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

Single cell genomics applications in forensic science: Current state and future directions

Standard methods of mixture analysis involve subjecting a dried crime scene sample to a "bulk" DNA extraction method such that the resulting isolate compromises a homogenized DNA mixture from the individual donors. If, however, instead of bulk DNA extraction, a sufficient number of individual cells from the mixed stain are subsampled prior to genetic analysis then it should be possible to recover highly probative single source, non-mixed scDNA profiles from each of the donors. This approach can detect low DNA level minor donors to a mixture that otherwise would not be identified using standard methods and can also resolve rare mixtures comprising first degree relatives and thereby also prevent the false inclusion of non-donor relatives. This literature landscape review and associated commentary reports on the history and increasing interest in current and potential future applications of scDNA in forensic genomics, and critically evaluates opportunities and impediments to further progress.

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.

Improving the Utilization of STRmix™ Variance Parameters as Semi-Quantitative Profile Modeling Metrics

Distributions of the variance parameter values developed during the validation process. Comparisons of these prior distributions to the run-specific average are one measure used by analysts to assess the reliability of a STRmix deconvolution. This study examined the behavior of three different STRmix variance parameters under standard amplification and interpretation conditions, as well as under a variety of challenging conditions, with the goal of making comparisons to the prior distributions more practical and meaningful. Using information found in STRmix v2.8 Interpretation Reports, we plotted the log₁₀ of each variance parameter against the log₁₀ of the template amount of the highest-level contributor (Tc) for a large set of mixture data amplified under standard conditions. We observed nonlinear trends in these plots, which we regressed to fourth-order polynomials, and used the regression data to establish typical ranges for the variance parameters over the Tc range. We then compared the typical variance parameter ranges to log₁₀(variance parameter) v log₁₀(Tc) plots for mixtures amplified and interpreted under a variety of challenging conditions. We observed several distinct patterns to variance parameter shifts in the challenged data interpretations in comparison to the unchallenged data interpretations, as well as distinct shifts in the unchallenged variance parameters away from their prior gamma distribution modes over specific ranges of Tc. These findings suggest that employing empirically determined working ranges for variance parameters may be an improved means of detecting whether aberrations in the interpretation were meaningful enough to trigger greater scrutiny of the electropherogram and genotype interpretation.

Probabilistic Genotyping of Single Cell Replicates from Mixtures Involving First-Degree Relatives Prevents the False Inclusions of Non-Donor Relatives

Analysis of complex DNA mixtures comprised of related individuals requires a great degree of care due to the increased risk of falsely including non-donor first-degree relatives. Although alternative likelihood ratio (LR) propositions that may aid in the analysis of these difficult cases can be employed, the prior information required for their use is not always known, nor do these alternative propositions always prevent false inclusions. For example, with a father/mother/child mixture, conditioning the mixture on the presence of one of the parents is recommended. However, the definitive presence of the parent(s) is not always known and an assumption of their presence in the mixture may not be objectively justifiable. Additionally, the high level of allele sharing seen with familial mixtures leads to an increased risk of underestimating the number of contributors (NOC) to a mixture. Therefore, fully resolving and identifying each of the individuals present in familial mixtures and excluding related non-donors is an important goal of the mixture deconvolution process and can be of great investigative value. Here, firstly, we further investigated and confirmed the problems encountered with standard bulk analysis of familial mixtures and demonstrated the ability of single cell analysis to fully distinguish first-degree relatives (FDR). Then, separation of each of the individual donors via single cell analysis was carried out by a combination of direct single cell subsampling (DSCS), enhanced DNA typing, and probabilistic genotyping, and applied to three complex familial 4-person mixtures resulting in a probative gain of LR for all donors and an accurate determination of the NOC. Significantly, non-donor first-degree relatives that were falsely included (LRs > 102−108) by a standard bulk sampling and analysis approach were no longer falsely included using DSCS.

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.

