Crime Stain Research Articles

Body Fluid Identification in Samples Collected after Intimate and Social Contact: A Comparison of Two mRNA Profiling Methods and the Additional Information Gained by cSNP Genotypes.

The ability to associate a contributor with a specific body fluid in a crime stain can aid casework investigation. The detection of body fluids combined with DNA analyses may supply essential information, but as the two tests are independent, they may not be associated. Recently, the analysis of coding region SNPs (cSNPs) within the RNA transcript has been proven to be a promising method to face this challenge. In this study, we performed targeted RNA sequencing of 158 samples (boxershorts, fingernail swabs and penile swabs) collected from 12 couples at different time points post-intimate contact and after non-intimate contact, using the Ion S5™ System and BFID-cSNP-6F assay. The aim of the study was to compare the performance of the MPS and CE methods in the detection of mRNA markers, and to associate body fluids with contributors by their cSNP genotypes. The results of the study show a lower success rate in the detection of vaginal mucosa by the MPS compared to the CE method. However, the additional information obtained with the cSNP genotypes could successfully associate body fluids with contributors in most cases.

Internal validation for analytical and stochastic thresholds in the GlobalFiler system

Interpretation of crime stain profiles is one of the challenging tasks of forensic scientists. Before using the probabilistic genotyping system, forensic scientists should interpret crime stain profiles using analytical and stochastic thresholds. According to the guidelines presented by the Scientific Working Group on DNA Analysis Methods, these thresholds shall be based on and supported by applicable internal validation studies. In this study, we performed the internal validation study of our DNA typing system to determine these thresholds. A total of 350 DNA samples and 11 negative controls were amplified using GlobalFilerTM PCR Amplification Kit with 30 cycles and PCR products were then analyzed on a SeqStudio Genetic Analyzer. As a result, the analytical threshold was set to 110 RFU, which has high sensitivity to obtain almost full profiles of 0.063 ng DNA, and has high specificity not to detect most of the pull-up and drop-in peaks. The stochastic threshold was set to 890 RFU allowing a 1% probability of allelic drop-out based on logistic regression. These thresholds are useful for interpreting crime stain profiles in our GlobalFiler system.

A Nonparametric Bayesian Approach to the Rare Type Match Problem

The “rare type match problem” is the situation in which, in a criminal case, the suspect’s DNA profile, matching the DNA profile of the crime stain, is not in the database of reference. Ideally, the evaluation of this observed match in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person) should be based on the calculation of the likelihood ratio and depends on the population proportions of the DNA profiles that are unknown. We propose a Bayesian nonparametric method that uses a two-parameter Poisson Dirichlet distribution as a prior over the ranked population proportions and discards the information about the names of the different DNA profiles. This model is validated using data coming from European Y-STR DNA profiles, and the calculation of the likelihood ratio becomes quite simple thanks to an Empirical Bayes approach for which we provided a motivation.

Value of evidence in the rare type match problem: common source versus specific source

Abstract In the so-called rare type match problem, the discrete characteristics of a crime stain have not been observed in the set of background material. To assess the strength of evidence, two competing statistical hypotheses need to be considered. The formulation of the hypotheses depends on which identification of source question is of interest (Ommen, 2017, Approximate statistical solutions to the forensic identification of source problem. (Phd thesis). South Dakota State University). Assuming that the evidence has been generated according to the beta-binomial model, two quantifications of the value of evidence can be found in the literature, but no clear indication is given when to use either of these. When the likelihood ratio is used to quantify the value of evidence, an estimate is needed for the frequency of the discrete characteristics. The central discussion is about whether or not one of the traces needs to be added to the background material when determining this estimate. In this article it is shown, using fully Bayesian methods, that one of the values of evidence from the literature corresponds to the so-called ‘identification of common source’ problem and the other to the ‘identification of specific source’ problem (Ommen, 2017, Approximate statistical solutions to the forensic identification of source problem. (Phd thesis). South Dakota State University). This means that the question whether or not one of the traces needs to be added to the background material reduces to the question whether a common source or specific source problem is under consideration. The distinction between the two values is especially important for the rare type match problem, since the values of evidence differ most in this situation.

