Mixed DNA Profiles Research Articles

Application of X-STRs for Forensic Identification in Mixed DNA Profile: A Case Report

Autosomal polymorphisms (STRs) or Y-Chromosome polymorphisms (Y-STRs) are usually used for the study and deconvolution of mixed DNA profiles in forensic genetics, accompanying data interpretation with biostatistical evaluations (e.g., RMP, RMNE, LR). Sometimes, however, some mixed DNA profiles are so complex that autosomal and Y markers are not sufficient for correct discrimination and identification. In this work is reported a robbery case in which the analysis of the polymorphic markers of the X Chromosome (X-STRs) was applied to the mixed profiles obtained from the traces. This falls outside the classic use of the X-STRs. Indeed, the aim of the authors is to encourage the usage of X-STRs not only in parental relationships, but also in pure forensic cases for interpreting complex mixed DNA profiles, since their application in case resolution could be more decisive than autosomal STRs and Y-STRs.

Deconvoluting multi-person biological mixtures and accurate characterization and identification of separated contributors using non-targeted single-cell DNA sequencing

The genetic characterization and identification of individuals who contributed to biological mixtures are complex and mostly unresolved tasks. These tasks are relevant in various fields, particularly in forensic investigations, which frequently encounters crime scene stains generated by more than one person. Currently, forensic mixture deconvolution is mostly performed subsequent to forensic DNA profiling at the level of the mixed DNA profiles, which comes with several limitations. Some previous studies attempted at separating single cells prior to forensic DNA profiling. However, these approaches are biased at selection of the cells and, due to their targeted DNA analysis on low template DNA, provide incomplete and unreliable forensic DNA profiles. We recently demonstrated the feasibility of performing mixture deconvolution prior to forensic DNA profiling through the utilization of a non-targeted single-cell transcriptome sequencing (scRNA-seq). In addition to individual-specific mixture deconvolution, this approach also allowed accurate characterisation of biological sex, biogeographic ancestry and individual identification of the separated mixture contributors. However, RNA has the forensic disadvantage of being prone to degradation, and sequencing RNA - focussing on coding regions - limits the number of single nucleotide polymorphisms (SNPs) utilized for genetic mixture deconvolution, characterization, and identification. These limitations can be overcome by performing single-cell sequencing on the level of DNA instead of RNA. Here, for the first time, we applied non-targeted single-cell DNA sequencing (scDNA-seq) by applying the scATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing) technique to address the challenges of mixture deconvolution in the forensic context. We demonstrated that scATAC-seq, together with our recently developed De-goulash data analysis pipeline, is capable of deconvoluting complex blood mixtures of five individuals from both sexes with varying biogeographic ancestries. We further showed that our approach achieved correct genetic characterization of the biological sex and the biogeographic ancestry of each of the separated mixture contributors and established their identity. Furthermore, by analysing in-silico generated scATAC-seq data mixtures, we demonstrated successful individual-specific mixture deconvolution of i) highly complex mixtures of 11 individuals, ii) balanced mixtures containing as few as 20 cells (10 per each individual), and iii) imbalanced mixtures with a ratio as low as 1:80. Overall, our proof-of-principle study demonstrates the general feasibility of scDNA-seq in general, and scATAC-seq in particular, for mixture deconvolution, genetic characterization and individual identification of the separated mixture contributors. Furthermore, it shows that compared to scRNA-seq, scDNA-seq detects more SNPs from fewer cells, providing higher sensitivity, that is valuable in forensic genetics.

Carrying out common DNA donor analysis using DBLR™ on two or five-cell mini-mixture subsamples for improved discrimination power in complex DNA mixtures

Probabilistic genotyping systems are able to analyse complex mixed DNA profiles and show good power to discriminate contributors from non-contributors. However, the abilities of the statistical analyses are still unavoidably bound by the quality of information being analysed. If a profile has a high number of contributors, or a contributor that is present in trace amounts, then the amount of information about those individuals in the DNA profile is limited. Recent work has shown the ability to gain better resolution of the genotypes of contributors to complex profiles using cell subsampling. This is the process of taking many sets of a limited number of cells and individually profiling each set. These ‘mini-mixtures’ can provide greater information about the genotypes of underlying contributors. In our work we take the resulting profiles from multiple subsamplings of complex DNA profiles in equal amounts and show how testing for, and then assuming, a common DNA donor can further improve the ability to resolve the genotypes of contributors. Using direct cell sub-sampling and statistical analysis software DBLR™, we were able to recover single source profiles of uploadable quality from five out of the six contributors of an equally proportioned mixture. Through the analysis of mixtures in this work we provide a template for carrying out common donor analysis for maximum effect.

