School of Forensic Medicine, Kunming Medical University, Kunming 650500, China.
Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China.
Yi Chuan. 2024 Oct;46(10):849-859. doi: 10.16288/j.yczz.24-150.
With the release of large-scale genomic resources from ancient and modern populations, advancements in computational biology tools, and the enhancement of data mining capabilities, the field of genomics is undergoing a revolutionary transformation. These advancements and changes have not only significantly deepened our understanding of the complex evolutionary processes of human origins, migration, and admixture but have also unveiled the impact of these processes on human health and disease. They have accelerated research into the genetic basis of human health and disease and provided new avenues for uncovering the evolutionary trajectories recorded in the human genome related to population history and disease genetics. The ancestral recombination graph (ARG) reconstructs the evolutionary relationships between genomic segments by analyzing recombination events and coalescence patterns across different regions of the genome. An ARG provides a record of all coalescence and recombination events since the divergence of the sequences under study and specifies a complete genealogy at each genomic position, which is the ideal data structure for genomic analysis. Here, we review the theoretical foundations and research advancements of the ARG, and explore its translational applications and future prospects across various disciplines, including forensic genomics, population genetics, evolutionary medicine, and medical genomics. Our goal is to promote the application of this technique in genomic research, thereby deepening our understanding of the human genome.
随着来自古代和现代人群的大规模基因组资源的发布、计算生物学工具的进步以及数据挖掘能力的增强，基因组学领域正在发生革命性的变革。这些进展和变化不仅极大地加深了我们对人类起源、迁徙和混合等复杂进化过程的理解，还揭示了这些过程对人类健康和疾病的影响。它们加速了对人类健康和疾病遗传基础的研究，并为揭示与人口历史和疾病遗传学相关的人类基因组中记录的进化轨迹提供了新途径。祖先重组图 (ARG) 通过分析基因组不同区域的重组事件和聚结模式，重建基因组片段之间的进化关系。ARG 记录了自研究序列分歧以来所有的聚结和重组事件，并在每个基因组位置指定了完整的系统发育，这是基因组分析的理想数据结构。在这里，我们回顾了 ARG 的理论基础和研究进展，并探讨了它在法医基因组学、群体遗传学、进化医学和医学基因组学等各个学科中的转化应用和未来前景。我们的目标是促进该技术在基因组研究中的应用，从而加深我们对人类基因组的理解。
