Establishing the Limits of TrueAllele® Casework: A Validation Study

The limits of the expert system, TrueAllele^® Casework (TA), were explored using challenging mock casework profiles that included 17 single‐source and 18 two‐, 15 three‐ and 7 four‐person DNA mixtures. The sensitivity (ability to detect a minor contributor) of the TA analysis process was examined by challenging the system with mixture DNA samples that exhibited allelic and locus dropout and other stochastic effects. The specificity (ability to exclude nondonors) was rigorously tested by interrogating TA derived genotypes with 100 nondonor profiles. The accuracy with which TA estimated mixture weights of contributors to the two‐person mixtures was examined. Finally, first‐degree relatives of donors were used to assess the ability of the system to exclude close relatives. TA demonstrated great accuracy, sensitivity, and specificity. TA correctly assigned mixture weights and excluded nearly all first‐degree relatives. This study demonstrates the analysis power of the TrueAllele^® Casework system.     

A discussion of the merits of random man not excluded and likelihood ratios

DNA mixture interpretation is undertaken either by calculating a LR or an exclusion probability (RMNE or its complement CPI). Debate exists as to which has the greater claim. The merits and drawbacks of the two approaches are discussed. We conclude that the two matters that appear to have real force are: (1) LRs are more difficult to present in court and (2) the RMNE statistic wastes information that should be utilised.     

Using the likelihood ratio in bloodstain pattern analysis

There is an apparent paradox that the likelihood ratio (LR) approach is an appropriate measure of the weight of evidence when forensic findings have to be evaluated in court, while it is typically not used by bloodstain pattern analysis (BPA) experts. This commentary evaluates how the scope and methods of BPA relate to several types of evaluative propositions and methods to which LRs are applicable. As a result of this evaluation, we show how specificities in scope (BPA being about activities rather than source identification), gaps in the underlying science base, and the reliance on a wide range of methods render the use of LRs in BPA more complex than in some other forensic disciplines. Three directions are identified for BPA research and training, which would facilitate and widen the use of LRs: research in the underlying physics; the development of a culture of data sharing; and the development of training material on the required statistical background. An example of how recent fluid dynamics research in BPA can lead to the use of LR is provided. We conclude that an LR framework is fully applicable to BPA, provided methodic efforts and significant developments occur along the three outlined directions.     

Probabilistic genotyping of single cell replicates from complex DNA mixtures recovers higher contributor LRs than standard analysis

DNA mixtures are a common source of crime scene evidence and are often one of the more difficult sources of biological evidence to interpret. With the implementation of probabilistic genotyping (PG), mixture analysis has been revolutionized allowing previously unresolvable mixed profiles to be analyzed and probative genotype information from contributors to be recovered. However, due to allele overlap, artifacts, or low-level minor contributors, genotype information loss inevitably occurs. In order to reduce the potential loss of significant DNA information from donors in complex mixtures, an alternative approach is to physically separate individual cells from mixtures prior to performing DNA typing thus obtaining single source profiles from contributors. In the present work, a simplified micro-manipulation technique combined with enhanced single-cell DNA typing was used to collect one or few cells, referred to as direct single-cell subsampling (DSCS). Using this approach, single and 2-cell subsamples were collected from 2 to 6 person mixtures. Single-cell subsamples resulted in single source DNA profiles while the 2-cell subsamples returned either single source DNA profiles or new mini-mixtures that are less complex than the original mixture due to the presence of fewer contributors. PG (STRmix™) was implemented, after appropriate validation, to analyze the original bulk mixtures, single source cell subsamples, and the 2-cell mini mixture subsamples from the original 2–6-person mixtures. PG further allowed replicate analysis to be employed which, in many instances, resulted in a significant gain of genotype information such that the returned donor likelihood ratios (LRs) were comparable to that seen in their single source reference profiles (i.e., the reciprocal of their random match probabilities). In every mixture, the DSCS approach gave improved results for each donor compared to standard bulk mixture analysis. With the 5- and 6- person complex mixtures, DSCS recovered highly probative LRs (≥10²⁰) from donors that had returned non-probative LRs (<10³) by standard methods.     

Fingermark Evidence Evaluation Based on Automated Fingerprint Identification System Matching Scores: The Effect of Different Types of Conditioning on Likelihood Ratios

In recent studies, the evidential value of the similarity of minutiae configurations of fingermarks and fingerprints, for example expressed by automated fingerprint identification systems (AFIS), is determined by likelihood ratios (LRs). The paper explores whether there is an effect on LRs if conditioning takes place on specified fingers, fingerprints, or fingermarks under competing hypotheses: In addition, an approach is explored where conditioning is asymmetric. Comparisons between fingerprints and simulated fingermarks with eight minutiae are performed to produce similarity score distributions for each type of conditioning, given a fixed AFIS matching algorithm. Both similarity scores and LRs are significantly different if the conditioning changes. Given a common‐source scenario, “LRs” resulting from asymmetric conditioning are on average higher. The difference may reach a factor of 2000. As conditioning on a suspect's finger(print) is labor‐intensive and requires a cooperating suspect, it is recommended to just condition on the number of minutiae in the fingermark.     

