Hinda Haned

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.

Combining results of forensic STR kits: HDplex validation including allelic association and linkage testing with NGM and Identifiler loci

The autosomal short tandem repeat (STR) kits that are currently used in forensic science have a high discrimination power. However, this discrimination power is sometimes not sufficient for complex kinship analyses or decreases when alleles are missing due to degradation of the DNA. The Investigator HDplex kit contains nine STRs that are additional to the commonly used forensic markers, and we validated this kit to assist human identification. With the increasing number of markers it becomes inevitable that forensic and kinship analyses include two or more STRs present on the same chromosome. To examine whether such markers can be regarded as independent, we evaluated the 30 STRs present in NGM, Identifiler and HDplex. Among these 30 markers, 17 syntenic STR pairs can be formed. Allelic association between these pairs was examined using 335 Dutch reference samples and no linkage disequilibrium was detected, which makes it possible to use the product rule for profile probability calculations in unrelated individuals. Linkage between syntenic STRs was studied by determining the recombination fraction between them in five three-generation CEPH families. The recombination fractions were compared to the physical and genetic distances between the markers. For most types of pedigrees, the kinship analyses can be performed using the product rule, and for those cases that require an alternative calculation method (Gill et al., Forensic Sci Int Genet 6:477–486, 2011), the recombination fractions as determined in this study can be used. Finally, we calculated the (combined) match probabilities, for the supplementary genotyping results of HDplex, NGM and Identifiler.

Consensus and pool profiles to assist in the analysis and interpretation of complex low template DNA mixtures

Forensic analysis of low template (LT) DNA mixtures is particularly complicated when (1) LT components concur with high template components, (2) more than three contributors are present, or (3) contributors are related. In this study, we generated a set of such complex LT mixtures and examined two methods to assist in DNA profile analysis and interpretation: the “n/2” consensus method (Benschop et al. 2011) and the pool profile approach. N/2 consensus profiles include alleles that are reproducibly amplified in at least half of the replications. Pool profiles are generated by injecting a blend of independently amplified PCR products on a capillary electrophoresis instrument. Both approaches resulted in a similar increase in the percentage of detected alleles compared to individual profiles, and both rarely included drop-in alleles in case mixtures of pristine DNAs were used. Interestingly, the consensus and the pool profiles often showed differences for the actual alleles detected for the LT component(s). We estimated the number of contributors using different methods. Better approximations were obtained with data in the consensus and pool profiles compared to the data of the individual profiles. Consensus profiles contain allele calls only, while pool profiles consist of both allele calls and peak height information, which can be of use in (statistical) profile analysis. All advantages and limitations of the various types of profiles were assessed, and based on the results we infer that both consensus and pool profiles (or a combination thereof) are helpful in the interpretation of complex LT DNA mixtures.

Exact computation of the distribution of likelihood ratios with forensic applications.

If complex DNA profiles, conditioned on multiple individuals are evaluated, it may be difficult to assess the strength of the evidence based on the likelihood ratio. A likelihood ratio does not give information about the relative weights that are provided by separate contributors. Alternatively, the observed likelihood ratio can be evaluated with respect to the distribution of the likelihood ratio under the defense hypothesis. We present an efficient algorithm to compute an exact distribution of likelihood ratios that can be applied to any LR-based model. The distribution may have several applications, but is used here to compute a p-value that corresponds to the observed likelihood ratio. The p-value is the probability that a profile under the defense hypothesis, substituted for a questioned contributor e.g. suspect, would attain a likelihood ratio which is at least the same magnitude as that observed. The p-value can be thought of as a scaled version of the likelihood ratio, giving a quantitative measure of the strength of the evidence relative to the specified hypotheses and the model used for the analysis. The algorithm is demonstrated on examples based on real data. R code for the algorithm is freely available in the R package euroMix.

Assessment of mock cases involving complex low template DNA mixtures: A descriptive study.

Complex DNA mixtures with low template (LT) components provide the most challenging cases to interpret and report. In this study, we designed such mixtures and we describe how reporting officers (ROs) at the Netherlands Forensic Institute (NFI) assess these when embedded in a mock case setting. DNA mixtures containing LT DNA from two to four contributors, sporadic contamination (mimicked by adding 6pg of DNA, which represents once cell equivalent) and/or DNA of relatives (brothers), were amplified four-fold using the AmpFlSTR(®) NGM™ PCR Amplification Kit. Consensus profiles were then generated which included the alleles detected in at least half of the replicates. Four mock cases were created by including reference profiles of a hypothetical victim and suspect. The mock cases were assessed by eight ROs following the stepwise interpretation approach currently in use at the NFI. With this approach, the results of the comparisons between the DNA profiles of the evidentiary trace and the reference profiles are classified into four categories of evidential value [1]. The interpretations by the ROs were compared to the likelihood ratios (LRs) obtained from a probabilistic model that allows a calculation of LRs to assist the interpretation of LT DNA evidence and both were compared to the true composition of the designed mixtures.

The effect of varying the number of contributors on likelihood ratios for complex DNA mixtures

Interpretation of DNA mixtures with three or more contributors, defined here as high order mixtures, is difficult because of the inevitability of allele sharing. Allele sharing complicates the estimation of the number of contributors, which is an important parameter to assess the probative value. Consequently, these mixtures may not be deemed suitable for interpretation and reporting. In this study, we generated three-, four- and five-person mixtures with little or no drop-out and with varying levels of allele sharing. For these DNA mixtures we computed likelihood ratios (LRs) using the LRmix model, and always using persons of interest that are true contributors. We assessed the influence of different scenarios on the LR, and used (1) the true or an incorrect number of contributors, (2) zero, one or two anchored individuals and (3) an equal number of contributors under Hp and Hd or an extra contributor under Hd. It was shown that the LR varied considerably when the hypotheses used an incorrect number of contributors, especially when individuals were anchored under the hypotheses. Overall, when analysing high order mixtures, there may occur a transition from LR greater than one to less than one if an incorrect number of contributors is conditioned. This is a result of allele sharing among the multiple contributors rather than allele drop-out, since this study only utilised samples with little or no drop-out.

Database extraction strategies for low-template evidence.

Often in forensic cases, the profile of at least one of the contributors to a DNA evidence sample is unknown and a database search is needed to discover possible perpetrators. In this article we consider two types of search strategies to extract suspects from a database using methods based on probability arguments. The performance of the proposed match scores is demonstrated by carrying out a study of each match score relative to the level of allele drop-out in the crime sample, simulating low-template DNA. The efficiency was measured by random man simulation and we compared the performance using the SGM Plus kit and the ESX 17 kit for the Norwegian population, demonstrating that the latter has greatly enhanced power to discover perpetrators of crime in large national DNA databases. The code for the database extraction strategies will be prepared for release in the R-package forensim.

Validation of probabilistic genotyping software for use in forensic DNA casework: Definitions and illustrations

A number of new computer programs have recently been developed to facilitate the interpretation and statistical weighting of complex DNA profiles in forensic casework. Acceptance of such software in the user community, and subsequent acceptance by the court, relies heavily upon their validation. To date, few guidelines exist that describe the appropriate and sufficient validation of such software used in forensic DNA casework. In this paper, we discuss general principles of software validation and how they could be applied to the interpretation software now being introduced into the forensic community. Importantly, we clarify the relationship between a statistical model and its implementation via software. We use the LRmix program to provide specific examples of how these principles can be implemented.