Comparison of the performance of different models for the interpretation of low level mixed DNA profiles.

DNA analyses from forensic casework samples commonly result in complex DNA profiles. Often, these profiles consist of multiple contributors and display multiple stochastic events such as peak height imbalance, allelic or locus drop-out, allelic drop-in, and excessive or indistinguishable stutter. This increased complexity has established a need for more sophisticated methods of DNA mixture interpretation. This study compares the effectiveness of statistical models in the interpretation of artificially created low template two person mixed DNA profiles at varying proportions and template quantities. Two binary models (combined probability of inclusion and random match probability), a semicontinuous (Lab retriever), and continuous model (STRmix™) were compared. Generally, as the sophistication of the models increases, the power of discrimination increases. Differences in discrimination often correlate to each model's ability to use observed data effectively. Binary models require static thresholds resulting in unused data and outliers that may lead to difficult or incorrect interpretation. Semicontinuous and continuous models eliminate the stochastic threshold, however Lab Retriever does not account for stochastic events beyond drop-out and drop-in leading to possible less effective use of the data. STRmix™ incorporates all stochastic events listed above into the calculation making the most effective use of the observed data.

A diagnosis of the primary difference between EuroForMix and STRmix™.

There is interest in comparing the output, principally the likelihood ratio, from the two probabilistic genotyping software EuroForMix (EFM) and STRmix™. Many of these comparison studies are descriptive and make little or no effort to diagnose the cause of difference. There are fundamental differences between EFM and STRmix™ that are causative of the largest set of likelihood ratio differences. This set of differences is for false donors where there are many instances of LRs just above or below 1 for EFM that give much lower LRs in STRmix™. This is caused by the separate estimation of parameters such as allele height variance and mixture proportion using MLE under Hp and Ha for EFM. This can result in very different estimations of these parameters under Hp and Ha . It results in a departure from calibration for EFM in the region of LRs just above and below 1.

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.

A tale of two PG systems: A comparison of the two most widely used continuous probabilistic genotyping systems in the United States.

The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two-, three-, and four-person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non-supportive, or inconclusive) for ~91% of contributor-specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor-specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low-template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non-supportive) and 12 were either inconclusive versus non-supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab-defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low-template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low-template, degraded contributors, and the deviation is generally predictable.

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.

Addressing uncertain assumptions in DNA evidence evaluation.

Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant's profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.

Addressing the alternate hypothesis: Transfer and persistence of saliva beneath fingernails.

This study investigated the transfer and persistence of salivary DNA under fingernails. This was performed to address a common alternate hypothesis presented to scientists in court, asserting that a relatively large quantity of DNA detected beneath the fingernails, typically from a victim of crime, originates from innocuous transfer of saliva in a casual setting. It was determined through these studies that contact with liquid saliva was an effective way to transfer foreign DNA beneath fingernails. However, when saliva was dried, DNA did not readily transfer through casual contact. When liquid saliva was placed directly beneath fingernails the amount of DNA detected from the saliva donor twenty-four hours later was several hundred-fold lower than the amount detected when sampling occurred immediately following deposition. Furthermore, when the recipients' hands were washed immediately following the deposition of liquid saliva beneath fingernails, the majority of foreign DNA was removed following one hand washing and all detectable foreign DNA was removed from most recipients' hands after three or six hand washings. This study demonstrates that casual contact with wet saliva can result in the transfer of substantial quantities of DNA beneath fingernails but that it does not typically persist for extended periods of time and is mostly removed if the hands are washed soon after deposition.

Combining artificial neural network classification with fully continuous probabilistic genotyping to remove the need for an analytical threshold and electropherogram reading.

