Ling Xiaohua, Han Shuang, Lin Xinyi, Bai Zhaochen, Zhang Nan, Li Jiayue, Wang Huan, Ou Xueling
Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, P. R. China.
Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University, Guangzhou, P. R. China.
Electrophoresis. 2025 Aug;46(15):1102-1111. doi: 10.1002/elps.202400140. Epub 2024 Oct 14.
In cases of serious crimes that involve challenging DNA samples from the perpetrator (e.g., a minor contributor to a mixture), there is justification to combine different mixture profiles. In our previous study, we developed a massively parallel sequencing (MPS)-based assay targeting 140 microhaplotype markers. In this study, we extended the use of the microhaplotype panel to common scenarios, such as determining the presence of a common contributor or relatedness between different mixture profiles when no reference source is available. Data interpretation was performed using the R package KinMix. Our findings revealed that correct assignments of a common contributor and relatedness were made between relatively balanced mixtures. However, when profiles suffered from allele imbalance, inclusive assignments were significantly associated with the suspect's mixture proportion. Additionally, our analysis showed zero false-positive rates in the studied scenarios. These results indicate that microhaplotype data can be reliably interpreted for identifying a common donor or related donors among different mixtures. Further research based on larger sample sizes may yield more reliable results, which could assist in solving issues related to complex scenarios where multiple mixed profiles were involved.
在涉及对犯罪者的DNA样本提出质疑的严重犯罪案件中(例如,混合样本中的次要贡献者),有理由合并不同的混合图谱。在我们之前的研究中,我们开发了一种基于大规模平行测序(MPS)的检测方法,靶向140个微单倍型标记。在本研究中,我们将微单倍型面板的应用扩展到常见场景,例如在没有参考样本的情况下,确定不同混合图谱之间是否存在共同贡献者或亲缘关系。使用R包KinMix进行数据解读。我们的研究结果表明,在相对平衡的混合样本之间能够正确确定共同贡献者和亲缘关系。然而,当图谱存在等位基因不平衡时,包容性赋值与嫌疑人的混合比例显著相关。此外,我们的分析表明在所研究的场景中假阳性率为零。这些结果表明,微单倍型数据可用于可靠地识别不同混合样本中的共同供体或相关供体。基于更大样本量的进一步研究可能会产生更可靠的结果,这有助于解决涉及多个混合图谱的复杂场景相关问题。