基因型填充技术在古DNA研究中的应用与潜在价值

doi:10.16288/j.yczz.25-166

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Hereditas(Beijing) ›› 2026, Vol. 48 ›› Issue (5): 471-482.doi: 10.16288/j.yczz.25-166

• Review • Previous Articles Next Articles

Application and potential value of genotype imputation techniques in ancient DNA research

Wenqi Wei¹^,²(), Dawei Cai¹^,²()

¹ Bioarchaeology Laboratory, Jilin University, Changchun 130012, China
² School of Archaeology, Jilin University, Changchun 130012, China

Received:2025-07-18 Revised:2025-10-13 Online:2025-11-13 Published:2025-11-13
Contact: Dawei Cai E-mail:weiwq0304@163.com;caidw@jlu.edu.cn
Supported by:
National Social Science Foundation of China(17ZDA221);Interdisciplinary Research Support Program for Doctoral Candidates of the Interdisciplinary Innovation Center, Jilin University(4190214243B6)

Abstract

Abstract:

Ancient DNA research plays a pivotal role in reconstructing history and understanding biological evolution. Modern genotype imputation techniques, which leverage reference panels to predict missing genotypes, have emerged as powerful tools for enhancing genetic information retrieval from ancient DNA. In this review, we examine state-of-the-art computational tools and algorithms—including GLIMPSE, Beagle, and QUILT—that enable more comprehensive analysis of genetic architecture and evolutionary patterns in ancient humans and animals. While analytical accuracy can be affected by variables such as sampling strategies and reference panel composition, the field shows tremendous potential for growth. Key future directions include: (1) algorithmic refinements, (2) computational efficiency improvements, (3) integration with emerging technologies, and (4) expansion into novel research domains. These advances are expected to offer new perspectives for advancing ancient DNA research and understanding ancient life systems.

Key words: ancient DNA, genotype imputation, genetic information recovery

Wenqi Wei, Dawei Cai. Application and potential value of genotype imputation techniques in ancient DNA research[J]. Hereditas(Beijing), 2026, 48(5): 471-482.

Figures/Tables 4

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Table 1

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工具名称	核心算法	数据类型	推荐测序深度	单倍型参考序列集要求	主要应用场景	参考文献
GLIMPSE	基于Li-Stephens模型的吉布斯采样	低覆盖度全基因组测序(whole genome sequencing, WGS)数据	≥0.1×	需要大规模高质量单倍型参考序列集	WGS数据，尤其适合古DNA研究和罕见变异填充	[30]
STITCH	基于隐马尔可夫模型(hidden Markov model, HMM)的祖先单倍型拼接	低覆盖度WGS数据	无特殊要求	不需要单倍型参考序列集	非人类物种及缺乏单倍型参考序列集的特殊群体研究	[32]
Beagle	基于身份血统(identity by descent, IBD)的隐马尔可夫模型	芯片数据/低覆盖度WGS数据	≥0.5×	依赖大规模单倍型参考序列集	大规模基因组数据填充，GWAS等准确性要求高的场景	[33]
Reveel	基于HMM的祖先单倍型推断	低覆盖度WGS数据	适应范围广	不需要单倍型参考序列集	缺乏单倍型参考序列集时的大规模群体基因分型	[34]
QUILT	迭代吉布斯采样结合读标签分组	低覆盖度WGS数据/长读长数据	≥0.25×	需要代表性单倍型参考序列集(≥1 k样本)	低覆盖度WGS数据，适合多样人群和罕见变异研究	[36]

Application and potential value of genotype imputation techniques in ancient DNA research

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