The BMFS Program receives NIJ funding for research in the field of forensic DNA analysis
New research into the characterization and development of low-template complex DNA mixtures is supported by NIJ (National Institute of Justice) Applied Research and Development in Forensic Science for Criminal Justice Purposes Grant. This project endeavors to develop an enhanced method to characterize complex DNA mixtures by the development of a complex DNA mixture interpretation tool designed to enhance traditional DNA interpretation by utilizing a likelihood ratio which makes no assumptions regarding the number of contributors or by determining the likelihood that a certain number of individuals contributed to the DNA mixture. We use statistical signal processing methods to accurately infer the number of contributors to a DNA stain. Specifically, we calculate the a posteriori probability (APP) of the number of contributors to a stain based on the genotyping results. The APP is the probability that the stain came from a certain number of contributors given what is observed during genotyping. If it is strongly peaked, i.e. the APP says that there is a particular number of contributors that is highly likely and all others are highly unlikely, then the APP tells us the number of contributors that gave rise to the stain. If not, the APP will nevertheless tell us the range in which the number of contributors is overwhelmingly likely to lie, which can then be used to calculate a range for the LR. The APP formulates the process of assigning a number of contributors, which currently must be performed by subjective judgment, into an accurate, objective process. This not only would help crime laboratory analysts in appropriately determining the number of individuals and the uncertainty with respect to the number assumed, but it would ultimately aid in the ability of these laboratories to state the likelihood that it is n individuals. Furthermore, the LR that is ultimately reported should and can incorporate the uncertainties of the number of individuals into the likelihood calculation.
This project is being done in collaboration with a number of highly motivated graduate students.