DNA commission of the International Society of Forensic Genetics: Recommendations on the interpretation of mixtures

A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation

Several fully continuous probabilistic genotyping software (PGS) use Markov chain Monte Carlo algorithms (MCMC) to assign weights to different proposed genotype combinations at a locus. Replicate interpretations of the same profile in these software are expected not to produce identical weights and likelihood ratio (LR) values due to the Monte Carlo aspect. This paper reports a detailed precision study under reproducibility conditions conducted as a collaborative exercise across the National Institute of Standards and Technology (NIST), Federal Bureau of Investigation (FBI), and Institute of Environmental Science and Research (ESR). Replicate interpretations generated across the three laboratories used the same input files, software version, and settings but different random number seed and different computers. This work demonstrates that using different computers to analyze replicate interpretations does not contribute to any variations in LR values. The study quantifies the magnitude of differences in the assigned LRs that is only due to run-to-run MCMC variability and addresses the potential explanations for the observed differences.

A comparison of likelihood ratios calculated from surface DNA mixtures using MPS and CE Technologies

This study evaluates the performance of analysing surface DNA samples using massively parallel sequencing (MPS) compared to traditional capillary electrophoresis (CE). A total of 30 samples were collected from various surfaces in an office environment and were analysed with CE and MPS. These were compared against 60 reference samples (office inhabitants). To identify contributors, likelihood ratios (LRs) were calculated for MPS and CE data using the probabilistic genotyping software MPSproto and EuroForMix respectively. Although a higher number of sequences/peaks were observed per DNA profile in MPS compared to CE, LR values were found to be lower for MPS data formats. This might be the result of the increased complexity of MPS data, along with a possible elevation of unknown alleles and/or artefacts. The study highlights avenues for improving MPS data quality and analysis to facilitate more robust interpretation of challenging casework-like samples.

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.

DNA profiling in India: Addressing issues of sample preservation, databasing, marker selection, & statistical approaches

DNA technology is the gold standard with respect to the identification of individuals from biological evidence. The technology offers the convenience of a universally similar approach and methodology for analysis across the globe. However, the technology has not realised its full potential in India due to the lack of a DNA database and lacunae in sample collection and preservation from the scene of crime and victims (especially those of sexual assault). Further, statistical interpretation of DNA results is non-existent in the majority of cases. Though the latest technologies and developments in the field of DNA analysis are being adopted and implemented,very little has been enacted practically to improve optimise sample collection and preservation. This article discusses current casework scenarios that highlight the pitfalls and ambiguous areas in the field of DNA analysis, especially with respect DNA databases, sampling, andstatistical approaches to genetic data analysis. Possible solutions and mitigation measures are suggested.

An exploratory view into allelic drop-out of sequenced autosomal STRs

As massively parallel sequencing is implemented in forensic genetics, an understanding of sequence data must accompany these advancements, that is, accurate modeling of data for proper statistical analysis. Allelic drop-out, a common stochastic effect seen in genetic data, is often modeled in statistical analysis of STR results. This proof-of-concept study sequenced several serial dilutions of a standard sample ranging from 4 ng to 7.82 pg to evaluate allelic drop-out trends on a select panel of autosomal STRs using the ForenSeq™ DNA Signature Prep Kit, Primer Set A on the Illumina MiSeq FGx. Parameters assessed included locus, profile, and run specific information. A majority of the allelic drop-out occurred in DNA concentrations less than 31.25 pg. Statistical results indicated a need for locus-specific modeling based on STR descriptors, like simple versus compound repeat patterns. No correlation was seen between average read count of scored alleles and allelic drop-out at a locus. A statistical correlation was observed between the amount of allelic drop-out and the starting amount of DNA in a sample, average read count of a sample, and total read count generated on a flow cell. This study supports using common allelic drop-out factors used in fragment length analysis on sequenced STRs while including additional locus, sample, and run specific information. Results demonstrate multiple factors that can be considered when developing probability of allelic drop-out models for sequenced autosomal STRs including locus-specific analysis, total read count of a profile, and total read count sequenced on a flow cell.

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.