Utilization of the CIFS DNA database to monitor recidivism

Since 2004, in a joint project of the Central Institute of Forensic Science (CIFS) and the Department of Correction, Ministry of Justice, CIFS has collected biological samples from prisoners with all offences who will be released within four months to one year. One aspect of this project is to prevent and monitor detained prisoners from repeated offences. Until July 2019, buccal cells on FTA paper were collected from more than 120,000 samples, and analyzed and uploaded to the CIFS DNA database. Presently, the CIFS DNA Database has reached 187,464 profiles, consisting of 16,521 DNA profiles from crime stain samples, 49,210 individuals related to a crime and 121,733 prisoners. Mainly autosomal STR markers were collected in the DNA database. Y-STR markers are also analyzed and uploaded, especially crime stains from sexual assault cases and their potential suspects. Approximately 2150 hits were reported on various offenses such as possession of explosives, unrest, homicide, sexual assault, and drug smuggling. Interestingly, 146 prisoners were found to match against samples from sexual assaults (10), assaults or homicides (23), drug smuggling (79), theft (11), and other crimes (23). All the prisoners were imprisoned and then released few years ago so-called ex-prisoners. The number of match reports demonstrated that some ex-prisoners tend to commit new crimes again or recidivism. Consequently, the DNA database can be applied to monitor repeated offenders and also protect them from false accusation. To expand the capabilities of DNA Database investigations CIFS plans to collect newly registered prisoners and all prisoners in Thailand in order to monitor repeated offenders and perhaps increase the hit rate. However, this report does not intend to state that all prisoners will commit a new crime again.

How to avoid driving DNA caseworkers crazy: CaseSolver, an expert system to investigate complex crime scenes

DNA analyses can be used for both investigative (crime scene-focused), or evaluative (suspect-focused) reporting. Investigative, DNA-led exploration of serious crimes always involves the comparison of hundreds of biological samples submitted by the authorities for analysis. Crime stain comparisons include both evidence to evidence profiles and reference to evidence profiles. When many complex DNA results (mixtures, low template LT-DNA samples) are involved in the investigation of a crime, the manual comparison of DNA profiles is very time-consuming and prone to manual errors. In addition, if the person of interest is a minor contributor, the classical approach of performing searches of national DNA databases is problematic because it is realistically restricted to clear major contributors and the occurrence of masking and drop-out means that there will not be a definitive DNA profile to perform the search with.CaseSolver is an open source expert system that automates analysis of complex cases. It does this by three sequential steps: a) simple allele comparison b) likelihood ratio (LR) based on a qualitative model (forensim) c) LR based on a quantitative model (EuroForMix). The software generates a list of potential match candidates, ranked according to the LRs, which can be exported as a report. The software can also identify contributors from small or large databases (e.g., staff database or 1 mill. individuals). In addition, an informative graphical network plot is generated that easily identifies contributors in common to multiple stains. Here we describe recent improvements made to the software in version v1.5.0, made in response to user requirements during intensive casework usage.

Forensic genealogy—A comparison of methods to infer distant relationships based on dense SNP data

The concept forensic genealogy was discussed already in 2005 but has recently emerged in relation to the use of public genealogy databases to find relatives of the donor of a crime stain. In this study we explored the results and evaluation of searches conducted in such databases. In particular, we focused on the statistical classification that entails from the search and study the variation observed for different relationship classes. The forensic guidelines advocate the use of the likelihood ratio (LR) as a mean to measure the weight of evidence, which requires exact formulation of competing hypotheses. We contrast the LR approach with alternative approaches relying on identical by state (IBS) measures to estimate the total length of shared genomic segments as well as identical by descent (IBD) coefficients for a pair of individuals.We used freely accessible data from the 1000 Genome project to perform extensive simulations, generating data for a number of distinct relationships. Specifically we studied some overarching relationship classes and the performance of the above-mentioned evaluative approaches to classify a known pair of relatives into each class.The results indicate that the traditional LR approach as a single source of classification is as good as, and in some cases even better than, the alternative approaches. In particular the true classification rate is higher for some distant relationship. However, the LR approach is both computer-intensive and sensitive to population frequencies as well as genetic maps (positions of the markers). We further showed that when combining different classification approaches, a lower false classification rate was achieved while still maintaining a high true classification rate.

