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

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.

An overview of log likelihood ratio cost in forensic science - Where is it used and what values can we expect?

There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost (Cllr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. Cllr = 0 indicates perfection while Cllr = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" Cllr is unclear. Aiming to provide handles on when a Cllr is "good", we studied 136 publications on (semi-)automated LR systems. Results show Cllr use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting Cllr remains stable. Noticeably, Cllr values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field.

Determining the number and size of background samples derived from an area adjacent to the target sample that provide the greatest support for a POI in a target sample

When sampling an item or surface for DNA originating from an action of interest, one is likely to collect DNA unrelated to the action of interest (background DNA). While adding to the complexity of a generated DNA profile, background DNA has been shown to aid in resolving the genotypes of contributors in a targeted sample, and where references of donors to the background DNA are not available, strengthen the LR supporting a person of interest contributing to the targeted sample. This is possible thanks to advances in probabilistic genotyping, where forensic labs are able to deconvolute complex DNA profiles to obtain lists of genotypes and their associated weights. Coupled with DBLR™, one can then compare multiple evidentiary profiles to each other to determine the contribution of common, but unknown, contributors. Here, we consider factors associated with taking background samples and whether one should collect multiple background samples that all relate to a single target sample, or if one should collect larger background samples rather than smaller samples. Background samples consisted of DNA accumulated on the items primarily by one or both occupants of a single household, while targeted samples were generated from touch deposits, or saliva deposits that had been left to air dry. Samples were collected from areas of various sizes, consisting of only the background, the target and the background directly beneath it, and the target and additional surrounding background. A broad range of DNA quantities were recovered, with larger background samples (400 cm2) yielding significantly more DNA than smaller background samples (30 cm2). Significant differences in DNA quantities between target samples were not observed. Generated DNA profiles were interpreted using STRmix™ and DBLR™, and where there was support for a common donor between the background and target sample, pairwise comparisons were performed to observe the effect on the LR supporting the target DNA donor contributing to the targeted sample when conditioning on one (or two) common donor between the targeted sample and 1-8 background samples. Multiple background samples gave significantly higher LRs compared to a single background sample, the larger sampled background area resulted in larger LR gains than the smaller areas, and four or more background samples reduced LR variability considerably. Here we provide recommendations for the minimum and ideal number of additional background samples that should be collected, and that several smaller samples may be more beneficial than a single larger sample.

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

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

The invisible witness: air and dust as DNA evidence of human occupancy in indoor premises

Humans constantly shed deoxyribonucleic acid (DNA) into the surrounding environment. This DNA may either remain suspended in the air or it settles onto surfaces as indoor dust. In this study, we explored the potential use of human DNA recovered from air and dust to investigate crimes where there are no visible traces available-for example, from a recently vacated drugs factory where multiple workers had been present. Samples were collected from three indoor locations (offices, meeting rooms and laboratories) characterized by different occupancy types and cleaning regimes. The resultant DNA profiles were compared with the reference profiles of 55 occupants of the premises. Our findings showed that indoor dust samples are rich sources of DNA and provide an historical record of occupants within the specific locality of collection. Detectable levels of DNA were also observed in air and dust samples from ultra-clean forensic laboratories which can potentially contaminate casework samples. We provide a Bayesian statistical model to estimate the minimum number of dust samples needed to detect all inhabitants of a location. The results of this study suggest that air and dust could become novel sources of DNA evidence to identify current and past occupants of a crime scene.

Phenotype predictions of two-person mixture using single cell analysis

Over a decade after the publication of the first forensic DNA phenotyping (FDP) studies, DNA-based appearance predictions are now becoming a reality in routine crime scene investigations. The significant number of publications dedicated to the subject of FDP clearly demonstrates a sustained interest and a strong need for further method development. However, the implementation of FDP in routine work still encounters obstacles, and one of these challenges is making phenotype predictions from DNA mixtures. In this study, we examined single-cell sequencing as a potential tool to enable reliable phenotyping of contributors within mixtures. Two mock mixtures, each containing two contributors with similar and different physical appearances, were analyzed using two different workflows. In the first workflow, the mixtures were sequenced using the Ion AmpliSeq™ PhenoTrivium Panel, which includes 41 HIrisPlex-S (HPS) markers. Subsequently, the genotypes were analyzed using the HPS Deconvolution Tool to predict the phenotypes of both contributors. The second workflow involved the introduction of single-cell separation and collection using the DEPArray™ PLUS System. Two different PhenoTrivium amplification protocols were tested, and the phenotype predictions from single cells were compared with the results obtained using the HPS Tool. Our results suggest that the approach presented here allows for the obtainment of nearly complete HIrisPlex-S profiles with accurate genotypes and reliable phenotype predictions from single cells. This method proves successful in deconvoluting mixtures submitted to forensic DNA phenotyping.