A practical treatment of sensitivity analyses in activity level evaluations

Evaluations of forensic observations considering activity level propositions are becoming more common place in forensic institutions. A measure that can be taken to interrogate the evaluation for robustness is called sensitivity analysis. A sensitivity analysis explores the sensitivity of the evaluation to the data used when assigning probabilities, or to the level of uncertainty surrounding a probability assignment, or to the choice of various assumptions within the model. There have been a number of publications that describe sensitivity analysis in technical terms, and demonstrate their use, but limited literature on how that theory can be applied in practice. In this work we provide some simplified examples of how sensitivity analyses can be carried out, when they are likely to show that the evaluation is sensitive to underlying data, knowledge or assumptions, how to interpret the results of sensitivity analysis, and how the outcome can be reported. We also provide access to an application to conduct sensitivity analysis.

A quantifiler™ trio-based HRM screening assay for the accurate prediction of single source versus mixed biological samples

At present, the forensic DNA workflow is not capable of providing information about the contributor status (single source vs. multiple contributors) of evidentiary samples prior to end-point analysis. This exacerbates the challenges inherent to mixtures and low-template DNA samples. If additional sample information could be provided earlier in the workflow, protocols could be implemented to mitigate these challenges. An integrated Quantiplex®- high resolution melt (HRM) assay was shown to be effective in distinguishing between single source and mixture DNA samples; however, integration of the HRM assay into a more commonly used chemistry would be beneficial to the practitioner community. Thus, the assay was redesigned as an integrated Quantifiler™ Trio-HRM assay, which included the identification of a new DNA-binding dye, an increased reaction volume, and the establishment of new data analysis and standard curve metrics for all targets. This redesigned assay produced quantification values and qualitative values that were comparable to those produced when the same samples were tested using the standard Quantifiler™ Trio chemistry and settings. Further, STR profiles generated with quantification values produced from the integrated Quantifiler™ Trio-HRM assay and standard Quantifiler™ Trio chemistry were complete and fully concordant. Most importantly, the integrated Quantifiler™ Trio-HRM assay was able to accurately predict whether a sample was single source or a mixture 79.2% of the time, demonstrating the potential of this approach. With the incorporation of an expanded training set for prediction modeling, and completion of critical developmental validation studies, this assay could prove useful to the forensic DNA practitioner community.

Evaluation of large-scale highly polymorphic microhaplotypes in complex DNA mixtures analysis using RMNE method

DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.

Forty years of research and development on forensic genetics: A bibliometric analysis

The current study aims to investigate the research publication trends in the field of forensic genetics using Bibliometric analysis. An extensive search of the Scopus database was conducted to identify scholarly articles on forensic genetics published between 1977 and 2022, and a data set comprising 2945 articles was obtained. The analysis was carried out using VOSviewer, RStudio, MS Excel and MS Access to investigate the annual publication trend, most productive journals, organizations/authors/countries, authorship and citation patterns, most cited documents/articles and co-occurrence of keywords. The results revealed the first article in the field of forensic genetics was published in 1977. By the end of 1999, only 15 articles were published. Since then, there has been a considerable increase in the yearly number of publications and post-2006, there were more than 100 yearly published articles. USA, China, Spain, Germany and United Kingdom were found to be the most productive countries. Among various organizations, the Institute of Legal Medicine, Innsbruck Medical University, Austria was found to be the most productive organization. In terms of the number of publications and citations, Morling N. was found to be the most prolific author. The highest number of articles were published in Forensic Science International: Genetics, contributing about 34% of the total articles published in different sources/journals. The document with the highest number of citations was "HOMER N, 2008, PLOS GENET", with a total of 750 citations. The most frequent keywords were forensic genetics and forensic science, followed by STR, population genetics, DNA, mt-DNA and DNA-typing. The results also revealed that there had been collaborative research among countries, organizations and authors, which helps in the exchange of ideas across disciplines, developing new skills, getting access to financial resources and generating quality results.

