Abstract：
DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors\' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.
摘要：
DNA混合解释是法医学中最具挑战性的问题之一。当存在多于两个贡献者或相关贡献者时,复杂的DNA混合物更难以分析。微单倍型（MHs）是最近发现并用于DNA混合物分析的多态性遗传标记。然而,MH基因分型数据的证据解释需要更多的辩论。随机人不排除（RMNE）方法分析DNA混合物，而不使用等位基因峰高数据或贡献者数量（NoC）假设。本研究旨在评估RMNE对混合MH基因分型数据的解释程度。我们根据它们的Ae值将来自1000基因组项目数据库的MH基因座分类成组。然后,我们进行了具有2-10个不相关的贡献者的DNA混合物和具有一对同胞贡献者的DNA混合物的模拟。对于每个模拟的DNA混合物，估计了三种类型的非贡献者的错误纳入比率：随机男性，贡献者的父母，和贡献者的兄弟姐妹。同时,计算贡献者和三种类型的非贡献者的RMNE概率，允许基因座不匹配。结果表明,MH数,MHAe值，NoC影响了混合物的RMNE概率和不正确的非贡献者比率。当有更多的MHs时，Ae值较高的MHs，和NoC较少的混合物，RMNE概率，错误的纳入比率下降。混合物中亲缘关系的存在使混合物解释变得复杂。作为混合物中的非贡献者和相关贡献者的贡献者\'亲属增加了对遗传标记的要求，以正确识别贡献者。当使用500个Ae值高于5的高度多态MHs时，这四种个体类型可以根据RMNE概率来区分。这项研究揭示了MH作为混合DNA解释的遗传标记以及RMNE的扩展作为指示特定个体与DNA混合物在DNA数据库搜索中的关系的参数的有希望的潜力。
