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.     

The design of "the first experimental study exploring DNA interpretation"

This letter explains why a recent study purporting to show that exposure to domain-irrelevant information caused DNA analysts to misinterpret a complex mixture does not warrant this conclusion on the basis of the data from the study.     

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.     

Subjectivity and bias in forensic DNA mixture interpretation

The objectivity of forensic science decision making has received increased attention and scrutiny. However, there are only a few published studies experimentally addressing the potential for contextual bias. Because of the esteem of DNA evidence, it is important to study and assess the impact of subjectivity and bias on DNA mixture interpretation. The study reported here presents empirical data suggesting that DNA mixture interpretation is subjective. When 17 North American expert DNA examiners were asked for their interpretation of data from an adjudicated criminal case in that jurisdiction, they produced inconsistent interpretations. Furthermore, the majority of 'context free' experts disagreed with the laboratory's pre-trial conclusions, suggesting that the extraneous context of the criminal case may have influenced the interpretation of the DNA evidence, thereby showing a biasing effect of contextual information in DNA mixture interpretation.     

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.     

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.     

A validation study for the extraction and analysis of DNA from human nail material and its application to forensic casework.

A validation study was conducted to demonstrate that deoxyribonucleic acid (DNA) could be successfully extracted from human nail material and analyzed using short tandem repeat (STR) profiling and/or mitochondrial DNA (mtDNA) sequencing. This study involved the development of a DNA extraction protocol that includes a cleaning procedure designed to remove external contaminants (e.g., biological, chemical). This protocol was used to test human nail material that had been soaked in whole blood from a second donor and coated with gold-palladium to simulate scanning electron microscopic analysis. The results showed no indication of a mixture and were consistent with that of the nail donor. Fresh human nail material usually yielded both STR profiles and mtDNA sequence information; however, aged human nail material (approximately eight years old) yielded only mtDNA sequence information. Upon completion of the validation study, the extraction protocol was used for the analysis of a torn fingernail fragment recovered from the scene of a violent homicide in 1983. A partial STR profile and mtDNA sequence information indicated that the fingernail fragment was excluded as originating from the suspect and was, in fact, consistent with originating from one of the victims.     

Applications and extensions of subpopulation theory: a caseworkers guide

This paper extends the calculation of conditional probabilities from those given by Balding and Nichols to casework situations where a series of possible DNA types are possible. Such situations may occur when a sample is identified containing a mixture of DNA from two or more people or where extra information can be determined about the subpopulation under consideration by analysis of additional samples. Using this approach, the error in the estimated likelihood ratios is expected to reduce as the number of additional individuals typed from the subpopulation increases.     

Unusual results due to transfused blood.

During a murder enquiry, the forensic science investigation used PGM and EAP blood grouping systems and detected a mixture of blood on the deceased's jacket. The blood groups matched those of the deceased and accused. The results of DNA analysis, however, proved that only a single source of DNA, matching the deceased, was present. Supplementary information relating to the transfusion of the individual whilst still wearing his clothing led the authors to conclude that a mixture of transfused blood and the individual's own blood had effused from his body via a stab wound, and onto his clothing.     

Linear mixture analysis: a mathematical approach to resolving mixed DNA samples.

With the advent of PCR-based STR typing systems, mixed samples can be separated into their individual DNA profiles. Quantitative peak information can help in this analysis. However, despite such advances, forensic mixture analysis still remains a laborious art, with the high cost and effort often precluding timely reporting. We introduce here a new automated approach to resolving forensic DNA mixtures. Our linear mixture analysis (LMA) is a straightforward mathematical approach that can integrate all the quantitative PCR data into a single rapid computation. LMA has application to diverse mixture problems. As demonstrated here on laboratory STR data, LMA can assess the quality and utility of its solutions. Such rapid and robust methods for computer-based analysis of DNA mixtures may help in reducing crime.     

Cognitive forensics and experimental research about bias in forensic casework

Understanding and coping with cognitive bias in forensic science requires multiple studies, utilizing both laboratory-based experiments and data from casework. Neither type of studies has ever been conducted to examine bias in mixture DNA interpretations. A study that includes both types of data has recently been published in Science and Justice. The data and statistical analysis clearly — at the very least — suggest that bias may potentially influence DNA mixture interpretation. This is due, in part, to the subjective elements in interpretation of mixture DNA. The issue of bias and other cognitive influences is of a sensitive nature and presents complex experimental challenges. Our study takes a step in examining these issues and calls for more research.     

Identification and separation of DNA mixtures using peak area information

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture. It can also be used to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed.     

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.     

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.     

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.     

A preliminary assessment of the effect of PreCR™ DNA repair treatment on mixture ratios in two person mixtures

In this study, DNA extracted from known buccal samples was combined into two component mixture samples. These were subjected to UV exposure prior to their amplification with the Promega PowerPlex® 16HS amplification kit, and subsequent capillary electrophoresis on the ABI 3130xl instrument. Damaged samples were subjected to enzymatic repair treatment and retested to assess the amount of repair. Data showed that there is fidelity associated with the application with profile concordance after its use, and a corresponding increase in the amount of recovered alleles post damage. Results also showed changes in the stochastic relationship between mixture components that appear to be induced by the repair process itself. The mixture ratios of DNA samples were altered from an approximate original 1:3 ratio, to a ratio of 1:2 or greater. This variation can have a significant effect regarding the ability to reliably de-convolute DNA mixtures that have been subjected to the repair process.     

二组分混合DNA样品STR图谱解释

对混合样品STR图谱的结果进行解释。实验模拟二组分DNA混合样品 ,复合扩增荧光检测 10个基因座 ,比较混合样品谱带 ,计算等位基因峰面积比。结果发现 :二组分DNA混合样品的等位基因数增加 ,样品的混合比例不同就出现峰面积的不平衡。在等位基因峰面积比值与样品组分混合比例接近时 ,可由峰面积比值推断混合样品的混合比例。在混合比例为 1∶2 0时 ,基本上检测不到来自少量混合成分的等位基因 ,表现为单一组分图谱 ;在混合比例为 1∶10时 ,含量低的组分的等位基因峰面积接近与主要组分的“Stutter”峰面积 ,与来自主要组分的等位基因峰面积差异很明显。能检出混合样品中少量成分等位基因的最高混合比例为 1∶10     

Calculating likelihood ratios for a mixed DNA profile when a contribution from a genetic relative of a suspect is proposed

This technical note describes a practical method for evaluating evidence in the case of a two person conditioned DNA mixture where the defence proposition is that the unknown contributor is genetically related to the suspect. A conditioned mixture is one where the presence of DNA from one of two individuals is accepted by both prosecution and defence. A typical example would be a vaginal swab in an alleged rape case, where the presence of the complainant's DNA would be expected and samples have been taken from the complainant and a suspect. Much has been written about the interpretation of such mixtures and the calculation of the conditional genotype probabilities that must be carried out. In general, such treatments assume that the unknown contributor, under the defence proposition, is unrelated to the known individuals. In this paper, we consider the case where the defence proposition is that the unknown contributor is genetically related to the suspect. We describe a method, incorporating a flow chart and reference tables that facilitate manual calculations of the likelihood ratio for several postulated genetic relationships.