Interpreting a major component from a mixed DNA profile with an unknown number of minor contributors

Modern interpretation strategies typically require an assignment of the number of contributors (N) to a DNA profile. This can prove to be a difficult task, particularly when dealing with higher order mixtures or mixtures where one or more contributors have donated low amounts of DNA. Differences in the assigned N at interpretation can lead to differences in the likelihood ration (LR). If the number of contributors cannot reasonably be assigned, then an interpretation of the profile may not be able to be progressed.In this study, we investigate mixed DNA profiles of varying complexity and interpret them altering the assigned N. We assign LRs for true- and non- contributors and compare the results given different assignments of N over a range of mixture proportions. When a component of a mixture had a proportion of at least 10%, a ratio of at least 1.5:1 to the next highest component, and a DNA amount (as determined by STRmix™) of at least 50 rfu, the LR of the component for a true contributor was not significantly affected by varying N and was therefore suitable for interpretation and the assignment of an LR. LRs produced for minor contributors were found to vary significantly as the assigned N was changed. These heuristics may be used to identify profiles suitable for interpretation.     

The effect of wild card designations and rare alleles in forensic DNA database searches

Forensic DNA databases are powerful tools used for the identification of persons of interest in criminal investigations. Typically, they consist of two parts: (1) a database containing DNA profiles of known individuals and (2) a database of DNA profiles associated with crime scenes. The risk of adventitious or chance matches between crimes and innocent people increases as the number of profiles within a database grows and more data is shared between various forensic DNA databases, e.g. from different jurisdictions.The DNA profiles obtained from crime scenes are often partial because crime samples may be compromised in quantity or quality. When an individual's profile cannot be resolved from a DNA mixture, ambiguity is introduced. A wild card, F, may be used in place of an allele that has dropped out or when an ambiguous profile is resolved from a DNA mixture.Variant alleles that do not correspond to any marker in the allelic ladder or appear above or below the extent of the allelic ladder range are assigned the allele designation R for rare allele. R alleles are position specific with respect to the observed/unambiguous allele. The F and R designations are made when the exact genotype has not been determined. The F and R designation are treated as wild cards for searching, which results in increased chance of adventitious matches. We investigated the probability of adventitious matches given these two types of wild cards.     

Interpreting low template DNA profiles

We discuss the interpretation of DNA profiles obtained from low template DNA samples. The most important challenge to interpretation in this setting arises when either or both of “drop-out” and “drop-in” create discordances between the crime scene DNA profile and the DNA profile expected under the prosecution allegation. Stutter and unbalanced peak heights are also problematic, in addition to the effects of masking from the profile of a known contributor. We outline a framework for assessing such evidence, based on likelihood ratios that involve drop-out and drop-in probabilities, and apply it to two casework examples. Our framework extends previous work, including new approaches to modelling homozygote drop-out and uncertainty in allele calls for stutter, masking and near-threshold peaks. We show that some current approaches to interpretation, such as ignoring a discrepant locus or reporting a “Random Man Not Excluded” (RMNE) probability, can be systematically unfair to defendants, sometimes extremely so. We also show that the LR can depend strongly on the assumed value for the drop-out probability, and there is typically no approximation that is useful for all values. We illustrate that ignoring the possibility of drop-in is usually unfair to defendants, and argue that under circumstances in which the prosecution relies on drop-out, it may be unsatisfactory to ignore any possibility of drop-in.     

Investigating a common approach to DNA profile interpretation using probabilistic software

Recently there has been a drive for standardisation of DNA profile interpretation within and between different forensic laboratories. The continuous interpretation software STRmix⢢ has been adopted for use by laboratories in Australia and New Zealand for profile interpretation. Within this paper we examine the concordance in profile interpretation of three crime samples by twenty different analysts across twelve different international laboratories using STRmix⢢. The three profiles selected for this study exhibited a range of template and complexity. The use of probabilistic software has compelled a level of concordance between different analysts however there remain differences within profile interpretation, particularly with the objective assignment of the number of contributors to profiles.     

Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles

The interpretation of DNA evidence can entail analysis of challenging STR typing results. Genotypes inferred from low quality or quantity specimens, or mixed DNA samples originating from multiple contributors, can result in weak or inconclusive match probabilities when a binary interpretation method and necessary thresholds (such as a stochastic threshold) are employed. Probabilistic genotyping approaches, such as fully continuous methods that incorporate empirically determined biological parameter models, enable usage of more of the profile information and reduce subjectivity in interpretation. As a result, software-based probabilistic analyses tend to produce more consistent and more informative results regarding potential contributors to DNA evidence. Studies to assess and internally validate the probabilistic genotyping software STRmix™ for casework usage at the Federal Bureau of Investigation Laboratory were conducted using lab-specific parameters and more than 300 single-source and mixed contributor profiles. Simulated forensic specimens, including constructed mixtures that included DNA from two to five donors across a broad range of template amounts and contributor proportions, were used to examine the sensitivity and specificity of the system via more than 60,000 tests comparing hundreds of known contributors and non-contributors to the specimens. Conditioned analyses, concurrent interpretation of amplification replicates, and application of an incorrect contributor number were also performed to further investigate software performance and probe the limitations of the system. In addition, the results from manual and probabilistic interpretation of both prepared and evidentiary mixtures were compared.The findings support that STRmix™ is sufficiently robust for implementation in forensic laboratories, offering numerous advantages over historical methods of DNA profile analysis and greater statistical power for the estimation of evidentiary weight, and can be used reliably in human identification testing. With few exceptions, likelihood ratio results reflected intuitively correct estimates of the weight of the genotype possibilities and known contributor genotypes. This comprehensive evaluation provides a model in accordance with SWGDAM recommendations for internal validation of a probabilistic genotyping system for DNA evidence interpretation     

Allgemeine Empfehlungen der Spurenkommission zur statistischen Bewertung von DNA-Datenbank-Treffern

Speculative searches in a National DNA database using DNA profiles from unsolved crime cases are a powerful tool to identify individuals who cannot be excluded from being contributors of these DNA profiles, and thus may be considered suspects in these cases. When a crime scene profile is matching a person’s profile following a database search, a statistical evaluation of the weight of evidence of this database match is often requested by the investigating authorities. The German Stain Commission has developed recommendations how to adequately take into account the probability of an adventitious match on the background of the database size. Following these recommendations, the relevant match probability can be derived from the frequency of the DNA profile corrected by the actual number of persons in the database. Based on theoretical considerations and using simple examples, a statistical concept is described that allows to calculate either a match probability or a likelihood ratio without overestimating the weight of evidence following a database search.     

Testing likelihood ratios produced from complex DNA profiles

The performance of any model used to analyse DNA profile evidence should be tested using simulation, large scale validation studies based on ground-truth cases, or alignment with trends predicted by theory. We investigate a number of diagnostics to assess the performance of the model using H_d true tests. Of particular focus in this work is the proportion of comparisons to non-contributors that yield a likelihood ratio (LR) higher than or equal to the likelihood ratio of a known contributor (LR_POI), designated as p, and the average LR for H_d true tests. Theory predicts that p should always be less than or equal to 1/LR_POI and hence the observation of this in any particular case is of limited use. A better diagnostic is the average LR for H_d true which should be near to 1. We test the performance of a continuous interpretation model on nine DNA profiles of varying quality and complexity and verify the theoretical expectations.     

Familial searching on DNA mixtures with dropout

Familial searching, the act of searching a database for a relative of an unknown individual whose DNA profile has been obtained, is usually restricted to cases where the DNA profile of that person has been unambiguously determined. Therefore, it is normally applied only with a good quality single source profile as starting point. In this article we investigate the performance of the method if applied to mixtures with and without allelic dropout, when likelihood ratios are computed with a semi-continuous (binary) model. We show that mixtures with dropout do not necessarily perform worse than mixtures without, especially if some separation between the donors is possible due to their different dropout probabilities. The familial searching true and false positive rates of mixed profiles on 15 loci are in some cases better than those of single source profiles on 10 loci. Thus, the information loss due to the fact that the person of interest's DNA has been mixed with that of other, and is affected by dropout, can be less than the loss of information corresponding to having 5 fewer loci available for a single source trace. Profiles typed on 10 autosomal loci are often involved in familial searching casework since many databases, including the Dutch one, in part consist of such profiles. Therefore, from this point of view, there seems to be no objection to extend familial searching to mixed or degraded profiles.     

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to ‘explain’ an observed set of alleles. The other procedure is probabilistic using Bayes’ theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.     

