数百个基因组的全基因组谱系的稳健和准确的贝叶斯推断，这一成果由加州大学Yun S. Song研究团队经过不懈努力而取得。2025年9月8日出版的《自然—遗传学》杂志发表了这一最新研究成果。

为了应对这些挑战，该团队引入了SINGER（重组谱系的采样和推断），这种方法将ARG从后向分布中采样的速度提高了两个数量级，从而能够对数百个全基因组序列进行准确的推断和不确定性量化。通过大量的仿真，该团队证明了与现有方法相比，SINGER提高了模型错配的准确性和灵活性。研究组通过将SINGER应用于1000基因组计划中的英国和非洲后裔个体，证明了它的实用性，识别了种群分化、古代渗入的信号，并有力地支持了灵长类动物共有的人类白细胞抗原区域的古代多态性。

据介绍，祖先重组图（ARG）描述了基因组样本的谱系历史，是种群基因组学和生物医学研究的重要工具。最近的进展大大提高了ARG重建的可扩展性，但它们依赖于可能降低准确性的近似值，特别是在模型规格错误的情况下。此外，他们只重建了一个单一的ARG拓扑结构，无法量化与ARG推断相关的相当大的不确定性。

附：英文原文

Title: Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes

Author: Deng, Yun, Nielsen, Rasmus, Song, Yun S.

Issue&Volume: 2025-09-08

Abstract: The Ancestral Recombination Graph (ARG), which describes the genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have substantially increased ARG reconstruction scalability, but they rely on approximations that can reduce accuracy, especially under model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. Here, to address these challenges, we introduce SINGER (sampling and inferring of genealogies with recombination), a method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for hundreds of whole-genome sequences. Through extensive simulations, we demonstrate SINGER’s enhanced accuracy and robustness to model misspecification compared to existing methods. We demonstrate the utility of SINGER by applying it to individuals of British and African descent within the 1000 Genomes Project, identifying signals of population differentiation, archaic introgression and strong support for ancient polymorphism in the human leukocyte antigen region shared across primates.

DOI: 10.1038/s41588-025-02317-9

Source: https://www.nature.com/articles/s41588-025-02317-9