A top-down approach to DNA mixtures

Improved individual identification in DNA mixtures of unrelated or related contributors through massively parallel sequencing

DNA mixtures are a common sample type in forensic genetics, and we typically assume that contributors to the mixture are unrelated when calculating the likelihood ratio (LR). However, scenarios involving mixtures with related contributors, such as in family murder or incest cases, can also be encountered. Compared to the mixtures with unrelated contributors, the kinship within the mixture would bring additional challenges for the inference of the number of contributors (NOC) and the construction of probabilistic genotyping models. To evaluate the influence of potential kinship on the individual identification of the person of interest (POI), we conducted simulations of two-person (2 P) and three-person (3 P) DNA mixtures containing unrelated or related contributors (parent-child, full-sibling, and uncle-nephew) at different mixing ratios (for 2 P: 1:1, 4:1, 9:1, and 19:1; for 3 P: 1:1:1, 2:1:1, 5:4:1, and 10:5:1), and performed massively parallel sequencing (MPS) using MGIEasy Signature Identification Library Prep Kit on MGI platform. In addition, in silico simulations of mixtures with unrelated and related contributors were also performed. In this study, we evaluated 1): the MPS performance; 2) the influence of multiple genetic markers on determining the presence of related contributors and inferring the NOC within the mixture; 3) the probability distribution of MAC (maximum allele count) and TAC (total allele count) based on in silico mixture profiles; 4) trends in LR values with and without considering kinship in mixtures with related and unrelated contributors; 5) trends in LR values with length- and sequence-based STR genotypes. Results indicated that multiple numbers and types of genetic markers positively influenced kinship and NOC inference in a mixture. The LR values of POI were strongly dependent on the mixing ratio. Non- and correct-kinship hypotheses essentially did not affect the individual identification of the major POI; the correct kinship hypothesis yielded more conservative LR values; the incorrect kinship hypothesis did not necessarily lead to the failure of POI individual identification. However, it is noteworthy that these considerations could lead to uncertain outcomes in the identification of minor contributors. Compared to length-based STR genotyping, using sequence-based STR genotype increases the individual identification power of the POI, concurrently improving the accuracy of mixing ratio inference using EuroForMix. In conclusion, the MGIEasy Signature Identification Library Prep kit demonstrated robust individual identification power, which is a viable MPS panel for forensic DNA mixture interpretations, whether involving unrelated or related contributors.

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.

Characterizing the amplification of STR markers in multiplex polymerase chain displacement reaction using massively parallel sequencing

Polymerase chain displacement reaction (PCDR) showed advantages in forensic low-template DNA analysis with improved amplification efficiency, higher allele detection capacity, and lower stutter artifact than PCR. However, characteristics of STR markers after PCDR amplification remain unclarified for the limited resolving power of capillary electrophoresis (CE). This issue can be addressed by massively parallel sequencing (MPS) technology with higher throughput and discriminability. Here, we developed a multiplex PCDR system including 24 STRs and amelogenin. In addition, a PCR reference was established for comparison. After amplification, products were subjected to PCR-free library construction and sequenced on the Illumina NovaSeq system. We implemented a sequence-matching pipeline to separate different amplicon types of PCDR products from the combination of primers. In the sensitivity test, the PCDR multiplex obtained full STR profiles with as low as 125 pg 2800M control DNA. Based on that, single-source DNA samples were tested. First, highly concordant genotypes were observed among the PCDR multiplex, the PCR reference, and CE-based STR kits. Next, read counts of different PCDR amplicon types were investigated, showing a relative abundance of 78:12:12:1 for the shortest amplicon S, the two medium amplicons M1 and M2, and the longest amplicon L. We also analyzed the stutter artifacts for distinct amplicon types, and the results revealed the reduction of N - 1 and N - 2 contraction stutters, and the increase of N + 1 and N + 2 elongation stutters in PCDR samples. Moreover, we confirmed the feasibility of PCDR for amplifying degraded DNA samples and unbalanced DNA mixtures. Compared to the previous proof of principle study, our work took a further step to characterize the complete profile of STR markers in the PCDR context. Our results suggested that the PCDR-MPS workflow is an effective approach for forensic STR analysis. Corresponding findings in this study may help the development of PCDR-based assays and probabilistic methods in future studies.

