Mixture Interpretation: Defining the Relevant Features for Guidelines for the Assessment of Mixed DNA Profiles in Forensic Casework

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.

A novel three-step DNA extraction method for mixed bloodstains

Mixed DNA samples from at least two contributors can be present at a crime scene, which could be the most crucial piece of genetic evidence. The mixed stains in sexual assault cases are typically separated using differential lysis procedures (a two-step method). Blood mixed stains, however, are usually difficult to separate. In this work, we propose that a mixed stain comprises three layers, that is, (1) the upper layer which is primarily made up of cells from one contributor; (2) the middle layer which is a similar mixture from two contributors; and (3) the lower layer which primarily comprises cells from the other contributor. Based on this concept, a novel three-step DNA extraction method was proposed to solve the challenge involving bloodstains from two contributors. In the experiment, we extracted three layers DNA from mixed bloodstains using three steps. As a result, single-source DNA and approximate single-source DNA were detected from steps 1 and 3, respectively. This study demonstrates that the DNA from some mixed blood stains could be effectively separated following an appropriate extraction strategy, providing valuable insights, and serving as a reference for future examination of blood mixtures.

Single-cell transcriptome sequencing allows genetic separation, characterization and identification of individuals in multi-person biological mixtures

Identifying individuals from biological mixtures to which they contributed is highly relevant in crime scene investigation and various biomedical research fields, but despite previous attempts, remains nearly impossible. Here we investigated the potential of using single-cell transcriptome sequencing (scRNA-seq), coupled with a dedicated bioinformatics pipeline (De-goulash), to solve this long-standing problem. We developed a novel approach and tested it with scRNA-seq data that we de-novo generated from multi-person blood mixtures, and also in-silico mixtures we assembled from public single individual scRNA-seq datasets, involving different numbers, ratios, and bio-geographic ancestries of contributors. For all 2 up to 9-person balanced and imbalanced blood mixtures with ratios up to 1:60, we achieved a clear single-cell separation according to the contributing individuals. For all separated mixture contributors, sex and bio-geographic ancestry (maternal, paternal, and bi-parental) were correctly determined. All separated contributors were correctly individually identified with court-acceptable statistical certainty using de-novo generated whole exome sequencing reference data. In this proof-of-concept study, we demonstrate the feasibility of single-cell approaches to deconvolute biological mixtures and subsequently genetically characterise, and individually identify the separated mixture contributors. With further optimisation and implementation, this approach may eventually allow moving to challenging biological mixtures, including those found at crime scenes.

New Perspectives for Whole Genome Amplification in Forensic STR Analysis

Modern PCR-based analytical techniques have reached sensitivity levels that allow for obtaining complete forensic DNA profiles from even tiny traces containing genomic DNA amounts as small as 125 pg. Yet these techniques have reached their limits when it comes to the analysis of traces such as fingerprints or single cells. One suggestion to overcome these limits has been the usage of whole genome amplification (WGA) methods. These methods aim at increasing the copy number of genomic DNA and by this means generate more template DNA for subsequent analyses. Their application in forensic contexts has so far remained mostly an academic exercise, and results have not shown significant improvements and even have raised additional analytical problems. Until very recently, based on these disappointments, the forensic application of WGA seems to have largely been abandoned. In the meantime, however, novel improved methods are pointing towards a perspective for WGA in specific forensic applications. This review article tries to summarize current knowledge about WGA in forensics and suggests the forensic analysis of single-donor bioparticles and of single cells as promising applications.

A New Computational Deconvolution Algorithm for the Analysis of Forensic DNA Mixtures with SNP Markers

Single nucleotide polymorphisms (SNPs) support robust analysis on degraded DNA samples. However, the development of a systematic method to interpret the profiles derived from the mixtures is less studied, and it remains a challenge due to the bi-allelic nature of SNP markers. To improve the discriminating power of SNPs, this study explored bioinformatic strategies to analyze mixtures. Then, computer-generated mixtures were produced using real-world massively parallel sequencing (MPS) data from the single samples processed with the Precision ID Identity Panel. Moreover, the values of the frequency of major allele reads (F_MAR) were calculated and applied as key parameters to deconvolve the two-person mixtures and estimate mixture ratios. Four custom R language scripts (three for autosomes and one for Y chromosome) were designed with the K-means clustering method as a core algorithm. Finally, the method was validated with real-world mixtures. The results indicated that the deconvolution accuracy for evenly balanced mixtures was 100% or close to 100%, which was the same as the deconvolution accuracy of inferring the genotypes of the major contributor of unevenly balanced mixtures. Meanwhile, the accuracy of inferring the genotypes of the minor contributor decreased as its proportion in the mixture decreased. Moreover, the estimated mixture ratio was almost equal to the actual ratio between 1:1 and 1:6. The method proposed in this study provides a new paradigm for mixture interpretation, especially for inferring contributor profiles of evenly balanced mixtures and the major contributor profile of unevenly balanced mixtures.

