Abstract：
DNA mixtures are a common sample type in forensic genetics, and we typically assume that contributors to the mixture are unrelated when calculating the likelihood ratio (LR). However, scenarios involving mixtures with related contributors, such as in family murder or incest cases, can also be encountered. Compared to the mixtures with unrelated contributors, the kinship within the mixture would bring additional challenges for the inference of the number of contributors (NOC) and the construction of probabilistic genotyping models. To evaluate the influence of potential kinship on the individual identification of the person of interest (POI), we conducted simulations of two-person (2 P) and three-person (3 P) DNA mixtures containing unrelated or related contributors (parent-child, full-sibling, and uncle-nephew) at different mixing ratios (for 2 P: 1:1, 4:1, 9:1, and 19:1; for 3 P: 1:1:1, 2:1:1, 5:4:1, and 10:5:1), and performed massively parallel sequencing (MPS) using MGIEasy Signature Identification Library Prep Kit on MGI platform. In addition, in silico simulations of mixtures with unrelated and related contributors were also performed. In this study, we evaluated 1): the MPS performance; 2) the influence of multiple genetic markers on determining the presence of related contributors and inferring the NOC within the mixture; 3) the probability distribution of MAC (maximum allele count) and TAC (total allele count) based on in silico mixture profiles; 4) trends in LR values with and without considering kinship in mixtures with related and unrelated contributors; 5) trends in LR values with length- and sequence-based STR genotypes. Results indicated that multiple numbers and types of genetic markers positively influenced kinship and NOC inference in a mixture. The LR values of POI were strongly dependent on the mixing ratio. Non- and correct-kinship hypotheses essentially did not affect the individual identification of the major POI; the correct kinship hypothesis yielded more conservative LR values; the incorrect kinship hypothesis did not necessarily lead to the failure of POI individual identification. However, it is noteworthy that these considerations could lead to uncertain outcomes in the identification of minor contributors. Compared to length-based STR genotyping, using sequence-based STR genotype increases the individual identification power of the POI, concurrently improving the accuracy of mixing ratio inference using EuroForMix. In conclusion, the MGIEasy Signature Identification Library Prep kit demonstrated robust individual identification power, which is a viable MPS panel for forensic DNA mixture interpretations, whether involving unrelated or related contributors.
摘要：
DNA混合物是法医遗传学中常见的样本类型,并且我们通常假设在计算似然比（LR）时对混合物的贡献是不相关的。然而,涉及与相关贡献者混合的场景，比如在家庭谋杀或乱伦案件中，也可以遇到。与不相关贡献者的混合物相比，混合物中的亲缘关系将为推断贡献者数量(NOC)和构建概率基因分型模型带来额外的挑战.为了评估潜在的亲属关系对感兴趣的人（POI）的个人身份的影响，我们模拟了包含无关或相关贡献者（父母-子女，兄弟姐妹,和叔叔-侄子）以不同的混合比（对于2P：1：1、4：1、9：1和19：1；对于3P：1：1：1、2：1：1、5：4：1和10：5：1），并在MGI平台上使用MGIEasy签名鉴定文库制备试剂盒进行大规模平行测序(MPS)。此外,还对具有无关和相关贡献者的混合物进行了计算机模拟。在这项研究中,我们评估了1）：MPS性能；2）多种遗传标记对确定混合物中相关贡献者的存在并推断NOC的影响；3）基于计算机混合谱的MAC（最大等位基因计数）和TAC（总等位基因计数）的概率分布；4）LR值的趋势，考虑了与相关和无关贡献者的混合物中的亲缘关系；5）LR值与基于长度和序列的STR基因型的趋势。结果表明，多种数量和类型的遗传标记对混合物中的亲缘关系和NOC推断有积极影响。POI的LR值强烈依赖于混合比。非亲属关系假设和正确亲属关系假设基本上不会影响主要POI的个体识别；正确的亲属关系假设产生了更保守的LR值；不正确的亲属关系假设并不一定导致POI个体识别的失败。然而,值得注意的是，这些考虑因素可能会导致次要贡献者的识别结果不确定。与基于长度的STR基因分型相比，使用基于序列的STR基因型增加了POI的个体识别能力，同时使用EuroForMix提高混合比推断的准确性。总之,MGIEasy签名识别库准备套件展示了强大的个人识别能力，这是一个可行的MPS小组，用于法医DNA混合物解释，是否涉及无关或相关的贡献者。