The Sexome ‐ A proof of concept study into microbial transfer between heterosexual couples after sexual intercourse

The detection and recovery of male DNA post-assault is important in sexual assault investigations, particularly where an offender is unknown to the victim. The collection of DNA evidence often occurs when the female victim undergoes a forensic medical assessment. Analysis regularly results in mixed autosomal DNA profiles with both victim and perpetrator DNA, often making it difficult to interpret a male profile suitable for DNA database searching. While short tandem repeat (STR) profiling of the male Y-chromosome is often used to overcome this challenge, successful identification of an individual can be hindered by the paternal inheritance pattern of Y-STRs and small Y-STR databases. Human microbiome research has suggested that a person’s microbial diversity is unique. Therefore microbiome analysis using Massively Parallel Sequencing (MPS) could serve as a useful adjunct method of perpetrator identification. This study aimed to identify bacteria taxa that were unique to each participant and compare the bacterial communities found on their genitals both pre- and post-coitus. Samples were collected from six male-female sexual partner pairs. Volunteers were asked to self-collect low vaginal (females) and penis shaft and glans (males) samples before and after intercourse. Samples were extracted using the PureLink™ Microbiome DNA Purification Kit. Extracted DNA underwent library preparation using primers targeting the V3-V4 hypervariable regions of the bacterial 16S rRNA gene (∼450 bp). Libraries were sequenced on the Illumina MiSeq® platform. From the sequence data derived, statistical analysis was performed to investigate if bacteria sequences could be used to infer contact between each male-female pairing. Unique bacterial signatures were detected in low frequencies (<1%) in male and female participants pre-coitus. The data indicated a significant disruption to microbial diversity post-coitus in all samples. A transfer of the female microbiome during intercourse was most significant. As expected, one couple who did not use a barrier contraceptive yielded the most microbial transfer and disruption to diversity demonstrating a proof-of-concept in the utility of microbiome interrogation for sexual assault cases. Further genomic analysis is needed to confirm species and subspecies classification of bacteria that may produce a unique microbial profile that could then be used to identify a specific individual.

Validation of Probabilistic Genotyping Software for Single Cell STR Analysis

Probabilistic genotyping (PG) and its associated software has greatly aided in forensic DNA mixture analysis, with it primarily being applied to mixed DNA profiles obtained from bulk cellular extracts. However, these software applications do not always result in probative information about the identity of all donors to said mixtures/extracts. This is primarily due to mixture complexity caused by overlapping alleles and the presence of artifacts and minor donors. One way of reducing mixture complexity is to perform direct single cell subsampling of the bulk mixture prior to genotyping and interpretation. The analysis of low template DNA samples, including from single or few cells, has also benefited from the application of PG methods. With the application of PG, multiple cell subsamples originating from the same donor can be combined into a single analysis using the software replicate analysis function often resulting in full DNA profile donor information. In the present work, we demonstrate how two PG software systems, STRmixTM and EuroForMix, were successfully validated for single or few cell applications.