Evaluating Mixture Solution™- rapid and non-MCMC probabilistic mixture analysis

We compare DNA mixture analysis via DNAˑVIEW® Mixture Solution™ and the current combined probability of inclusion (CPI) method of the South African Police Service (SAPS). South Africa has a high incidence of property-related crimes and sexual offences and consequently a great deal of low-template (LT-DNA) forensic DNA mixture casework and a perpetual backlog. A range of casework and laboratory-prepared sexual assault mixtures with initial male DNA amounts varying from about 2 to 200 cells were analysed to evaluate the benefits of a continuous model program. Unfortunately CPI methods are nearly useless for LT-DNA cases because of dropout-common from a mixture contribution of fewer than 20 or 30 cells. We further argue that proposed CPI elaborations to circumvent dropout lack supporting research or even explanation. Mixture Solution models mixture data as continuous rather than binary, with a mathematically coherent ("probabilistic") model which incorporates dropout and other phenomena realistically. Much more information is thereby utilised resulting in applicability to more cases (7 or fewer contributor cells suffice), stronger evidence against a suspect who is a mixture contributor and stronger evidence to absolve a non-contributor. Mixture Solution incidentally provides information which, along with rfu data, allows estimating contributions in terms of number of cells, which is a useful perspective. The method of calculation is explained.

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

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.

Searching CODIS with binary conversions of STRmix interpretations

The predominant approach to interpreting autosomal STR DNA typing results is through the use of probabilistic genotyping software. The primary output from such software is a list of genotypes and associated genotype weights representing the likelihood of observing the typing results if an individual or set of individuals with that genotype(s) was the donor of the evidence DNA. While such lists are used by probabilistic software to calculate likelihood ratios based upon a pair of propositions regarding specific donors of the DNA, they are not directly amendable to entry into law enforcement databases that use the Combined DNA Index System (CODIS) software. An approach to creating CODIS-compatible profiles from the output of the probabilistic genotyping program STRmix is discussed and assessed for sensitivity and specificity. Combining this information with an assessment of the weight-of-evidence potential contained in the STRmix interpretation, pragmatic guidance was developed regarding the creation and searching of CODIS profiles in a way that balances the detection of investigative leads with the potential burdensome effects on workload.

MMDIT: A tool for the deconvolution and interpretation of mitochondrial DNA mixtures

Short tandem repeats of the nuclear genome have been the preferred markers for analyzing forensic DNA mixtures. However, when nuclear DNA in a sample is degraded or limited, mitochondrial DNA (mtDNA) markers provide a powerful alternative. Though historically considered challenging, the interpretation and analysis of mtDNA mixtures have recently seen renewed interest with the advent of massively parallel sequencing. However, there are only a few software tools available for mtDNA mixture interpretation. To address this gap, the Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) was developed. MMDIT is an interactive application complete with a graphical user interface that allows users to deconvolve mtDNA (whole or partial genomes) mixtures into constituent donor haplotypes and estimate random match probabilities on these resultant haplotypes. In cases where deconvolution might not be feasible, the software allows mixture analysis directly within a binary framework (i.e. qualitatively, only using data on allele presence/absence). This paper explains the functionality of MMDIT, using an example of an in vitro two-person mtDNA mixture with a ratio of 1:4. The uniqueness of MMDIT lies in its ability to resolve mixtures into complete donor haplotypes using a statistical phasing framework before mixture analysis and evaluating statistical weights employing a novel graph algorithm approach. MMDIT is the first available open-source software that can automate mtDNA mixture deconvolution and analysis. The MMDIT web application can be accessed online at https://www.unthsc.edu/mmdit/. The source code is available at https://github.com/SammedMandape/MMDIT_UI and archived on zenodo (https://doi.org/10.5281/zenodo.4770184).

