The ReAct project: Analysis of data from 23 different laboratories to characterise DNA recovery given two sets of activity level propositions.

Abstract

The ReAct (Recovery, Activity) project is an ENFSI (European Network of Forensic Science Institutes) supported initiative comprising a large consortium of laboratories. Here, the results from more than 23 laboratories are presented. The primary purpose was to design experiments simulating typical casework circumstances; collect data and to implement Bayesian networks to assess the value (i.e., likelihood ratio) of DNA results given activity level propositions. Two different experimental designs were used to simulate a robbery, where a screwdriver was used to force a door or window. Propositions and case information were chosen following laboratory feedback listing typical casework circumstances (included in the paper). In a direct transfer experiment, the defendant owned and used the screwdriver, but he did not force the door/window in question. An unknown person used the defendant's stolen screwdriver. In an indirect transfer experiment, the defendant neither owned, saw, nor used the screwdriver, nor did they force the door or window. For the second experiment, given the defence view, the defendant never held the screwdriver. We envisaged the situation where an object manipulated by the defendant (or the defendant himself/herself) would be touched by the unknown offender who would then force the window. It was found for the direct transfer experiment that unless a single contributor profile aligning with the known person's of interest profile was retrieved, the results did not allow to discriminate between propositions. On the other hand, for the indirect transfer experiment, both single and major contributor profiles that aligned with the person of interest (POI) supported the proposition that the person used the tool rather than an unknown person who had touched an object, when indeed the former was true. There was considerable variation in median recoveries of DNA between laboratories (between 200pg-5ng) for a given experiment if quantities are taken into account. These differences affect the likelihood ratios given activity level propositions. More than 2700 samples were analysed in the course of this study. Two different Bayesian Networks are made available via an open source application written in Shiny R: Shiny_React(). For comparison, all datasets were analysed using a qualitative method categorised into absent, single, major or other given contributors. The importance of standardising methods is emphasised, alongside the necessity of developing new approaches to assign the probability of laboratory-dependent DNA recovery. Freely accessible open databases play a crucial role in supporting these efforts.