DNA transfer when using gloves in burglary simulations

Several studies have demonstrated that DNA can be indirectly transferred from an individual onto a surface. Therefore, the presence of DNA that is compatible with a given person does not necessarily mean that this person has touched the surface on which the DNA was recovered. The present work simulates cases, where DNA is recovered on a door handle and compared to several reference DNA profiles. The DNA profile of the trace shares DNA components with a person of interest (POI). When asked about the DNA results, the POI says he has nothing to do with the incident and has never been at the scene. However, a possibility would be that the DNA came from his recently stolen gloves. Someone else, the alternative offender (AO), could have opened the door wearing his gloves (POI’s gloves), and transferred his DNA (POI’s DNA). Based on the above-mentioned scenario, 60 burglary simulations experiments were carried out to generate data to assess DNA results given these allegations. The quantity and quality of DNA profiles (NGM SElect) recovered when the POI opened/closed the door bare-handed or when someone else performed the same activity but using POI’s gloves, were compared. The gloves were regularly worn during at least three months by their owner during the winter. On the contrary, the AO wore them only for two minutes. Among the traces collected on the door handles, less than 50% of the traces led to interpretable DNA profiles. In 30% of the cases (3/10), when the door was opened/closed with bare hands, the DNA found on the door handle led to a mixed DNA profile with the POI’s DNA aligning with the major contributor. For the experiments where the AO opened/closed the door with the POI’s gloves, the POI’s DNA was compatible with 22% (11/50) of the mixed DNA profile, aligning with the major in 8% of the cases (4/50). The DNA profiles of the offices’ occupants were observed on the door handles, but not the AO’s. In addition to the results of the experiments, we show two examples of how one can assess results observed in casework. Given the possibility of indirect transfer of minute DNA quantities, this research emphasizes the need to evaluate DNA results given the activities when the POI has a legitimate reason that can explain the presence of their DNA.

Compound and Conditioned Likelihood Ratio Behavior within a Probabilistic Genotyping Context.

In cases where multiple questioned individuals are separately supported as contributors to a mixed DNA profile, guidance documents recommend performing a comparison to see if there is support for their joint contribution. Anecdotal observations suggest the summed log of the individual likelihood ratios (LR), termed the simple LR product, should be roughly equivalent to or less than the log(LR) for the joint likelihood ratio, termed the compound LR. To assist casework analysts in evaluating statistical weights applied to a case at hand, this study assessed how consistently compound LRs conform to an additive behavior when compared to the simple LR product counterparts. Two-, three-, and four-person DNA mixture data, of various mixture proportions and DNA inputs, were interpreted by STRmix® version 2.8 Probabilistic Genotyping Software. Relative magnitudes of LR increases were found to be dependent on both template level and mixture composition. The distribution of log(LR) differences between all compound/simple LR comparisons was ~-2.7 to ~28.3. This level of information gain was similar to that for compound LR comparisons, with and without interpretation conditioning (~-3.2 to ~27.7). In both scenarios, the probability density peaked at approximately 0.5, indicating the information gain from constrained genotype combinations has a comparable impact on the outcome of LR calculations whether the restriction is applied before or after interpretation.

Y-STR mixture deconvolution by single-cell analysis.

Since Y-STR typing only amplifies male Y chromosomal DNA, it can simplify the interpretation of some DNA mixtures that contain female DNA. However, if there are multiple male contributors, mixed Y-STR DNA profiles will often be obtained. Y-STR mixture analysis cases are particularly challenging though as, currently, there are no validated probabilistic genotyping (PG) software solutions commercially available to aid in their interpretation. One approach to fully deconvoluting these challenging mixtures into their individual donors is to conduct single-cell genotyping by isolating individual cells from a mixture prior to conducting DNA typing. In this work, a physical micromanipulation technique involving a tungsten needle and direct PCR with decreased reaction volume and increased cycle number was applied to equimolar 2- and 3-person buccal cell male DNA mixtures and a mock touch DNA case scenario involving the consecutive firing of a handgun by two males. A consensus DNA profiling approach was then utilized to obtain YFiler™ Plus Y-STR haplotypes. Buccal cells were used to optimize and test the direct single-cell subsampling approach, and 2-3 person male buccal cell mixtures were fully deconvoluted into their individual donor Y-STR haplotypes. Single-cell (or agglomerated cell clump) subsampling from the gun's trigger recovered single-source Y-STR profiles from both individuals who fired the gun, the owner, and the other unrelated male. Only the non-owner's DNA was found in the cells recovered from the handle. In summary, direct single-cell subsampling as described represents a potential simple way to analyze and interpret Y-STR mixtures.

Use of hormone-specific antibody probes for differential labeling of contributor cell populations in trace DNA mixtures.

