Success Story: Advancing Mixture Interpretation Analysis with NOCIt

National Institute of Justice and Rutgers University

Date

September 2020

Overview

DNA samples recovered from crime scenes often contain at least two contributors. Complex forensic DNA mixture interpretation can be challenging and requires computational advancements that support its use. Using forensic probabilistic tools to identify a DNA sample’s number of contributors (NOC) is crucial to accurately computing the weight of evidence for a person of interest. Traditionally, calculating the NOC for a forensic short tandem repeat (STR) DNA profile involves evaluating peaks per locus and dividing by two, ratios of alleles/allelic balance at a locus, and review to ensure all loci fit the estimated NOC. However, this method provides only an estimate about the minimum NOC that could explain the mixture rather than the probability of a certain NOC. Furthermore, there can be variation between analysts which introduces subjectivity when using this method to determine NOC. Thus, there is a need for objective, continuous, probabilistic frameworks that use all the information present in an STR profile to draw inferences for the NOC. Drs. Catherine Grgicak and Desmond Lun at Rutgers University developed and validated a probabilistic system, “NOCIt”, that determines a probability distribution on the NOC given an STR electropherogram.