Development of enhanced sensitivity protocols on the RapidHIT™ 200 with a view to processing casework material

The RapidHIT™ 200 device from IntegenX® provides a sample-to-profile platform that is capable of processing a variety of sample types. In this study we review the sensitivity of the ‘Run Other’ protocol for processing crime stain type samples containing various input quantities of DNA using the AmpFℓSTR® NGMSElect™ Express PCR Amplification Kit cartridges available from IntegenX®. The range of DNA inputs which achieved useable results were not as desired and therefore various enhancements to the instruments extraction processes were investigated. These studies showed an improvement in the range of DNA input templates that could by processed on the RapidHIT™ 200 by using the enhanced methods and resulted in three new run protocols.

The first Australian conviction resulting from a familial search

ABSTRACTFamilial Searching has been used in numerous jurisdictions globally since the turn of the century and has provided successful investigative leads and prosecutions. We present a familial search performed at Forensic Science SA in conjunction with the South Australian Police that resulted in an intelligence lead, arrest and conviction of an individual, which we believe is the first such conviction in Australia. A DNA profile of male origin was obtained from exhibits collected from two different sexual assaults which did not match any individuals on the National DNA database. A familial search was conducted resulting in a candidate list of potential relatives. After further exclusionary DNA work using Y-STRs, a single individual gave strong support as a possible child or parent of the unknown male who left the crime stain. After further police investigation of this male, he was subsequently arrested and ultimately pleaded guilty to the two assaults. We briefly discuss the police and scientific work involved in this case and also some of the more general issues and processes around familial searching in Australia.

Effectiveness of CIFS DNA database in Thailand

In 2004, the Central Institute of Forensic Science (CIFS) established a DNA database. The DNA database was originally utilized as intelligence to support law enforcement agencies solving organized crimes in the Deep South of Thailand. This area covers three border provinces of Thailand and Malaysia. There were 1600 hits from various incidents of this area. Recently, additional offences are being kept in the CIFS DNA database such as homicide, murder, sexual assault, burglary and drug smuggling. Until March 2017, the CIFS DNA database contains approximately 145,000 DNA profiles including 130,000 profiles of individuals (potential suspects and prisoners) and 14,000 profiles from crime stain samples. Mainly autosomal STR markers were collected in the DNA database. Y-STR markers are also analyzed and uploaded, especially crime stains from sexual assault cases and their potential suspects. Other 200 hits were reported on other offences such as homicide, sexual assault, and drug smuggling. Almost 2000 hits were found and reported to law enforcement agencies. CIFS plans to expand the capabilities of DNA investigations such as collecting newly registered prisoners and all prisoners in Thailand in order to monitor serial offense and increase the hit rate. CIFS DNA database also plans to work on DNA searching and data exchange with national and international law enforcement agencies. An ultimate goal of DNA database is to help to solve transnational crimes such as human trafficking, migrant smuggling, and illegal labors.

Сrime stain as a forensic object: Some essential aspects of examination

The key to a successful crime investigation includes working with forensic information effectively. However, essential complication of forensic technologies, requiring a number of narrow specialized professionals, results in excessive differentiation of modern forensic knowledge. Besides, at the background of enormous achievements in the field of DNA analysis, in practice other methods of the examination of a crime stain important for the forensic assessment may be eclipsed. For instance, according to available data, BPA (bloodstain pattern analysis) is used much less frequently than DNA analysis and serological examination of bloodstains, therefore, the mechanism of trace formation is often not taken into account. Under these conditions there may be a disadvantage due to the loss of the vision of the integrated information picture which is necessary for the successful crime investigation. The integrated view is necessary both for the forensic examination and the crime investigation. The lack of such vision can result in the failure to use all the potential of the available expert arsenal to solve the tasks of the particular investigation. It may also lead to inappropriating appointment of the succession of forensic examinations without taking into account the destructive/non-destructive nature of the methods; inefficient use of the stain’s material; poor data interpretation, etc. The total algorithm of the forensic examination should take into consideration all information (relevant to the particular case) which could be retrieved from the object under study.

