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.     

Validating TrueAllele® DNA mixture interpretation

Abstract: DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all‐or‐none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two‐person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min = 2.32, max = 10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min = 1.00, max = 11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in‐depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele^® DNA mixture interpretation and establish a significant information improvement over human review.     

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.     

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.     

Empirical analysis of the STR profiles resulting from conceptual mixtures

Samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors can be challenging and associated uncertainties can have dramatic effects on the interpretation of testing results. Using an FBI genotypes dataset, containing complete genotype information from the 13 Combined DNA Index System (CODIS) loci for 959 individuals, all possible mixtures of three individuals were exhaustively and empirically computed. Allele sharing between pairs of individuals in the original dataset, a randomized dataset and datasets of generated cousins and siblings was evaluated as were the number of loci that were necessary to reliably deduce the number of contributors present in simulated mixtures of four or less contributors. The relatively small number of alleles detectable at most CODIS loci and the fact that some alleles are likely to be shared between individuals within a population can make the maximum number of different alleles observed at any tested loci an unreliable indicator of the maximum number of contributors to a mixed DNA sample. This analysis does not use other data available from the electropherograms (such as peak height or peak area) to estimate the number of contributors to each mixture. As a result, the study represents a worst case analysis of mixture characterization. Within this dataset, approximately 3% of three-person mixtures would be mischaracterized as two-person mixtures and more than 70% of four-person mixtures would be mischaracterized as two- or three-person mixtures using only the maximum number of alleles observed at any tested locus.     

Establishing the Limits of TrueAllele® Casework: A Validation Study

The limits of the expert system, TrueAllele^® Casework (TA), were explored using challenging mock casework profiles that included 17 single‐source and 18 two‐, 15 three‐ and 7 four‐person DNA mixtures. The sensitivity (ability to detect a minor contributor) of the TA analysis process was examined by challenging the system with mixture DNA samples that exhibited allelic and locus dropout and other stochastic effects. The specificity (ability to exclude nondonors) was rigorously tested by interrogating TA derived genotypes with 100 nondonor profiles. The accuracy with which TA estimated mixture weights of contributors to the two‐person mixtures was examined. Finally, first‐degree relatives of donors were used to assess the ability of the system to exclude close relatives. TA demonstrated great accuracy, sensitivity, and specificity. TA correctly assigned mixture weights and excluded nearly all first‐degree relatives. This study demonstrates the analysis power of the TrueAllele^® Casework system.     

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.     

DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: Combining quantitative data for greater identification information

Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells (‘binomial sampling’) from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function.As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50 ± 35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. Finally, we discuss the implications of this work for routine forensic practice.     

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.     

Estimating the Number of Contributors to Forensic DNA Mixtures: Does Maximum Likelihood Perform Better Than Maximum Allele Count?

Abstract: Determining the number of contributors to a forensic DNA mixture using maximum allele count is a common practice in many forensic laboratories. In this paper, we compare this method to a maximum likelihood estimator, previously proposed by Egeland et al., that we extend to the cases of multiallelic loci and population subdivision. We compared both methods’ efficiency for identifying mixtures of two to five individuals in the case of uncertainty about the population allele frequencies and partial profiles. The proportion of correctly resolved mixtures was >90% for both estimators for two‐ and three‐person mixtures, while likelihood maximization yielded success rates 2‐ to 15‐fold higher for four‐ and five‐person mixtures. Comparable results were obtained in the cases of uncertain allele frequencies and partial profiles. Our results support the use of the maximum likelihood estimator to report the number of contributors when dealing with complex DNA mixtures.     

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).     

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.     

Mucosal Cell Isolation and Analysis from Cellular Mixtures of Three Contributors

In forensic genetic analyses, mixtures of various biological materials are common samples. Micromanipulation, which is performed based on differences in cellular morphology, is an effective method for the isolation of cells from mixtures. In this study, mucosal cell was isolated from somatic cellular mixtures (blood and saliva) based on micromanipulation and a low volume‐PCR (LV‐PCR) platform. One hundred and twenty‐six parallel LV‐PCR processes were performed using an Identifiler^® kit, with 107 reactions yielding single‐source DNA profiles. Among them, 54 full profiles (50%) and 37 partial profiles (13–15 loci) were obtained. Based on the above method, we obtained a single‐source DNA profile from a cigarette butt contaminated by two victims’ blood in a murder case. The generated genotype was used to query a DNA database, and a perfect match was found.     