Complex DNA mixture analysis in a forensic context: Evaluating the probative value using a likelihood ratio model

The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors.The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a ‘gold standard’ LR, to the LR that would be obtained in casework. The ‘gold standard’ LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The ‘casework LR’ was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim.We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework.     

Optimal strategies for familial searching

Familial searching is the process of finding potential relatives of the donor of a crime scene profile in a DNA database. Several authors have proposed strategies for generating candidate lists of potential relatives. This paper reviews four strategies and investigates theoretical properties as well as empirical behavior, using a comprehensive simulation study on mock databases. The effectiveness of a familial search is shown to highly depend on the case profile as well as on the tuning parameters. We give recommendations for proceeding in an optimal way and on how to choose tuning parameters both in general and on a case-by-case basis. Additionally we treat searching heterogeneous databases (not all profiles comprise the same loci) and composite searching for multiple types of kinship. An R-package for reproducing results in a particular case is released to help decision-making in familial searching.     

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.     

DNA accumulation and transfer within an operational forensic exhibit storeroom

The increased sensitivity of current DNA profiling technologies allows the detection of trace amounts of DNA. With these advancements, there is an increased probability of detecting trace levels of DNA from contamination. Studies which investigate the accumulation and transfer of DNA within forensic laboratories provide insight into the possible mechanisms which may result in the contamination of exhibits. To gain a greater understanding of the level of DNA transfer between exhibit packaging and forensic workspaces, the accumulation of DNA within an operational forensic exhibit storeroom was investigated. Samples were collected from previously cleaned forensic exhibit storeroom shelves at various time points over a 14-week period. To determine the source of accumulating DNA, profiles generated from shelf samples were compared to the laboratory staff elimination database and the profiles generated from exhibits stored on each of the shelves sampled over the course of the study. Additionally, all samples were compared using STRmix™ mixture-to-mixture profile analysis, to identify the presence of common non-staff DNA donors and DNA from exhibits stored on the shelves sampled. As sampling time intervals increased, there was a significant increase in DNA quantity (ng) and number of profile contributors. The shelf height was also observed to influence the number of profile contributors, with higher numbers of contributors being found on lower shelves. DNA profiles generated from the shelf samples were matched to DNA from forensic staff members who enter the storeroom and police employees, who do not enter the storeroom. There were three instances where a common DNA profile contributor was identified between a shelf sample and the profile generated from an exhibit.This study provides insight into whether current exhibit storage procedures are still adequate given the highly sensitive DNA profiling systems currently used.     

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.     

Shedding light on the relative DNA contribution of two persons handling the same object

Traces collected on crime scene objects frequently result in challenging DNA mixtures from several contributors in different DNA proportions. Understanding how the relative proportion of DNA deposited by different persons who handled the same object evolves through time has important bearings. For instance, this information may help determine whether the major contributor in a mixed DNA profile is more likely to correspond to the object owner or to the person who may have stolen this object. In this perspective, a simulation-based protocol was designed where randomly paired participants were asked to act either as first (object owner) or second (last) users. The first user was asked to handle/wear 9 different plastic-, metal-, nitrile- and fabric-made objects, commonly found at burglary/robbery crime scenes, for a minimum of 20 min during 8 or 10 consecutive days. The second user subsequently used them for 5, 30 or 120 min in three distinct simulation sessions. The analysis of the relative DNA contribution on the resulting 234 mock DNA traces revealed a large variability in the contribution depending on the time, substrate and pairs of participants. Despite this, a progressive increase of the second user’s DNA contribution, relative to the first user, was observed over time in 93% of the traces. The second user was shown to become the major contributor in approximately 15%, 33% and 55% of the traces recovered from objects used for 5, 30 and 120 min, respectively. Single-source DNA profiles were shown to represent only 1% of the traces. In addition, the DNA profiles of 165 out of 234 (71%) simulated traces displayed extra alleles. Most of these occurred in the minor fraction of mixed DNA profiles and were interpreted as artefacts. Nevertheless, DNA profiles of known participants either involved or not in the simulations were observed in 9 cases (4%). This confirms that indirect DNA transfer should be taken into account when interpreting “touch” DNA evidence.     

