The impact of considering different numbers of contributors in identification problems involving real casework mixture samples

Increasingly complex genetic samples are analyzed in forensic genetics routine, including mixtures to which more than one individual contributed. The standard problem relies on identification, aiming to quantify the likelihood of the donor of a reference sample being a contributor to the mixture. This is computed through a likelihood ratio (LR) and requires using devoted probabilistic genotyping software that may consider the quantity of the mixture's DNA (quantitative tools), beyond only the presence/absence of specific alleles (qualitative tools). In any case, the mixture's number of contributors (NoC) is a parameter that the user must introduce. Due to its nature, NoC is unknown for most real casework samples and needs to be estimated, which may be challenging due to poor DNA quality and quantity. This study aims to evaluate the impact of considering different NoC of real mixture samples (both over- and underestimating it after a first assessment of the expert) in identification problems through the pairwise comparison of LRs, using for the statistical assessment of both qualitative (LRmix Studio) and quantitative tools (EuroForMix and STRmix™). Different computational models showed different variations of the results, but for all, the impact was greater when considering a smaller NoC than the one initially estimated by the expert. Quantitative tools showed more sensitivity to NoC variation. Taking advantage of using real data, whose possible complexities surpass those of mock ones, this work highlights the impact that the NoC may have on the quantification of the proof, reinforcing the importance of its proper estimation.