An analysis of bulletproof as probabilistic genotyping software for forensic DNA analysis casework

Abstract

Using computer systems for probabilistic genotyping on DNA evidence in forensic casework is beneficial as it allows a complete analysis of the data available for a wide range of profiles, a range that is limited when analyzed manually. One such software, Bulletproof, uses the exact method as the statistical foundation of its web-based interface to estimate the likelihood ratio of two hypotheses that explain the given evidence. In this investigation, the capability of Bulletproof was examined by analyzing the effects of evidence and reference sample template amount, injection time, and stutter filter utilization on likelihood ratio. In terms of likelihood ratio, deconvolution by the software is more efficient in cases in which evidence samples of high contrast ratios (such as 1:9 vs. 1:1) and low contributor count have high template, and when sample injection times are low. Reference sample template amount and injection time are less impactful than that of evidentiary samples. As with unknown samples, reference samples should be analyzed beforehand and artifacts removed for better deconvolution.