The efficacy of DNA mixture to mixture matching

Standard practice in forensic science is to compare a person of interest’s (POI) reference DNA profile with an evidence DNA profile and calculate a likelihood ratio that considers propositions including and excluding the POI as a DNA donor. A method has recently been published that provides the ability to compare two evidence profiles (of any number of contributors and of any level of resolution) comparing propositions that consider the profiles either have a common contributor, or do not have any common contributors. Using this method, forensic analysts can provide intelligence to law enforcement by linking crime scenes when no suspects may be available. The method could also be used as a quality assurance measure to identify potential sample to sample contamination. In this work we analyse a number of constructed mixtures, ranging from two to five contributors, and with known numbers of common contributors, in order to investigate the performance of using likelihood ratios for mixture to mixture comparisons. Our findings demonstrate the ability to identify common donors in DNA mixtures with the power of discrimination depending largely on the least informative mixture of the pair being considered. The ability to match mixtures to mixtures may provide intelligence information to investigators by identifying possible links between cases which otherwise may not have been considered connected.     

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.     

Identifying the most likely contributors to a Y-STR mixture using the discrete Laplace method

In some crime cases, the male part of the DNA in a stain can only be analysed using Y chromosomal markers, e.g. Y-STRs. This may be the case in e.g. rape cases, where the male components can only be detected as Y-STR profiles, because the fraction of male DNA is much smaller than that of female DNA, which can mask the male results when autosomal STRs are investigated. Sometimes, mixtures of Y-STRs are observed, e.g. in rape cases with multiple offenders. In such cases, Y-STR mixture analysis is required, e.g. by mixture deconvolution, to deduce the most likely DNA profiles from the contributors.We demonstrate how the discrete Laplace method can be used to separate a two person Y-STR mixture, where the Y-STR profiles of the true contributors are not present in the reference dataset, which is often the case for Y-STR profiles in real case work. We also briefly discuss how to calculate the weight of the evidence using the likelihood ratio principle when a suspect's Y-STR profile fits into a two person mixture. We used three datasets with between 7 and 21 Y-STR loci: Denmark (n = 181), Somalia (n = 201) and Germany (n = 3443). The Danish dataset with 21 loci was truncated to 15 and 10 loci to examine the effect of the number of loci. For each of these datasets, an out of sample simulation study was performed: A total of 550 mixtures were composed by randomly sampling two haplotypes, h₁ and h₂, from the dataset.We then used the discrete Laplace method on the remaining data (excluding h₁ and h₂) to rank the contributor pairs by the product of the contributors’ estimated haplotype frequencies. Successful separation of mixtures (defined by the observation that the true contributor pair was among the 10 most likely contributor pairs) was found in 42–52% of the cases for 21 loci, 69–75% for 15 loci and 92–99% for 10 loci or less depending on the dataset and how the discrete Laplace model was chosen. Y-STR mixtures with many loci are difficult to separate, but even haplotypes with 21 Y-STR loci can be separated.     

A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles

The investigation of the performance of models to interpret complex DNA profiles is best undertaken using real DNA profiles. Here we used a data set to reflect the variety typically encountered in real casework. The “crime-stains” were constructed from known individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted from blood, 2–3 person mixtures, degradation/no-degradation, differences in allele sharing, dropout/no dropout, etc. Two siblings were also included in the test-set in order to challenge the systems. Two kinds of analyses were performed, namely tests on whether a person of interest is a contributor based on weight-of-evidence (likelihood ratio) calculations, and deconvolution test to estimate the profile of unknown constituent parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool. When we classified persons of interests into being true contributors or non-contributors, we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels compared to LRmix. In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor. Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool.     

Separation of SNP profiles from DNA mixtures with two contributors via massively parallel sequencing technology

ABSTRACT

Massively parallel sequencing technology offers the opportunity to analyse forensically challenging samples, such as degraded samples and mixtures. In the current study, we developed a perl-based pipeline to separate the DNA mixture into its components and to predict the most probable single nucleotide polymorphism (SNP) genotypes of each contributor to the mixed profile. We examined the usefulness of this method by detecting both artificially constructed DNA mixtures and mixtures from crime cases using the Precision ID Identity Panel on the Ion PGM platform. The separated genotypes of mixtures were validated by genotypes of each of the donors detected independently. The results indicate that the method performed well in identifications of both the artificially constructed mixtures and case-type mixtures, even when the two contributors are immediate relatives (mother and son), which demonstrated the practical usefulness of this method in forensic casework. Our research presents an efficient and different strategy for identification and paternity testing of DNA mixtures in forensic genetics.