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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Until recently, forensic DNA profile interpretation was predominantly a manual, time-consuming process undertaken by analysts using heuristics to determine those genotype combinations that could reasonably explain a recovered profile. Probabilistic genotyping (PG) has now become commonplace in the interpretation of DNA profiling evidence. As the complexity of PG necessitates the use of algorithms and modern computing power it has been dubbed by some critics as a "black box" approach. Here we discuss the wealth of information that is provided within the output of STRmix™, one example of a continuous PG system. We discuss how this information can be evaluated by analysts either to give confidence in the results or to indicate that further interpretation may be warranted. Specifically, we discuss the "primary" and "secondary" diagnostics output by STRmix™ and give some context to the values that may be observed.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  We investigate a method to identify a common contributor in two mixed DNA profiles  The discrimination power is limited by the smallest DNA contribution to the profiles  We show good ability to find pairs of profiles with a common contributor  This tool gives the ability to provide intelligence information

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