A significant proportion of casework analyzed by forensic science laboratories is often "touch" or trace forensic DNA evidence, which is deposited through physical contact and is comprised of sloughed epidermal cells. These samples can be challenging to analyze due to low DNA concentrations, frequent degradation, and the presence of cells from multiple individuals in the same sample. To address these challenges, we investigated a new approach for characterizing trace evidence prior to DNA profiling that labels epidermal cells with antibody probes targeting hormone molecules testosterone and dihydrotestosterone (DHT). The goal was to test whether cell populations derived from separate individuals showed different binding efficiencies to hormone probes and, thus, could be used to detect the presence of multiple cell populations. Additionally, we investigated whether antibody probes could be used to isolate contributor cell populations from an epidermal cell mixture and facilitate deconvolution of mixed DNA profiles recovered from touch/trace evidence. Results showed that cell populations from some individuals could differentiated in trace samples based on fluorescence histograms following probe labeling. However, certain pairs of contributors showed largely or completely overlapping histogram profiles and could not be resolved. Preliminary efforts to separate cell populations that could be differentiated with hormone probes with fluorescence-activated cell sorting (FACS) coupled to DNA profiling and probabilistic modeling indicated that it is possible to enrich contributor cell populations from touch/trace samples and produce more probative DNA profiles compared to the original mixture sample. The variability in labeling, differentiation, and physical separation of cell populations may be impacted by similarities in biochemical profiles across some contributors as well as imbalance of contributor DNA quantities in certain mixtures as is typical in casework involving touch/trace evidence. Ultimately, screening and separation of trace DNA samples with this approach may be presumptive and constrained by sample-specific parameters of the original mixture.

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.

Allele frequencies and minor contributor match statistic convergence using simulated population replicates.

Probabilistic genotyping permits a comparison of forensic evidence given hypotheses regarding the origin of observed short tandem repeat alleles in a mixed DNA profile. Using the publicly available R package forensim, it has been proposed that mixtures with non-contributors from low genetic diversity populations are more likely to be mistakenly identified as contributors to a mixture than non-contributors from high genetic diversity populations. We hypothesized that these observations are attributed to the unique distribution of alleles in the reference population and may not generalize to other samplings of the same population. We used forensim to simulate 200 US populations (50 each of self-reported African-American, Asian-American, European-American, and Hispanic descent). We compared likelihood ratios for 2400 mixtures to those derived from published data and identified stark differences. A minimum of ten population replicates were required to reduce observed differences relative to published data. Deviations from Hardy-Weinberg equilibrium and allele frequency distributions suggest that simulated populations should be sufficiently evaluated for expectations of population genetic parameters prior to use in DNA mixture modeling experiments. Overall, our findings support the utility of forensim and further describe its suitability to model population genetic parameters but suggest that a single population replicate (directly ascertained or simulated) may be insufficient to make conclusions about a given DNA mixture.

Identification of missing persons through kinship analysis by microhaplotype sequencing of single-source DNA and two-person DNA mixtures

In forensic applications, there is an increasing demand for the analysis of DNA profiles arising from missing person identification (MPI) cases. A specific DNA profile may originate from a single source or more than one contributor (i.e., a DNA mixture). When direct references are not available, indirect relative references can be used to identify missing persons by kinship analysis. As a novel kind of multiallelic marker, microhaplotypes have proven promising for relatedness determination and mixture deconvolution. Herein, we developed a large panel of 185 microhaplotype markers and demonstrated its application in different scenarios of relationship inference through a simulation study and real pedigree analysis, combined with probabilistic genotyping models for data interpretation. Based on single-source profiles, it was shown that the present microhaplotype panel was sufficient for pairwise close relative testing (parent/child, full-sibling and 2nd-degree relative). For more distant relatives (3rd-degree relatives), there was a clear improvement when data from one well-chosen extra relative were available. We further sought to evaluate the theoretical systematic effectiveness and actual performance of microhaplotype markers in identifying the contribution of a missing pedigree member to a two-person mixture (as a minor donor). It was observed that 100% correct assignments were made in the balanced mixtures (with no dropout) when referenced to close relatives. When the mixture profiles suffered from dropout, incorrect assignments of minor donors were markedly associated with relatedness and the dropout level. Meanwhile, the studied scenarios generally exhibited zero or very low false-positive rates, indicating a low probability of incorrectly assigning an unrelated contributor as a close relative of the reference. Our results indicate that microhaplotype data can be reliably interpreted for identifying missing persons through kinship analysis based on DNA profiles of single-source samples or two-person mixtures. Furthermore, this study could be extended to more complex scenarios, such as determining the relatedness of contributors in (or among) mixed DNA profiles, if combined with different statistical frameworks.

