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).     

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.     

Evaluating DNA profiles in a case where the defence is "it was my brother".

The widespread application of DNA profiling has brought to some prominence the issue of close relatives sharing body fluid types. In a particular crime case it may be suggested that a close relative of the suspect was the person responsible. This paper explains how an assessment may be made in the context of single locus profiling and where the suggestion is that a full sibling is involved. A formula is established for the likelihood ratio which is then worked out in detail for the particular case where the suspect profile has two distinct bands. Generalization to other types of case is briefly explained and there is a short discussion of the impact of the evidence in the context of a trial. For all parties, justice will best be done if the scientist has ample notice, before the trial, of relationship issues such as this one.     

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.     

Chromosomal duplications along the Y-chromosome and their potential impact on Y-STR interpretation

Y-chromosome short tandem repeat (Y-STR) markers are being used as potential tools for distinguishing low levels of male DNA in the presence of excess female DNA as is present in many sexual assault samples. Usually single copy Y-STR loci produce a single amplicon in single source samples, and thus the observation of multiple peaks at such a locus could suggest to an analyst that a mixture of more than one male contributor is present in the tested sample. However, many regions of the Y-chromosome are duplicated or even triplicated in some individuals and this fact can thus complicate potential mixture interpretation. Reasons for the presence of duplications at multiple loci within a single sample are explored in the context of Y-STR marker location along the chromosome. True male-male mixtures commonly exhibit more than one locus-specific PCR product across multiple Y-STR loci that are not adjacent to one another on the Y-chromosome. In addition, duplicated loci typically possess alleles that differ by only a single repeat unit and possess similar peak heights.     

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.     

DNA typing of forensic material with mixed genotypes using allele-specific enzymatic amplification (polymerase chain reaction).

Biological material in forensic casework frequently contains a mixture of genotypes, with a predominance of material from the victim and only trace amounts from the person committing the crime. Physical separation of the two genotypes or preferential lysis of different cell types may sometimes be possible. However, it is often difficult to achieve complete separation due to the lysis of cells or lack of material. We have developed an enzymatic amplification system for the HLA DQA1 locus, that will allow the presence of individual alleles in a sample with mixed genotypes to be determined, independent of their initial proportion in the sample. This system permits the identification of an allele representing less than 10(-4) of the background genotype. Use of polymerase chain reaction (PCR) with general primers allows only alleles representing more than about 1% to be detected, while the allele-specific amplification represents up to a 1000-fold increase in sensitivity. This method was applied to a rape case and a combined rape and murder case; in both cases the biological evidential materials contained a mixture of alleles from the victim and the rapist. Allele-specific PCR revealed the presence of alleles identical to those of the suspect using DNA from a vaginal swab taken after a rape incident, whereas by using general primers in the PCR only trace amounts of alleles other than those of the victim were found. Similarly, allele-specific amplification of DNA from vaginal swabs from the murder case revealed the presence of alleles identical to those of the suspect, while standard PCR only indicated the presence of genetic material from the victim.     

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.     

Identification of biological samples in a case of contamination of a cytological slide preparation

Here we report a case where a discrete section of the cytological slide preparation of a female individual was obviously contaminated with pleura liquid of a female tumor patient. Analysis of the cancerous pleura liquid and the healthy cells of the slide preparation showed different DNA profiles, indicating that the material originated from two different female individuals. The DNA profile of the cell mixture revealed a heterogenous pattern whereby the alleles could be assigned to the healthy and the tumor patient. Loss of heterozygosity (LOH) was observed in four of eight short tandem repeat systems for the pleura liquid and the cell mixture. Despite the low amount of DNA on the slide preparation and the occurrence of LOH, it was possible to clarify the case and to support the assumption that a drop of cancerous pleura liquid contaminated the cytological slide.     

Inclusion Probabilities and Dropout

Abstract: Recent discussions on a forensic discussion group highlighted the prevalence of a practice in the application of inclusion probabilities when dropout is possible that is of significant concern. In such cases, there appears to be an unpublished practice of calculation of an inclusion probability only for those loci at which the profile of interest (hereafter the suspect) is fully included among the alleles present in the crime scene sample and to omit those loci at which the suspect has alleles that are not fully represented among the alleles in the mixture. The danger is that this approach may produce apparently strong evidence against a surprisingly large fraction of noncontributors. In this paper, the risk associated with the approach of ignoring loci with discordant alleles is assessed by simulation.     

“YFlag” – A Single‐base Extension Primer Based Method for Gender Determination

Assigning the gender of a DNA contributor in forensic analysis is typically achieved using the amelogenin test. Occasionally, this test produces false‐positive results due to deletions occurring on the Y chromosome. Here, a four‐marker “YFlag” method is presented to infer gender using single‐base extension primers to flag the presence (or absence) of Y‐chromosome DNA within a sample to supplement forensic STR profiling. This method offers built‐in redundancy, with a single marker being sufficient to detect the presence of male DNA. In a study using 30 male and 30 female individuals, detection of male DNA was achieved with c. 0.03 ng of male DNA. All four markers were present in male/female mixture samples despite the presence of excessive female DNA. In summary, the YFlag system offers a method that is reproducible, specific, and sensitive, making it suitable for forensic use to detect male DNA.     

