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通过下一代测序技术开发和验证一种新型的小而高效的法医遗传学微单倍型面板。

Developmental and validation of a novel small and high-efficient panel of microhaplotypes for forensic genetics by the next generation sequencing.

机构信息

Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550025, China.

Ningbo HEALTH Gene Technology Co., Ltd, Ningbo, 315042, China.

出版信息

BMC Genomics. 2024 Oct 14;25(1):958. doi: 10.1186/s12864-024-10880-4.

DOI:10.1186/s12864-024-10880-4
PMID:39402483
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11475632/
Abstract

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 and 1-1.6 × 10, 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.

摘要

背景

在法医学领域,亲属关系鉴定和混合拆分技术的应用至关重要,为解决复杂案件提供了强有力的科学证据。微单倍型作为新兴的遗传标记类别,由于其高度多态性和优异的稳定性,在法医学中得到了广泛研究。

结果与讨论

本研究采用下一代测序技术开发了一个新型、高效的整合 33 个微单倍型基因座和一个性别决定基因座的检测panel。此外,我们还评估了其法医学效用,并深入研究了其用于亲属关系分析和混合拆分的能力。贵州汉族人群 33 个微单倍型基因座的平均有效等位基因数(Ae)为 6.06,30 个基因座的 Ae 值大于 5。新型 panel 在贵州汉族人群中的累积鉴别力和累积排除力值分别为 1-5.6×10 和 1-1.6×10。在模拟亲属关系分析中,该 panel 能够有效区分亲子、全同胞、半同胞、祖孙、叔侄和无关个体,但在区分表亲与无关个体时不确定性率明显增加。对于混合样本,新型 panel 与机器学习方法结合使用,在估计 1 至 5 个贡献者的混合样本中的贡献者数量方面表现出优异的性能。

结论

总之,我们开发了一种小型、高效的法医学遗传学检测 panel,可为法医复杂亲属关系检测和混合拆分提供新的思路。

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Performance of a 74-Microhaplotype Assay in Kinship Analyses.74 微单倍型检测在血缘关系分析中的表现。
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Using simulated microhaplotype genotyping data to evaluate the value of machine learning algorithms for inferring DNA mixture contributor numbers.
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Development and evaluation of a novel panel containing 188 microhaplotypes for 2nd-degree kinship testing in the Hebei Han population.开发并评估了一种包含 188 个微单倍型的新型panel,用于河北汉族人群的二级亲缘关系检测。
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