Impact on touch DNA of an alcohol-based hand sanitizer used in COVID-19 prevention

In the last years, forensic research has been focused on touch DNA in order to improve its evidential value in criminal activity investigations as well as to understand the variables impacting touch DNA. One of the emerging variables is represented by the use of alcohol-based sanitizers, which was suggested for hand hygiene during the COVID-19 pandemic. The aims of the present study were to assess the effect of a hand sanitizer on touch DNA deposition, transfer, and recovery and also to evaluate STR typing success, quality of DNA profiles, and personal identification. Before and after the use of an alcohol-based hand sanitizer, 20 volunteers deposited on glass surfaces 120 fingerprints, containing skin-derived or salivary DNA. Samples were quantified by real-time quantitative PCR (q-PCR), and 76 samples yielding > 15 pg/μl were typed for 21 autosomal STRs by GlobalFiler® PCR Amplification Kit. DNA profiles were classified into single source, mixed, and inconclusive profiles, and a LR assessment was performed by comparison to the reference samples using LRmix Studio software. After the use of hand sanitizer, samples yielded lower quantities of recovered transferred DNA, especially considering samples containing salivary DNA (p < 0.05 by Friedman test). All the 76 amplified samples (63.3% of the total) showed at least 10 typed loci, and 83-100% of profiles were consistent with the reference ones on the basis of a LR value ≥ 10⁶. Results showed that, although the hand sanitizer reduces the DNA recovering, touch DNA samples might still be useful for forensic personal identification even when hand sanitizers are used.

Weight of evidence of Y-STR matches computed with the discrete Laplace method: Impact of adding a suspect's profile to a reference database

The discrete Laplace method is recommended by multiple parties (including the International Society for Forensic Genetics, ISFG) to estimate the weight of evidence in criminal cases when a suspect's Y-STR profile matches the crime scene Y-STR profile. Unfortunately, modelling the distribution of Y-STR profiles in the population reference database is time-consuming and requires expert knowledge. When the suspect's Y-STR profile is added to the database, as would be the protocol in many cases, the parameters of the discrete Laplace model must be re-estimated. We found that the likelihood ratios with and without adding the suspect's Y-STR profile were almost identical with 1,000 or more Y-STR profiles in the database for Y-STR profiles with 8, 12, and 17 loci. Thus, likelihood ratio calculations can be performed in seconds if an established discrete Laplace model based on at least 1,000 Y-STR profiles is used. A match in a population reference database with 17 Y-STR loci from at least 1,000 male individuals results in a likelihood ratio above 10,000 in approximately 94% of the cases, and above 100,000 in approximately 82% of the cases. We offer free software accessible without restrictions to estimate a discrete Laplace model using a Y-STR reference database and subsequently to calculate likelihood ratios.

Likelihood Ratio Calculation Using LRmix Studio

LRmix Studio performs statistical analyses on forensic casework samples by calculating a likelihood ratio (LR) following a semi-continuous, unrestricted approach. The software utilizes a basic probabilistic model allowing the comparison of two alternative hypotheses regarding the evidence profile to include known and/or unknown contributors, for a maximum of a 4-person mixture. Other statistical factors that are included in this model are the incorporation of multiple probability of drop-out values, probability of drop-in, a correction factor for population substructure, assumed contributor inclusion, and inclusion of an unknown relative in the defense hypothesis. A range of plausible probability of drop-out values can be calculated for various contributors and hypotheses based on a Monte Carlo probability method and included in the likelihood ratio calculation. The software also includes several ways to test the validity and robustness of the probabilistic model. A sensitivity analysis can be performed by calculating likelihood ratios for the given profile against a range of drop-out values. Additionally, a non-contributor test can be performed on the crime scene sample and the chosen LR parameters to test the robustness of the model. This can give a point of comparison of the likelihood ratio generated for the person of interest (POI) compared to "random man" profiles generated from uploaded allelic frequencies. Finally, the analysis can be printed in a well-structured and user-friendly report that includes all analysis parameters. Within this chapter, the reader will learn the steps to calculate a likelihood ratio using the semi-continuous software, LRmix Studio. Additional tools supplied through the software will also be explained and demonstrated.

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.

Internal Validation of MaSTR™ Probabilistic Genotyping Software for the Interpretation of 2–5 Person Mixed DNA Profiles

Mixed human deoxyribonucleic acid (DNA) samples present one of the most challenging pieces of evidence that a forensic analyst can encounter. When multiple contributors, stochastic amplification, and allele drop-out further complicate the mixture profile, interpretation by hand becomes unreliable and statistical analysis problematic. Probabilistic genotyping software has provided a tool to address complex mixture interpretation and provide likelihood ratios for defined sets of propositions. The MaSTR™ software is a fully continuous probabilistic system that considers a wide range of STR profile data to provide likelihood ratios on DNA mixtures. Mixtures with two to five contributors and a range of component ratios and allele peak heights were created to test the validity of MaSTR™ with data similar to real casework. Over 280 different mixed DNA profiles were used to perform more than 2600 analyses using different sets of propositions and numbers of contributors. The results of the analyses demonstrated that MaSTR™ provided accurate and precise statistical data on DNA mixtures with up to five contributors, including minor contributors with stochastic amplification effects. Tests for both Type I and Type II errors were performed. The findings in this study support that MaSTR™ is a robust tool that meets the current standards for probabilistic genotyping.