Standard processing of electrophoretic data within a forensic DNA laboratory is for one (or two) analysts to designate peaks as either artefactual or non-artefactual in a process commonly referred to as profile 'reading'. Recently, FaSTR™ DNA has been developed to use artificial neural networks to automatically classify fluorescence within an electropherogram as baseline, allele, stutter or pull-up. These classifications are based on probabilities assigned to each timepoint (scan) within the electropherogram. Instead of using the probabilities to assign fluorescence into a category they can be used directly in the profile analysis. This has a number of advantages; increased objectivity in DNA profile processing, the removal for the need for analysts to read profiles, the removal for the need of an analytical threshold. Models within STRmix™ were extended to incorporate the peak label probabilities assigned by FaSTR™ DNA. The performance of the model extensions was tested on a DNA mixture dataset, comprising 2-4 person samples. This dataset was processed in a 'standard' manner using an analytical threshold of 50rfu, analyst peak designations and STRmix™ V2.9 models. The same dataset was then processed in an automated manner using no analytical threshold, no analysts reading the profile and using the STRmix™ models extended to incorporate peak label probabilities. Both datasets were compared to the known DNA donors and a set of non-donors. The result between the two processes was a very close performance, but with a large efficiency gain in the 0rfu process. Utilising peak label probabilities opens up the possibility for a range of workflow process efficiency gains, but beyond this allows full use of all data within an electropherogram.

Exploring how the LR of a POI in a target sample is impacted by awareness of the profile of the background derived from an area adjacent to the target sample.

DNA unrelated to an action of interest (background DNA) is routinely collected when sampling an area for DNA that may have originated from an action of interest. Background DNA can add to the complexity of a recovered DNA profile and could impact the discrimination power when comparing it to the reference profile of a person of interest. Recent advances in probabilistic genotyping and the development of new tools, now allow for the comparison of multiple evidentiary profiles to query for a common DNA donor. Here, we explore the additional discrimination power that can be gained by having an awareness of the background DNA present on a surface prior to the deposition of target DNA. Samples with varying number of contributors and DNA quantities were generated on cleaned plastic pipes (where ground truth was known) and items used by occupants of a single household (where ground truth was not known). The background consisted of deposits made by hands (touch) while target deposits were both touch and saliva. Samples were collected from areas consisting of only the background (A), the target and the background directly beneath it (B), and the target and additional surrounding background (B+C). Samples B and B+C yielded similar DNA amounts when the target consisted of saliva, but when the target consisted of touch, significantly more DNA was recovered from B+C. Subsequently generated DNA profiles were interpreted using STRmix™ and DBLR™. The first approach involved no conditioning while the second approach involved conditioning on the reference profiles of the known background DNA donors. The third approach involved conditioning on one common DNA donor between A and B or A and B+C. The fourth and final approach involved conditioning on two common DNA donors between A and B or A and B+C. As more information was applied to the analysis, the greater the increase in the LR for the comparison of the target sample to the POI. Conditioning on two common donors between the target and the background provided almost the same amount of information as conditioning on the references of the known background DNA donors. This resulted in an increase in the LR that was over 10 orders of magnitude for known donors in the target sample. Here we have demonstrated the value in collecting additional background samples from an area adjacent to a targeted sample, and that this has the potential to improve discrimination power.

DNA transfer when using gloves in burglary simulations.

Several studies have demonstrated that DNA can be indirectly transferred from an individual onto a surface. Therefore, the presence of DNA that is compatible with a given person does not necessarily mean that this person has touched the surface on which the DNA was recovered. The present work simulates cases, where DNA is recovered on a door handle and compared to several reference DNA profiles. The DNA profile of the trace shares DNA components with a person of interest (POI). When asked about the DNA results, the POI says he has nothing to do with the incident and has never been at the scene. However, a possibility would be that the DNA came from his recently stolen gloves. Someone else, the alternative offender (AO), could have opened the door wearing his gloves (POI's gloves), and transferred his DNA (POI's DNA). Based on the above-mentioned scenario, 60 burglary simulations experiments were carried out to generate data to assess DNA results given these allegations. The quantity and quality of DNA profiles (NGM SElect) recovered when the POI opened/closed the door bare-handed or when someone else performed the same activity but using POI's gloves, were compared. The gloves were regularly worn during at least three months by their owner during the winter. On the contrary, the AO wore them only for two minutes. Among the traces collected on the door handles, less than 50% of the traces led to interpretable DNA profiles. In 30% of the cases (3/10), when the door was opened/closed with bare hands, the DNA found on the door handle led to a mixed DNA profile with the POI's DNA aligning with the major contributor. For the experiments where the AO opened/closed the door with the POI's gloves, the POI's DNA was compatible with 22% (11/50) of the mixed DNA profile, aligning with the major in 8% of the cases (4/50). The DNA profiles of the offices' occupants were observed on the door handles, but not the AO's. In addition to the results of the experiments, we show two examples of how one can assess results observed in casework. Given the possibility of indirect transfer of minute DNA quantities, this research emphasizes the need to evaluate DNA results given the activities when the POI has a legitimate reason that can explain the presence of their DNA.

