利用新一代测序技术开发和验证用于法医遗传学的新型小型高效微单型面板。

IF 3.5 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Changyun Gu, Weipeng Huo, Xiaolan Huang, Li Chen, Shunyi Tian, Qianchong Ran, Zheng Ren, Qiyan Wang, Meiqing Yang, Jingyan Ji, Yubo Liu, Min Zhong, Kang Wang, Danlu Song, Jiang Huang, Hongling Zhang, Xiaoye Jin
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引用次数: 0

摘要

背景:在法医学领域,亲缘关系鉴定和混合物解旋技术的应用至关重要,可为复杂案件的侦破提供有力的科学证据。微单体型作为一类新兴的遗传标记,因其高多态性和出色的稳定性,在法医学中得到了广泛的研究:在这项研究中,我们利用新一代测序技术开发了一个新颖高效的小组,该小组整合了 33 个微单体型位点和一个性别决定位点。此外,我们还评估了它在法医方面的实用性,并深入研究了它在亲缘关系分析和混合解构方面的能力。贵州汉族 33 个微单型位点的平均有效等位基因数(Ae)为 6.06,其中 30 个位点的 Ae 值大于 5。贵州汉族人群中新型面板的累积区分度和累积排除度分别为 1-5.6 × 10- 43 和 1-1.6 × 10- 15。在模拟亲缘关系分析中,面板能有效区分亲子、全同胞、半同胞、祖孙、姑侄和非亲缘关系个体,但在区分嫡表亲和非亲缘关系个体时,不确定率明显增加。在混合物方面,新型面板与机器学习方法相结合,在估计 1 至 5 个贡献者的混合物贡献者数量方面表现出色:总之,我们为法医遗传学开发了一个小型高效面板,可为法医复杂亲缘关系测试和混合物解卷积提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developmental and validation of a novel small and high-efficient panel of microhaplotypes for forensic genetics by the next generation sequencing.

Background: In the domain of forensic science, the application of kinship identification and mixture deconvolution techniques are of critical importance, providing robust scientific evidence for the resolution of complex cases. Microhaplotypes, as the emerging class of genetic markers, have been widely studied in forensics due to their high polymorphisms and excellent stability.

Results and discussion: In this research, a novel and high-efficient panel integrating 33 microhaplotype loci along with a sex-determining locus was developed by the next generation sequencing technology. In addition, we also assessed its forensic utility and delved into its capacity for kinship analysis and mixture deconvolution. The average effective number of alleles (Ae) of the 33 microhaplotype loci in the Guizhou Han population was 6.06, and the Ae values of 30 loci were greater than 5. The cumulative power of discrimination and cumulative power of exclusion values of the novel panel in the Guizhou Han population were 1-5.6 × 10- 43 and 1-1.6 × 10- 15, respectively. In the simulated kinship analysis, the panel could effectively distinguish between parent-child, full-sibling, half-sibling, grandfather-grandson, aunt-nephew and unrelated individuals, but uncertainty rates clearly increased when distinguishing between first cousins and unrelated individuals. For the mixtures, the novel panel had demonstrated excellent performance in estimating the number of contributors of mixtures with 1 to 5 contributors in combination with the machine learning methods.

Conclusions: In summary, we have developed a small and high-efficient panel for forensic genetics, which could provide novel insights into forensic complex kinships testing and mixture deconvolution.

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来源期刊
BMC Genomics
BMC Genomics 生物-生物工程与应用微生物
CiteScore
7.40
自引率
4.50%
发文量
769
审稿时长
6.4 months
期刊介绍: BMC Genomics is an open access, peer-reviewed journal that considers articles on all aspects of genome-scale analysis, functional genomics, and proteomics. BMC Genomics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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