Forensic Genetics Unit, University Center of Legal Medicine, Lausanne - Geneva, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Fondation pour la formation continue Universitaire Lausannoise (UNIL-EPFL), Dorigny 1015, Switzerland.
Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand.
Forensic Sci Int Genet. 2021 May;52:102481. doi: 10.1016/j.fsigen.2021.102481. Epub 2021 Feb 9.
In casework, laboratories may be asked to compare DNA mixtures to multiple persons of interest (POI). Guidelines on forensic DNA mixture interpretation recommend that analysts consider several pairs of propositions; however, it is unclear if several likelihood ratios (LRs) per person should be reported or not. The propositions communicated to the court should not depend on the value of the LR. As such, we suggest that the propositions should be functionally exhaustive. This implies that all propositions with a non-zero prior probability need to be considered, at least initially. Those that have a significant posterior probability need to be used in the final evaluation. Using standard probability theory we combine various propositions so that collectively they are exhaustive. This involves a prior probability that the sub-proposition is true, given that the primary proposition is true. Imagine a case in which there are two possible donors: i and j. We focus our analysis first on donor i so that the primary proposition is that i is one of the sources of the DNA. In this example, given that i is a donor, we would further consider that j is either a donor or not. In practice, the prior weights for these sub-propositions may be difficult to assign. However, the LR is often linearly related to these priors and its behaviour is predictable. We also believe that these priors are unavoidable and are hidden in alternative methods. We term the likelihood ratio formed from these context-exhaustive propositions LR. LR is trialed in a set of two- and three-person mixtures. For two-person mixtures, LR is often well approximated by LR, where the subscript ij describes the proposition that i and j are the donors and ja describes the proposition that j and an alternate, unknown individual (a), who is unrelated to both i and j, are the donors. For three-person mixtures, LR is often well approximated by LR where the subscript ijk describes the proposition that i, j, and k are the donors and jka describes the proposition that j, k, and an unknown, unrelated (to i, j, and k) individual (a) are the donors. In our simulations, LR had fewer inclusionary LRs for non-contributors than the unconditioned LR (LR).
在案例工作中，实验室可能会被要求将 DNA 混合物与多个感兴趣的人（POI）进行比较。法医 DNA 混合物解释指南建议分析人员考虑几对命题; 然而，尚不清楚是否应该报告每个人的几个似然比（LR）。传达给法庭的命题不应取决于 LR 的值。因此，我们建议命题应该具有功能上的完整性。这意味着需要考虑所有具有非零先验概率的命题，至少在最初阶段是这样。那些具有显著后验概率的命题需要在最终评估中使用。我们使用标准概率论将各种命题结合在一起，使它们具有完整性。这涉及到给定主要命题为真时，子命题为真的先验概率。想象一个有两个可能供体的案例：i 和 j。我们首先将分析重点放在供体 i 上，因此主要命题是 i 是 DNA 的来源之一。在这个例子中，由于 i 是供体，我们将进一步考虑 j 是供体还是不是供体。在实践中，这些子命题的先验权重可能难以分配。然而，LR 通常与这些先验呈线性相关，其行为是可预测的。我们还认为这些先验是不可避免的，并且隐藏在替代方法中。我们将这些上下文完整的命题形成的似然比称为 LR。LR 在两组和三组混合物中进行试验。对于两人混合物，LR 通常可以很好地由 LR 近似，其中下标 ij 描述命题 i 和 j 是供体，而 ja 描述命题 j 和一个未知的、与 i 和 j 都无关的、另一个个体（a）是供体。对于三人混合物，LR 通常可以很好地由 LR 近似，其中下标 ijk 描述命题 i、j 和 k 是供体，jka 描述命题 j、k 和一个未知的、与 i、j 和 k 都无关的个体（a）是供体。在我们的模拟中，LR 对非贡献者的包容性 LR 比未条件的 LR（LR）少。
