We have released a software named EuroForMix to analyze STR DNA profiles in a user-friendly graphical user interface. The software implements a model to explain the allelic peak height on a continuous scale in order to carry out weight-of-evidence calculations for profiles which could be from a mixture of contributors. Through a properly parameterized model we are able to do inference on mixture proportions, the peak height properties, stutter proportion and degradation. In addition, EuroForMix includes models for allele drop-out, allele drop-in and sub-population structure. EuroForMix supports two inference approaches for likelihood ratio calculations. The first approach uses maximum likelihood estimation of the unknown parameters. The second approach is Bayesian based which requires prior distributions to be specified for the parameters involved. The user may specify any number of known and unknown contributors in the model, however we find that there is a practical computing time limit which restricts the model to a maximum of four unknown contributors. EuroForMix is the first freely open source, continuous model (accommodating peak height, stutter, drop-in, drop-out, population substructure and degradation), to be reported in the literature. It therefore serves an important purpose to act as an unrestricted platform to compare different solutions that are available. The implementation of the continuous model used in the software showed close to identical results to the R-package DNAmixtures, which requires a HUGIN Expert license to be used. An additional feature in EuroForMix is the ability for the user to adapt the Bayesian inference framework by incorporating their own prior information.

No abstract

The investigation of the performance of models to interpret complex DNA profiles is best undertaken using real DNA profiles. Here we used a data set to reflect the variety typically encountered in real casework. The "crime-stains" were constructed from known individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted from blood, 2-3 person mixtures, degradation/no-degradation, differences in allele sharing, dropout/no dropout, etc. Two siblings were also included in the test-set in order to challenge the systems. Two kinds of analyses were performed, namely tests on whether a person of interest is a contributor based on weight-of-evidence (likelihood ratio) calculations, and deconvolution test to estimate the profile of unknown constituent parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool. When we classified persons of interests into being true contributors or non-contributors, we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels compared to LRmix. In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor. Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool.

Bayesian logistic regression is used to model the probability of DNA recovery following direct and secondary transfer and persistence over a 24 h period between deposition and sample collection. Sub-source level likelihood ratios provided the raw data for activity-level analysis. Probabilities of secondary transfer are typically low, and there are challenges with small data-sets with low numbers of positive observations. However, the persistence of DNA over time can be modelled by a single logistic regression for both direct and secondary transfer, except that the time since deposition must be compensated by an offset value for the latter. This simplifies the analysis. Probabilities are used to inform an activity-level Bayesian Network that takes account of alternative propositions e.g. time of assault and time of social activities. The model is extended in order to take account of multiple contacts between person of interest and 'victim'. Variables taken into account include probabilities of direct and secondary transfer, along with background DNA from unknown individuals. The logistic regression analysis is Bayesian -for each analysis, 4000 separate simulations were carried out. Quantile assignments enable calculation of a plausible range of probabilities and sensitivity analysis is used to describe the corresponding variation of s that occur when modelled by the Bayesian network. It is noted that there is need for consistent experimental design, and analysis, to facilitate inter-laboratory comparisons. Appropriate recommendations are made. The open-source program written in R-code ALTRaP (Activity Level, Transfer, Recovery and Persistence) enables analysis of complex multiple transfer propositions that are commonplace in cases-work e.g. between those who cohabit. A number of case examples are provided. ALTRaP can be used to replicate the results and can easily be modified to incorporate different sets of data and variables.

No abstract

Likelihood ratios (LR) differences between the probabilistic genotyping software EuroForMix and STRmix™ are examined. After considering differences in the allele probabilities, the LRs from both software for an unambiguous single-source profile were identical (four significant figures). LRs from both software for an unambiguous single-source profile with alleles previously unseen in the allele frequency database (rare alleles) were the same (three significant figures) for θ = 0.01. Due to differences in the minimum allele frequencies, the LRs differed by three orders of magnitude when θ = 0. For both software, the LRs for a single-source dilution series decreased as the input amount decreased. The LRs from both software were within an order of magnitude for known contributors. The largest difference was where the target input amount was 0.0156 ng: The LR EuroForMix was 2.1 × 10 25 and the LR STRmix was 8.0 × 10 24 . Both software show similar LR behavior with respect to mixture ratio. For two person mixtures the LR increases for both the major and the minor as the ratio moves away from 1:1. The LR for the major stabilizes at about 3:1 whereas the LR for the minor reaches its maximum at about 3:1 and then declines. Greater differences in LR were observed between EuroForMix and STRmix™ for mixtures. One-hundred and twenty-nine mixtures from the PROVEDIt dataset were compared. LRs for 84% of the comparisons for known contributors without rare alleles were within two orders of magnitude. Five divergent results were investigated, and a manual intervention approach was applied where appropriate.

No abstract

No abstract