How to avoid driving DNA caseworkers crazy: CaseSolver,an expert system to investigate complex crime scenes

DNA analyses can be used for both investigative (crime scene-focused), or evaluative (suspect-focused) reporting. Investigative, DNA-led exploration of serious crimes always involves the comparison of hundreds of biological samples submitted by the authorities for analysis. Crime stain comparisons include both evidence to evidence profiles and reference to evidence profiles. When many complex DNA results (mixtures, low template LT-DNA samples) are involved in the investigation of a crime, the manual comparison of DNA profiles is very time-consuming and prone to manual errors. In addition, if the person of interest is a minor contributor, the classical approach of performing searches of national DNA databases is problematic because it is realistically restricted to clear major contributors and the occurrence of masking and drop-out means that there will not be a definitive DNA profile to perform the search with.CaseSolver is an open source expert system that automates analysis of complex cases. It does this by three sequential steps: a) simple allele comparison b) likelihood ratio (LR) based on a qualitative model (forensim) c) LR based on a quantitative model (EuroForMix). The software generates a list of potential match candidates, ranked according to the LRs, which can be exported as a report. The software can also identify contributors from small or large databases (e.g., staff database or 1 mill. individuals). In addition, an informative graphical network plot is generated that easily identifies contributors in common to multiple stains. Here we describe recent improvements made to the software in version v1.5.0, made in response to user requirements during intensive casework usage.     

Multiplex short tandem repeat amplification of low template DNA samples with the addition of proofreading enzymes

Abstract:  With <100 pg of template DNA, routine short tandem repeat (STR) analysis often fails, resulting in no or partial profiles and increased stochastic effects. To overcome this, some have investigated preamplification methods that include the addition of proofreading enzymes to the PCR cocktail. This project sought to determine whether adding proofreading polymerases directly in the STR amplification mixture would improve the reaction when little template DNA is available. Platinum Taq High Fidelity and GeneAmp High Fidelity were tested in Profiler Plus? STR reactions alone and in combination with AmpliTaq^® Gold. All reactions included the additional step of a post‐PCR purification step. With both pristine low template DNA and casework samples, the addition of these polymerases resulted in comparable or no improvement in the STR amplification signal. Further, stochastic effects and artifacts were observed equally across all enzyme conditions. Based on these studies, the addition of these proofreading enzymes to a multiplex STR amplification is not recommended for low template DNA work.     

New York State TrueAllele® Casework Validation Study

DNA evidence can pose interpretation challenges, particularly with low‐level or mixed samples. It would be desirable to make full use of the quantitative data, consider every genotype possibility, and objectively produce accurate and reproducible DNA match results. Probabilistic genotype computing is designed to achieve these goals. This validation study assessed TrueAllele^® probabilistic computer interpretation on 368 evidence items in 41 test cases and compared the results with human review of the same data. Whenever there was a human result, the computer's genotype was concordant. Further, the computer produced a match statistic on 81 mixture items (for 87 inferred matching genotypes) in the test cases, while human review reported a statistic on 25 of these items (30.9%). Using match statistics to quantify information, probabilistic genotyping was shown to be sensitive, specific, and reproducible. These results demonstrate that objective probabilistic genotyping of biological evidence can reliably preserve DNA identification information.     

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.     

Complex mixtures: A critical examination of a paper by Homer et al.

DNA evidence in criminal cases may be challenging to interpret if several individuals have contributed to a DNA-mixture. The genetic markers conventionally used for forensic applications may be insufficient to resolve cases where there is a small fraction of DNA (say less than 10%) from some contributors or where there are several (say more than 4) contributors. Recently methods have been proposed that claim to substantially improve on existing approaches [1]. The basic idea is to use high-density single nucleotide polymorphism (SNP) genotyping arrays including as many as 500,000 markers or more and explicitly exploit raw allele intensity measures. It is claimed that trace fractions of less than 0.1% can be reliably detected in mixtures with a large number of contributors. Specific forensic issues pertaining to the amount and quality of DNA are not discussed in the paper and will not be addressed here. Rather our paper critically examines the statistical methods and the validity of the conclusions drawn in Homer et al. (2008) [1].We provide a mathematical argument showing that the suggested statistical approach will give misleading results for important cases. For instance, for a two person mixture an individual contributing less than 33% is expected to be declared a non-contributor. The quoted threshold 33% applies when all relative allele frequencies are 0.5. Simulations confirmed the mathematical findings and also provide results for more complex cases. We specified several scenarios for the number of contributors, the mixing proportions and allele frequencies and simulated as many as 500,000 SNPs.A controlled, blinded experiment was performed using the Illumina GoldenGate^® 360 SNP test panel. Twenty-five mixtures were created from 2 to 5 contributors with proportions ranging from 0.01 to 0.99. The findings were consistent with the mathematical result and the simulations.We conclude that it is not possible to reliably infer the presence of minor contributors to mixtures following the approach suggested in Homer et al. (2008) [1]. The basic problem is that the method fails to account for mixing proportions.     

DNA profile evidence in complex disputed paternity cases: The analysis of 300 real cases

In disputed paternity cases where the putative father is unavailable DNA from one or more of his relatives could be used. However, interpreting results is often difficult, because of the partial information regarding the parental genotype obtained from his relatives. We analyzed results obtained in 300 real paternity cases performed through close relatives of the real father (sib, half-sibs, one grandparent and/or uncle). DNA was typed with PowerPlex (Promega) and the LR estimated with the Software BDGen. As expected the higher LR values were achieved with sibs and half-sibs (in such cases where his/her mother was available for testing). The LR values were tight related to the number of uninformative loci, which varied between 0 and 13. In 10% of the reviewed cases, 10 or more non-informative loci were observed; all of them associated LR values below 0.01. Thus, providing evidence in favor of no relatedness.