Overall Proportion of Total DNA Consistent with an Individual Briefly Handling a Firearm.

In forensic investigations, DNA profiles are routinely obtained from firearms evidence and alternative hypotheses may be proposed for consideration on the activity level. DNA profiles found to be consistent with the DNA profile of a specific individual could be a result of directly handling the firearm or other modes of transfer of DNA. Sixteen law-enforcement-owned firearms were evaluated with samples collected from the frame and slide area, the trigger and trigger guard area, and the front and rear sights after brief handling by laboratory personnel. Twenty-two out of forty-eight samples resulted in DNA profiles suitable for comparison, of which six resulted in likelihood ratios (LR) that demonstrated support for the hypothesis that included the brief handler as a contributor to the DNA profile obtained from the sample. Five of these samples were obtained from the frame and slide and one was from the trigger and trigger guard area. None of the DNA profiles obtained from the sights supported the inclusion of the brief handler as a contributor to the DNA profile. Gaining knowledge and supporting data on the nature of DNA profiles typically obtained from both owners and brief handlers can be useful for the purposes of evaluative reporting when considering results obtained from firearm evidence.

Uncertainty in probabilistic genotyping of low template DNA: A case study comparing STRMix™ and TrueAllele™.

Two probabilistic genotyping (PG) programs, STRMix™ and TrueAllele™, were used to assess the strength of the same item of DNA evidence in a federal criminal case, with strikingly different results. For STRMix, the reported likelihood ratio in favor of the non-contributor hypothesis was 24; for TrueAllele it ranged from 1.2 million to 16.7 million, depending on the reference population. This case report seeks to explain why the two programs produced different results and to consider what the difference tells us about the reliability and trustworthiness of these programs. It uses a locus-by-locus breakdown to trace the differing results to subtle differences in modeling parameters and methods, analytic thresholds, and mixture ratios, as well as TrueAllele's use of an ad hoc procedure for assigning LRs at some loci. These findings illustrate the extent to which PG analysis rests on a lattice of contestable assumptions, highlighting the importance of rigorous validation of PG programs using known-source test samples that closely replicate the characteristics of evidentiary samples. The article also points out misleading aspects of the way STRMix and TrueAllele results are routinely presented in reports and testimony and calls for clarification of forensic reporting standards to address those problems.

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.

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.

Quantification of forensic genetic evidence: Comparison of results obtained by qualitative and quantitative software for real casework samples.

To overcome the multifactorial complexity associated with the analysis and interpretation of the capillary electrophoresis results of forensic mixture samples, probabilistic genotyping methods have been developed and implemented as software, based on either qualitative or quantitative models. The former considers the electropherograms' qualitative information (detected alleles), whilst the latter also takes into account the associated quantitative information (height of allele peaks). Both models then quantify the genetic evidence through the computation of a likelihood ratio (LR), comparing the probabilities of the observations given two alternative and mutually exclusive hypotheses. In this study, the results obtained through the qualitative software LRmix Studio (v.2.1.3), and the quantitative ones: STRmix™ (v.2.7) and EuroForMix (v.3.4.0), were compared considering real casework samples. A set of 156 irreversibly anonymized sample pairs (GeneMapper files), obtained under the scope of former cases of the Portuguese Scientific Police Laboratory, Judiciary Police (LPC-PJ), were independently analyzed using each software. Sample pairs were composed by (i) a mixture profile with either two or three estimated contributors, and (ii) a single contributor profile associated. In most cases, information on 21 short tandem repeat (STR) autosomal markers were considered, and the majority of the single-source samples could not be a priori excluded as belonging to a contributor to the paired mixture sample. This inter-software analysis shows the differences between the probative values obtained through different qualitative and quantitative tools, for the same input samples. LR values computed in this work by quantitative tools showed to be generally higher than those obtained by the qualitative. Although the differences between the LR values computed by both quantitative software showed to be much smaller, STRmix™ generated LRs are generally higher than those from EuroForMix. As expected, mixtures with three estimated contributors showed generally lower LR values than those obtained for mixtures with two estimated contributors. Different software products are based on different approaches and mathematical or statistical models, which necessarily result in the computation of different LR values. The understanding by the forensic experts of the models and their differences among available software is therefore crucial. The better the expert understands the methodology, the better he/she will be able to support and/or explain the results in court or any other area of scrutiny.