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.     

relMix: An open source software for DNA mixtures with related contributors

In both criminal cases and relationship inference there is an increasing demand for analysis of DNA mixtures where relatives are involved. The goal might be to identify the contributors to a mixture where the donors may or may not be related, or to determine the relationship between individuals based on a mixture. relMix is an open source software for analysing DNA mixtures involving relatives, available as a graphical user interface in R. We explain the model behind relMix and give an overview of the new features (including improved checking of input) in the latest version.     

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.     

A Y-chromosome STR marker should be added to commercial multiplex STR kits

Abstract:  Autosomal short tandem repeat (STR) analysis has become highly relevant in the identification of victims from mass disasters and terrorist attacks. In such events, gender misidentification can be of grave consequences, yet the list reporting amelogenin amplification failure using STR multiplex kits continues to grow. Presented here are three such examples. In the first case, we present two male suspects who demonstrated amelogenin Y-deficient results using two commercial kit procedures. The presence of their Y chromosomes was proven by obtaining a Y-haplotype. The second case demonstrated a profile from a third male suspect where only the Y homolog of the XY pair was amplified. In events such as mass disasters or terrorist attacks, timely and reliable high throughput DNA typing results are essential. As the number of reported cases of amplification failure at the amelogenin gene continues to grow, we suggest that the incorporation of a better gender identification tool in commercial kits is crucial.     

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.     

Examining the judgments of pedophiles in relation to a non-sexual offense

ABSTRACT

Even though pedophilia is not synonymous with child sexual abuse, it remains a highly stigmatized phenomenon. As such, non-offending pedophilic individuals are judged as being dangerous, abnormal, amoral, and in need of punishment. It is unknown, however, whether a pedophilic individual would be judged more harshly than a heterosexual individual and a homosexual individual in relation to a nonsexual, nonviolent offense. This was the aim of the present study. A final sample of 309 participants were recruited online and allocated to one of three sexual orientation conditions. Participants read the same hypothetical crime report (breaking and entering) followed by a suspect profile that was identical across conditions except for sexual orientation. They then provided sentencing and moral character judgements of the suspect. Participants also completed a punitive attitudes scale pertaining to pedophilia. Results showed that the pedophilic individual received harsher judgments relative to the heterosexual individual, but only at higher levels of pre-existing punitive attitudes. These findings suggest that anti-pedophilia stigma can bias judgments about offenses that are not sexual. We argue that this is due to a ‘reverse halo effect’. However, we consider an alternative explanation based on the idea of interpreting information in a schema-consistent manner. Limitations and suggestions for future research are discussed.     

The defence of therapeutic privilege in Australia

Therapeutic privilege is a defence that excuses a medical practitioner or other health professional from complying with the requirements of full disclosure to a patient in circumstances where it is reasonably considered that such disclosure would be harmful to that patient's health or welfare. Although the concept originated in the United States, the defence has been applied in Australia, and was specifically endorsed as part of Australian law by the High Court in Rogers v Whitaker (1992) 175 CLR 479. However, there has been negligible application of the defence since that endorsement. This article examines the doctrine of therapeutic privilege in the present Australian medico-legal environment. After an examination of the concept and its three constituetent elements, the article canvasses the limited instances of judicial approval of the defence prior to Rogers v Whitaker. The author then analyses, by reference to reported and unreported case law, why the defence has been so narrowly interpreted since, such that it has come to occupy an almost untenable position in Australia's medical jurisprudence.     

Assessment of forensic findings when alternative explanations have different likelihoods—“Blame-the-brother”-syndrome

Assessment of forensic findings with likelihood ratios is for several cases straightforward, but there are a number of situations where contemplation of the alternative explanation to the evidence needs consideration, in particular when it comes to the reporting of the evidentiary strength. The likelihood ratio approach cannot be directly applied to cases where the proposition alternative to the forwarded one is a set of multiple propositions with different likelihoods and different prior probabilities. Here we present a general framework based on the Bayes' factor as the quantitative measure of evidentiary strength from which it can be deduced whether the direct application of a likelihood ratio is reasonable or not. The framework is applied on DNA evidence in forms of an extension to previously published work. With the help of a scale of conclusions we provide a solution to the problem of communicating to the court the evidentiary strength of a DNA match when a close relative to the suspect has a non-negligible prior probability of being the source of the DNA.     

'The story of Abraham,Isaac and Jacob" or "am I my brother's keeper?"

Presented is a case report of a violent sexual assault where the DNA profile obtained from an item of evidence was compared to a suspect's profile. The profiles did not match, but the sharing of such a large number of alleles raised the suspicion that perhaps the real perpetrator was a blood relative of the suspect. The investigators requested a sample from the suspect's brother, and a match was defined. In an era of technological breakthroughs in the field of forensic DNA analysis, the importance of the scientist's attention to the evidence presented in each case is stressed.