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微单倍型的最新技术进展

State of the Art for Microhaplotypes.

机构信息

Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA.

出版信息

Genes (Basel). 2022 Jul 24;13(8):1322. doi: 10.3390/genes13081322.

DOI:10.3390/genes13081322
PMID:35893059
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9329722/
Abstract

In recent years, the number of publications on microhaplotypes has averaged more than a dozen papers annually. Many have contributed to a significant increase in the number of highly polymorphic microhaplotype loci. This increase allows microhaplotypes to be very informative in four main areas of forensic uses of DNA: individualization, ancestry inference, kinship analysis, and mixture deconvolution. The random match Probability (RMP) can be as small as 10−100 for a large panel of microhaplotypes. It is possible to measure the heterozygosity of an MH as the effective number of alleles (Ae). Ae > 7.5 exists for African populations and >4.5 exists for Native American populations for a smaller panel of two dozen selected microhaplotypes. Using STRUCTURE, at least 10 different ancestral clusters can be defined by microhaplotypes. The Ae for a locus is also identical to the Paternity Index (PI), the measure of how informative a locus will be in parentage testing. High Ae loci can also be useful in missing persons cases. Finally, high Ae microhaplotypes allow the near certainty of seeing multiple additional alleles in a mixture of two or more individuals in a DNA sample. In summary, a panel of higher Ae microhaplotypes can outperform the standard CODIS markers.

摘要

近年来,关于微单倍型的出版物数量平均每年超过十几篇。许多研究都为高度多态性微单倍型基因座的数量显著增加做出了贡献。这种增加使微单倍型在 DNA 法医应用的四个主要领域非常有信息价值:个体识别、祖籍推断、亲属关系分析和混合物解析。对于一个大的微单倍型面板,随机匹配概率 (RMP) 可以小到 10−100。可以通过有效等位基因数 (Ae) 来衡量 MH 的杂合度。对于一个由二十几个选定的微单倍型组成的较小面板,非洲人群的 Ae > 7.5,美洲原住民人群的 Ae > 4.5。使用 STRUCTURE,可以通过微单倍型定义至少 10 个不同的祖先簇。一个基因座的 Ae 也与亲权指数 (PI) 相同,PI 是衡量一个基因座在亲子鉴定中的信息量的指标。高 Ae 基因座在失踪人员案件中也很有用。最后,高 Ae 微单倍型允许在 DNA 样本中两个或更多个体的混合物中看到多个额外等位基因的几乎确定性。总之,一组更高 Ae 的微单倍型可以胜过标准的 CODIS 标记。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/3934656d7c80/genes-13-01322-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/79c7382f30c6/genes-13-01322-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/3813c489b0af/genes-13-01322-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/4515747afc19/genes-13-01322-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/f99fc1946a8b/genes-13-01322-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/4812284c54a9/genes-13-01322-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/3934656d7c80/genes-13-01322-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/79c7382f30c6/genes-13-01322-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/3813c489b0af/genes-13-01322-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/4515747afc19/genes-13-01322-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/f99fc1946a8b/genes-13-01322-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/4812284c54a9/genes-13-01322-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e1/9329722/3934656d7c80/genes-13-01322-g006.jpg

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