The comparison of DNA mixture profiles with multiple persons of interest

When we compare a DNA mixture profile to a single person of interest, there are often just two competing explanations considered, and the comparison of how likely these are to lead to the observed mixture is summarized by a likelihood ratio. However, in more complex cases this does not suffice, e.g., when there are multiple persons of interest. One can then compute several likelihood ratios, corresponding to several pairs of hypotheses, and subsequently decide which one(s) to report. This may lead to the computation of a rather large number of such likelihood ratios. In this article we advocate a systematic approach that starts by describing all relevant hypotheses. For each hypothesis, we then compute its likelihood (i.e., the probability to see the genetic data if the hypothesis is true). Based on the likelihoods of all considered hypotheses, one can then make a summary of the findings to report. This may be on the level of the considered hypotheses and/or with likelihood ratios per person of interest. We illustrate with several examples how this approach assists interpretation. The likelihoods summarize how the trace can help to distinguish between the considered hypotheses, in the sense that they transform the prior odds on them into posterior odds, without having to assign prior probabilities on the hypotheses for the calculation of the likelihoods themselves. On the other hand likelihood ratios (LR’s) for individual PoI’s cannot be obtained without these priors. In many cases these LR’s will be quite insensitive to the choice of prior probabilities but in other cases they will be; we give examples of both.We argue that the table of likelihoods of the considered hypotheses is a more natural analog of the LR provided in the simple case with one PoI and two considered hypotheses, compared to the computation of a LR per PoI. We end with a discussion of the choice of prior probabilities, of the existing recommendations for this situation, and on reporting.     

Variational inference accelerates accurate DNA mixture deconvolution

We investigate a class of DNA mixture deconvolution algorithms based on variational inference, and we show that this can significantly reduce computational runtimes with little or no effect on the accuracy and precision of the result. In particular, we consider Stein Variational Gradient Descent (SVGD) and Variational Inference (VI) with an evidence lower-bound objective. Both provide alternatives to the commonly used Markov-Chain Monte-Carlo methods for estimating the model posterior in Bayesian probabilistic genotyping. We demonstrate that both SVGD and VI significantly reduce computational costs over the current state of the art. Importantly, VI does so without sacrificing precision or accuracy, presenting an overall improvement over previously published methods.     

Hamiltonian Monte Carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution

Motivation: Analysing mixed DNA profiles is a common task in forensic genetics. Due to the complexity of the data, such analysis is often performed using Markov Chain Monte Carlo (MCMC)-based genotyping algorithms. These trade off precision against execution time. When default settings (including default chain lengths) are used, as large as a 10-fold changes in inferred log-likelihood ratios (LR) are observed when the software is run twice on the same case. So far, this uncertainty has been attributed to the stochasticity of MCMC algorithms. Since LRs translate directly to strength of the evidence in a criminal trial, forensic laboratories desire LR with small run-to-run variability.Results:We present the use of a Hamiltonian Monte Carlo (HMC) algorithm that reduces run-to-run variability in forensic DNA mixture deconvolution by around an order of magnitude without increased runtime. We achieve this by enforcing strict convergence criteria. We show that the choice of convergence metric strongly influences precision. We validate our method by reproducing previously published results for benchmark DNA mixtures (MIX05, MIX13, and ProvedIt). We also present a complete software implementation of our algorithm that is able to leverage GPU acceleration for the inference process. In the benchmark mixtures, on consumer-grade hardware, the runtime is less than 7 min for 3 contributors, less than 35 min for 4 contributors, and less than an hour for 5 contributors with one known contributor.     

STRmix™ collaborative exercise on DNA mixture interpretation

An intra and inter-laboratory study using the probabilistic genotyping (PG) software STRmix™ is reported. Two complex mixtures from the PROVEDIt set, analysed on an Applied Biosystems™ 3500 Series Genetic Analyzer, were selected. 174 participants responded.For Sample 1 (low template, in the order of 200 rfu for major contributors) five participants described the comparison as inconclusive with respect to the POI or excluded him. Where LRs were assigned, the point estimates ranging from 2 × 10⁴ to 8 × 10⁶. For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 10²⁸ to 2 × 10²⁹. Where LRs were calculated, the differences between participants can be attributed to (from largest to smallest impact):

• •varying number of contributors (NoC),
• •the exclusion of some loci within the interpretation,
• •differences in local CE data analysis methods leading to variation in the peaks present and their heights in the input files used,
• •and run-to-run variation due to the random sampling inherent to all MCMC-based methods.

