Choice of population database for forensic DNA profile analysis

When evaluating the weight of evidence (WoE) for an individual to be a contributor to a DNA sample, an allele frequency database is required. The allele frequencies are needed to inform about genotype probabilities for unknown contributors of DNA to the sample. Typically databases are available from several populations, and a common practice is to evaluate the WoE using each available database for each unknown contributor. Often the most conservative WoE (most favourable to the defence) is the one reported to the court. However the number of human populations that could be considered is essentially unlimited and the number of contributors to a sample can be large, making it impractical to perform every possible WoE calculation, particularly for complex crime scene profiles. We propose instead the use of only the database that best matches the ancestry of the queried contributor, together with a substantial F_ST adjustment. To investigate the degree of conservativeness of this approach, we performed extensive simulations of one- and two-contributor crime scene profiles, in the latter case with, and without, the profile of the second contributor available for the analysis. The genotypes were simulated using five population databases, which were also available for the analysis, and evaluations of WoE using our heuristic rule were compared with several alternative calculations using different databases. Using F_ST = 0.03, we found that our heuristic gave WoE more favourable to the defence than alternative calculations in well over 99% of the comparisons we considered; on average the difference in WoE was just under 0.2 bans (orders of magnitude) per locus. The degree of conservativeness of the heuristic rule can be adjusted through the F_ST value. We propose the use of this heuristic for DNA profile WoE calculations, due to its ease of implementation, and efficient use of the evidence while allowing a flexible degree of conservativeness.     

Application of Random Match Probability Calculations to Mixed STR Profiles

Mixed DNA profiles are being encountered more frequently as laboratories analyze increasing amounts of touch evidence. If it is determined that an individual could be a possible contributor to the mixture, it is necessary to perform a statistical analysis to allow an assignment of weight to the evidence. Currently, the combined probability of inclusion (CPI) and the likelihood ratio (LR) are the most commonly used methods to perform the statistical analysis. A third method, random match probability (RMP), is available. This article compares the advantages and disadvantages of the CPI and LR methods to the RMP method. We demonstrate that although the LR method is still considered the most powerful of the binary methods, the RMP and LR methods make similar use of the observed data such as peak height, assumed number of contributors, and known contributors where the CPI calculation tends to waste information and be less informative.     

TrueAllele® Genotype Identification on DNA Mixtures Containing up to Five Unknown Contributors

Computer methods have been developed for mathematically interpreting mixed and low‐template DNA. The genotype modeling approach computationally separates out the contributors to a mixture, with uncertainty represented through probability. Comparison of inferred genotypes calculates a likelihood ratio (LR), which measures identification information. This study statistically examined the genotype modeling performance of Cybergenetics TrueAllele^® computer system. High‐ and low‐template DNA mixtures of known randomized composition containing 2, 3, 4, and 5 contributors were tested. Sensitivity, specificity, and reproducibility were established through LR quantification in each of these eight groups. Covariance analysis found LR behavior to be relatively invariant to DNA amount or contributor number. Analysis of variance found that consistent solutions were produced, once a sufficient number of contributors were considered. This study demonstrates the reliability of TrueAllele interpretation on complex DNA mixtures of representative casework composition. The results can help predict an information outcome for a DNA mixture analysis.     

Likelihood ratios for complex mixtures with relatives

Likelihood ratios used for the analysis of complex DNA mixtures depend on a number of modeling assumptions and parameter estimates. In particular, the LR does not give information about the relative weight of the separate contributors for hypotheses conditioned on several contributors. An alternative is to evaluate the observed LR with respect to likelihood ratios expected under the defense hypothesis. Further, a p-value corresponding to the LR can be calculated. The p-value is the probability of observing a LR equally large or larger than the one observed, if the defense hypothesis is true. In this paper we investigate the distribution of likelihood ratios for mixtures with drop-in and drop-out and related contributors. Disregarding a plausible close relative of the suspect as an alternative contributor may overestimate the LR against a suspect.     

Validating TrueAllele® Interpretation of DNA Mixtures Containing up to Ten Unknown Contributors

Most DNA evidence is a mixture of two or more people. Cybergenetics TrueAllele^® system uses Bayesian computing to separate genotypes from mixture data and compare genotypes to calculate likelihood ratio (LR) match statistics. This validation study examined the reliability of TrueAllele computing on laboratory-generated DNA mixtures containing up to ten unknown contributors. Using log(LR) match information, the study measured sensitivity, specificity, and reproducibility. These reliability metrics were assessed under different conditions, including varying the number of assumed contributors, statistical sampling duration, and setting known genotypes. The main determiner of match information and variability was how much DNA a person contributed to a mixture. Observed contributor number based on data peaks gave better results than the number known from experimental design. The study found that TrueAllele is a reliable method for analyzing DNA mixtures containing up to ten unknown contributors.     