Computation of likelihood ratios in fingerprint identification for configurations of any number of minutiae

Recent court challenges have highlighted the need for statistical research on fingerprint identification. This paper proposes a model for computing likelihood ratios (LRs) to assess the evidential value of comparisons with any number of minutiae. The model considers minutiae type, direction and relative spatial relationships. It expands on previous work on three minutiae by adopting a spatial modeling using radial triangulation and a probabilistic distortion model for assessing the numerator of the LR. The model has been tested on a sample of 686 ulnar loops and 204 arches. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutiae data. The metric used to assess similarity between two feature vectors is based on an Euclidean distance measure. Tippett plots and rates of misleading evidence have been used as performance indicators of the model. The model has shown encouraging behavior with low rates of misleading evidence and a LR power of the model increasing significantly with the number of minutiae. The LRs that it provides are highly indicative of identity of source on a significant proportion of cases, even when considering configurations with few minutiae. In contrast with previous research, the model, in addition to minutia type and direction, incorporates spatial relationships of minutiae without introducing probabilistic independence assumptions. The model also accounts for finger distortion.     

A mixed DNA profile controversy revisited

Semaan et al. (J Forensic Res, 2020, 11, 453) discuss a mock case “where eight different individuals [P₁ through P₈] could not be excluded in a mixed DNA analysis. Even though … expert DNA mixture analysis software was used.” Two of these are the true donors. The LRs reported are incorrect due to the incorrect entry of propositions into LRmix Studio. This forced the software to account for most of the alleles as drop-in, resulting in LRs 60–70 orders of magnitude larger than expected. P₁, P₂, P₄, P₅, and P₈ can be manually excluded using peak heights. This has relevance when using LRmix which does not use peak heights. We extend the work using the same two reference genotypes who were the true contributors as Semaan et al. (J Forensic Res, 2020, 11, 453). We simulate three two-donor mixtures with peak heights using these two genotypes and analyze using STRmix?. For the simulated 1:1 mixture, one of the non-donors’ LRs supported him being a contributor when no conditioning was used. When considered in combination with any other potential donors (i.e., with conditioning), this non-donor was correctly eliminated. For the 3:1 mixture, all results correctly supported that the non-donors were not contributors. The low-template 4:1 mixture LRs with no conditioning showed support for all eight profiles as donors. However, the results from pair-wise conditioning showed that only the two ground truth donors had LRs supporting that they were contributors to the mixture. We recommend the use of peak heights and conditioning profiles, as this allows better sensitivity and specificity even when the persons share many alleles.     

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.     

常染色体STR祖孙亲缘关系鉴定分析

目的概括归纳一种简便易行的祖孙亲缘关系鉴定分析方法,对实际检案起到指导作用。方法根据孟德尔遗传规律、概率论和亲权指数(PI)值基本概念,归纳推导适用于常染色体STR各种基因型组合的祖孙亲缘关系似然率(LR)计算式及其代入参数。并应用于一个三代家系的祖孙鉴定及实际案例。结果祖孙亲缘似然率(LR)计算总共有三种计算式,它们概括了祖孙亲缘关系鉴定中的各种基因型遗传组合情形。公式中祖父母遗传可能的生父基因的概率可以简单地根据可能生父基因在祖父母基因型中所占的比例数来推定。祖孙亲缘关系鉴定分为孩子生母参与和不参与两种情况,同样的遗传指标检验分析后,前一种情形LRs明显升高,后一种情形LRs明显降低。结论用常染色体STR检验进行祖孙亲缘关系鉴定分析,在祖父、祖母均参与的情况下,其祖孙亲缘关系似然率的计算简单、直观、可操作性强;孩子生母尽可能参与检验,否则需要增加检验STR位点的数量才能获得更加明确的结论。     

Evidence evaluation in fingerprint comparison and automated fingerprint identification systems--modelling within finger variability

