Transfer, persistence and recovery of DNA and mRNA vaginal mucosa markers after intimate and social contact with Bayesian network analysis for activity level reporting.

In sexual assault cases, it can be challenging to identify the type of body fluids/ cell types present in a crime scene sample, especially the origin of epithelial cells. Therefore, more labs are applying mRNA body fluid analysis for saliva, skin and vaginal mucosa markers. To address activity level propositions, it is necessary to assign probabilities of transfer, persistence, prevalence and recovery of DNA and mRNA markers. In this study we analysed 158 samples (fingernail swabs, penile swabs and boxershorts) from 12 couples collected at different time points post intimate contact and after non-intimate contact in order to detect DNA from the person of interest (POI) and mRNA vaginal mucosa markers. Samples were DNA and RNA co-extracted and analysed with PowerPlex®Fusion 6C System and 19-plex mRNA primer mix respectively, using Endpoint PCR and the CE platform. Vaginal mucosa was detected up to 36 h post intimate contact, but also detected in one non-intimate contact sample. In 94% of intimate contact and 50 % of non-intimate contact samples the DNA results support the proposition that POI is the donor (LR ≥ 10,000). There was a strong association between the detection of vaginal mucosa and the average RFU value of the POI. The data were used to instantiate a comprehensive Bayesian network to evaluate the evidence at activity level, given alternate propositions conditioned upon indirect or direct transfer events. It is shown that the value of the evidence is mainly affected by the high DNA quantity (measured as mean RFU) that is recovered from the POI. The detection of vaginal mucosa had low impact upon the resultant likelihood ratio.

Evaluating the performance of five up-to-date DNA/RNA co-extraction methods for forensic application.

The importance of RNA evidence is growing with new developments in RNA profiling methods and purposes. As forensic samples often can be of small quantity, extraction methods with high yields of both DNA and RNA are desirable. In order to identify the optimal DNA/RNA co-extraction workflow for forensic samples, we evaluated the performance of three frequently-used methods, two new approaches for DNA/RNA co-extraction and a manual phenol/chloroform RNA-only extraction method on blood and saliva samples. Based on a comprehensive analysis of the RNA and DNA quantities, as well as the STR genotyping and mRNA profiling results, we conclude that the two frequently-used co-extraction methods, combining commercially available DNA and RNA extraction kits, achieved the best performance. However, not any combination of commercially available DNA and RNA extraction kits works well and extensive optimization is necessary, as seen in the poor results of the two new approaches.

Who Packed the Drugs? Application of Bayesian Networks to Address Questions of DNA Transfer, Persistence, and Recovery from Plastic Bags and Tape.

When DNA from a suspect is detected in a sample collected at a crime scene, there can be alternative explanations about the activity that may have led to the transfer, persistence and recovery of his/her DNA. Previous studies have shown that DNA can be indirectly transferred via intermediate surfaces and that DNA on a previously used object can persist after subsequent use of another individual. In addition, it has been shown that a person's shedder status may influence transfer, persistence, prevalence, and recovery of DNA. In this study we have investigated transfer persistence and recovery on zip-lock bags and tape, which are commonly encountered in drug cases and how the shedder status of the participants influenced the results. A probabilistic framework was developed which was based on a previously described Bayesian network with case-specific modifications. Continuous modelling of data was used to inform the Bayesian networks and two case scenarios were investigated. In the specific scenarios only moderate to low support for Hp was obtained. Applying a continuous model based on the profile quality can change the LRs.