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

There is interest in comparing the output, principally the likelihood ratio, from the two probabilistic genotyping software EuroForMix (EFM) and STRmix™. Many of these comparison studies are descriptive and make little or no effort to diagnose the cause of difference. There are fundamental differences between EFM and STRmix™ that are causative of the largest set of likelihood ratio differences. This set of differences is for false donors where there are many instances of LRs just above or below 1 for EFM that give much lower LRs in STRmix™. This is caused by the separate estimation of parameters such as allele height variance and mixture proportion using MLE under Hp and Ha for EFM. This can result in very different estimations of these parameters under Hp and Ha . It results in a departure from calibration for EFM in the region of LRs just above and below 1.

To overcome the multifactorial complexity associated with the analysis and interpretation of the capillary electrophoresis results of forensic mixture samples, probabilistic genotyping methods have been developed and implemented as software, based on either qualitative or quantitative models. The former considers the electropherograms\' qualitative information (detected alleles), whilst the latter also takes into account the associated quantitative information (height of allele peaks). Both models then quantify the genetic evidence through the computation of a likelihood ratio (LR), comparing the probabilities of the observations given two alternative and mutually exclusive hypotheses. In this study, the results obtained through the qualitative software LRmix Studio (v.2.1.3), and the quantitative ones: STRmix™ (v.2.7) and EuroForMix (v.3.4.0), were compared considering real casework samples. A set of 156 irreversibly anonymized sample pairs (GeneMapper files), obtained under the scope of former cases of the Portuguese Scientific Police Laboratory, Judiciary Police (LPC-PJ), were independently analyzed using each software. Sample pairs were composed by (i) a mixture profile with either two or three estimated contributors, and (ii) a single contributor profile associated. In most cases, information on 21 short tandem repeat (STR) autosomal markers were considered, and the majority of the single-source samples could not be a priori excluded as belonging to a contributor to the paired mixture sample. This inter-software analysis shows the differences between the probative values obtained through different qualitative and quantitative tools, for the same input samples. LR values computed in this work by quantitative tools showed to be generally higher than those obtained by the qualitative. Although the differences between the LR values computed by both quantitative software showed to be much smaller, STRmix™ generated LRs are generally higher than those from EuroForMix. As expected, mixtures with three estimated contributors showed generally lower LR values than those obtained for mixtures with two estimated contributors. Different software products are based on different approaches and mathematical or statistical models, which necessarily result in the computation of different LR values. The understanding by the forensic experts of the models and their differences among available software is therefore crucial. The better the expert understands the methodology, the better he/she will be able to support and/or explain the results in court or any other area of scrutiny.

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.

Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.

Likelihood ratios (LR) differences between the probabilistic genotyping software EuroForMix and STRmix™ are examined. After considering differences in the allele probabilities, the LRs from both software for an unambiguous single-source profile were identical (four significant figures). LRs from both software for an unambiguous single-source profile with alleles previously unseen in the allele frequency database (rare alleles) were the same (three significant figures) for θ = 0.01. Due to differences in the minimum allele frequencies, the LRs differed by three orders of magnitude when θ = 0. For both software, the LRs for a single-source dilution series decreased as the input amount decreased. The LRs from both software were within an order of magnitude for known contributors. The largest difference was where the target input amount was 0.0156 ng: The LREuroForMix was 2.1 × 1025 and the LRSTRmix was 8.0 × 1024 . Both software show similar LR behavior with respect to mixture ratio. For two person mixtures the LR increases for both the major and the minor as the ratio moves away from 1:1. The LR for the major stabilizes at about 3:1 whereas the LR for the minor reaches its maximum at about 3:1 and then declines. Greater differences in LR were observed between EuroForMix and STRmix™ for mixtures. One-hundred and twenty-nine mixtures from the PROVEDIt dataset were compared. LRs for 84% of the comparisons for known contributors without rare alleles were within two orders of magnitude. Five divergent results were investigated, and a manual intervention approach was applied where appropriate.

Since the first shedder test was formulated almost 20 years ago, a plethora of different test strategies has emerged. The amount of data generated so far is considerable. However, because of the limited reproducibility of its results, the reliability of the shedder concept is frequently questioned. This study provides a literature overview of applied shedder tests that capture the diversity of the concept. It is pointed out to what extent different classification criteria, workflows, and trace evaluation can impair the classification outcome. The robustness of shedder status was assessed by applying a promising approach established by Fonneløp et al. (Forensic Sci Int Genet 29:48-60, 21). Data provide similar results to those in recent studies but also ambiguous shedder classifications. The applied shedder test was adapted based on our own as well as the reviewed data. With novel classification parameters, promising results were achieved. This study reveals uncertainties and inconsistencies of the shedder concept. Recommendations for harmonization and transparency are proposed. Implementation of the recommendations may result in an increased impact on casework and transfer studies, including activity-level assessments. Furthermore, this study shows that moisturizers affect participants\' shedder status as well as DNA transfer. The impact appears to remain relevant even 60 min post ointment application but depends greatly on the type of moisturizer applied.

The interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out, for instance from sample degradation. Mixture interpretation can be improved by increasing the number of STRs and/or loci with a higher discriminatory power. Both capillary electrophoresis (CE, 6-dye) and massively parallel sequencing (MPS) provide a platform for analysing relatively large numbers of autosomal STRs. In addition, MPS enables distinguishing between sequence variants, resulting in enlarged discriminatory power. Also, MPS allows for small amplicon sizes for all loci as spacing is not an issue, which is beneficial with degraded DNA. Altogether, MPS has the potential to increase the weights of evidence for true contributors to (complex) DNA profiles. In this study, likelihood ratio (LR) calculations were performed using STR profiles obtained with two different MPS systems and analysed using different settings: 1) MPS PowerSeq™ Auto System profiles analysed using FDSTools equipped with optimized settings such as noise correction, 2) ForenSeq™ DNA Signature Prep Kit profiles analysed using the default settings in the Universal Analysis Software (UAS), and 3) ForenSeq™ DNA Signature Prep Kit profiles analysed using FDSTools empirically adapted to cope with one-directional reads and provisional, basic settings. The LR calculations used genotyping data for two- to four-person mixtures varying for mixture proportion, level of drop-out and allele sharing and were generated with the continuous model EuroForMix. The LR results for the over 2000 sets of propositions were affected by the variation for the number of markers and analysis settings used in the three approaches. Nevertheless, trends for true and non-contributors, effects of replicates, assigned number of contributors, and model validation results were comparable for the three MPS approaches and alike the trends known for CE data. Based on this analogy, we regard the probabilistic interpretation of MPS STR data fit for forensic DNA casework. In addition, guidelines were derived on when to apply LR calculations to MPS autosomal STR data and report the corresponding results.

The increased interest in the use of Massively Parallel Sequencing (MPS) technologies to type traditional autosomal STR markers raises multiple questions regarding interpretation of the results via probabilistic genotyping. To begin to address some of those questions, we examined the effects of using differing degrees of sequence information, pre-filtering, and data modeling to interpret complex MPS-STR mixtures in a probabilistic genotyping software. Sixty ForenSeq typing results for mixtures of from two to four contributors were: 1) represented using three separate formats that captured different degrees of sequence information, and 2) were analyzed using three different filtering approaches prior to probabilistic interpretation. All mixtures for the different format and filtering variants were subsequently interpreted with respect to ten reference profiles, using both qualitative (LRmix) and quantitative (EuroForMix) models to calculate the likelihood ratio (LR). The LR results indicated moderate information gain when the STR nomenclature was based upon the longest uninterrupted stretch (LUS) compared with conventional capillary electrophoresis repeat units (RU), whereas additional gains were very small when the complete sequence information was utilised. Use of a static analytical threshold for data pre-filtering improved LRs compared to a dynamic (percentage-based) threshold, as the static threshold prevented excessive filtering of alleles originating from minor contributors. For interpretations performed using a quantitative model, a small improvement in performance was observed if a stutter model was employed instead of using stutter thresholds to pre-filter the data, whereas - as expected - performance worsened considerably under the qualitative model when stutter was not pre-filtered. Given the empirical and theoretical findings in this study we discuss the value of utilizing sequence-level information and potential paths forward to increase information gain using MPS systems.

Identification of the minor contributor in DNA mixture of close relatives remains a dilemma in forensic genetics. Massively parallel sequencing (MPS) can analyze multiple short tandem repeats (STRs) and single nucleotide polymorphism (SNPs) concurrently and detect non-overlapping alleles of the minor contributors in DNA mixtures. A commercial kit for MPS of 59 identity informative STRs (iiSTRs) and 94 autosomal identity-informative SNPs (iiSNPs) was used to analyzed 34 nondegraded and 33 highly degraded two-person artificial DNA mixtures of close relatives with various minor to major ratios (1:9, 1:19, 1:29, 1:39, 1:79, 1:99). EuroForMix software was used to determine the minor contributors in the mixtures based on the likelihood ratios calculated from the MPS data, and relMix software was used to perform kinship analysis of the contributors. The STRs and SNPs of the 34 nondegraded and 33 degraded DNA mixtures were genotyped using MPS. Using EuroForMix based on the genotypes of autosomal iiSTRs and autosomal iiSNPs, 82.4% (28/34) and 54.5% (18/33) of minor donors could be accurately assigned for the nondegraded and degraded DNA mixtures, respectively. The relMix software correctly inferred the relationship between contributors in 97.1% (33/34) of nondegraded mixtures and in 97.0% (32/33) of degraded mixtures. In conclusion, combined EuroForMix and MPS data of STRs and SNPs can assist in the assignment of minor donors in nondegraded DNA mixtures of close relatives, and relMix can be used to infer relationship among contributors.