Pairwise kinship inference and pedigree reconstruction using 91 microhaplotypes.

Kinship inference has been a major issue in forensic genetics, and it remains to be solved when there is no prior hypothesis and the relationships between multiple individuals are unknown. In this study, we genotyped 91 microhaplotypes from 46 pedigree samples using massive parallel sequencing and inferred their relatedness by calculating the likelihood ratio (LR). Based on simulated and real data, different treatments were applied in the presence and absence of relatedness assumptions. The pedigree of multiple individuals was reconstructed by calculating pedigree likelihoods based on real pedigree samples. The results showed that the 91 MHs could discriminate pairs of second-degree relatives from unrelated individuals. And more highly polymorphic loci were needed to discriminate the pairs of second-degree or more distant relative from other degrees of relationship, but correct classification could be obtained by expanding the suspected relationship searched to other relationships with lower LR values. Multiple individuals with unknown relationships can be successfully reconstructed if they are closely related. Our study provides a solution for kinship inference when there are no prior assumptions, and explores the possibility of pedigree reconstruction when the relationships of multiple individuals are unknown.

Exploration of identifying individual tumor tissue based on probabilistic model.

Variations in the tumor genome can result in allelic changes compared to the reference profile of its homogenous body source on genetic markers. This brings a challenge to source identification of tumor samples, such as clinically collected pathological paraffin-embedded tissue and sections. In this study, a probabilistic model was developed for calculating likelihood ratio (LR) to tackle this issue, which utilizes short tandem repeat (STR) genotyping data. The core of the model is to consider tumor tissue as a mixture of normal and tumor cells and introduce the incidence of STR variants (φ) and the percentage of normal cells (Mxn) as a priori parameters when performing calculations. The relationship between LR values and φ or Mxn was also investigated. Analysis of tumor samples and reference blood samples from 17 colorectal cancer patients showed that all samples had Log 10(LR) values greater than 1014. In the non-contributor test, 99.9% of the quartiles had Log 10(LR) values less than 0. When the defense's hypothesis took into account the possibility that the tumor samples came from the patient's relatives, LR greater than 0 was still obtained. Furthermore, this study revealed that LR values increased with decreasing φ and increasing Mxn. Finally, LR interval value was provided for each tumor sample by considering the confidence interval of Mxn. The probabilistic model proposed in this paper could deal with the possibility of tumor allele variability and offers an evaluation of the strength of evidence for determining tumor origin in clinical practice and forensic identification.

Corrigendum: A novel SNP-STR system based on a capillary electrophoresis platform.

[This corrects the article DOI: 10.3389/fgene.2021.636821.].

Objectifying evidence evaluation for gunshot residue comparisons using machine learning on criminal case data.

Comparative gunshot residue analysis addresses relevant forensic questions such as 'did suspect X fire shot Y?'. More formally, it weighs the evidence for hypotheses of the form H1: gunshot residue particles found on suspect's hands are from the same source as the gunshot residue particles found on the crime scene and H2: two sets of particles are from different sources. Currently, experts perform this analysis by evaluating the elemental composition of the particles using their knowledge and experience. The aim of this study is to construct a likelihood-ratio (LR) system based on representative data. Such an LR system can support the expert by making the interpretation of the results of electron microscopy analysis more empirically grounded. In this study we chose statistical models from the machine learning literature as candidates to construct this system, as these models have been shown to work well for large and high-dimensional datasets. Using a subsequent calibration step ensured that the system outputs well-calibrated LRs. The system is developed and validated on casework data and an additional validation step is performed on an independent dataset of cartridge data. The results show that the system performs well on both datasets. We discuss future work needed before the method can be implemented in casework.

RFU derived LRs for activity level assignments using Bayesian Networks.

A comparative study has been carried out, comparing two different methods to estimate activity level likelihood ratios (LRa) using Bayesian Networks. The first method uses the sub-source likelihood ratio (log10LRϕ) as a 'quality indicator'. However, this has been criticised as introducing potential bias from population differences in allelic proportions. An alternative method has been introduced that is based upon the total RFU of a DNA profile that is adjusted using the mixture proportion (Mx) which is calculated from quantitative probabilistic genotyping software (EuroForMix). Bayesian logistic regressions of direct transfer data showed that the two methods were comparable. Differences were attributed to sampling error, and small sample sizes of secondary transfer data. The Bayesian approach facilitates comparative studies by taking account of sampling error; it can easily be extended to compare different methods.

An LR framework incorporating sensitivity analysis to model multiple direct and secondary transfer events on skin surface.