Kinship assignment with the ForenSeq™ DNA Signature Prep Kit: Sources of error in simulated and real cases

Kinship recognition between anonymous DNA samples is becoming a relevant issue in forensics, more so with the increasing number of DNA profiles in databanks. Also, NGS-based genotyping is being increasingly used in routine personal identification, to simultaneously type large numbers of markers of different kind. In the present work, we explored computationally and experimentally the performance of the ForenSeq™ DNA Signature Prep Kit in identifying the true relationship between two anonymous samples, distinguishing it from other possible relationships. We analyzed with Familias R series of 10,000 pairs with 9 different simulated relationships, corresponding to different degrees of autosomal sharing. For each pair we obtained likelihood ratios for five kinship hypotheses vs. unrelatedness, and used their ranking to identify the preferred relationship. We also typed 21 subjects from two pedigrees, representing from parent-child to 4th cousins relationships. As expected, the power for identifying the true relationship decays in the order of autosomal sharing. Parent-child and full siblings can be robustly identified against other relationships. For half-siblings the chance of reaching a significant conclusion is already small. For more distant relationships the proportion of cases correctly and significantly identified is 10% or less. Bidirectional errors in kinship attribution include the suggestion of relatedness when this does not exist (10-50%), and the suggestion of independence in pairs of individuals more than 4 generations apart (25-60%). The real cases revealed a relevant effect of genotype miscalling at some loci, which could only be partly avoided by modulating the analysis parameters. In conclusion, with the exception of first degree relatives, the kit can be useful to inform additional investigations, but does not usually provide probatory results.

An in-field evaluation of rapid DNA instruments for disaster victim identification

In 2019 and 2020, disaster victim identification (DVI) simulations were conducted at the Australian Facility for Taphonomic Experimental Research. Whole and fragmented cadavers were positioned to replicate a building collapse scenario and left to decompose for up to 4 weeks. This study evaluated the utility of the ANDE™ 6C Rapid DNA System and the RapidHIT^TM ID System for DVI in the field and mortuary. Applying post-mortem nail and tissue biopsy samples showed promise, with the added benefit of minimally invasive collection procedures and limited preparation requirements. The preferred platform will depend on a number of factors, including its intended use and operating environment.

Enhanced mixture interpretation with macrohaplotypes based on long-read DNA sequencing

Deconvoluting mixture samples is one of the most challenging problems confronting DNA forensic laboratories. Efforts have been made to provide solutions regarding mixture interpretation. The probabilistic interpretation of Short Tandem Repeat (STR) profiles has increased the number of complex mixtures that can be analyzed. A portion of complex mixture profiles, particularly for mixtures with a high number of contributors, are still being deemed uninterpretable. Novel forensic markers, such as Single Nucleotide Variants (SNV) and microhaplotypes, also have been proposed to allow for better mixture interpretation. However, these markers have both a lower discrimination power compared with STRs and are not compatible with CODIS or other national DNA databanks worldwide. The short-read sequencing (SRS) technologies can facilitate mixture interpretation by identifying intra-allelic variations within STRs. Unfortunately, the short size of the amplicons containing STR markers and sequence reads limit the alleles that can be attained per STR. The latest long-read sequencing (LRS) technologies can overcome this limitation in some samples in which larger DNA fragments (including both STRs and SNVs) with definitive phasing are available. Based on the LRS technologies, this study developed a novel CODIS compatible forensic marker, called a macrohaplotype, which combines a CODIS STR and flanking variants to offer extremely high number of haplotypes and hence very high discrimination power per marker. The macrohaplotype will substantially improve mixture interpretation capabilities. Based on publicly accessible data, a panel of 20 macrohaplotypes with sizes of ~ 8 k bp and the maximum high discrimination powers were designed. The statistical evaluation demonstrates that these macrohaplotypes substantially outperform CODIS STRs for mixture interpretation, particularly for mixtures with a high number of contributors, as well as other forensic applications. Based on these results, efforts should be undertaken to build a complete workflow, both wet-lab and bioinformatics, to precisely call the variants and generate the macrohaplotypes based on the LRS technologies.