Distinguishing between donors and their relatives in complex DNA mixtures with binary models

While likelihood ratio calculations were until the recent past limited to the evaluation of mixtures in which all alleles of all donors are present in the DNA mixture profile, more recent methods are able to deal with allelic dropout and drop-in. This opens up the possibility to obtain likelihood ratios for mixtures where this was not previously possible, but it also means that a full match between the alleged contributor and the crime stain is no longer necessary. We investigate in this article what the consequences are for relatives of the actual donors, because they typically share more alleles with the true donor than an unrelated individual. We do this with a semi-continuous binary approach, where the likelihood ratios are based on the observed alleles and the dropout probabilities for each donor, but not on the peak heights themselves. These models are widespread in the forensic community. Since in many cases a simple model is used where a uniform dropout probability is assumed for all (or for all unknown) contributors, we explore the extent to which this alters the false positive probabilities for relatives of donors, compared to what would have been obtained with the correct probabilities of dropout for each donor.

A probabilistic approach for the interpretation of RNA profiles as cell type evidence

DNA profiles can be used as evidence to distinguish between possible donors of a crime stain. In some cases, both the prosecution and the defence claim that the cell material was left by the suspect but they dispute which cell type was left behind. For example, in sexual offense cases the prosecution could claim that the sample contains semen cells where the defence argues that the sample contains skin cells. In these cases, traditional methods (e.g. a phosphatase test) can be used to examine the cell type contained in the sample. However, there are some drawbacks when using these methods. For instance, many of these techniques need to be carried out separately for each cell type and each of them requires part of the available sample, which reduces the amount that can be used for DNA analysis.Another option is messenger RNA (mRNA) evidence. mRNA expression levels vary among cell types and can be used to make (probability) statements about the cell type(s) present in a sample. Existing methods for the interpretation of RNA profiles as evidence for the presence of certain cell types aim at making categorical statements. Such statements limit the possibility to report the associated uncertainty. Some of these existing methods will be discussed. Most notably, a method based on a ‘n/2’ scoring rule (Lindenbergh et al. [2]) and a method using marker values and cell type scoring thresholds (Roeder et al. [3]).From a statistical point of view, a probabilistic approach is the most obvious choice. Two approaches (multinomial logistic regression and naïve Bayes’) are suggested. All methods are compared, using two different datasets and several criteria regarding their ability to assess the evidential value of RNA profiles.We conclude that both the naïve Bayes’ method and a method based on multinomial logistic regression, that produces a probabilistic statement as measure of the evidential value, are an important improvement of the existing methods. Besides a better performance, they are flexible and can be adapted to other situations. For example, they could potentially assist in the combination of RNA with DNA evidence.

Estimating allele dropout probabilities by logistic regression: Assessments using Applied Biosystems 3500xL and 3130xl Genetic Analyzers with various commercially available human identification kits.

Phenomena called allele dropouts are often observed in crime stain profiles. Allele dropouts are generated because one of a pair of heterozygous alleles is underrepresented by stochastic influences and is indicated by a low peak detection threshold. Therefore, it is important that such risks are statistically evaluated. In recent years, attempts to interpret allele dropout probabilities by logistic regression using the information on peak heights have been reported. However, these previous studies are limited to the use of a human identification kit and fragment analyzer. In the present study, we calculated allele dropout probabilities by logistic regression using contemporary capillary electrophoresis instruments, 3500xL Genetic Analyzer and 3130xl Genetic Analyzer with various commercially available human identification kits such as AmpFℓSTR® Identifiler® Plus PCR Amplification Kit. Furthermore, the differences in logistic curves between peak detection thresholds using analytical threshold (AT) and values recommended by the manufacturer were compared. The standard logistic curves for calculating allele dropout probabilities from the peak height of sister alleles were characterized. The present study confirmed that ATs were lower than the values recommended by the manufacturer in human identification kits; therefore, it is possible to reduce allele dropout probabilities and obtain more information using AT as the peak detection threshold.

