The efficacy of DNA mixture to mixture matching

Value of DNA mixture-to-mixture comparisons within an operational context

Since 1995, national forensic DNA databases have used a maximum number of contributors, and a minimum number of loci to reduce the risk of providing false leads. DNA profiles of biological traces that do not meet these criteria cannot be loaded into these databases. In 2023, about 10 % of more than 15,000 trace DNA profiles analyzed in western Switzerland were not compared at the national level, even though they were considered to be interpretable, mainly because they contained the DNA from more than two persons. In this situation, police services can request local comparisons with DNA profiles of known persons and/or with other traces, but this occurs in only a small proportion of cases, so that DNA mixtures are rarely used to help detect potential series. The development of probabilistic genotyping software and its associated tools have made possible the efficient performance of this type of comparison, which is based on likelihood ratios (LR) rather than on the number of shared alleles. To highlight potential common contributors for investigation and intelligence purposes, the present study used the mixture-to-mixture tool of the software STRmix v2.7 to compare 235 DNA profiles that cannot be searched the Swiss DNA database. These DNA profiles originated from traces collected by six different police services in 2021 and 2022. Traces were selected by the police based on information that indicated that they were from potential series. Associations between profiles were compared with expected investigative associations to define the value of this approach. Among the 27,495 pairwise comparisons of DNA profiles, 88 pairs (0.3 %) showed at least one potential common contributor when using a LR threshold of 1000. Of these 88 pairs, 60 (68.2 %) were qualified by the police services as "expected" (60/88), 22 (25.0 %) as "possible", and six (6.8 %) as "unexpected". Although it is important to consider the limits of this approach (e.g., adventitious or missed associations, cost/benefit evaluation, integration of DNA mixture comparison in the process), these findings indicate that non CODIS loadable DNA mixtures could provide police agencies with information concerning potential series at both the local and national level.

Determining the number and size of background samples derived from an area adjacent to the target sample that provide the greatest support for a POI in a target sample

When sampling an item or surface for DNA originating from an action of interest, one is likely to collect DNA unrelated to the action of interest (background DNA). While adding to the complexity of a generated DNA profile, background DNA has been shown to aid in resolving the genotypes of contributors in a targeted sample, and where references of donors to the background DNA are not available, strengthen the LR supporting a person of interest contributing to the targeted sample. This is possible thanks to advances in probabilistic genotyping, where forensic labs are able to deconvolute complex DNA profiles to obtain lists of genotypes and their associated weights. Coupled with DBLR™, one can then compare multiple evidentiary profiles to each other to determine the contribution of common, but unknown, contributors. Here, we consider factors associated with taking background samples and whether one should collect multiple background samples that all relate to a single target sample, or if one should collect larger background samples rather than smaller samples. Background samples consisted of DNA accumulated on the items primarily by one or both occupants of a single household, while targeted samples were generated from touch deposits, or saliva deposits that had been left to air dry. Samples were collected from areas of various sizes, consisting of only the background, the target and the background directly beneath it, and the target and additional surrounding background. A broad range of DNA quantities were recovered, with larger background samples (400 cm2) yielding significantly more DNA than smaller background samples (30 cm2). Significant differences in DNA quantities between target samples were not observed. Generated DNA profiles were interpreted using STRmix™ and DBLR™, and where there was support for a common donor between the background and target sample, pairwise comparisons were performed to observe the effect on the LR supporting the target DNA donor contributing to the targeted sample when conditioning on one (or two) common donor between the targeted sample and 1-8 background samples. Multiple background samples gave significantly higher LRs compared to a single background sample, the larger sampled background area resulted in larger LR gains than the smaller areas, and four or more background samples reduced LR variability considerably. Here we provide recommendations for the minimum and ideal number of additional background samples that should be collected, and that several smaller samples may be more beneficial than a single larger sample.

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.