Micromanipulation of single cells and fingerprints for forensic identification

Crime scene samples often include biological stains, handled items, or worn clothes and may contain cells from various donors. Applying routine sample collection methods by using a portion of a biological stain or swabbing the entire suspected touched area of the evidence followed by DNA extraction often leads to DNA mixtures. Some mixtures can be addressed with sophisticated interpretation protocols and probabilistic genotyping software resulting in DNA profiles of their contributors. However, many samples remain unresolved, providing no investigative information. Samples with many contributors are often the most challenging samples in forensic biology. Examples include gang rape situations or where the perpetrator's DNA is present in traces among the overwhelming amounts of the victim's DNA. If this is the only available evidence in a case, it is of paramount importance to generate usable information. An alternative approach, to address biological mixtures, could be the collection of individual cells directly from the evidence and testing them separately. This method could prevent cells from being inadvertently blended during the extraction process, thus resulting in DNA mixtures. In this study, multiple tools coupled with adhesive microcarriers to collect single cells were evaluated. These were tested on epithelial (buccal) and sperm cells, as well as on touched items. Single cells were successfully collected but fingerprints were swabbed in their entirety to account for the extracellular DNA of these samples and the poor DNA quality of shed skin flakes. Furthermore, micromanipulation devices, such as the P.A.L.M.® and the Axio Zoom.V16 operated manually or with a robotic arm aureka®, were compared for their effectiveness in collecting cells. The P.A.L.M.® was suitable for single cell isolation when smeared on membrane slides. Manual or robotic manipulations, by utilizing the Axio Zoom.V16, have wider applications as they can be used to isolate cells from various substrates such as glass or membrane slides, tapes, or directly from the evidence. Manipulations using the Axio Zoom.V16, either with the robotic arm aureka® or manually, generated similar outcomes which were significantly better than the outcomes by using the P.A.L.M.®. Robotic manipulations using the aureka® produced more consistent results, but operating the aureka® required training and often needed re-calibrations. This made the process of cell manipulations slower than when manually operated. Our preferred method was the manual manipulations as it was fast, cost effective, required little training, but relied on a steady hand of the technician.

Evaluation of two 13-loci STR multiplex system regarding identification and origin discrimination of Brazilian Cannabis sativa samples

According to the Brazilian Federal Police (BFP), the Brazilian Cannabis sativa illicit market is mainly supplied by drugs originated from Paraguay and Northeastern Brazil (Marijuana Polygon region). These two known routes, the increasing indoor cultivations (supported by online market), and drugs from Uruguay are also in BFP's sight. Forensic tools to aid police intelligence were published in the past years. In genetics, microsatellites have gained attention due to their individualization capability. This study aims to evaluate the effectiveness and efficiency of two STR multiplex systems previously proposed in 94 Cannabis sativa samples seized in Brazil. Principal coordinate analyses (PCoA), forensic parameters, and genetic structure analysis were executed. Both panels were effective in individualizing and origin discriminating all samples, and the system proposed in 2015 demonstrated better results. For this marker set, the probability of identity for a random individual is approximately one in 65 billion; also, the PCoA shows a clear genetic distinction among samples according to its origin. Bayesian inference populational structure analysis indicated a significant genetic diversity among seizure groups, matching with its origin. Overall, the STR multiplex systems were able to achieve its purpose in individualizing and differentiating, according to geographic region, Brazilian Cannabis sp. samples.

Developmental validation of the monSTR identity panel, a forensic STR multiplex assay for massively parallel sequencing