Exploring likelihood ratios assigned for siblings of the true mixture contributor as an alternate contributor

Relatives tend to have more DNA in common than unrelated people. The closer the biological relationship, the higher the chance of alleles being identical by descent between the individuals. Therefore, when considering a mixed DNA profile, close relatives of the true contributor may not always be excluded as a possible contributor to a mixture due to allele sharing. In these situations, it might be more appropriate under the alternate proposition to consider that the DNA could have originated from a relative of the person of interest rather than an unrelated individual. The probabilistic genotyping software STRmix™ automatically provides LRs considering close biological relatives as alternate sources of the DNA. In this paper, we investigate the support for siblings of the true contributor to a mixture (who are not present in the mixture themselves). We interpret the mixtures and assign LRs using STRmix™ and investigate whether the resulting LRs could be used to indicate whether the true contributor could be a sibling of the POI. Most siblings will have one or more alleles that are not observed in the mixture profile. Support for siblings to have contributed can only occur when allelic dropout is a possibility at the loci where the siblings have alleles that are not observed in the profile. In these data, that was only observed in components with assigned template of 588 rfu or less.

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

DNA profiling involves the analysis of sequences of an individual or mixed DNA profiles to identify the persons that these profiles belong to. A critically important application of DNA profiling is in forensic science to identify criminals by finding a match between their blood samples and the DNA profile found on the crime scene. Other applications include paternity tests, disaster victim identification, missing person investigations, and mapping genetic diseases. A crucial task in DNA profiling is the determination of the number of contributors in a DNA mixture profile, which is challenging due to issues that include allele dropout, stutter, blobs, and noise in DNA profiles; these issues negatively affect the estimation accuracy and the computational complexity. Machine-learning-based methods have been applied for estimating the number of unknowns; however, there is limited work in this area and many more efforts are required to develop robust models and their training on large and diverse datasets. In this paper, we propose and develop a software tool called TAWSEEM that employs a multilayer perceptron (MLP) neural network deep learning model for estimating the number of unknown contributors in DNA mixture profiles using PROVEDIt, the largest publicly available dataset. We investigate the performance of our developed deep learning model using four performance metrics, namely accuracy, F1-score, recall, and precision. The novelty of our tool is evident in the fact that it provides the highest accuracy (97%) compared to any existing work on the most diverse dataset (in terms of the profiles, loci, multiplexes, etc.). We also provide a detailed background on the DNA profiling and literature review, and a detailed account of the deep learning tool development and the performance investigation of the deep learning method.

Interpretation and reporting of mixed DNA profiles by seven forensic laboratories in the UK and Ireland

In 2014, the UK Forensic Science Regulator (FSR) commissioned a collaborative trial to assess the methods used by forensic service providers (FSPs) in the UK and Ireland for analysis, interpretation and reporting of mixed DNA profiles. Five different mixed samples of varying complexity with supporting mock case circumstances were tested using SGMPlus™ and the newly introduced DNA-17(+) multiplexes and reported by participating laboratories. The results demonstrated a high degree of consistency in analytical methods and allele designations, but some variation in the statistical evaluation and reporting of results. Some of the differences noted were attributable to the major technology change to 17(+)-STR systems which had recently been implemented across the UK at that time. The FSR made recommendations based on the trial outcomes which were intended to produce a more consistent approach to mixtures analysis, interpretation and reporting.Four years later, the Association of Forensic Science Providers (AFSP) repeated the trial, with all major UK and Ireland FSPs (both public sector and private companies) again participating. This second trial used the same mixture set as the 2014 trial but was focussed on the methods for interpretation and evaluation. Since 2014, all UK and Ireland FSPs have implemented probabilistic statistical software using continuous models enabling statistical evaluation of more complex mixtures than was possible in 2014. The trial was therefore aimed at investigating the value of these improved capabilities and also to investigate if there appeared to be marked differences between the different software tools in use in the UK.The results demonstrate a high degree of concordance within and between FSPs and across different evaluation models, and will provide important support for the use of such models in evaluation of mixed DNA profiles.