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

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

A tool for simulating single source and mixed DNA profiles

Simulation studies play an important role in the study of probabilistic genotyping systems, as a low cost and fast alternative to in vitro studies. With ongoing calls for further study of the behaviour of probabilistic genotyping systems, there is a continuous need for such studies. In most cases, researchers use simplified models, for example ignoring complexities such as peak height variability due to lack of availability of advanced tools. We fill this void and describe a tool that can simulate DNA profiles in silico for the validation and investigation of probabilistic genotyping software. Contributor genotypes are simulated by randomly sampling alleles from selected allele frequencies. Some or all contributors may be related to a pedigree and the genotypes of non-founders are obtained by random gene dropping. The number of contributors per profile, and ranges for parameters such as DNA template amount and degradation parameters can be configured. Peak height variability is modelled using a lognormal distribution or a gamma distribution. Profile behaviour of simulated profiles is shown to be broadly similar to laboratory generated profiles though the latter shows more variation. Simulation studies do not remove the need for experimental data. The tool has been made available as an R-package named simDNAmixtures.

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.

Statistefix 4.0: A novel probabilistic software tool

Latest innovations indicate that continuous tools are promising DNA trace assessment methods. In this study, we present the continuous software solution Statistefix 4.0. The software supports DNA experts in deducing DNA profiles for database queries and can help to preselect DNA samples suitable for further processing using advanced probabilistic search engines. The novel tool weights genotype contributions and deduces major contributors from high- and low-quality DNA traces. Peak height, degradation, stutter as well as allelic drop-in/-out events are incorporated in the statistical model. We analyzed reference and casework samples as well as artificially generated mixture samples for software evaluation. The tool offers the completely automated assessment of reference and mixture samples. Deconvolution outcomes of mixtures are compared with EuroForMix, GenoProof Mixture 3 and STRmix™. Data show that Statistefix 4.0 is as successful as analogously tested and implemented software. Deduced DNA profiles from casework samples highlight the potential benefit in routine casework. Statistefix 4.0 is freely available, works with replicates of different autosomal kits and enables bulk sample processing. This inter-laboratory study includes a variety of sample types and indicates a timesaving, robust and easily implemented software that supports DNA analysts in evaluating DNA traces.

Evaluating DNA evidence possibly involving multiple (mixed) samples, common donors and related contributors

Forensic DNA profiling is used in various circumstances to evaluate support for two competing propositions with the assignment of a likelihood ratio. Many software implementations exist that tackle a range of inference problems spanning identification and relationship testing. We propose a flexible likelihood ratio framework that caters to inference problems in forensic genetics. The framework allows for investigation of the degree of support for the contribution of multiple persons to multiple samples allowing for persons to be related according to a pedigree, including inbred relationships. We explain how a number of routine as well as more complex problems can be treated within this framework.

The importance of considering common sources of unknown DNA when evaluating findings given activity level propositions

Evaluating forensic biological evidence considering activity level propositions is becoming more prominent around the world. In such evaluations it is common to combine results from multiple items associated with the alleged activities. The results from these items may not be conditionally independent, depending on the mechanism of cell/DNA transfer being considered and it is important that the evaluation takes these dependencies into account. Part of this consideration is to incorporate our understanding of prevalent DNA and of background DNA on objects and people, and how activities can lead to common sources of unknown DNA being deposited on items. We demonstrate a framework for evaluation of DNA evidence in such a scenario using Object-Oriented Bayesian Networks and apply it to a motivating case from South Australia.

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.

Inter-sample contamination detection using mixture deconvolution comparison

A recent publication has provided the ability to compare two mixed DNA profiles and consider their probability of occurrence if they do, compared to if they do not, have a common contributor. This ability has applications to both quality assurance (to test for sample to sample contamination) and for intelligence gathering purposes (did the same unknown offender donate DNA to multiple samples). We use a mixture to mixture comparison tool to investigate the prevalence of sample to sample contamination that could occur from two laboratory mechanisms, one during DNA extraction and one during electrophoresis. By carrying out pairwise comparisons of all samples (deconvoluted using probabilistic genotyping software STRmix™) within extraction or run batches we identify any potential common DNA donors and investigate these with respect to their risk of contamination from the two proposed mechanisms. While not identifying any contamination, we inadvertently find a potential intelligence link between samples, showing the use of a mixture to mixture comparison tool for investigative purposes.