The 21-plex STR panel monSTR was designed for high-fidelity forensic genotyping on the Illumina MiSeq platform. In this study, the panel's performance was validated according to the recommended validation guidelines of the Scientific Working Group for DNA Analysis Methods (SWGDAM). Concordance, repeatability and reproducibility, sensitivity of detection, mixture analysis, species-specificity, and the ability to analyze mock samples were assessed. Sequence data was analyzed using the genotyping software toaSTR. The assay performance was evaluated by measuring the read on-target ratio, the genotype accuracy, the inter-locus balance, the heterozygosity balance, and the signal-to-noise ratio. Results showed that profiles of NIST reference DNA samples as well as GEDNAP proficiency samples were fully concordant with CE-based methods. In addition, inter-run and intra-run variation experiments indicated high precision. Furthermore, full profiles could be obtained using 62.5 pg of DNA input amount with proper inter-locus balance and read on-target ratio; 76.4% of alleles were correctly called with 7.8 pg DNA input amount. It was demonstrated that 94.4% of minor contributor alleles were resolved accurately in a 1:49 mixture. Results suggested that the minor contribution could be precisely calculated based on the minor component allele frequency. Validation results described here demonstrate that the monSTR forensic identity panel is a valid tool for forensic STR genotyping using massively parallel sequencing.

A multivariate statistical approach for the estimation of the ethnic origin of unknown genetic profiles in forensic genetics

DNA typing and genetic profile data interpretation are among the most relevant topics in forensic science; among other applications, genetic profile's capability to distinguish biogeographic information about population groups, subgroups and affiliations have been largely explored in the last decade. In fact, for investigative and intelligence purposes, it is extremely useful to identify subjects and estimate their biogeographic origins by examining the recovered DNA profiles from evidence on a crime scene. Current approaches for BiogeoGraphic Ancestry (BGA) estimation using STRs profiles are usually based on Bayesian methods, which quantify the evidence in terms of likelihood ratio, supporting or not the hypothesis that a certain profile belongs to a specific ethnic group. The present study provides an alternative approach to the likelihood ratio method that involves multivariate data analysis strategies for the estimation of multiple populations. Starting from the well-known NIST US autosomal STRs dataset involving African-American, Asian, and Caucasian individuals, and moving towards further and more geographically restricted populations (such as Northern Africans vs sub-Saharan Africans, Afghans vs Iraqis and Italians vs Romanians), powerful multivariate techniques such as Sparse and Logistic Principal Component Analysis (SL-PCA), Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) and Support Vector Machines (SVM) were employed and their discriminating power was also compared. Both sPLS-DA and SVM techniques provided robust classifications, yielding high sensitivity and specificity models capable of discriminating populations on ethnic basis. This application may represent a powerful and dynamic tool for law enforcement agencies whenever a standard autosomal STR profile is obtained from the biological evidence collected at a crime scene or recovered during mass-disaster and missing person investigations.

Detection and analysis of the cause of false-tetra-allelic patterns of locus D10S1435 at the sequence level

A number of artifacts produced in forensic DNA typing make the interpretation more complicated and even lead to typing errors. Here, we reported the cause of false-tetra-allelic patterns of STR locus D10S1435 at the sequence level. To confirm the true genotyping, the sample with four allelic peaks was re-amplified and sequenced. The amplicon sequences of D10S1435, D20S482, D6S1017, and D10S1248 loci were analyzed by software BioXM and RNAstructure. We successfully reproduced the four-peak phenomenon by adding various concentration of magnesium chloride into the loading mixtures to simulate the suboptimal electrophoresis conditions. The false four allelic peaks may be caused by the specific nucleotide sequence of locus D10S1435 which tends to form secondary structures under the suboptimal electrophoresis conditions. The relatively high GC content and extremely uneven distribution give the amplicon a potency to resist complete denaturation at the phase of sample preparation and a tendency to form intra- and intermolecular secondary structures during post-injection.

Probabilistic genotyping software: An overview

The interpretation of mixed profiles from DNA evidentiary material is one of the more challenging duties of the forensic scientist. Traditionally, analysts have used a "binary" approach to interpretation where inferred genotypes are either included or excluded from the mixture using a stochastic threshold and other biological parameters such as heterozygote balance, mixture ratio, and stutter ratios. As the sensitivity of STR multiplexes and capillary electrophoresis instrumentation improved over the past 25 years, coupled with the change in the type of evidence being submitted for analysis (from high quality and quantity (often single-source) stains to low quality and quantity (often mixed) "touch" samples), the complexity of DNA profile interpretation has equally increased. This review provides a historical perspective on the movement from binary methods of interpretation to probabilistic methods of interpretation. We describe the two approaches to probabilistic genotyping (semi-continuous and fully continuous) and address issues such as validation and court acceptance. Areas of future needs for probabilistic software are discussed.