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.

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.

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

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

Identification and characterization of novel DIP-STRs from whole-genome sequencing data

In an attempt to enhance forensic DNA mixture deconvolution several alternative DNA typing approaches have been developed. Among these, DIP-STR compound markers can resolve extremely unbalanced two-source DNA mixtures of same-or-opposite sex donors, up to a 1:1000 minor:major DNA ratio. A forensic set of 10 markers was validated for casework and a larger set of 23 DIP-STRs has proven suitable to biogeographic ancestry inference and for prenatal paternity testing. Yet, to promote the widespread use of this original approach, more markers and multiplex panels need to be developed. To this end, here we describe an extended set of forensic DIP-STRs identified using currently available whole-genome sequencing datasets. Complete lists of Indels and STRs were obtained from reported frequencies of genetic variants of 76,156 genomes. About 3000 identified DIP-STRs candidates were shorter than 200 bp and 500 showed high haplotype variability estimated using the genotypes of individuals homozygous for the DIP or the STR. Here, we present 23 additional DIP-STRs validated for sensitivity, specificity and Swiss population variability. Finally, a set of 30 markers comprising seven previously validated ones is proposed for the prospective development of a forensic DIP-STR multiplex panel.

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.

Comparison of genotyping and weight of evidence results when applying different genotyping strategies on samples from a DNA transfer experiment

In this study, we assessed to what extent data on the subject of TPPR (transfer, persistence, prevalence, recovery) that are obtained through an older STR typing kit can be used in an activity-level evaluation for a case profiled with a more modern STR kit. Newer kits generally hold more loci and may show higher sensitivity especially when reduced reaction volumes are used, and this could increase the evidential value at the source level. On the other hand, the increased genotyping information may invoke a higher number of contributors in the weight of evidence calculations, which could affect the evidential values as well. An activity scenario well explored in earlier studies [1,2] was redone using volunteers with known DNA profiles. DNA extracts were analyzed with three different approaches, namely using the optimal DNA input for (1) an older and (2) a newer STR typing system, and (3) using a standard, volume-based input combined with replicate PCR analysis with only the newer STR kit. The genotyping results were analyzed for various aspects such as percentage detected alleles and relative peak height contribution for background and the contributors known to be involved in the activity. Next, source-level LRs were calculated and the same trends were observed with regard to inclusionary and exclusionary LRs for persons who had or had not been in direct contact with the sampled areas. We subsequently assessed the impact on the outcome of the activity-level evaluation in an exemplary case by applying the assigned probabilities to a Bayesian network. We infer that data from different STR kits can be combined in the activity-level evaluations.

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.

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 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.

Post hoc deconvolution of human mitochondrial DNA mixtures by EMMA 2 using fine-tuned Phylotree nomenclature

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MtDNA mixtures are observed frequently and difficult to deconvolute.

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Most previous methods require raw data or quantitative information.

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EMMA 2 produces valid splittings from consensus sequences of any sequencing technology.

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EMMA 2 can deconvolute 2 and 3 person mixtures in a fast and traceable way.

In this paper we present a new algorithm for splitting (partial) human mitogenomes into components with high similarity to haplogroup motifs of Phylotree. The algorithm reads a (partial) mitogenome coded by the differences to the reference (rCRS) and outputs the estimated haplogroups of the putative components. The algorithm requires no special information on the raw data of the sequencing process and is therefore suited for the post hoc analysis of mixtures of any sequencing technology. The software EMMA 2 implementing the algorithm will be made available via the EMPOP (https://empop.online) database and extends the nine years old software EMMA for haplogrouping single mitogenomes to mixtures with at most three components.