Recent challenges and errors in fingerprint identification have highlighted the need for assessing the information content of a papillary pattern in a systematic way. In particular, estimation of the statistical uncertainty associated with this type of evidence is more and more called upon. The approach used in the present study is based on the assessment of likelihood ratios (LRs). This evaluative tool weighs the likelihood of evidence given two mutually exclusive hypotheses. The computation of likelihood ratios on a database of marks of known sources (matching the unknown and non-matching the unknown mark) allows an estimation of the evidential contribution of fingerprint evidence. LRs are computed taking advantage of the scores obtained from an automated fingerprint identification system and hence are based exclusively on level II features (minutiae). The AFIS system attributes a score to any comparison (fingerprint to fingerprint, mark to mark and mark to fingerprint), used here as a proximity measure between the respective arrangements of minutiae. The numerator of the LR addresses the within finger variability and is obtained by comparing the same configurations of minutiae coming from the same source. Only comparisons where the same minutiae are visible both on the mark and on the print are therefore taken into account. The denominator of the LR is obtained by cross-comparison with a database of prints originating from non-matching sources. The estimation of the numerator of the LR is much more complex in terms of specific data requirements than the estimation of the denominator of the LR (that requires only a large database of prints from an non-associated population). Hence this paper addresses specific issues associated with the numerator or within finger variability. This study aims at answering the following questions: (1) how a database for modelling within finger variability should be acquired; (2) whether or not the visualisation technique or the choice of different minutiae arrangements may influence that modelling and (3) what is the magnitude of LRs that can be expected from such a model. Results show that within finger variability is affected by the visualisation technique used on the mark, the number of minutiae and the minutiae configuration. They also show that the rates of misleading evidence in the likelihood ratios obtained for one of the configurations examined are low.     

Issues and opportunities: The application of the numerical likelihood ratio framework to forensic speaker comparison

Across forensic speech science, the likelihood ratio (LR) is increasingly becoming accepted as the logically and legally correct framework for the expression of expert conclusions. However, there remain a number of theoretical and practical shortcomings in the procedures applied for computing LRs based on speech evidence. In this paper we review how the LR is currently applied to speaker comparison evidence and outline three specific areas which deserve further investigation: namely statistical modelling, issues relating to the relevant population and the combination of LRs from correlated parameters. We then consider future directions for confronting these issues and discuss the implications for forensic comparison evidence more generally.     

Surface granularity as a discriminating feature of illicit tablets

In this paper we propose an innovative methodology for automated profiling of illicit tablets by their surface granularity; a feature previously unexamined for this purpose. We make use of the tiny inconsistencies at the tablet surface, referred to as speckles, to generate a quantitative granularity profile of tablets. Euclidian distance is used as a measurement of (dis)similarity between granularity profiles. The frequency of observed distances is then modelled by kernel density estimation in order to generalize the observations and to calculate likelihood ratios (LRs). The resulting LRs are used to evaluate the potential of granularity profiles to differentiate between same-batch and different-batches tablets. Furthermore, we use the LRs as a similarity metric to refine database queries. We are able to derive reliable LRs within a scope that represent the true evidential value of the granularity feature. These metrics are used to refine candidate hit-lists form a database containing physical features of illicit tablets. We observe improved or identical ranking of candidate tablets in 87.5% of cases when granularity is considered.     

Resolving paternity relationships using X-chromosome STRs and Bayesian networks

X-chromosomal short tandem repeats (X-STRs) are very useful in complex paternity cases because they are inherited by male and female offspring in different ways. They complement autosomal STRs (as-STRs) allowing higher paternity probabilities to be attained. These probabilities are expressed in a likelihood ratio (LR). The formulae needed to calculate LR depend on the genotype combinations of suspected pedigrees. LR can also be obtained by the use of Bayesian networks (BNs). These are graphical representations of real situations that can be used to easily calculate complex probabilities. In the present work, two BNs are presented, which are designed to derive LRs for half-sisters/half-sisters and mother/daughter/paternal grandmother relationships. These networks were validated against known formulae and show themselves to be useful in other suspect pedigree situations than those for which they were developed. The BNs were applied in two paternity cases. The application of the mother/daughter/paternal grandmother BN highlighted the complementary value of X-STRs to as-STRs. The same case evaluated without the mother underlined that missing information tends to be conservative if the alleged father is the biological father and otherwise nonconservative. The half-sisters case shows a limitation of statistical interpretations in regard to high allelic frequencies.     

Bayesian networks and the value of the evidence for the forensic two-trace transfer problem*

Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). We first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. We illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Our approach allows us to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces.     