13-loci STR multiplex system for Brazilian seized samples of marijuana: individualization and origin differentiation

It is known that Cannabis in Brazil could either originate from Paraguay or be cultivated in Brazil. While consumer markets in the North and Northeast regions are maintained by national production, the rest of the country is supplied with Cannabis from Paraguay. However, the Brazilian Federal Police (BFP) has exponentially increased the seizure number of Cannabis seeds sent by mail. For this reason, the aim of the study was to assess the 13-loci short tandem repeat (STR) multiplex system proposed by Houston et al. (2015) to evaluate the power of such markers in individualization and origin differentiation of Cannabis sativa samples seized in Brazil by the BFP. To do so, 72 Cannabis samples seized in Brazil by BFP were analyzed. The principal coordinate analysis (PCoA) and probability identity (PI) analysis were computed. Additionally, the Cannabis samples' genotypes were subjected to comparison by Kruskal-Wallis H, followed by a multiple discriminant analysis (MDA). All samples analyzed revealed a distinct genetic profile. PCoA clearly discriminated the seizure sets based on their geographic origin. A combination of seven loci was enough to differentiate samples' genotypes, and the PI for a random sample is approximately one in 50 billion. The Cannabis samples were 100% correct as classified by Kruskal-Wallis H, followed by an MDA. The results of this study demonstrate that the 13-loci STR multiplex system successfully achieved the aim of sample individualization and origin differentiation and suggest that it could be a useful tool to help BFP intelligence in tracing back-trade routes.

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.

A novel multiplex assay of SNP-STR markers for forensic purpose

Like DIP-STR markers (deletion/insertion polymorphism-short tandem repeat combinations), SNP-STR markers (single nucleotide polymorphism-STR combinations) are also valuable in forensic DNA mixture analysis. In this study, eight SNP-STRs were selected, and a stable and sensitive multiplex polymerase chain reaction (PCR) assay was developed for amplifying these SNP-STRs and the Amelogenin gender marker according to the principle of amplification refractory mutation system (ARMS). This novel multiplex set allows detection of the minor DNA contributor in a DNA mixture of any gender and cellular origin with high resolution (beyond a DNA ratio of 1:20). In addition, SNP-STR haplotype frequencies were estimated based on a survey of 350 unrelated individuals from Chinese Han population, and the combined power of discrimination (PD) and power of exclusion (PE) of the eight SNP-STRs were calculated as 0.99999999965 and 0.9996, which were obviously higher than that of the eight STR loci: 0.9999999954 and 0.9989 respectively. The results indicated that the SNP-STR compound markers have higher application value in forensic identification compared to standard autosomal STRs, especially in the analysis of imbalanced DNA mixtures.

DNA commission of the International society for forensic genetics: Assessing the value of forensic biological evidence - Guidelines highlighting the importance of propositions: Part I: evaluation of DNA profiling comparisons given (sub-) source propositions

The interpretation of evidence continues to be one of the biggest challenges facing the forensic community. This is the first of two papers intended to provide advice on difficult aspects of evaluation and in particular on the formulation of propositions. The scientist has a dual role: investigator (crime-focused), where often there is no suspect available and a database search may be required; evaluator (suspect-focused), where the strength of evidence is assessed in the context of the case. In investigative mode, generally the aim is to produce leads regarding the source of the DNA. Sub-source level propositions will be adequate to help identify potential suspects who can be further investigated by the authorities. Once in evaluative mode, given the defence version of events of the person of interest, it may become necessary to consider alternatives that go beyond the source of the DNA (i.e., to consider activity level propositions). In the evaluation phase, it is crucial that formulation of propositions allows the assessment of all the results that will help with the issue at hand. Propositions should therefore be precise (indication of the number of contributors, information on the relevant population etc.), be about causes, not effects (e.g. a 'matching' DNA profile) and to avoid bias, must not be findings-led. This means that ideally, propositions should be decided based on the case information and before the results of the comparisons are known. This paper primarily reflects upon what has been coined as "sub-source level propositions". These are restricted to the evaluation of the DNA profiles themselves, and help answer the issue regarding the source of the DNA. It is to be emphasised that likelihood ratios given sub-source level propositions cannot be carried over to a different level - for example, activity level propositions, where the DNA evidence is put into the context of the alleged activities. This would be highly misleading and could give rise to miscarriages of justice; this will be discussed in a second paper. The value of forensic results depends not only on propositions, but also on the type of results (e.g. allelic designations, peak heights, replicates) and upon the model used: it is therefore important to discuss these aspects. Finally, since communication is key to help understanding by courts, we will explore how to convey the value of the results and explain the importance of avoiding the practice of transposing the conditional.

