南加州大学 Charleston W. K. Chiang研究团队揭示了基于可能性的从家谱树进行人口统计推断的框架。相关论文发表在2025年3月20日出版的《自然—遗传学》杂志上。

在这里，小组提出了一个称为谱系可能性（gLike）的人口统计学推断框架。他们的方法采用一种基于图的结构来总结基因谱系树中所有谱系之间的关系，这些谱系具有随时间变化的所有可能的种群成员轨迹，并在参数化人口统计模型下推导出树之间的全似然。研究小组通过模拟和经验应用表明，对于经历过多次外加剂的种群，gLike可以准确地估计数十个人口统计学参数，包括祖先种群规模、外加剂时间和外加剂比例，并且它优于传统的以站点频谱为主题的人口统计学推断方法。综上所述，他们提出的gLike框架利用底层谱系信息，在推断人类和其他物种的复杂人口统计数据时提供了高灵敏度和准确性。

研究人员表示，一个种群的人口统计历史是遗传变异模式的基础，并被编码在样本单倍型的基因谱系树中。

附：英文原文

Title: A likelihood-based framework for demographic inference from genealogical trees

Author: Fan, Caoqi, Cahoon, Jordan L., Dinh, Bryan L., Ortega-Del Vecchyo, Diego, Huber, Christian D., Edge, Michael D., Mancuso, Nicholas, Chiang, Charleston W. K.

Issue&Volume: 2025-03-20

Abstract: The demographic history of a population underlies patterns of genetic variation and is encoded in the gene-genealogical trees of the sampled haplotypes. Here we propose a demographic inference framework called the genealogical likelihood (gLike). Our method uses a graph-based structure to summarize the relationships among all lineages in a gene-genealogical tree with all possible trajectories of population memberships through time and derives the full likelihood across trees under a parameterized demographic model. We show through simulations and empirical applications that for populations that have experienced multiple admixtures, gLike can accurately estimate dozens of demographic parameters, including ancestral population sizes, admixture timing and admixture proportions, and it outperforms conventional demographic inference methods using the site frequency spectrum. Taken together, our proposed gLike framework harnesses underused genealogical information to offer high sensitivity and accuracy in inferring complex demographies for humans and other species.

DOI: 10.1038/s41588-025-02129-x

Source: https://www.nature.com/articles/s41588-025-02129-x