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. 2021 May;52:102449. doi: 10.1016/j.fsigen.2020.102449. Epub 2020 Dec 25.
Two main applications of forensic DNA analysis are the investigation of possible relatedness and the investigation whether a person left DNA in a trace. Both of these are usually carried out by the calculation of likelihood ratios. In the kinship case, it is standard to let the likelihood ratio express the support in favour of the investigated relatedness versus no relatedness, and in the investigation of traces, one by default compares the hypothesis that the person of interest contributed DNA, versus that he is unrelated to any of the actual contributors. In both cases however, we can also view the probabilistic procedure as an inference of the profile of the person we look for: in other words, in both cases we carry out probabilistic genotyping. In this article we use this general analogy to develop various more specific analogies between kinship and mixture likelihood ratios. These analogies help to understand the concepts that play a role, and also to understand the importance of the statistical modeling needed for DNA mixtures. In this article, we apply our findings to consider what we can and cannot conclude from a likelihood ratio in favour of contribution to a mixed DNA profile, if that is computed by a model whose specifics are not entirely known to us, or where we do not know whether they provide a good description of the stochastic effects involved in the generation of DNA trace profiles. We show that, if unrelated individuals are adequately modeled, we can give bounds on how often LR's coming from certain types of black box models may arise, both for persons who are actual contributors and who are unrelated. In particular we show that no model, provided it satisfies basic requirements, can overestimate the evidence found for actual contributors both often and strongly.
法医 DNA 分析有两个主要应用，一是调查可能的亲缘关系，二是调查某人是否在痕迹中留下了 DNA。这两种情况通常都是通过计算似然比来进行的。在亲属关系案件中，标准做法是让似然比表示调查的亲缘关系相对于无亲缘关系的支持程度，而在痕迹调查中，默认情况下是将有兴趣的人贡献了 DNA 的假设与他与任何实际贡献者都没有关系的假设进行比较。然而，在这两种情况下，我们也可以将概率过程视为对我们要寻找的人的特征进行推断：换句话说，在这两种情况下，我们都进行概率基因分型。在本文中，我们使用这种一般类比来发展亲属关系和混合物似然比之间的各种更具体的类比。这些类比有助于理解所涉及的概念，也有助于理解 DNA 混合物所需的统计建模的重要性。在本文中，我们应用我们的发现来考虑，如果计算似然比支持对混合 DNA 谱的贡献的模型的具体细节我们不完全了解，或者我们不知道它们是否为生成 DNA 痕迹谱所涉及的随机效应提供了良好的描述，那么我们可以从支持贡献的似然比中得出什么结论。我们表明，如果对无关个体进行了充分的建模，我们可以为来自某些类型的黑盒模型的似然比出现的频率设定界限，无论是对于实际贡献者还是无关个体。特别是，我们表明，只要满足基本要求，任何模型都不能经常且强烈地高估为实际贡献者找到的证据。