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.

Assessment of the effectiveness of the EUROFORGEN NAME and Precision ID Ancestry panel markers for ancestry investigations

The EUROFORGEN NAME panel is a regional ancestry panel designed to differentiate individuals from the Middle East, North Africa, and Europe. The first version of the panel was developed for the MassARRAY system and included 111 SNPs. Here, a custom AmpliSeq EUROFORGEN NAME panel with 102 of the original 111 loci was used to sequence 1098 individuals from 14 populations from Europe, the Middle East, North Africa, North-East Africa, and South-Central Asia. These samples were also sequenced with a global ancestry panel, the Precision ID Ancestry Panel. The GenoGeographer software was used to assign the AIM profiles to reference populations and calculate the weight of the evidence as likelihood ratios. The combination of the EUROFORGEN NAME and Precision ID Ancestry panels led to fewer ambiguous assignments, especially for individuals from the Middle East and South-Central Asia. The likelihood ratios showed that North African individuals could be separated from European and Middle Eastern individuals using the Precision ID Ancestry Panel. The separation improved with the addition of the EUROFORGEN NAME panel. The analyses also showed that the separation of Middle Eastern populations from European and South-Central Asian populations was challenging even when both panels were applied.

Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset

A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.

A 2b-RAD parentage analysis pipeline for complex and mixed DNA samples

Next-generation sequencing technology has revolutionized genotyping in many fields of study, yet parentage analysis often still relies on microsatellite markers that are costly to generate and are currently available only for a limited number of species. 2b-RAD sequencing (2b-RAD) is a DNA sequencing technique developed for ecological population genomics that utilizes type IIB restriction enzymes to generate consistent, uniform fragments across samples. This technology is inexpensive, effective with low DNA inputs, and robust to DNA degradation. Here, we developed a probabilistic genotyping-by-sequencing genetic testing pipeline for parentage analysis by using 2b-RAD for inferring familial relationships from mixed DNA samples and populations. Our approach to partial paternity assignment utilizes a novel weighted outlier paternity index (WOPI) adapted for next-generation sequencing data and an identity-by-state (IBS) matrix-based clustering method for pedigree reconstruction. The combination of these two parentage assignment methods overcomes two major obstacles faced by other genetic testing methods: 1) It allows detection of parentage when closely related or inbred individuals are in the alleged parent population (e.g., in laboratory strains); and 2) it resolves mixed DNA samples. We successfully demonstrate this novel approach by correctly inferring paternity for samples pooled from multiple offspring (i.e., entire clutches) in a highly inbred population of an East African cichlid fish. The unique advantages of 2b-RAD in combination with our bioinformatics pipeline enable straightforward and cost-effective parentage analysis in any species regardless of genomic resources available.

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).

Evaluation of probability distribution models for stutter ratios in the typing system of GlobalFiler and 3500xL Genetic Analyzer

As DNA typing systems have become increasingly sensitive in recent years, probability distribution models for back, forward, double-back, and minus 2-nt stutter ratios have been desired to be considered in DNA evidence interpretation using specific software programs. However, experimental investigations have been insufficient, especially for forward, double-back, and minus 2-nt stutters. In this study, we experimentally reevaluated the probability distribution models for each stutter ratio in the typing systems of GlobalFiler™ PCR Amplification Kit and 3500xL Genetic Analyzer from Thermo Fisher Scientific. In addition, to enhance the reliability of longest uninterrupted stretch (LUS) values and corrected allele numbers used in previously developed models for stutter ratios using sequence information (i.e., LUS model and multi-seq model), we propose the weighted average of LUS values and corrected allele numbers based on the number of observations in sequence-based population data. Back stutter ratios demonstrated a positive correlation with allele numbers (allele model) in eight loci, LUS values (LUS model) in eight loci, and corrected allele numbers (multi-seq model) in five loci. The forward stutter ratios (FSRs) of D22S1045 followed the LUS model. FSRs other than D22S1045 and double-back stutter ratios followed the LUS model by considering multiple loci together. Minus 2-nt stutter ratios observed in SE33 and D1S1656 did not increase with the increase in the allele numbers. The adopted models for each stutter ratio can be implemented in software programs for DNA evidence interpretation and enable a reliable interpretation of crime stain profiles in forensic caseworks.

