Euroforgen-NoE collaborative exercise on LRmix to demonstrate standardization of the interpretation of complex DNA profiles.

There has been very little work published on the variation of reporting practices of mixtures between laboratories, but it has been previously demonstrated that there is little consistency. This is because there is no current uniformity of practice, so different laboratories will operate using different rules. The interpretation of mixtures is not solely a matter of using some software to provide 'an answer'. An assessment of a case will usually begin with a consideration of the circumstances of a crime. Assumptions made about the numbers of contributors follow from an examination of the electropherogram(s)--and these may differ between the prosecution and the defence hypotheses. There may be a necessity to evaluate several sets of hypotheses for any given case if the circumstances are uncertain. Once the hypotheses are formulated, the mathematical analysis is complex and can only be accomplished by the use of specialist software. In order to obtain meaningful results, it is essential that scientists are trained, not only in the use of the software, but also in the methodology to understand the likelihood ratio concept that is used. The Euroforgen-NoE initiative has developed a training course that utilizes the LRmix program to carry out the calculations. This software encompasses the recommendations of the ISFG DNA commissions on mixture interpretation and is able to interpret samples that may come from two or more contributors and may also be partial profiles. Recently, eighteen different laboratories were trained in the methodology. Afterwards they were asked to independently analyze two different cases with partial mixture DNA evidence and to write a statement court-report. We show that by introducing a structured training programme, it is possible to demonstrate, for the first time, that a high degree of standardization, leading to uniformity of results can be achieved by participating laboratories.

A new methodological framework to interpret complex DNA profiles using likelihood ratios.

Although likelihood ratio (LR) based methods to analyse complex mixtures of two or more individuals, that exhibit the twin phenomena of drop-out and drop-in has been in the public domain for more than a decade, progress towards widespread implementation in to casework has been slow. The aim of this paper is to establish a LR-based framework using principles of the basic model recommended by the ISFG DNA commission. We use the tools in the form of open-source software (LRmix) in the Forensim package for the R software. A generalised set of guidelines has been prepared that can be used to evaluate any complex mixture. In addition, a validation framework has been proposed in order to evaluate LRs that are generated on a case-specific basis. This process is facilitated by replacing the reference profile of interest (typically the suspect's profile) with simulated random man using Monte-Carlo simulations and comparing the resulting distributions with the estimated LR. Validation is best carried out by comparison with a standard. Because LRmix is open-source we proposed that it is ideally positioned to be adopted as a standard basic model for complex DNA profile tests. This should not be confused with 'the best model' since it is clear that improvements could be made over time. Nevertheless, it is highly desirable to have a methodology in place that can show whether an improvement has been achieved should additional parameters, such as allele peak heights, are incorporated into the model. To facilitate comparative studies, we provide all of the necessary data for three test examples, presented as standard tests that can be utilised to carry out comparative studies. We envisage that the resource of standard test examples will be expanded over coming years so that a range of different case-types that are included will be used in order to improve the efficacy of models; to understand their advantages; conversely, to understand any limitations and to provide training material.

Exploratory data analysis for the interpretation of low template DNA mixtures.

The interpretation of DNA mixtures has proven to be a complex problem in forensic genetics. In particular, low template DNA samples, where alleles can be missing (allele drop-out), or where alleles unrelated to the crime-sample are amplified (allele drop-in), cannot be analysed with classical approaches such as random man not excluded or random match probability. Drop-out, drop-in, stutters and other PCR-related stochastic effects, create uncertainty about the composition of the crime-sample, making it difficult to attach a weight of evidence when (a) reference sample(s) is (are) compared to the crime-sample. In this paper, we use a probabilistic model to calculate likelihood ratios when there is uncertainty about the composition of the crime-sample. This model is essentially exploratory in the sense that it allows the exploration of LRs when two key-parameters, drop-out and drop-in are varied within their plausible ranges of variation. We build on the work of Curran et al., and improve their probabilistic model to allow more flexibility in the way the model parameters are applied. Two new main modifications are brought to their model: (i) different drop-out probabilities can be applied to different contributors, and (ii) different parameters can be used under the prosecution and the defence hypotheses. We illustrate how the LRs can be explored when the drop-out and drop-in parameters are varied, and suggest the use of Monte Carlo simulations to derive plausible ranges for the probability of drop-out. Although the model is suited for both high and low template samples, we illustrate the advantages of the exploratory approach through two DNA mixtures (involving two and at least three individuals) with low template components.