Bayesian logistic regression is used to model the probability of DNA recovery following direct and secondary transfer and persistence over a 24 h period between deposition and sample collection. Sub-source level likelihood ratios provided the raw data for activity-level analysis. Probabilities of secondary transfer are typically low, and there are challenges with small data-sets with low numbers of positive observations. However, the persistence of DNA over time can be modelled by a single logistic regression for both direct and secondary transfer, except that the time since deposition must be compensated by an offset value for the latter. This simplifies the analysis. Probabilities are used to inform an activity-level Bayesian Network that takes account of alternative propositions e.g. time of assault and time of social activities. The model is extended in order to take account of multiple contacts between person of interest and 'victim'. Variables taken into account include probabilities of direct and secondary transfer, along with background DNA from unknown individuals. The logistic regression analysis is Bayesian - for each analysis, 4000 separate simulations were carried out. Quantile assignments enable calculation of a plausible range of probabilities and sensitivity analysis is used to describe the corresponding variation of LRs that occur when modelled by the Bayesian network. It is noted that there is need for consistent experimental design, and analysis, to facilitate inter-laboratory comparisons. Appropriate recommendations are made. The open-source program written in R-code ALTRaP (Activity Level, Transfer, Recovery and Persistence) enables analysis of complex multiple transfer propositions that are commonplace in cases-work e.g. between those who cohabit. A number of case examples are provided. ALTRaP can be used to replicate the results and can easily be modified to incorporate different sets of data and variables.

Evaluating Likelihood Ratio (LR) for firearm evidence identifications in forensic science based on the Congruent Matching Cells (CMC) method.

Firearm evidence identification has been challenged by the 2008 and 2009 National Research Council (NRC) reports and by legal proceedings on its fundamental assumptions, its procedure involving subjective interpretations, and the lack of a statistical foundation for evaluation of error rates or other measures for the weight of evidence. To address these challenges, researchers of the National Institute of Standards and Technology (NIST) recently developed a Congruent Matching Cells (CMC) method for automatic and objective firearm evidence identification and quantitative error rate evaluation. Based on the CMC method, a likelihood ratio (LR) procedure is proposed in this paper aiming to provide a scientific basis for firearm evidence identification and a method for evaluation of the weight of evidence. The initial LR evaluations using two sets of 9mm cartridge cases' breech face impression images with different sample sizes, imaging methods and ammunition showed that for all the declared identifications of the tested 2D and 3D image pairs, the evaluated LRs for the least favorable scenario were well above an order of 106, which provides Extremely Strong Support for a prosecution proposition (e.g. a same-source proposition) in a Bayesian frame. The LR evaluations also showed that for all the declared exclusions of the tested 3D image pairs, the evaluated LRs for the least favorable scenario were above an order of 102, which provides Moderately Strong Support for a defense proposition (e.g. a different-source proposition) in a Bayesian frame.

Sibling assessment based on likelihood ratio and total number of shared alleles using 21 short tandem repeat loci included in the GlobalFiler™ kit.

Sibling assessment using the 15 autosomal short tandem repeat (STR) loci included in the Identifiler® kit can be difficult when comparing an unidentified party to an alleged sibling. Therefore, we investigated the likelihood ratio (LR) and the total number of shared alleles (TNSA) for sibship determination using the 21 autosomal STR loci included in the GlobalFiler™ kit. We computationally generated the genotypes of 10,000 sibling pairs and 10,000 unrelated pairs based on previously reported allele frequencies of the 15 Identifiler loci and the remaining 6 GlobalFiler loci. The LR and the TNSA were then calculated in each pair using the 15 and 21 loci. Next, these calculations were applied to 22 actual sibling pairs. LR values ⩾ 10,000 were observed in 48% of the sibling pairs using the 15 loci and in 80% of the sibling pairs using the 21 loci. The TNSA distribution between siblings and unrelated pairs was more divergent in GlobalFiler than in Identifiler. TNSA values ⩾ 20 were found only in true siblings in Identifiler, while TNSA values ⩾24 in GlobalFiler. In Identifiler, all pairs with TNSA ⩾ 24 had LR values ⩾ 10,000 and the same was true in GlobalFiler for TNSA ⩾29. Therefore, increasing the number of loci is very efficient for sibship determination. The LR is most reliable for determining sibship. However, TNSA values may be useful for the preliminary method of LR values because LR value demonstrated a significantly positive correlation with TNSA value in both Identifiler and GlobalFiler.

Towards source level evaluation of the evidential value of fibre examinations.

This paper aims to provide the first steps towards a numerical source level evaluation of fibre evidence. For that purpose, likelihood ratio equations are derived for four generic scenarios, in which the source frequency, the number of references and trace types investigated, and the number of matches vary. Previous experimental studies into the evaluation of fibre evidence are reviewed and we demonstrate how the results of these studies, as well as other data, can be used to evaluate the derived equations for the four scenarios. Evaluation is not straightforward and requires a number of assumptions. This is mainly because the relevant population under consideration in a specific case cannot be sufficiently evaluated. In addition, the subjective match-criterion in current forensic fibre examinations makes it impossible to implement a good evaluation of the within-variation of samples. As a result, the discrimination power, currently calculated for discrimination studies, is only valid for samples with negligible heterogeneity. We conclude that reporting a numerical evidential value for forensic fibre examinations is not yet feasible as the data are available for only a few types of fibres and cannot be used without several assumptions. We propose a number of developments that are required to improve the accuracy and numerical analysis.