Analysis of mixed DNA profiles from the RapidHIT™ ID platform using probabilistic genotyping software STRmix™.

Rapid DNA instruments are gaining interest in the forensic community as a means to generate DNA profile information more quickly than standard laboratory workflows, and with the potential to be carried out at the scene where samples are taken. Due to the many years that DNA profiles have been generated in a standard laboratory workflow, there have been numerous studies into profiling performance. These have flowed into probabilistic means of evaluating DNA profiles produced in those laboratories. In this study we examined the DNA profiling performance of the RapidHIT™ ID system on artificial mixtures constructed from raw DNA, mixtures constructed from body fluids, and touch DNA samples. We calibrated the probabilistic genotyping system STRmix™ for use on results produced by the RapidHIT ID system. Identical DNA samples were split, with half analysed on the RapidHIT ID system and the other half analysed in the standard laboratory workflow. Profiles produced from these paired samples were compared with regards to their composition and discrimination power. In general, profiles produced using the RapidHIT ID system showed good discrimination power, but less than those produced via the standard laboratory workflow. This is the expected trade-off for the advantages of speed and portability.

A mixed DNA profile controversy revisited.

Semaan et al. (J Forensic Res, 2020, 11, 453) discuss a mock case "where eight different individuals [P1 through P8 ] could not be excluded in a mixed DNA analysis. Even though … expert DNA mixture analysis software was used." Two of these are the true donors. The LRs reported are incorrect due to the incorrect entry of propositions into LRmix Studio. This forced the software to account for most of the alleles as drop-in, resulting in LRs 60-70 orders of magnitude larger than expected. P1 , P2 , P4 , P5 , and P8 can be manually excluded using peak heights. This has relevance when using LRmix which does not use peak heights. We extend the work using the same two reference genotypes who were the true contributors as Semaan et al. (J Forensic Res, 2020, 11, 453). We simulate three two-donor mixtures with peak heights using these two genotypes and analyze using STRmix™. For the simulated 1:1 mixture, one of the non-donors' LRs supported him being a contributor when no conditioning was used. When considered in combination with any other potential donors (i.e., with conditioning), this non-donor was correctly eliminated. For the 3:1 mixture, all results correctly supported that the non-donors were not contributors. The low-template 4:1 mixture LRs with no conditioning showed support for all eight profiles as donors. However, the results from pair-wise conditioning showed that only the two ground truth donors had LRs supporting that they were contributors to the mixture. We recommend the use of peak heights and conditioning profiles, as this allows better sensitivity and specificity even when the persons share many alleles.

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.

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.

A comparison of likelihood ratios obtained from EuroForMix and STRmix™.

Likelihood ratios (LR) differences between the probabilistic genotyping software EuroForMix and STRmix™ are examined. After considering differences in the allele probabilities, the LRs from both software for an unambiguous single-source profile were identical (four significant figures). LRs from both software for an unambiguous single-source profile with alleles previously unseen in the allele frequency database (rare alleles) were the same (three significant figures) for θ = 0.01. Due to differences in the minimum allele frequencies, the LRs differed by three orders of magnitude when θ = 0. For both software, the LRs for a single-source dilution series decreased as the input amount decreased. The LRs from both software were within an order of magnitude for known contributors. The largest difference was where the target input amount was 0.0156 ng: The LREuroForMix was 2.1 × 1025 and the LRSTRmix was 8.0 × 1024 . Both software show similar LR behavior with respect to mixture ratio. For two person mixtures the LR increases for both the major and the minor as the ratio moves away from 1:1. The LR for the major stabilizes at about 3:1 whereas the LR for the minor reaches its maximum at about 3:1 and then declines. Greater differences in LR were observed between EuroForMix and STRmix™ for mixtures. One-hundred and twenty-nine mixtures from the PROVEDIt dataset were compared. LRs for 84% of the comparisons for known contributors without rare alleles were within two orders of magnitude. Five divergent results were investigated, and a manual intervention approach was applied where appropriate.