We report a large compilation of the internal validations of the probabilistic genotyping software STRmix™. Thirty one laboratories contributed data resulting in 2825 mixtures comprising three to six donors and a wide range of multiplex, equipment, mixture proportions and templates. Previously reported trends in the LR were confirmed including less discriminatory LRs occurring both for donors and non-donors at low template (for the donor in question) and at high contributor number. We were unable to isolate an effect of allelic sharing. Any apparent effect appears to be largely confounded with increased contributor number.

Highlights Inter-laboratory study with 174 participants using STRmix™  CE analysis settings resulted in larger differences in LR than PG software  Differences in log(LR) due to MCMC variation were less than one order of magnitude Abstract (max 400 words)An intra and inter-laboratory study using the probabilistic genotyping (PG) software STRmix™ is reported. Two complex mixtures from the PROVEDIt set, analysed on an Applied Biosystems™ 3500 Series Genetic Analyzer, were selected. 174 participants responded.LRs were assigned, the point estimates ranging from 2 × 10 4 to 8 × 10 6 . For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 10 28 to 2 × 10 29 . Where LRs were calculated, the differences between participants can be attributed to (from largest to smallest impact): varying number of contributors (NoC), the exclusion of some loci within the interpretation,  differences in local CE data analysis methods leading to variation in the peaks present and their heights in the input files used,  and run-to-run variation due to the random sampling inherent to all MCMC-based methods.This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.

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The interpretation of DNA profiles typically starts with an assessment of the number of contributors. In the last two decades, several methods have been proposed to assist with this assessment. We describe a relatively simple method using decision trees, that is fast to run and fully transparent to a forensic analyst. We use mixtures from the publicly available PROVEDIt dataset to demonstrate the performance of the method. We show that the performance of the method crucially depends on the performance of filters for stutter and other artefacts. We compare the performance of the decision tree method with other published methods for the same dataset.