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核酸替代模型选择对于贝叶斯推断的原生态与行为良好的先来说是不必要的
Luiza Guimarães Fabreti1,2, Sebastian Höhna1,2
1GeoBio-Center, Ludwig-Maximilians-Universität München, 80333 Munich, Germany.
Systematic biology
|July 17, 2023
概括
当使用适当的先验时,贝叶斯系遗传学对过度参数化的模型是强大的. 新的先前设置防止了基因树推断中的偏见,使得模型选择不那么重要.
科学领域:
- 进化生物学是进化的生物学.
- 计算生物学是一种计算生物学.
- 统计建模 统计建模
背景情况:
- 在遗传学中,模型选择对于选择替代和分割模型至关重要.
- 替代模型的低参数化可能会偏向遗传学研究.
- 过度参数化的对贝叶斯系遗传推理的影响仍然不太被探索.
研究的目的:
- 为了研究替代模型过度参数化在贝叶斯系遗传推理中的影响.
- 在真对复杂模型下比较家族遗传树拓和长度估计.
- 评估不同先前分布对模型过度参数化效应的影响.
主要方法:
- 使用 Jukes-Cantor 模型作为真实模型进行模拟.
- 与复杂的GTR+Γ+I替代模型进行了比较.
- 测试了四种先前的分发选择:来自MrBayes的默认先验,BEAST2,RevBayes,以及一个新的"Tame"先验.
主要成果:
- 贝叶斯的基因推理证明了替代模型过度参数化和过度分区的稳定性.
- 这种稳定性取决于新设计的"Tame"预设的应用.
- 来自MrBayes,BEAST2和RevBayes的默认先验引入了估计树长的偏差.
结论:
- 替代和分区模型选择在贝叶斯系遗传学中可能是多余的,因为它具有精确选择的先验.
- 未来的努力应优先考虑开发更复杂和生物学上更现实的替代模型.
- 选择先前分布显著影响了在模型错误规范下遗传学推断的可靠性.
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