This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.     

Potential DNA mixtures introduced through kissing

The use of saliva samples is an alternative to blood samples when a large number of control samples are to be compared by DNA investigations. The most convenient and safe method is by using cotton wool swabs. In this investigation the average DNA content of saliva samples taken by three different sampling techniques (i. e. cotton wool swab, filter paper, liquid saliva) was compared. In addition the possibility of a DNA mixture of saliva samples after intensive kissing was investigated by taking samples from voluntary pairs. Mixed STR patterns were found in five samples but restricted to the first sampling after kissing within max. 60 s. Received: 2 January 1998 / Received in revised form: 19 February 1998     

The number of alleles in DNA mixtures with related contributors

The maximum allele count (MAC) across loci and the total allele count (TAC) are often used to gauge the number of contributors to a DNA mixture. Computational strategies that predict the total number of alleles in a mixture arising from a certain number of contributors of a given population have been developed. Previous work considered the restricted case where all of the contributors to a mixture are unrelated. We relax this assumption and allow mixture contributors to be related according to a pedigree. We introduce an efficient computational strategy. This strategy based on first determining a probability distribution on the number of independent alleles per locus, and then conditioning on this distribution to compute a distribution of the number of distinct alleles per locus. The distribution of the number of independent alleles per locus is obtained by leveraging the Identical by Descent (IBD) pattern distribution which can be computed from the pedigree. We explain how allelic dropout and a subpopulation correction can be accounted for in the calculations.     

The Yara Gambirasio case: Combining evidence in a complex DNA mixture case

Here, we illustrate how statistical methods can help extract information from mixed DNA profiles pertaining to an Italian case, referred to by the media as The murder of Yara Gambirasio. We base the analysis on a model for DNA mixtures that takes fully into account the peak heights and possible artefacts, like stutter and dropout that might occur in the DNA amplification process. We show how to combine the evidence from multiple samples and from different marker systems all within the model framework. The combined evidence is used for deconvolution, where the focus is to find likely profiles for the donors to the sample. We also show how a mixture can be used to establish familial relationships between a reference profile and a donor to the mixed DNA sample. We compare results based on a single mixed DNA profile, combination of replicates, combinations of different samples and combinations of different kits. Based on the Yara case, we discuss just a few of the plethora of possibilities of combining evidential information.     

DNA reviews: the national DNA database of the United Kingdom

The national DNA database in United Kingdom has now been operational for over 10 years. This review looks at the history and development of this investigative resource. From the development of commercial DNA profiling kits to the current statistics for matches obtained in relation to criminal investigation in the United Kingdom, before moving onto discussing potential future direction that national DNA databases might take, including international collaboration on a European and global scale.     

Assessment of changes to DNA database interrogation at Forensic Science SA

ABSTRACT

The ultimate goal of database casework is to supply investigative information to authority agencies. Traditionally this has been accomplished by direct matching. Recently, Forensic Science SA (FSSA) made changes to the DNA profile upload criteria for direct matching. This was made possible in-house by the introduction of a new Laboratory Information Management System (LIMS). In conjunction with direct matching for database investigations, FSSA also routinely perform mixture searches on unresolved DNA mixtures using the expert forensic software STRmix. Data from the FSSA no suspect workflow was collected for a 10 month period from September 2017 to June 2018 to determine the impact of these two changes compared with previous direct matching processes over the same period. The data demonstrate an increase in investigative information provided to the South Australian Police (SAPOL) by FSSA.     