Mini-STRs: A powerful tool to identify genetic profiles in samples with small amounts of DNA

Degraded human remains and crime scene evidences with small amounts of DNA typically reveal incomplete or null genetic profiles when using standard (large) STR amplicons. The technology of mini-STRs, using reduced-size STR amplicons, can help to recover information from these samples. In our Forensic Genetic Service several genetic profiles were obtained or completed using MiniFiler kit (Applied Biosystems) increasing the success rate in sample typing. In all studied cases no inconsistencies were found between profiles obtained with MiniFiler and Identifiler, suggesting that this mini-STR kit can be used to include low copy number (LCN) evidence profiles in STR databases.     

Statistical analysis tools of mixture DNA samples: When the same software provides different results

The high complexity of the genetic analysis of crime scene samples is mainly related to the unknown number of contributors, low DNA quantity and quality, and associated stochastic effects. The difficulty and subjectivity of interpreting casework samples was the motto for the development of software to mitigate these conditions and allow the quantification of the genetic evidence. Currently, there are several tools for statistical analysis of mixture samples based on either qualitative or quantitative models. The first considers the electropherograms’ qualitative information, while the latter also considers the associated quantitative information. This work’s main goal was to evaluate the effect that parameters’ settings variation may have on the LR computation, specifically the drop-in frequency parameter. For that, a qualitative – LRmix Studio – and two quantitative software – STRmix™ and EuroForMix – were considered and an intra-software analysis was performed, using as input real casework samples. The drop-in frequency variation showed an impact, leading to differences higher than four units (log₁₀ scale) for some pairs of samples. In addition, for some cases, no comparisons were performed either because the tool computed a null LR value or displayed an error message. Thus, this work reinforces the importance of proper parameters’ modeling and estimation in forensic casework evaluation.     

Simulated approach to estimate the number and combination of known/unknown contributors in mixed DNA samples using 15 short tandem repeat loci

The calculation of likelihood ratios (LRs) for DNA mixture analysis is necessary to establish an appropriate hypothesis based on the estimated number of contributors and known contributor genotypes. In this paper, we recommend a relevant analytical method from the 15 short tandem repeat typing system (the Identifiler multiplex), which is used as a standard in Japanese forensic practice and incorporates a flowchart that facilitates hypothesis formulation. We postulate that: (1) all detected alleles need to be above the analytical threshold (e.g., 150 relative fluorescence unit (RFU)); (2) alleles of all known contributors should be detected in the mixture profile; (3) there should be no contribution from close relatives. Furthermore, we deduce that mixtures of four or more persons should not be interpreted by Identifiler as the LR values of 100,000 simulated cases have a lower expectation of exceeding our temporal LR threshold (10,000) which strongly supports the prosecution hypothesis. We validated the method using various computer-based simulations. The estimated number of contributors is most likely equal to the actual number if all alleles detected in the mixture can be assigned to those from the known contributors. By contrast, if an unknown contributor(s) needs to be designated, LRs should be calculated from both two-person and three-person contributions. We also consider some cases in which the unknown contributor(s) is genetically related to the known contributor(s).     

Using the Nondonor Distribution to Improve Communication and Inform Decision Making for Low LRs from Minor Contributors in Mixed DNA Profiles

The reporting of a likelihood ratio (LR) calculated from probabilistic genotyping software has become more popular since 2015 and has allowed for the use of more complex mixtures at court. The meaning of “inconclusive” LRs and how to communicate the significance of low LRs at court is now important. We present a method here using the distribution of LRs obtained from nondonors. The nondonor distribution is useful for examining calibration and discrimination for profiles that have produced LRs less than about 10⁴. In this paper, a range of mixed DNA profiles of varying quantity were constructed and the LR distribution considering the minor contributor for a number of nondonors was compared to the expectation given a calibrated system. It is demonstrated that conditioning genotypes should be used where reasonable given the background information to decrease the rate of nondonor LRs above 1. In all 17 cases examined, the LR for the minor donor was higher than the nondonor LRs, and in 12 of the 17 cases, the 99.9 percentile of the nondonor distribution was lower when appropriate conditioning information was used. The output of the tool is a graph that can show the position of the LR for the person of interest set against the nondonor LR distribution. This may assist communication between scientists and the court.     