Separation/extraction, detection, and interpretation of DNA mixtures in forensic science (review)

Interpreting mixed DNA samples containing material from multiple contributors has long been considered a major challenge in forensic casework, especially when encountering low-template DNA (LT-DNA) or high-order mixtures that may involve missing alleles (dropout) and unrelated alleles (drop-in), among others. In the last decades, extraordinary progress has been made in the analysis of mixed DNA samples, which has led to increasing attention to this research field. The advent of new methods for the separation and extraction of DNA from mixtures, novel or jointly applied genetic markers for detection and reliable interpretation approaches for estimating the weight of evidence, as well as the powerful massively parallel sequencing (MPS) technology, has greatly extended the range of mixed samples that can be correctly analyzed. Here, we summarized the investigative approaches and progress in the field of forensic DNA mixture analysis, hoping to provide some assistance to forensic practitioners and to promote further development involving this issue.

The forensic value of X-linked markers in mixed-male DNA analysis

Autosomal genetic markers and Y chromosome markers have been widely applied in analysis of mixed stains at crime scenes by forensic scientists. However, true genotype combinations are often difficult to distinguish using autosomal markers when similar amounts of DNA are contributed by multiple donors. In addition, specific individuals cannot be determined by Y chromosomal markers because male relatives share the same Y chromosome. X-linked markers, possessing characteristics somewhere intermediate between autosomes and the Y chromosome, are less universally applied in criminal casework. In this paper, X markers are proposed to apply to male mixtures because their true genes can be more easily and accurately recognized than the decision of the genotypes of AS markers. In this study, an actual two-man mixed stain from a forensic case file and simulated male-mixed DNA were examined simultaneously with the X markers and autosomal markers. Finally, the actual mixture was separated successfully by the X markers, although it was unresolved by AS-STRs, and the separation ratio of the simulated mixture was much higher using Chr X tools than with AS methods. We believe X-linked markers provide significant advantages in individual discrimination of male mixtures that should be further applied to forensic work.

Validation of haplotype-specific extraction for separating a mitochondrial DNA model mixture and application to simulated casework

Haplotype-specific extraction (HSE) is a new field of application for the separation of mitochondrial DNA (mtDNA) mixtures and is developed to identify the mtDNA haplotypes of the contributors subsequently by sequencing. Here we show the validation of HSE with an exemplary mitochondrial DNA mixture into its individual haplotypes according to our laboratory standards. These specify several critical areas of assay performance to be tested, such as sensitivity, robustness and mixture studies comprising varying proportions of their components,degraded samples and samples of different qualities and material. Wereport the successful and unambiguous analysis of the exemplary separated mitochondrial DNA mixture under various conditions as well as simulated casework samples, which manifest as mixed nucleotide calls at single base positions previously. Here we demonstrate that the HSE assay is high sensitive, stable against degradation and applicable in a wide range of sample qualities. Based on our findings from the validation study, we believe that this assay has great potential power and may be useful for distinguishing among the mtDNA of individuals and their geographical origin in mixed DNA samples.

Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model

In criminal investigations, forensic scientists need to evaluate DNA mixtures. The estimation of the number of contributors and evaluation of the contribution of a person of interest (POI) from these samples are challenging. In this study, we developed a new open-source software “Kongoh” for interpreting DNA mixture based on a quantitative continuous model. The model uses quantitative information of peak heights in the DNA profile and considers the effect of artifacts and allelic drop-out. By using this software, the likelihoods of 1–4 persons’ contributions are calculated, and the most optimal number of contributors is automatically determined; this differs from other open-source software. Therefore, we can eliminate the need to manually determine the number of contributors before the analysis. Kongoh also considers allele- or locus-specific effects of biological parameters based on the experimental data. We then validated Kongoh by calculating the likelihood ratio (LR) of a POI’s contribution in true contributors and non-contributors by using 2–4 person mixtures analyzed through a 15 short tandem repeat typing system. Most LR values obtained from Kongoh during true-contributor testing strongly supported the POI’s contribution even for small amounts or degraded DNA samples. Kongoh correctly rejected a false hypothesis in the non-contributor testing, generated reproducible LR values, and demonstrated higher accuracy of the estimated number of contributors than another software based on the quantitative continuous model. Therefore, Kongoh is useful in accurately interpreting DNA evidence like mixtures and small amounts or degraded DNA samples.