Typing of semen-containing mixtures using ARMS-based semen-specific CpG-InDel/STR markers

Mixed traces are common biological materials found at crime scenes, and their identification remains a significant challenge in the field of forensic genetics. In recent years, DNA methylation has been considered as a promising approach for body fluid identification, and length polymorphic loci are still the preferred markers for personal identification. In this study, we used tissue-specific CpG sites with linked insertion or deletion (InDel) or short tandem repeat (STR) markers (CpG-InDel/STR) for both body fluid and individual identification. The tissue-specific CpG loci, which were all selected from the previous reports, were analyzed using a combination of bisulfite conversion and amplification refractory mutation system-multiprimer-PCR technology. InDels or STRs, which were selected within 400 bp upstream or downstream of the semen-specific CpG loci, were analyzed using a capillary electrophoresis platform. Eventually, we successfully constructed a panel containing 17 semen-specific CpG-InDel/STR compound markers compassing 21 InDels/STRs, 3 body-fluid positive controls (vaginal secretion-, saliva-, and blood-specific CpG), and 1 gender identification locus. Using this panel, full genotyping of individuals could be obtained successfully with 50 ng DNA input. Semen stains stored at room temperature for 7 months and degraded samples that were heat treated for up to 6 h were still identified efficiently. For semen containing mixed stains, it is also useful when the semen content is as low as 3.03%. Moreover, the cumulative discrimination power of this panel is 0.9999998. In conclusion, it is a robust panel enabling the validation of both the tissue source and individual identification of semen containing mixed stains and can be employed as an alternative solution for forensic case investigation.

Open practices in our science and our courtrooms

Advocates of transparency in science often point to the benefits of open practices for the scientific process. Here, we focus on a possibly underappreciated effect of standards for transparency: their influence on non-scientific decisions. As a case study, we consider the current state of probabilistic genotyping software in forensics.

The population genetics characteristics of a 90 locus panel of microhaplotypes

Single-nucleotide polymorphisms (SNPs) and small genomic regions with multiple SNPs (microhaplotypes, MHs) are rapidly emerging as novel forensic investigative tools to assist in individual identification, kinship analyses, ancestry inference, and deconvolution of DNA mixtures. Here, we analyzed information for 90 microhaplotype loci in 4009 individuals from 79 world populations in 6 major biogeographic regions. The study included multiplex microhaplotype sequencing (mMHseq) data analyzed for 524 individuals from 16 populations and genotype data for 3485 individuals from 63 populations curated from public repositories. Analyses of the 79 populations revealed excellent characteristics for this 90-plex MH panel for various forensic applications achieving an overall average effective number of allele values (A_e) of 4.55 (range 1.04–19.27) for individualization and mixture deconvolution. Population-specific random match probabilities ranged from a low of 10^–115 to a maximum of 10^–66. Mean informativeness (I_n) for ancestry inference was 0.355 (range 0.117–0.883). 65 novel SNPs were detected in 39 of the MHs using mMHseq. Of the 3018 different microhaplotype alleles identified, 1337 occurred at frequencies > 5% in at least one of the populations studied. The 90-plex MH panel enables effective differentiation of population groupings for major biogeographic regions as well as delineation of distinct subgroupings within regions. Open-source, web-based software is available to support validation of this technology for forensic case work analysis and to tailor MH analysis for specific geographical regions.

A novel approach for rapid cell assessment to estimate DNA recovery from human bone tissue

We report on the use of a DNA staining dye to locate and record nucleated osteocytes and other bone-related cells within sections of archived formalin-fixed and paraffin-embedded human tibia from which informative DNA profiles were obtained. Eleven of these archived tibia samples were sectioned at a thickness of 5 µm. Diamond™ Nucleic Acid Dye was applied to the sections and cells within the matrix of the bone fluoresced so that their location and number of cells could be photographed. DNA was isolated from these 11 samples using a standard extraction process and the yields were quantified by real-time PCR. Complete STR profiles were generated from ten bone extracts where low-level inhibition was recorded with an incomplete STR profile obtained from one sample with higher inhibition. The stained image of this sample showed that few cells were present. There was a significant relationship between the number of DD-stained cells and the number of alleles obtained (p < 0.05). Staining cells to determine the prevalence of bone cell nuclei allows a triage of samples prior to any subsequent DNA profiling.

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.