Probabilistic Strategies for Familial DNA Searching

SummaryFamilial searching is the process of searching in a deoxyribonucleic acid (DNA) database for relatives of a certain individual. Typically, this individual is the source of a crime stain that can be reasonably attributed to the offender. If this crime stain has not given rise to a match in a DNA database, then in some jurisdictions a familial search may be carried out to attempt to identify a relative of the offender, thus giving investigators a lead. We discuss two methods to select a subset from the database that contains a relative (if present) with a probability that we can control. The first method is based on a derivation of the likelihood ratio for each database member in favour of being the relative, taking all DNA profiles in the database into account. This method needs prior probabilities and yields posterior probabilities. The second method is based on case-specific false positive and false negative rates. We discuss the relationship between the approaches and assess the methods with familial searching carried out in the Dutch national DNA database. We also give practical recommendations on the usefulness of both methods.

DNA transfer: Review and implications for casework

DNA-bearing cellular material can come to be present on a surface by either direct or indirect transfer. Direct transfer includes contact, but also includes activities within the vicinity of an item that may result in the transfer of DNA directly from an individual without any contact, such as speaking, coughing, and sneezing. Indirect transfer of DNA is when DNA from an individual comes to be on an item via an intermediary surface. It is important to consider indirect transfer in the evaluation of trace DNA in casework. The term 'trace DNA' in this review refers solely to DNA that cannot be attributed to an identifiable body fluid. This review presents and considers data from trace DNA experiments to establish whether the quantity of DNA recovered from a crime stain and/or the quality of a DNA profile obtained can be used to infer the likely mechanism of transfer. The data show that varied results are obtained from apparently similar trace DNA samples, presumably due to the many factors that affect the detection of trace DNA. The nature and effect of these varying factors and the application of the data to casework is considered generally and with specific reference to DNA transfer to skin, DNA beneath fingernails, 'wearer DNA', and various contamination considerations.

The database search problem: A question of rational decision making

This paper applies probability and decision theory in the graphical interface of an influence diagram to study the formal requirements of rationality which justify the individualization of a person found through a database search. The decision-theoretic part of the analysis studies the parameters that a rational decision maker would use to individualize the selected person. The modeling part (in the form of an influence diagram) clarifies the relationships between this decision and the ingredients that make up the database search problem, i.e., the results of the database search and the different pairs of propositions describing whether an individual is at the source of the crime stain. These analyses evaluate the desirability associated with the decision of ‘individualizing’ (and ‘not individualizing’). They point out that this decision is a function of (i) the probability that the individual in question is, in fact, at the source of the crime stain (i.e., the state of nature), and (ii) the decision maker's preferences among the possible consequences of the decision (i.e., the decision maker's loss function). We discuss the relevance and argumentative implications of these insights with respect to recent comments in specialized literature, which suggest points of view that are opposed to the results of our study.

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to ‘explain’ an observed set of alleles. The other procedure is probabilistic using Bayes’ theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.

A Bayesian network approach to the database search problem in criminal proceedings

BackgroundThe ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity.MethodsAs a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population.ResultsThis paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches.ConclusionsThe proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method’s graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

Match probabilities for multiple siblings

A defendant whose dna profile matches that of a crime stain may argue that he has several, say n, brothers and that one of them may have been the origin of the crime stain. If the probability for any of the brothers considered separately to match the crime stain profile is p, we show that the probability that at least one of the n brothers match is strictly smaller than np. This latter quantity therefore is an easy to compute and conservative value to report.