Costa Camila, Figueiredo Carolina, Costa Sandra, Ferreira Paulo Miguel, Amorim António, Prieto Lourdes, Pinto Nádia
Faculdade de Ciências, Universidade Do Porto, Porto, Portugal.
i3S - Instituto de Investigação E Inovação Em Saúde, Universidade Do Porto, R. Alfredo Allen 208, 4200 - 135, Porto, Portugal.
Int J Legal Med. 2025 May 9. doi: 10.1007/s00414-025-03500-7.
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.
在法医遗传学常规工作中，分析的基因样本越来越复杂，包括有多个个体贡献的混合样本。标准问题在于识别，旨在量化参考样本的提供者是混合样本贡献者的可能性。这通过似然比（LR）来计算，并且需要使用专门的概率基因分型软件，该软件可能会考虑混合样本DNA的数量（定量工具），而不仅仅是特定等位基因的存在与否（定性工具）。在任何情况下，混合样本的贡献者数量（NoC）都是用户必须输入的参数。由于其性质，对于大多数实际案件样本来说，NoC是未知的，需要进行估计，而由于DNA质量和数量不佳，这可能具有挑战性。本研究旨在通过似然比的成对比较，评估在识别问题中考虑真实混合样本的不同NoC（在专家首次评估后高估和低估它）的影响，使用定性工具（LRmix Studio）和定量工具（EuroForMix和STRmix™）进行统计评估。不同的计算模型显示出结果的不同变化，但对于所有模型来说，当考虑的NoC小于专家最初估计的NoC时，影响更大。定量工具对NoC变化表现出更高的敏感性。利用使用真实数据的优势，其可能的复杂性超过模拟数据，这项工作突出了NoC对证据量化可能产生的影响，强化了正确估计它的重要性。