Expanding beyond the current core STR loci: An exploration of 73 STR markers with increased diversity for enhanced DNA mixture deconvolution

Current approaches to mixture deconvolution of complex biological samples at times do not have the capability to resolve component contributors in DNA evidence. Additional short tandem repeat (STR) loci were sought that may improve the forensic genetic analysis of mixtures. This study presents exploratory data of a multiplex comprised of 73 highly polymorphic STRs (referred herein as the 73Plex) that were selected because of their high diversity due to sequence variation. These STRs (or a subset of them) may be considered as candidates that may augment current core markers capabilities for DNA mixture deconvolution. Population genetics analyses were performed for each locus using DNA samples from 451 individuals comprising three U.S. populations. Sequence-based heterozygosities ranged from 72% to 98%, where only two loci (D10A97 and D6A7) fell below 80%. Mixture deconvolution capabilities for two-person mixtures were assessed based on complete allele resolution per locus (i.e., four alleles observed) of pairwise mixtures using in silico methods. A subset of 20 highly informative loci (referred herein as the 20Plex) from the 73Plex was compared to the 20 CODIS core loci on all population samples with full DNA profiles for both panels (i.e., no locus dropout; n = 443). Based on proportion of loci displaying four alleles, the 20Plex outperformed the CODIS core loci with increases of 82.6% and 89.3% using length-based and sequence-based alleles, respectively. A combination of 17 STR from the 20Plex and 3 CODIS loci gave the highest capacity for resolving allelic components per locus. These data illustrate the increased value of utilizing sequenced-based alleles of additional STR loci. Furthermore, there are a number of candidate STR loci that could notably augment the current core STR loci and enhance mixture interpretation capabilities.     

ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model

Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.     

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.     

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.     

Development and validation of Kongoh ver. 3.0.1: Open-source software for DNA mixture interpretation in the GlobalFiler system based on a quantitative continuous model

Probabilistic genotyping software based on continuous models is effective for interpreting DNA profiles derived from DNA mixtures and small DNA samples. In this study, we updated our previously developed Kongoh software (to ver. 3.0.1) to interpret DNA profiles typed using the GlobalFiler™ PCR Amplification Kit. Recently, highly sensitive typing systems such as the GlobalFiler system have facilitated the detection of forward, double-back, and minus 2-nt stutters; therefore, we implemented statistical models for these stutters in Kongoh. In addition, we validated the new version of Kongoh using 2–4-person mixtures and DNA profiles with degradation in the GlobalFiler system. The likelihood ratios (LRs) for true contributors and non-contributors were well separated as the information increased (i.e., larger peak height and fewer contributors), and these LRs tended to neutrality as the information decreased. These trends were observed even in profiles with DNA degradation. The LR values were highly reproducible, and the accuracy of the calculation was also confirmed. Therefore, Kongoh ver. 3.0.1 is useful for interpreting DNA mixtures and degraded DNA samples in the GlobalFiler system.     

Probabilistic genotyping software: An overview

The interpretation of mixed profiles from DNA evidentiary material is one of the more challenging duties of the forensic scientist. Traditionally, analysts have used a “binary” approach to interpretation where inferred genotypes are either included or excluded from the mixture using a stochastic threshold and other biological parameters such as heterozygote balance, mixture ratio, and stutter ratios. As the sensitivity of STR multiplexes and capillary electrophoresis instrumentation improved over the past 25 years, coupled with the change in the type of evidence being submitted for analysis (from high quality and quantity (often single-source) stains to low quality and quantity (often mixed) “touch” samples), the complexity of DNA profile interpretation has equally increased. This review provides a historical perspective on the movement from binary methods of interpretation to probabilistic methods of interpretation. We describe the two approaches to probabilistic genotyping (semi-continuous and fully continuous) and address issues such as validation and court acceptance. Areas of future needs for probabilistic software are discussed.     