A method for forensic gasoline comparison in fire debris samples: A numerical likelihood ratio system

In many arson cases gasoline is used as a means to start the fire. In this paper results are presented for a likelihood-ratio (LR) system aimed at comparing gasoline traces from fire debris to a reference gasoline. The LR-system is able to deal with disturbing effects caused by burning and exposure to surroundings: pyrolysis products, preferential adsorption and evaporation. This paper focusses on the criminalistic and statistical aspects of the design of the LR-system, and presents results on performance of the LR-system. The details of trace gasoline recovery from fire debris will be presented in an accompanying paper.Validation and performance measures show that this system gives well-calibrated LRs for comparisons involving trace samples with a spread in quantity of gasoline, evaporation levels, and matrices that are typically encountered in casework. Rates of misleading evidence are less than 3.5%.We conclude that, despite limitations in experimental design, this LR-system can be useful to the comparison of gasoline profiles in casework practice.     

Simulated approach to estimate the number and combination of known/unknown contributors in mixed DNA samples using 15 short tandem repeat loci

The calculation of likelihood ratios (LRs) for DNA mixture analysis is necessary to establish an appropriate hypothesis based on the estimated number of contributors and known contributor genotypes. In this paper, we recommend a relevant analytical method from the 15 short tandem repeat typing system (the Identifiler multiplex), which is used as a standard in Japanese forensic practice and incorporates a flowchart that facilitates hypothesis formulation. We postulate that: (1) all detected alleles need to be above the analytical threshold (e.g., 150 relative fluorescence unit (RFU)); (2) alleles of all known contributors should be detected in the mixture profile; (3) there should be no contribution from close relatives. Furthermore, we deduce that mixtures of four or more persons should not be interpreted by Identifiler as the LR values of 100,000 simulated cases have a lower expectation of exceeding our temporal LR threshold (10,000) which strongly supports the prosecution hypothesis. We validated the method using various computer-based simulations. The estimated number of contributors is most likely equal to the actual number if all alleles detected in the mixture can be assigned to those from the known contributors. By contrast, if an unknown contributor(s) needs to be designated, LRs should be calculated from both two-person and three-person contributions. We also consider some cases in which the unknown contributor(s) is genetically related to the known contributor(s).     

Validation of a 17-locus Y-STR multiplex system

Internal validation of a commercial 17-locus Y-STR system (AmpFlSTR^® Yfiler™, Applied Biosystems) has been performed on the ABI PRISM^® 3130 Genetic Analyzer for use in forensic cases. The Yfiler™ kit was validated according to SWGDAM guidelines. Our results show that it is possible to obtain full profiles with as little as 30 pg of male DNA even in the presence of 20,000-fold amounts of female DNA. Reaction volume was optimized for 10 μl. Male-male mixtures yielded full profiles of the minor contributor with 10-fold excess of the major contributor. Stutter values for each locus were determined from data generated for the population study which included Y-STR profiles from 156 caucasian males from the Montreal and Lac St.-Jean areas of Québec, Canada. The study recorded 141 different haplotypes of which 131 were unique with a haplotype diversity of 0.9965. A number of non-probative forensic samples from rape kit epithelial fractions and fingernail scrapings were also successfully tested.     

From unknown to known: Identification of the remains at the mausoleum of fosse Ardeatine

During the Second World War, on 24th March 1944, 335 Italians were massacred near Rome by the occupying forces of Nazi Germany. Four months later forensic examination led to the identification of 323 out of 335 victims. After approximately 60?years, the identification of the remaining unidentified twelve victims began with anthropological and genetic analysis carried out by a team of Italian forensic experts. Anthropological analysis was performed in field in order to confirm the sex of each victim and verify the presence of only one individual in each grave for a correct sampling. Selected bone fragments for each individual were then collected and transferred to the laboratory for genetic analysis. Although the anthropological ante mortem information was limited, morphological and metrical data was collected for a possible future identification of the victims. Subsequently, the typing of autosomal loci, Y-STR and mtDNA D-loop region of all bone and available reference samples was conducted. LR and cumulative LRs obtained from autosomal STR and Y-STR results confirmed the alleged relationship between three victims and their relatives with values over 10⁴ (one sample) and 10⁶ (two samples). Therefore, the genetic analysis offered the families the possibility of replacing the number of the grave with the name of the victim.     

The blurred vision of Lady Justice for minors with mental disorders: Records of the juvenile court in Belgium

Purpose

This study examined (1) the information present in juvenile court records in Belgium (Flanders) and (2) whether there are differences in information between records that mention a mental disorder and those that do not.

Method

The file study sample included 107 court records, and we used a Pearson's chi-square test and a t-test to analyze the information within those records.

Results

Information in juvenile court records varied considerably. This variability was evident when we compared juvenile court records with and without mention of a mental disorder. Significantly more information about school-related problems, the functioning of the minor, and the occurrence of domestic violence was included in records that mentioned a mental disorder compared with records that did not.

Conclusion

The content of the juvenile court records varied, particularly with regard to the mental health status of the minor in question. We suggest guidelines to standardize the information contained in juvenile court records.