Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands; VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.
Forensic Sci Int Genet. 2020 May;46:102250. doi: 10.1016/j.fsigen.2020.102250. Epub 2020 Feb 5.
Presently, there exist many different models and algorithms for determining, in the form of a likelihood ratio, whether there is evidence that a person of interest contributed to a mixed trace profile. These methods have in common that they model the whole trace, hence all its contributors, which leads to the computation time being mostly determined by the number of contributors that is assumed. At some point, these calculations are no longer feasible. We present another approach, in which we target the contributors of the mixture in the order of their contribution. With this approach the calculation time now depends on how many contributors are queried. This means that any trace can be subjected to calculations of likelihood ratios in favor of being a relatively prominent contributor, and we can choose not to query it for all its contributors, e.g., if that is computationally not feasible, or not relevant for the case. We do so without using a quantitative peak height model, i.e., we do not define a peak height distribution. Instead, we work with subprofiles derived from the full trace profile, carrying out likelihood ratio calculations on these with a discrete method. This lack of modeling makes our method widely applicable. The results with our top-down method are slightly conservative with respect to the one of a continuous model, and more so as we query less and less prominent contributors. We present results on mixtures with known contributors and on research data, analyzing traces with plausibly 6 or more contributors. If a top-k of most prominent contributors is targeted, it is not necessary to know how many other contributors there are for LR calculations, and the more prominent the queried contributor is relatively to all others, the less the evidential value depends on the specifics of a chosen peak height model. For these contributors the qualitative statement that more input DNA leads to larger peaks suffices. The evidential value for a comparison with minor contributors on the other hand, potentially depends much more on the chosen model. We also conclude that a trace's complexity, as meaning its (in)ability to yield large LR's that are not too model-dependent, is not measured by its number of contributors; rather, it is the equality of contribution that makes it harder to obtain strong evidence.
目前，有许多不同的模型和算法可用于确定是否有证据表明感兴趣的人对混合痕迹特征作出了贡献，其形式为似然比。这些方法的共同点是它们对整个痕迹进行建模，因此对所有贡献者进行建模，这导致计算时间主要取决于所假设的贡献者数量。在某些时候，这些计算不再可行。我们提出了另一种方法，其中我们按混合体中贡献者的贡献顺序对其进行定位。通过这种方法，计算时间现在取决于要查询的贡献者数量。这意味着可以对任何痕迹进行似然比计算，以确定其是否为相对突出的贡献者，并且我们可以选择不对其所有贡献者进行查询，例如，如果计算上不可行，或者与案件无关。我们这样做不使用定量峰高模型，即我们不定义峰高分布。相反，我们使用源自完整痕迹特征的子特征来完成工作，并使用离散方法对这些子特征进行似然比计算。这种缺乏建模使我们的方法具有广泛的适用性。与连续模型相比，我们的自上而下方法的结果略为保守，而且随着我们查询的贡献者越来越不突出，结果也越来越保守。我们在已知贡献者的混合物和研究数据上展示了结果，分析了可能有 6 个或更多贡献者的痕迹。如果目标是最突出的贡献者的前 k 名，则无需知道还有多少其他贡献者需要进行 LR 计算，而且所查询的贡献者相对于所有其他贡献者越突出，证据价值就越不依赖于所选择的峰高模型的具体情况。对于这些贡献者，关于更多输入 DNA 会导致更大峰的定性陈述就足够了。另一方面，与次要贡献者进行比较的证据价值可能更多地取决于所选择的模型。我们还得出结论，痕迹的复杂性，即其（不能）产生不依赖于模型的较大似然比的能力，不是通过其贡献者的数量来衡量的；相反，是贡献的平等性使得获得有力的证据变得更加困难。