PowerPlex® Fusion 6C System: evaluation study for analysis of casework and database samples

AIM

To report on the successful analysis of amplicons obtained with PowerPlex® Fusion 6C System, a highly robust 27-plex genotyping kit developed for human identification laboratories, on the Applied Biosystems® 3500 Genetic Analyzer.

METHOD

We performed characterization and evaluation studies following the Scientific Working Group on DNA Analysis Methods (SWGDAM) validation guidelines, examining several critical areas of kit performance. We report the results of sensitivity, robustness, heterozygous peak height ratio, precision, concordance, caseworks, and mixture interpretations. We tested sensitivity, using serial dilutions of control DNA.

RESULTS

The minimum amount of input DNA resulting in a full profile was 125 pg. Inhibition, inducted by urea, showsed a progressively fragmentation of DNA and a full profile was obtained until 1M of inhibitor factor. To test the profile quality, casework samples were extracted with different extraction methods: Chelex®100, QIAmp DNA Micro Kit and Phenol-Chloroform extraction. The results demonstrated that extraction chemistries do not have affect on amplification performance. Concordance check was performed by typing some casework samples and comparing the typing results with those obtained with other available kits. Thus, concordance was expected and supported by the data.

CONCLUSION

Reliable DNA typing results can be obtained using this new kit, demonstrating its effectiveness and utility in forensic analysis.

A Comparison and Integration of MiSeq and MinION Platforms for Sequencing Single Source and Mixed Mitochondrial Genomes

Single source and multiple donor (mixed) samples of human mitochondrial DNA were analyzed and compared using the MinION and the MiSeq platforms. A generalized variant detection strategy was employed to provide a cursory framework for evaluating the reliability and accuracy of mitochondrial sequences produced by the MinION. The feasibility of long-read phasing was investigated to establish its efficacy in quantitatively distinguishing and deconvolving individuals in a mixture. Finally, a proof-of-concept was demonstrated by integrating both platforms in a hybrid assembly that leverages solely mixture data to accurately reconstruct full mitochondrial genomes.

Evaluation of forensic DNA mixture evidence: protocol for evaluation, interpretation, and statistical calculations using the combined probability of inclusion

Background

The evaluation and interpretation of forensic DNA mixture evidence faces greater interpretational challenges due to increasingly complex mixture evidence. Such challenges include: casework involving low quantity or degraded evidence leading to allele and locus dropout; allele sharing of contributors leading to allele stacking; and differentiation of PCR stutter artifacts from true alleles. There is variation in statistical approaches used to evaluate the strength of the evidence when inclusion of a specific known individual(s) is determined, and the approaches used must be supportable. There are concerns that methods utilized for interpretation of complex forensic DNA mixtures may not be implemented properly in some casework. Similar questions are being raised in a number of U.S. jurisdictions, leading to some confusion about mixture interpretation for current and previous casework.

Results

Key elements necessary for the interpretation and statistical evaluation of forensic DNA mixtures are described. Given the most common method for statistical evaluation of DNA mixtures in many parts of the world, including the USA, is the Combined Probability of Inclusion/Exclusion (CPI/CPE). Exposition and elucidation of this method and a protocol for use is the focus of this article. Formulae and other supporting materials are provided.

Conclusions

Guidance and details of a DNA mixture interpretation protocol is provided for application of the CPI/CPE method in the analysis of more complex forensic DNA mixtures. This description, in turn, should help reduce the variability of interpretation with application of this methodology and thereby improve the quality of DNA mixture interpretation throughout the forensic community.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0429-7) contains supplementary material, which is available to authorized users.

