使用基于全基因组基因谱的复合概率推断复杂的人口历史
Drew DeHaas1, Zhibai Jia1, Leo Speidel2
1Department of Computational Biology, Cornell University.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
从祖先重组图 (ARG) 准确推断人口统计是具有挑战性的. 新的工具mrpast提供了一种多功能解决方案,用于使用ARG和复合概率推断复杂的人口遗传史.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 对复杂的人口模型进行准确的参数推断仍然是人口遗传学的挑战.
- 祖先重组图 (ARG) 为人口推断提供比总结统计数据更丰富的数据.
研究的目的:
- 介绍mrpast,这是一个新的计算工具,用于从ARG推断复杂的人口历史.
- 评估不同种群遗传模型中mrpast的准确性和多功能性.
主要方法:
- mrpast采用一种复合概率方法,该方法基于来自ARG的双对样本凝聚时间.
- 采用连续时间的马尔科夫过程用于凝聚概率.
- 对各种人口情景的模拟和推断的ARG进行评估.
主要成果:
- mrpast 准确地推断出包括分裂时间,人口规模和基因流动在内的人口参数.
- 在诸如不对称迁移,人口规模变化和混合等复杂模型上表现出高准确度.
- 通过使用"幽灵"人口方法成功重建了美国混合历史.
结论:
- mrpast为ARG的复杂人口推理提供了一个强大的和多功能管道.
- 该工具促进模拟,推断和操纵ARG用于模型测试.
- mrpast 提高了人口遗传人口推理的准确性和方便性.
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