Casework comparison of DNA sampling success from steering wheels and car seats in tropical Australia

ABSTRACT Investigations of unlawful use of motor vehicle (UUMV) offences often rely on detection of the offender’s DNA inside the vehicle. Steering wheels are likely to retain the DNA of a recent driver; however, they often recover insufficient DNA for forensic DNA profiling or complex mixed DNA profiles unsuitable for comparison. In contrast, samples taken from the driver’s side seat often included the recent driver’s profile, with less DNA from other individuals. Further, in warmer climates, offenders are likely to dress in lightweight clothing, including shorts, which increases skin contact with car seats, particularly during the warmer months. Thus, to test the suitability of sampling car seats in comparison with steering wheels in a casework context, we employed a paired sampling approach to every suitable vehicle examined by forensic officers in a tropical Australian regional city over a six-month period. There was a significant difference between the overall DNA sampling results from car seats and steering wheels; however, there was little difference in the numbers of uploadable profiles or offender identifications between the sampled areas. These data contribute substantially to our knowledge of DNA transfer under real-world conditions and inform operational practices aimed at maximizing evidence recovery from crime scenes.

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.

Developmental validation of a software implementation of a flexible framework for the assignment of likelihood ratios for forensic investigations

The developmental validation of the software DBLR™ (‘database likelihood ratios’) is described. DBLR™ implements a flexible framework for the assignment of likelihood ratios for forensic DNA profiles. This framework allows the user to assign a likelihood ratio for virtually any conceivable scenario using any combination of single-source or mixed DNA profiles. Propositions are defined by constructing pedigrees and then linking DNA typing results to pedigree individuals. Complex kinship scenarios including incestuous relationships can be evaluated. Additional functionality within DBLR™ allows the user to explore the discriminatory power of any DNA profile (Explore Deconvolution), search a profile against a database of known profiles using flexible search strategies (Search Database), investigate whether two mixtures share one or more common donors (Mixture to Mixture), and search a database of known profiles for an assumed common donor between two or more profiles (Common Donor). Developmental validation was carried out in accordance to the FBI Quality Assurance Standards for Forensic DNA Testing Laboratories (2020) and included functional and reliability testing as well as accuracy, precision, sensitivity, and specificity studies. The results of developmental validation demonstrate that each of the functions within DBLR™ is performing as expected.

Analysis of Additional Methods of purification of Mixtures while Conducting Differential Lysis under Sexual Crimes

The article is focused on the problem of studying mixed biological traces during the research of the growing number and severity of sexual crimes. The presence of blood and epithelial cells with a small amount of sperm on things removed at the crime scene and smears from the victim’s body, complicates the molecular and genetic examination and the likelihood of obtaining a quality DNA profile from the sperm of the rapist. Therefore, the author has defined chemical basis of the standard method of differential lysis and the correlation of the components of the mixture of epithelial cells and sperm, which can help to get mixed and pure DNA profiles while amplify using the sets AmpFlSTR® Identifiler® PCR Amplification Kit, GlobalFiler STR PCR Amp Afication ™ PCR Amplification Kit. The author has analyzed literature data on the developed and perspective methods and additional stages of the differential lysis technique for purification of the mixture and aimed at sorting and reducing the number of epithelial cells. Chemical methods of mixture purification have been studied, including additional washing and incubation of the mortar, use of DNases, SDS lysis buffer and magnetic sorting with the use of antibodies (MOSPD3 and RH-20), and physical methods such as laser microdissection, acoustic lysis, hydrodynamic effect in small Reynolds numbers, microchips with additional integration of Cialyl Lewis oligosaccharide sequence and DEPArray technology. The main advantages and disadvantages of the procedures, the possibility of combining with other types, the impact on subsequent stages of molecular and genetic examination and the possibility of automating the research process have been provided for the methods. For the methods, the author has studied the probability of contamination during the procedure and the possibility of using for obsolete traces of sperm, where the destruction of sperm structure occurred. The author has indicated the possibility of automation of physical systems for forensic examination of molecular and genetic analysis by integrating the stages of sample preparation, cell counting and DNA isolation on a single device, which will reduce the time of the analysis of research objects in the future. The analysis of the literature data showed an improvement in the quality of DNA profiles while using the above methods, so it has been offered to use the presented methods for testing in expert practice.