Development and validation of a fast and automated DNA identification line

The importance of DNA evidence for gaining investigative leads demands a fast workflow for forensic DNA profiling performed in large volumes. Therefore, we developed software solutions for automated DNA profile analysis, contamination check, major donor inference, DNA database (DDB) comparison and reporting of the conclusions. This represents the Fast DNA IDentification Line (FIDL) and this study describes its development, validation and implementation in criminal casework at the authors' institute. This first implementation regards single donor profiles and major contributors to mixtures. The validation included testing of the software components on their own and examination of the performance of different DDB search strategies. Furthermore, end-to-end testing was performed under three conditions: (1) testing of scenarios that can occur in DNA casework practice, (2) tests using three months of previous casework data, and (3) testing in a casework production environment in parallel to standard casework practices. The same DNA database candidates were retrieved by this automated line as by the manual workflow. The data flow was correct, results were reproducible and robust, results requiring manual analysis were correctly flagged, and reported results were as expected. Overall, we found FIDL valid for use in casework practice in our institute. The results from FIDL are automatically reported within three working days from receiving the trace sample. This includes the time needed for registration of the case, DNA extraction, quantification, polymerase chain reaction and capillary electrophoresis. FIDL itself takes less than two hours from intake of the raw CE data to reporting. Reported conclusions are one of five options: (1) candidate retrieved from DDB, (2) no candidate retrieved from DDB, (3) high evidential value with regards to reference within the case, (4) results require examination of expert, or (5) insufficient amount of DNA obtained to generate a DNA profile. In our current process, the automated report is sent within three working days and a complete report, with confirmation of the FIDL results, and signed by a reporting officer is sent at a later time. The signed report may include additional analyses regarding e.g. minor contributors. The automated report with first case results is quickly available to the police enabling them to act upon the DNA results prior to receiving the full DNA report. This line enables a uniform and efficient manner of handling large numbers of traces and cases and provides high value investigative leads in the early stages of the investigation.

A New Computational Deconvolution Algorithm for the Analysis of Forensic DNA Mixtures with SNP Markers

Single nucleotide polymorphisms (SNPs) support robust analysis on degraded DNA samples. However, the development of a systematic method to interpret the profiles derived from the mixtures is less studied, and it remains a challenge due to the bi-allelic nature of SNP markers. To improve the discriminating power of SNPs, this study explored bioinformatic strategies to analyze mixtures. Then, computer-generated mixtures were produced using real-world massively parallel sequencing (MPS) data from the single samples processed with the Precision ID Identity Panel. Moreover, the values of the frequency of major allele reads (F_MAR) were calculated and applied as key parameters to deconvolve the two-person mixtures and estimate mixture ratios. Four custom R language scripts (three for autosomes and one for Y chromosome) were designed with the K-means clustering method as a core algorithm. Finally, the method was validated with real-world mixtures. The results indicated that the deconvolution accuracy for evenly balanced mixtures was 100% or close to 100%, which was the same as the deconvolution accuracy of inferring the genotypes of the major contributor of unevenly balanced mixtures. Meanwhile, the accuracy of inferring the genotypes of the minor contributor decreased as its proportion in the mixture decreased. Moreover, the estimated mixture ratio was almost equal to the actual ratio between 1:1 and 1:6. The method proposed in this study provides a new paradigm for mixture interpretation, especially for inferring contributor profiles of evenly balanced mixtures and the major contributor profile of unevenly balanced mixtures.

Who Packed the Drugs? Application of Bayesian Networks to Address Questions of DNA Transfer, Persistence, and Recovery from Plastic Bags and Tape

When DNA from a suspect is detected in a sample collected at a crime scene, there can be alternative explanations about the activity that may have led to the transfer, persistence and recovery of his/her DNA. Previous studies have shown that DNA can be indirectly transferred via intermediate surfaces and that DNA on a previously used object can persist after subsequent use of another individual. In addition, it has been shown that a person’s shedder status may influence transfer, persistence, prevalence, and recovery of DNA. In this study we have investigated transfer persistence and recovery on zip-lock bags and tape, which are commonly encountered in drug cases and how the shedder status of the participants influenced the results. A probabilistic framework was developed which was based on a previously described Bayesian network with case-specific modifications. Continuous modelling of data was used to inform the Bayesian networks and two case scenarios were investigated. In the specific scenarios only moderate to low support for H_p was obtained. Applying a continuous model based on the profile quality can change the LRs.