Analysis of red autofluorescence (650-670nm) in epidermal cell populations and its potential for distinguishing contributors to 'touch' biological samples

Interpretation of touch DNA mixtures poses a significant challenge for forensic caseworking laboratories.  Front end techniques that facilitate separation of contributor cell populations before DNA extraction are a way to circumvent this problem. The goal of this study was to survey intrinsic fluorescence of epidermal cells collected from touch surfaces and investigate whether this property could potentially be used to discriminate between contributor cell populations in a biological mixture.  Analysis of red autofluorescence (650-670nm) showed that some contributors could be distinguished on this basis. Variation was also observed between autofluorescence profiles of epidermal cell populations from a single contributor sampled on different days. This dataset suggests that red autofluorescence may be a useful marker for identifying distinct cell populations in some mixtures. Future efforts should continue to investigate the extrinsic or intrinsic factors contributing to this signature, and to identify additional biomarkers that could complement this system.

An assessment of the information content of likelihood ratios derived from complex mixtures

With the increasing sensitivity of DNA typing methodologies, as well as increasing awareness by law enforcement of the perceived capabilities of DNA typing, complex mixtures consisting of DNA from two or more contributors are increasingly being encountered. However, insufficient research has been conducted to characterize the ability to distinguish a true contributor (TC) from a known non-contributor (KNC) in these complex samples, and under what specific conditions. In order to investigate this question, sets of six 15-locus Caucasian genotype profiles were simulated and used to create mixtures containing 2-5 contributors. Likelihood ratios were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile, as well as comparisons of the evidence profile to TCs and KNCs. This work was intended to illustrate the best-case scenario, in which all alleles from the TC were detected in the simulated evidence samples. Therefore the possibility of drop-out was not modeled in this study. The computer program DNAMIX was then used to compute LRs comparing the evidence profile to TCs and KNCs. This resulted in 140,000 LRs for each of the two scenarios. These complex mixture simulations show that, even when all alleles are detected (i.e. no drop-out), TCs can generate LRs less than 1 across a 15-locus profile. However, this outcome was rare, 7 of 140,000 replicates (0.005%), and associated only with mixtures comprising 5 contributors in which the numerator hypothesis includes one or more unknown contributors. For KNCs, LRs were found to be greater than 1 in a small number of replicates (75 of 140,000 replicates, or 0.05%). These replicates were limited to 4 and 5 person mixtures with 1 or more unknowns in the numerator. Only 5 of these 75 replicates (0.004%) yielded an LR greater than 1,000. Thus, overall, these results imply that the weight of evidence that can be derived from complex mixtures containing up to 5 contributors, under a scenario in which no drop-out is required to explain any of the contributors, is remarkably high. This is a useful benchmark result on top of which to layer the effects of additional factors, such as drop-out, peak height, and other variables.

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles

Background

Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.

Results

To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.

Conclusions

Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0740-8) contains supplementary material, which is available to authorized users.

Effectiveness of Coupled Application of AmpFℓSTR Yfiler Kit and Reduced Size Y-chromosomal Short Tandem Repeat Analysis for Archeological Human Bones

The AmpFℓSTR Yfiler PCR Amplification (Yfiler) kit continues to be improved for a better analytical efficiency in cases of highly degraded DNA. The authors endeavored to determine whether coupling of the Yfiler kit with supplemental multiplex amplification of some Y-STR loci is a more efficient analytical mode for poorly preserved human femurs (n = 15) discovered at Korean archeological sites. To reveal locus profiles not easily obtained by Yfiler analysis, custom-designed primers were adopted for the DYS390, DYS391, DYS392, DYS438, DYS439, and DYS635 loci. The success rate for 16 Y-STR locus profiles obtained from the 15 femurs was improved from 18.33% (in the use of Yfiler kit only) to 49.17% (the coupled use of Yfiler and custom-designed primers). In this study, the authors established that the custom-designed primers offer a markedly improved success rate for obtainment of Y-STR profiles from degraded aDNA not easily identified by sole use of the Yfiler assay.

Separation of Y-chromosomal haplotypes from male DNA mixtures via multiplex haplotype-specific extraction

In forensic analysis, the interpretation of DNA mixtures is the subject of ongoing debate and requires expertise knowledge. Haplotype-specific extraction (HSE) is an alternative method that enables the separation of large chromosome fragments or haplotypes by using magnetic beads in conjunction with allele-specific probes. HSE thus allows physical separation of the components of a DNA mixture. Here, we present the first multiplex HSE separation of a Y-chromosomal haplotype consisting of six Yfiler short tandem repeat markers from a mixture of male DNA.