The impact of parameter variation in the quantification of forensic genetic evidence

Technological advancements have allowed the detection of increasingly complex forensic genetics samples, as minimum amounts of DNA can now be detected in crime scenes or other settings of interest. The weight of the evidence depends on several parameters regarding the population and sample-related analytical factors, the latter in a greater number when the DNA amount is considered. This led to the development of probabilistic genotyping software (PGS), able to deal with the associated complexities. This study aims to evaluate the impact on the evidence's weighing, when different analytical threshold values are used, and when different models and/or estimates for analytical artifacts, such as stutters or drop-in parameters, are considered. To reach this goal, three PGS, based on different statistical models, were used to analyze real casework pairs of samples composed of a mixture with either two or three estimated contributors, and a single-source sample associated. The obtained results show that the estimation of these parameters must not be overlooked, as they may considerably impact the outcome. This underlines the importance of proper parametrization in the analysis of forensic genetics identification problems when using complex samples, and the understanding by practitioners of how probabilistic genotyping informatics tools work to use them accurately.