What's on the bag? The DNA composition of evidence bags pre- and post-exhibit examination

Current forensic DNA profiling kits and techniques enable the detection of trace amounts of DNA. With advancements in kit sensitivity, there is an increased probability of detecting DNA from contamination. Research into DNA transfer within operational forensic laboratories provides insight into the possible mechanisms that may lead to exhibit contamination. To gain a greater understanding of the potential for evidence bags to act as DNA transfer vectors, the level of DNA accumulating on the exterior of evidence bags during the exhibit examination process was investigated. The exterior of 60 evidence bags were tapelifted before and after the examination of the exhibit inside of the bag resulting in 120 DNA profiles. These DNA profiles were compared to DNA profiles of staff working within the building and samples taken from the exhibit inside the bag. Common DNA profile contributors from each sample were also identified through STRmix™ mixture to mixture analysis. The average DNA quantity and number of profile contributors was higher in samples taken from the bag before exhibit examination than after examination. Fifty six percent of all samples taken identified a match between DNA recovered from the evidence bag and at least one staff member. On 11 bags, a common contributor was identified between the exhibit in the bag and the exhibit package post-examination. In one instance a DNA profile, matching that of a donor, on the exhibit bag before examination was also detected on a sample taken from the exhibit, raising the possibility of outer bag-to-exhibit DNA contamination. This study demonstrates that operational forensic laboratories must consider exhibit packages as a potential source of DNA contamination and evaluate their exhibit handling and storage procedures accordingly.

Using big data from probabilistic genotyping to solve crime

Forensic Science South Australia (FSSA) has been using STRmix™ software to deconvolute all reported DNA mixtures since 2012. Almost a decade of deconvolutions had led to a substantial repository of analysed profile data that can be interrogated to observe trends in case type, location or occurrence. In addition, deconvolutions can be compared in order to identify common DNA donors and reveal new intelligence information in cases where DNA profiling has previously provided no investigative information. As a proof of concept all samples deconvoluted as part of criminal casework (suspect or no-suspect) were interrogated and compared to each other using the mixture-to-mixture comparison feature in STRmix™. Within the Adelaide region there were 32 groups of cases that had evidence samples linked by a common DNA donor with LR > 1 million which was in addition to direct links and mixture searching links identified previously. These groups of cases can then be interrogated to reveal additional information to inform Police intelligence gathering. Our paper reports on the findings of this proof-of-concept study.

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.

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.

Prevalence of DNA of regular occupants in vehicles

People will deposit, redistribute and remove biological traces when they interact with their environment. Understanding the dynamics of trace DNA is crucial to assess both the optimal sampling strategy to recover traces and the relevance of DNA evidence in the context of a case. This paper addresses the prevalence of DNA of drivers, passengers, and unknown individuals in vehicles. Five vehicles with a regular driver only, and five vehicles with a regular driver and regular passenger have each been sampled at twenty locations. Based on the findings, we propose a sampling strategy for investigative purposes as well as for evaluative purposes when evaluating the findings given scenarios that propose the person-of-interest as either the driver or passenger in a vehicle.

Estimating the number of contributors to a DNA profile using decision trees

The interpretation of DNA profiles typically starts with an assessment of the number of contributors. In the last two decades, several methods have been proposed to assist with this assessment. We describe a relatively simple method using decision trees, that is fast to run and fully transparent to a forensic analyst. We use mixtures from the publicly available PROVEDIt dataset to demonstrate the performance of the method. We show that the performance of the method crucially depends on the performance of filters for stutter and other artefacts. We compare the performance of the decision tree method with other published methods for the same dataset.