Developments in forensic DNA analysis

The analysis of DNA from biological evidence recovered in the course of criminal investigations can provide very powerful evidence when a recovered profile matches one found on a DNA database or generated from a suspect. However, when no profile match is found, when the amount of DNA in a sample is too low, or the DNA too degraded to be analysed, traditional STR profiling may be of limited value. The rapidly expanding field of forensic genetics has introduced various novel methodologies that enable the analysis of challenging forensic samples, and that can generate intelligence about the donor of a biological sample. This article reviews some of the most important recent advances in the field, including the application of massively parallel sequencing to the analysis of STRs and other marker types, advancements in DNA mixture interpretation, particularly the use of probabilistic genotyping methods, the profiling of different RNA types for the identification of body fluids, the interrogation of SNP markers for predicting forensically relevant phenotypes, epigenetics and the analysis of DNA methylation to determine tissue type and estimate age, and the emerging field of forensic genetic genealogy. A key challenge will be for researchers to consider carefully how these innovations can be implemented into forensic practice to ensure their potential benefits are maximised.

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.

DNA on drugs! A preliminary investigation of DNA deposition during the handling of illicit drug capsules

DNA profiling from capsules and tablets offers a complementary tool to that of chemical profiling when investigating the manufacture and trade in illicit drugs. By sampling the outside of capsules, individuals who may have handled them during production, assembly or distribution may have deposited their DNA and can be identified if matched to a nominated profile or one on a relevant DNA database. The profiles can also be compared to those found on other capsules to potentially link various drug seizures. This study sampled the exterior of capsules after they had been handled in a controlled scenario to determine if informative DNA profiles could be generated from this brief contact. Two individuals of intermediate shedder status washed their hands and waited for 30 min before handling ten gelatine, vegetable, and enteric vegetable capsules each (n = 60). Contact was made for 15 s. Each capsule was swabbed and DNA isolated. The amount of recovered human DNA was quantified and profiled using the Verifiler Plus DNA profiling kit. Profiles were generated from 82% (49/60) of capsules tested with LR values above 1 × 10³ for the inclusion of the volunteer as a contributor. Inhibition of the PCR was detected in 24 of the 60 samples, however 16 of these still produced informative profiles when sufficient template DNA was available and only mild inhibition was detected, or by overcoming inhibition by dilution of the DNA extract. This pilot study demonstrates the potential for forensic science laboratories to recover human DNA from the exterior surface of capsules which are commonly used to encase illicit drugs such as MDMA, thus enabling both biological and chemical profiling methods to contribute to the investigation of clandestine drug production and distribution.

Validation of a top-down DNA profile analysis for database searching using a fully continuous probabilistic genotyping model

Slooten described a method of targeting major contributors in mixed DNA profiles and comparing them to individuals on a DNA database. The method worked by taking incrementally more peak information from the profile (based on the peak contribution), and using a semi-continuous model, calculating likelihood ratios for the comparison to database individuals. We describe the performance of this "top down approach" to profile interpretation within probabilistic genotyping software employing a fully continuous model. We interpret both complex constructed profiles where ground truth is known and casework profiles from non-suspect crimes. The interpretation of constructed four- and five- person mixtures demonstrated good discrimination power between contributors and non-contributors to the mixtures. Not all known contributors linked, and this is expected, particularly for minor contributors of DNA to the profile, or when the DNA from contributors was in relatively equal contributions. This finding was also reported by Slooten for the semi-continuous application of the approach. The maximum observed LR was shown to not exceed the LR obtained after a standard interpretation approach outside of that expected due to Monte Carlo variation. The interpretation of 91 complex profiles from no-suspect casework demonstrated that approximately 75% of profiles returned a link to someone on a database of known individuals. With a yearly average of 110 no-suspect cases that fall into this too-complex category at Forensic Science SA, the top down analysis, if applied to all such profiles, would represent an increase of 83 links per year of investigative information that could be provided to investigators.

How can courts take into account the uncertainty in a likelihood ratio?

As legal practitioners and courts become more aware of scientific methods and evidence evaluation, they are demanding measures of the reliability of expert opinion. In particular, there are calls for error rates to accompany opinion evidence in comparative forensic sciences. While error rates or confidence intervals can be useful for those disciplines that claim to identify the source of a trace, the call for these statistical tools has extended to sciences that present opinions in the form of a likelihood ratio. In this article we argue against presenting both a likelihood ratio and numerical measures of its uncertainty. We explain how the LR already encapsulates uncertainty. Instead we consider how sensitivity analyses can be used to guide the presentation of LRs that are informative to the court and not unfair to defendants.

Interpol review of forensic biology and forensic DNA typing 2016-2019

This review paper covers the forensic-relevant literature in biological sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

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.