Evaluation of the AGCU Expressmarker 30 Kit composed of 31 loci for forensic application

With the widespread use of STR in identification of individuals, paternity testing, as well as population genetics, many commercially robust and validated STR multiplex kits were developed. The AGCU Expressmarker 30 Kit is a new autosomal STR system that contains 29 autosomal STR loci (D3S1358, vWA, D1S1656, CSF1PO, D8S1132, D19S253, D3S3045, D8S1179, D21S11, D16S539, TPOX, D6S477, Penta D, D2S441, D5S818, TH01, FGA, D15S659, D22S1045, D19S433, D13S317, D7S820, D6S1043, D10S1435, D10S1248, D2S1338, D18S51, D12S391, and Penta E), one insertion/deletion polymorphic marker on the Y chromosome (Y indel), and the amelogenin locus. A series of validation studies were performed in this context according to the guidelines of "Validation Guidelines for Forensic DNA Analysis Methods". The sensitivity study showed that a full profile was observed with template DNA as low as 40 pg. In the stability study, all STR profiles were obtained at concentrations of humic acid up to 800 ng/μL, hematin up to 250 μM, and tannic acid up to 200 ng/μL. The mixture study demonstrated that all of the minor alleles could be called at ratios from 1:1-29:1 when the total DNA was 2 ng. In the population study, the total discrimination power for three population (Sichuan-Han, Gansu-Hui, and Guangxi-Zhuang) were above 0.9999999999999999999999999999999992, 0.999999999999999999999999999999998 and 0.999999999999999999999999999999994 as well as the cumulative probability of paternity exclusion were 0.999999999999953, 0.999999999999178, and 0.999999999999611 respectively. These results demonstrated that the AGCU Expressmarker 30 Kit is a useful tool for analyzing both forensic casework and database samples.

Oxidative stress-induced DNA damage and repair in primary human osteoarthritis chondrocytes: focus on IKKα and the DNA Mismatch Repair System

During osteoarthritis development, chondrocytes are subjected to a functional derangement. This increases their susceptibility to stressful conditions such as oxidative stress, a characteristic of the aging tissue, which can further provoke extrinsic senescence by DNA damage responses. It was previously observed that IκB kinase α knockdown increases the replicative potential of primary human OA chondrocytes cultured in monolayer and the survival of the same cells undergoing hypertrophic-like differentiation in 3-D. In this paper we investigated whether IKKα knockdown could modulate oxidative stress-induced senescence of OA chondrocytes undergoing a DDR and particularly the involvement in this process of the DNA mismatch repair system, the principal mechanism for repair of replicative and recombinational errors, devoted to genomic stability maintenance in actively replicating cells. This repair system is also implicated in oxidative stress-mediated DNA damage repair. We analyzed microsatellite instability and expression of the mismatch repair components in human osteoarthritis chondrocytes after IKKα knockdown and H₂O₂ exposure. Only low MSI levels and incidence were detected and exclusively in IKKα proficient cells. Moreover, we found that IKKα proficient and deficient chondrocytes differently regulated MMR proteins after oxidative stress, both at mRNA and protein level, suggesting a reduced susceptibility of IKKα deficient cells. Our data suggest an involvement of the MMR system in the response to oxidative stress that tends to be more efficient in IKKαKD cells. This argues for a partial contribution of the MMR system to the better ability to recover DNA damage already observed in these cells.

Examination of the usefulness of next-generation sequencing in mixed DNA samples

The identification of individuals from mixed DNA samples is an important application of DNA typing. Although the discriminatory power of DNA profiling has improved dramatically, a limiting factor is that individuals cannot be identified via short tandem repeat (STR) analysis. We used next-generation sequencing (NGS) to examine the mixed DNA samples. Our results showed that STR nucleotide sequences and single nucleotide polymorphisms (SNPs) analysis via NGS may enable the identification of each distinct subject from a DNA mixture containing DNA of the victim and suspect.

