel realism score. A user evaluation of the visualization shows positive opinions of end-users, which is ultimately the most appropriate metric in assessing explanations for real-world settings.

Using big data from probabilistic genotyping to solve crime

Forensic Science South Australia (FSSA) has been using STRmix™ software to deconvolute all reported DNA mixtures since 2012. Almost a decade of deconvolutions had led to a substantial repository of analysed profile data that can be interrogated to observe trends in case type, location or occurrence. In addition, deconvolutions can be compared in order to identify common DNA donors and reveal new intelligence information in cases where DNA profiling has previously provided no investigative information. As a proof of concept all samples deconvoluted as part of criminal casework (suspect or no-suspect) were interrogated and compared to each other using the mixture-to-mixture comparison feature in STRmix™. Within the Adelaide region there were 32 groups of cases that had evidence samples linked by a common DNA donor with LR > 1 million which was in addition to direct links and mixture searching links identified previously. These groups of cases can then be interrogated to reveal additional information to inform Police intelligence gathering. Our paper reports on the findings of this proof-of-concept study.

Open practices in our science and our courtrooms

Advocates of transparency in science often point to the benefits of open practices for the scientific process. Here, we focus on a possibly underappreciated effect of standards for transparency: their influence on non-scientific decisions. As a case study, we consider the current state of probabilistic genotyping software in forensics.

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.

A novel computational strategy to predict the value of the evidence in the SNP-based forensic mixtures

This study introduces a methodology for inferring the weight of the evidence (WoE) in the single nucleotide polymorphism (SNP)-typed DNA mixtures of forensic interest. First, we redefined some algebraic formulae to approach the semi-continuous calculation of likelihoods and likelihood ratios (LRs). To address the allelic dropouts, a peak height ratio index (“h,” an index of heterozygous state plausibility) was incorporated into semi-continuous formulae to act as a proxy for the “split-drop” model of calculation. Second, the original ratio at which a person of interest (POI) has entered into the mixture was inferred by evaluating the DNA amounts conferred by unique genotypes to any possible permutation of any locus of the typing protocol (unique genotypes are genotypes that appear just once in the relevant permutation). We compared this expected ratio (MR_ex) to all the mixing ratios emerging at all other permutations of the mixture (MR_obs) using several (1 - χ²) tests to evaluate the probability of each permutation to exist in the mixture according to quantitative criteria. At the level of each permutation state, we multiplied the (1 - χ²) value to the genotype frequencies and the h index. All the products of all the permutation states were finally summed to give a likelihood value that accounts for three independent properties of the mixtures. Owing to the (1 - χ²) index and the h index, this approach qualifies as a fully continuous methodology of LR calculation. We compared the MRs and LRs emerging from our methodology to those generated by the EuroForMix software ver. 3.0.3. When the true contributors were tested as POIs, our procedure generated highly discriminant LRs that, unlike EuroForMix, never overcame the corresponding single-source LRs. When false contributors were tested as POIs, we obtained a much lower LR value than that from EuroForMix. These two findings indicate that our computational method is more reliable and realistic than EuroForMix.

A highly polymorphic panel of 40-plex microhaplotypes for the Chinese Han population and its application in estimating the number of contributors in DNA mixtures

Microhaplotypes (MHs) have great potential in multiple forensic applications and have proven to be promising markers in complex DNA mixture analysis. In this study, we developed a multiplex panel of 40 highly polymorphic MHs for the Chinese Han population, evaluated its forensic values, and explored its application in predicting the number of contributors (NOCs) in DNA mixtures. The panel consisted of 20 newly proposed loci and 20 previously reported loci with lengths spanning less than 120 bp. The average effective number of alleles (A_e) was 3.77, and the cumulative matching probability (CMP) and the cumulative power of exclusion (CPE) reached 1.2E-37 and 1-2.1E-12, respectively, in the Chinese Han population from the 1000 Genomes Project. Further validation on 150 Chinese Han individuals showed that A_e ranged from 2.62 to 4.41 with a mean value of 3.61, and CMP and CPE were 3.61E-36 and 1-1.84E-12, respectively, indicating that this panel was informative for personal identification and paternity testing in the studied population. To estimate NOC in DNA mixtures, we developed a machine learning model based on this panel. As a result, the accuracies in artificial DNA mixtures reached 95.24% for 2- to 4-person mixtures and 83.33% for 2- to 6-person mixtures. Furthermore, the NOC estimation on simulated profiles with allele dropout showed that this panel was still robust under slight dropout. In conclusion, this panel has value for forensic identification and NOC estimation of DNA mixtures.

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.