DNA commission of the International Society of Forensic Genetics: Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods.

DNA profiling of biological material from scenes of crimes is often complicated because the amount of DNA is limited and the quality of the DNA may be compromised. Furthermore, the sensitivity of STR typing kits has been continuously improved to detect low level DNA traces. This may lead to (1) partial DNA profiles and (2) detection of additional alleles. There are two key phenomena to consider: allelic or locus 'drop-out', i.e. 'missing' alleles at one or more genetic loci, while 'drop-in' may explain alleles in the DNA profile that are additional to the assumed main contributor(s). The drop-in phenomenon is restricted to 1 or 2 alleles per profile. If multiple alleles are observed at more than two loci then these are considered as alleles from an extra contributor and analysis can proceed as a mixture of two or more contributors. Here, we give recommendations on how to estimate probabilities considering drop-out, Pr(D), and drop-in, Pr(C). For reasons of clarity, we have deliberately restricted the current recommendations considering drop-out and/or drop-in at only one locus. Furthermore, we offer recommendations on how to use Pr(D) and Pr(C) with the likelihood ratio principles that are generally recommended by the International Society of Forensic Genetics (ISFG) as measure of the weight of the evidence in forensic genetics. Examples of calculations are included. An Excel spreadsheet is provided so that scientists and laboratories may explore the models and input their own data.

Estimating drop-out probabilities in forensic DNA samples: a simulation approach to evaluate different models.

Allele drop-out is a well known phenomenon that is primarily caused by the stochastic effects associated with low quantity or low quality DNA samples. Recently, new interpretation models that employ the use of logistic regression have been utilised in order to estimate the probability of drop-out. The model parameters are estimated using profiles from samples of extracted DNA diluted to low template levels in order to induce drop-out. However, we propose that this approach is over-simplistic, because several sources of variability are not taken into account in this generalised model. For example, in real-life, small (discrete) crime-stains are analysed where cells are (or were) intact. The integrity of the paired chromosomes of the diploid cell is preserved. In extracted DNA that is diluted to low template levels, we argue that the paired-chromosome integrity is lost. This directly affects the outcome of the logistic model. To date, current experimentation procedures are more akin to haploid cells and thus, different logistic models are needed for haploid and diploid cells. In order to simplify the methodology to estimate the multiple logistic regressions, we propose the use of a simulation model of the entire process associated with the analysis of STR loci, as a supplement to the purely experimental approach to support the validation of new methods. We illustrate with an evaluation of some features of the logistic model proposed by Gill et al., 2009 [12] and discuss alternative models.

The predictive value of the maximum likelihood estimator of the number of contributors to a DNA mixture.

We propose to quantify the accuracy of a likelihood-based estimator that was recently proposed for the determination of the number of contributors to a DNA mixture, when genetic data alone is considered [H. Haned, L. Pène, J.R. Lobry, A.B. Dufour, D. Pontier, Estimating the number of contributors to forensic DNA mixtures: does maximum likelihood perform better than maximum allele count? J. Forensic Sci., in press]. Using Bayes' theorem, we derive a formula for the calculation of the predictive value (PV) of the likelihood-based estimator. The PV gives the probability that a DNA stain contains the DNAs of i people given that the maximum likelihood estimator gave an estimate of i contributors for this stain. We illustrate the PV calculations for two different types of DNA evidence: traces and body fluids. The PV varied according to the number of contributors involved in the DNA stain. Setting the maximum number of possible contributors to five, the lowest predictive values were scored for five-person mixtures with a minimum value of 0.26 for traces, but values were always above 0.94 for stains comprising one, two or three contributors, for both traces and body fluids. Values remained relatively high for four-person mixtures with a minimum value of 0.69. These findings confirm that likelihood-maximization is a powerful approach for the determination of the number of contributors to forensic DNA mixtures.