An integrated system for forensic DNA testing of sexual assault cases in the Philippines

In the Philippines, more than 7000 cases of sexual assault are reported annually. DNA technology is a powerful tool in identifying assailants. However, it is not routinely used in sexual assault investigations due to insufficient government support to cover the high cost of DNA testing and the absence of a national system for sample collection, handling, storage, and DNA testing of biological evidence. In itself, the nature of sexual assault samples containing DNA mixtures presents challenges to laboratory methods and interpretation of results. The sample recovered from the victim may only contain trace amounts of the assailant’s DNA, may have degraded due to prolonged storage in ambient conditions which is warm and humid in the tropics, or contaminated with inhibitors, such as in anal swabs. Hence, a closer evaluation of the processes of evidence collection and DNA testing is needed to increase the likelihood of success in generating conclusive results. In this paper, we propose an integrated system for DNA testing of biological samples collected from sexual assault victims considering the limitations of resources and the prevailing warm climate. Recommendations in this work should provide basis for formulating national guidelines for DNA analysis in aid of criminal investigations. The proposed scheme can be adopted by forensic DNA laboratories in the Philippines and in other countries facing similar challenges.

A Novel SNP-STR System Based on a Capillary Electrophoresis Platform

Various compound markers encompassing two or more variants within a small region can be regarded as generalized microhaplotypes. Many of these markers have been investigated for various forensic purposes, such as individual identification, deconvolution of DNA mixtures, or forensic ancestry inference. SNP-STR is a compound biomarker composed of a single nucleotide polymorphism (SNP) and a closely linked short tandem repeat polymorphism (STR), and possess the advantages of both SNPs and STRs. In addition, in conjunction with a polymerase chain reaction (PCR) technique based on the amplification refractory mutation system (ARMS), SNP-STRs can be used for forensic unbalanced DNA mixture analysis based on capillary electrophoresis (CE), which is the most commonly used platform in worldwide forensic laboratories. Our previous research reported 11 SNP-STRs, but few of them are derived from the commonly used STR loci, for which existing STR databases can be used as a reference. For maximum compatibility with existing DNA databases, in this study, we screened 18 SNP-STR loci, of which 14 were derived from the expanded CODIS core loci set. Stable and sensitive SNP-STR multiplex PCR panels based on the CE platform were established. Assays on simulated two-person DNA mixtures showed that all allele-specific primers could detect minor DNA components in 1:500 mixtures. Population data based on 113 unrelated Chengdu Han individuals were investigated. A Bayesian framework was developed for the likelihood ratio (LR) evaluation of SNP-STR profiling results obtained from two-person mixtures. Furthermore, we report on the first use of SNP-STRs in casework to show the advantages and limitations for use in practice. Compared to 2.86 × 10³ for autosomal STR kits, the combined LR reached 7.14 × 10⁷ using the SNP-STR method in this casework example.

Relaxing the assumption of unrelatedness in the numerator and denominator of likelihood ratios for DNA mixtures

DNA mixtures will have multiple donors under both the prosecution and alternate propositions when assigning a likelihood ratio for forensic DNA evidence. These donors are usually assumed to be unrelated to each other. In this paper, we make a small, preliminary examination of the potential effect of relaxing this assumption. We consider the simple situation of a two-person mixture with no dropout and a two-person major/minor mixture with dropout of the minor contributor. We make no adjustment for subpopulation effects. Mixtures were simulated under two assumptions: 1. that the donors were siblings 2. or that they were unrelated. Both unresolvable and major/minor mixtures were considered. We compared the likelihood ratio assuming sibship with the likelihood ratio assuming no relatedness. The LR for hypotheses assuming no relatedness is less than the LR assuming relatedness approximately 95% of the time when relatives are present in the mixture.

Rapid semen identification from mixed body fluids using methylation-sensitive high-resolution melting analysis of the DACT1 gene

In forensic genetics, a suspect is assigned to a component of a DNA mixture profile, and a probabilistic interpretation is then usually performed. However, it is difficult to determine what types of body fluid the component is from. Previous studies have reported that the fourth exon of the Dishevelled binding antagonist of beta catenin 1 (DACT1) gene is hypomethylated in a semen DNA-specific manner. In the present study, we evaluated whether the DACT1 gene could be effectively used to identify semen in body fluid mixtures and were able to semi-quantify the semen DNA content in mixed fluids. Our results showed that the DACT1 gene was useful in discriminating semen from venous blood and saliva. However, the amount of sperm in semen can affect semen identification. In addition, SI (the semen DNA content index), which we developed, was useful to determine whether the semen compromised majority, almost half, or was in the minority of the components in a mixed fluid. This technique is based on the methylation-sensitive high-resolution melting (MS-HRM) technology, which is time-, cost-, and labour-effective, and could be adopted in routine criminal investigations.

Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation

We demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalized logistic regression, can be employed when separation occurs in a two-class classification setting, and allow for the calculation of likelihood ratios. A case study of this framework is demonstrated on alcohol biomarker data for classifying chronic alcohol drinkers. The approach can be extended to applications in the fields of analytical and forensic chemistry, where it is a common feature to have a large number of biomarkers, and allows for flexibility in model assumptions such as multivariate normality. While some penalized regression methods have been introduced previously in forensic applications, our study is meant to encourage practitioners to use these powerful methods more widely. As such, based upon our proof-of-concept studies, we also introduce an R Shiny online tool with an intuitive interface able to perform several classification methods. We anticipate that this open-source and free-of-charge application will provide a powerful and dynamic tool to infer the LR value in case of classification tasks.

Interpreting Mixture Profiles: Comparison between Precision ID GlobalFiler™ NGS STR Panel v2 and Traditional Methods

Forensic investigation for the identification of offenders, recognition of human remains, and verification of family relationships requires the analysis of particular types of highly informative DNA markers, which have high discriminatory power and are efficient for typing degraded samples. These markers, called STRs (Short Tandem Repeats), can be amplified by multiplex-PCR (Polymerase Chain Reaction) allowing attainment of a unique profile through which it is possible to distinguish one individual from another with a high statistical significance. The rapid and progressive evolution of analytical techniques and the advent of Next-Generation Sequencing (NGS) have completely revolutionized the DNA sequencing approach. This technology, widely used today in the diagnostic field, has the advantage of being able to process several samples in parallel, producing a huge volume of data in a short time. At this time, although default parameters of interpretation software are available, there is no general agreement on the interpretation rules of forensic data produced via NGS technology. Here we report a pilot study aimed for a comparison between NGS (Precision ID GlobalFiler™ NGS STR Panel v2, Thermo Fisher Scientific, Waltham, MA, USA) and traditional methods in their ability to identify major and minor contributors in DNA mixtures from saliva and urine samples. A quantity of six mixed samples were prepared for both saliva and urine samples from donors. A total of 12 mixtures were obtained in the ratios of 1:2; 1:4; 1:6; 1:8; 1:10; and 1:20 between minor and major contributors. Although the number of analyzed mixtures is limited, our results confirm that NGS technology offers a huge range of additional information on samples, but cannot ensure a higher sensitivity in respect to traditional methods. Finally, the Precision ID GlobalFiler™ NGS STR Panel v2 is a powerful method for kinship analyses and typing reference samples, but its use in biological evidence should be carefully considered on the basis of the characteristics of the evidence.

DIP-microhaplotypes: new markers for detection of unbalanced DNA mixtures

The identification of a suspect in a degraded and unbalanced DNA mixture has been a challenge for the standard short tandem repeat polymorphisms (STR) typing. Several methods have been introduced to solve this problem, such as DIP-STR, DIP-SNP, and SNP-STR markers. In this study, we proposed DIP-microhaplotype (deletion/insertion linked a chain of SNPs) as a kind of new genetic marker to type the unbalanced and degraded DNA mixture. We established the detection method with ten DIP-microhaplotype markers including 26 SNPs using allele-specific multiplex PCR followed by SNaPshot assay. This novel compound marker allows us to detect the minor DNA with a sensitivity of 1:100 to 1:1000 in a DNA mixture of any gender. Most of the DIP-microhaplotype markers had a relatively high probability of informative alleles with an average informative value (I value) of 0.308. In all, we proposed DIP-microhaplotype as a novel type of DNA marker for the detection of minor contributor from unbalanced DNA mixtures. Due to their inherent shorter length, higher polymorphism, and sensitivity, DIP-microhaplotypes are promising markers for the examination of the degraded and unbalanced mixtures in forensic stains or clinical chimeras.