Evaluation of the MHSeqTyper47 kit for forensically challenging DNA samples

Microhaplotypes have been highly regarded for forensic mixture DNA deconvolution because they do not experience interference from stutters in the same way as short tandem repeat markers, and they tend to be more polymorphic than single nucleotide polymorphism markers. However, forensic microhaplotype kits have not been reported. The MHSeqTyper47 kit genotypes 47 microhaplotype loci. In this study, MiSeq FGx sequencing metrics for MHSeqTyper47 were presented, and the genotyping accuracy of this kit was examined. The sensitivity of MHSeqTyper47 reached 62.5 pg, and full genotyping results were obtained from degraded DNA samples with degradation indexes ≤ 3.00. Full genotypes were obtained in the presence of 100 ng/μL tannin, 50 μM heme, 25 ng/μL humic acid, and 1.25 μg/μL indigo dye. In DNA mixture studies, a minimum of 31 loci of the minor contributor were correctly genotyped at 1:99 or 99:1 mixing ratios, with the cumulative random matching probability of these loci reaching 4.54 × 10⁻²⁵. Mixing ratios could be reliably predicted from two-donor DNA mixtures based on the loci with four called alleles. Taken together, these data showed that the MHSeqTyper47 kit was effective for forensically challenging DNA analysis.     

A comparison of likelihood ratios with and without assuming relatedness for DNA mixtures interpreted using a continuous model

When evaluating support for the contribution of a person of interest (POI) to a mixed DNA sample, it is generally assumed that the mixture contributors are unrelated to the POI and to each other. In practice, there may be situations where this assumption is violated, for instance if two mixture contributors are siblings. The effect on the likelihood ratio of (in)correctly assuming relatedness between mixture contributors has previously been investigated using simulation studies based on simplified models ignoring peak heights. We revisit this problem using a simulation study that applies peak height models both in the simulation and mixture interpretation part of the study. Specifically, we sample sets of mixtures comprising both related and unrelated contributors and evaluate support for the contribution of the mixture donors as well as unrelated persons with and without incorporating an assumption of relatedness. The results show, consistent with earlier studies, that including a correct assumption of relatedness increases the capacity of the probabilistic genotyping system to distinguish between mixture donors and unrelated persons. Any effect of the relatedness is found to depend strongly on the mixture ratio. We further show that the results do not change materially when a sub-population correction is applied. Finally, we suggest and discuss a likelihood ratio approach that considers relatedness between mixture contributors using a prior probability.     

EFMrep: An extension of EuroForMix for improved combination of STR DNA mixture profiles

The EuroForMix model has been extended to create a new open-source software called EFMrep which enables the combination of STR DNA mixture samples from different multiplexes. In addition to calculating combined likelihood ratios and carrying out deconvolution, the software also includes the capability to specify related unknown individuals. A graphical user interface has been implemented to ease the analysis for practitioners in real case work. The effect of combining multiple samples based on the PROVEDIt dataset was investigated, either from the same or different multiplexes. The information gain increases when more samples are combined. A head-to-head comparison against EuroForMix shows the benefit of a more general model. Guidelines are provided. A real case example was used to demonstrate how EFMrep could be used to combine multiple samples when a proposition includes kinship.     

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.     

Leading-edge forensic DNA analyses and the necessity of including crime scene investigators,police officers and technicians in a DNA elimination database

In recent years, sophisticated technology has significantly increased the sensitivity and analytical power of genetic analyses so that very little starting material may now produce viable genetic profiles. This sensitivity however, has also increased the risk of detecting unknown genetic profiles assumed to be that of the perpetrator, yet originate from extraneous sources such as from crime scene workers. These contaminants may mislead investigations, keeping criminal cases active and unresolved for long spans of time. Voluntary submission of DNA samples from crime scene workers is fairly low, therefore we have created a promotional method for our staff elimination database that has resulted in a significant increase in voluntary samples since 2011. Our database enforces privacy safeguards and allows for optional anonymity to all staff members. We also offer information sessions at various police precincts to advise crime scene workers of the importance and success of our staff elimination database. This study, a pioneer in its field, has obtained 327 voluntary submissions from crime scene workers to date, of which 46 individual profiles (14%) have been matched to 58 criminal cases. By implementing our methods and respect for individual privacy, forensic laboratories everywhere may see similar growth and success in explaining unidentified genetic profiles in stagnate criminal cases.