Probabilistic genotyping of single cell replicates from complex DNA mixtures recovers higher contributor LRs than standard analysis

DNA mixtures are a common source of crime scene evidence and are often one of the more difficult sources of biological evidence to interpret. With the implementation of probabilistic genotyping (PG), mixture analysis has been revolutionized allowing previously unresolvable mixed profiles to be analyzed and probative genotype information from contributors to be recovered. However, due to allele overlap, artifacts, or low-level minor contributors, genotype information loss inevitably occurs. In order to reduce the potential loss of significant DNA information from donors in complex mixtures, an alternative approach is to physically separate individual cells from mixtures prior to performing DNA typing thus obtaining single source profiles from contributors. In the present work, a simplified micro-manipulation technique combined with enhanced single-cell DNA typing was used to collect one or few cells, referred to as direct single-cell subsampling (DSCS). Using this approach, single and 2-cell subsamples were collected from 2 to 6 person mixtures. Single-cell subsamples resulted in single source DNA profiles while the 2-cell subsamples returned either single source DNA profiles or new mini-mixtures that are less complex than the original mixture due to the presence of fewer contributors. PG (STRmix™) was implemented, after appropriate validation, to analyze the original bulk mixtures, single source cell subsamples, and the 2-cell mini mixture subsamples from the original 2–6-person mixtures. PG further allowed replicate analysis to be employed which, in many instances, resulted in a significant gain of genotype information such that the returned donor likelihood ratios (LRs) were comparable to that seen in their single source reference profiles (i.e., the reciprocal of their random match probabilities). In every mixture, the DSCS approach gave improved results for each donor compared to standard bulk mixture analysis. With the 5- and 6- person complex mixtures, DSCS recovered highly probative LRs (≥10²⁰) from donors that had returned non-probative LRs (<10³) by standard methods.     

Inferring the Number of Contributors to Complex DNA Mixtures Using Three Methods: Exploring the Limits of Low‐Template DNA Interpretation

In forensic DNA casework, the interpretation of an evidentiary profile may be dependent upon the assumption on the number of individuals from whom the evidence arose. Three methods of inferring the number of contributors—NOCIt, maximum likelihood estimator, and maximum allele count, were evaluated using 100 test samples consisting of one to five contributors and 0.5–0.016 ng template DNA amplified with Identifiler^® Plus and PowerPlex^® 16 HS. Results indicate that NOCIt was the most accurate method of the three, requiring 0.07 ng template DNA from any one contributor to consistently estimate the true number of contributors. Additionally, NOCIt returned repeatable results for 91% of samples analyzed in quintuplicate, while 50 single‐source standards proved sufficient to calibrate the software. The data indicate that computational methods that employ a quantitative, probabilistic approach provide improved accuracy and additional pertinent information such as the uncertainty associated with the inferred number of contributors.     

FaSTR DNA: A new expert system for forensic DNA analysis

The automation of DNA profile analysis of reference and crime samples continues to gain pace driven in part by a realisation by the criminal justice system of the positive impact DNA technology can have in aiding in the solution of crime and the apprehension of suspects. Expert systems to automate the profile analysis component of the process are beginning to be developed. In this paper, we report the validation of a new expert system FaSTR DNA, an expert system suitable for the analysis of DNA profiles from single source reference samples and from crime samples. We compare the performance of FaSTR DNA with that of other equivalent systems, GeneMapper™ ID v3.2 (Applied Biosystems, Foster City, CA) and FSS-i³ v4 (The Forensic Science Service^® DNA expert System Suite FSS-i³, Forensic Science Service, Birmingham, UK) with GeneScan^® Analysis v3.7/Genotyper^® v3.7 software (Applied Biosystems, Foster City, CA, USA) with manual review. We have shown that FaSTR DNA provides an alternative solution to automating DNA profile analysis and is appropriate for implementation into forensic laboratories. The FaSTR DNA system was demonstrated to be comparable in performance to that of GeneMapper™ ID v3.2 and superior to that of FSS-i³ v4 for the analysis of DNA profiles from crime samples.     

Recovery of single source DNA profiles from mixtures by direct single cell subsampling and simplified micromanipulation

Deconvolution of forensic DNA mixtures into their individual component DNA (geno)types is of great investigative value, though often complex and difficult. Two-person mixtures comprising a major and minor contributor are often easily interpreted although, when the DNA ratio of the two individuals is approximately equal (~1:1), deconvolution and interpretation becomes much more difficult. To address this issue, a physical separation of individual-, two- or three- cell subsamples prior to autosomal STR analysis was performed using a simplified micromanipulation technique paired with a decreased reaction volume and increased cycle number PCR. Using this method, single and multiple buccal epithelial cells were collected from a 1:1 two-person mixture (i.e. from individual 'A' and 'B') and directly amplified, omitting standard DNA extraction and purification steps. Single cell subsamples resulted in partial single-source profiles for both contributors while, in accordance with expectations of a quasi-binomial sampling schema, two- and three-cell subsamples resulted in single source informative partial profiles of individual A and individual B as well as complete consensus profiles, and equally mixed 1:1 (2-cell subsamples) and 2:1 (3-cell subsamples) admixed profiles of individual A and B. This proof-of-concept approach shows promise in permitting the DNA deconvolution of mixed samples where the individual contributors are present in similar amounts that would otherwise be difficult to interpret, resulting in an increase in evidentiary value. The subsampling approach can be readily investigated for DNA casework applications without additional investment in costly, new equipment, requiring only a stereo microscope and a tungsten needle.     

