考虑到人口结构和数据质量在人口推断与链接不平衡方法的人口推断
Enrique Santiago1, Carlos Köpke2, Armando Caballero3
1Departamento de Biología Funcional, Facultad de Biología, Universidad de Oviedo, Oviedo, Spain. esr@uniovi.es.
Nature communications
|July 1, 2025
概括
新的软件工具GONE2和currentNe2使用SNP数据估计有效人口规模 (Ne). 这些工具解释了复杂的人口结构和基因型错误,提高了人口统计推断的准确性.
科学领域:
- 人口遗传学 人口遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 人口推断通常使用泛美模型,这些模型可能不反映复杂的自然人口结构.
- 低于最佳的基因型数据质量可能会影响人口遗传分析的准确性.
研究的目的:
- 引入两个新的软件工具,GONE2和currentNe2,用于估计有效人口规模 (Ne).
- 通过适应复杂的人口结构和不完美的数据来解决现有方法的局限性.
主要方法:
- 开发了GONE2用于用遗传图推断最近的Ne变化,以及currentNe2用于没有遗传图的当代Ne估计.
- 利用来自单个人口样本的SNP数据.
- 纳入的方法分析人口结构 (FST,迁移率,亚种群数量) 并处理数据缺陷,如基因型错误和低测序深度.
主要成果:
- 通过模拟和实验室种群,在各种人口情景中验证了GONE2和当前Ne2.
- 对各种物种种群的扩展分析.
- 证明忽视人口细分往往导致低估有效人口规模 (Ne).
结论:
- 开发的软件工具提供了有效人口大小 (Ne) 的可靠估计,即使具有复杂的人口结构和低于最佳数据.
- 考虑到人口的细分对于准确的人口推断至关重要.
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