How to regulate forensic familial DNA searching in Hungary?

Forensic DNA analysis has the potential to provide useful information for criminal justice even in cases where there is no match, neither between the DNA profile generated from the crime scene and the existing DNA profiles in criminal databases, nor between the DNA collected at a crime scene and potential suspects. In contrast to traditional forensic genetic testing, forensic familial DNA searching does not provide evidence, but helps to generate investigative leads and narrow down the range of potential offenders. The aim of this study is to examine, whether there is a need for special regulation of this topic in Hungary.     

Criminal DNA databases: the European situation

In the last 5 years, a number of European countries have successfully introduced national databases holding the DNA profiles from suspected and convicted criminal offenders as well as from biological stain materials from unsolved crime cases. At present, DNA databases are fully or partially in operation in the UK, The Netherlands, Austria, Germany, Finland, Norway, Denmark, Switzerland and Sweden. Furthermore, in the other European countries, specific legislation will be enacted soon, or the introduction of such databases is being discussed to initiate a legislative process. Numerous differences exist regarding the criteria for a criminal offender to be included in the database, the storage periods and the possibility to remove database records, the possibility to keep reference samples from the offenders as long as their respective records are being held, and the role of judges in the process of entering a database record or to perform a database search. Nevertheless, harmonization has been achieved regarding the DNA information stored in national databases, and a European standard set of genetic systems has been recommended which is included either in part or completely in the DNA profiles of offenders and crime stains for all European databases. This facilitates the exchange of information from database records to allow the investigation of crime cases across national borders.     

Trace DNA success rates relating to volume crime offences

In this study, 252 trace DNA samples (from handled surfaces) from 201 burglary, robbery and drugs cases were compiled to assess success rates and to interpret the value of trace DNA evidence in volume crime investigations. The average amount of DNA recovered from the trace DNA samples collected was 1.7 ng. Full or major (12 or more alleles) profiles were recovered from 14% of samples. Samples from firearms and burglary points of entry were the least successful. Mixtures were recovered from 21% of samples, presenting a case for the collection of more elimination profiles to enable more samples to be used for database purposes. The research highlighted the difficulties in collecting data relating to the success rates of samples. Computerised automation of this process would be extremely beneficial in the assistance of policy development, method application, training, and investigative usefulness.     

Human Leukocyte Antigen alleles as an aid to STR in complex forensic DNA samples

Human biological samples with multiple contributors remain one of the most challenging aspects of DNA typing within a forensic science context. With the increasing sensitivity of commercially available kits allowing detection of low template DNA, complex mixtures are now a standard component of forensic DNA evidence. Over the years, various methods and techniques have been developed to try to resolve the issue of mixed profiles. However, forensic DNA analysis has relied on the same markers to generate DNA profiles for the past 30 years causing considerable challenges in the deconvolution of complex mixed samples. The future of resolving complicated DNA mixtures may rely on utilising markers that have been previously applied to gene typing of non-forensic relevance. With Massively Parallel Sequencing (MPS), techniques becoming more popular and accessible even epigenetic markers have become a source of interest for forensic scientists.The aim of this review is to consider the potential of alleles from the Human Leukocyte Antigen (HLA) complex as effective forensic markers. While Massively Parallel Sequencing of HLA is routinely used in clinical laboratories in fields such as transplantation, pharmacology or population studies, there have not been any studies testing its suitability for forensic casework samples.     

Automation of high volume MPS mixture interpretation using CaseSolver

Sixty samples were sequenced using the ForenSeq kit to produce complex STR DNA mixtures, represented with three separate formats capturing different degrees of sequence information. All mixtures were run through the (open-source) CaseSolver software for comparison against both 10 reference profiles. By comparing the performance of the qualitative and the quantitative models for the different allele formats we found the following preliminary results: The quantitative model performed better than the qualitative model, however the gain was only substantial when the LR was already large. The performance gain of using the longest uninterrupted stretch over using only repeat units was large, whereas the further gain of also using the whole sequence information was small.