概括贝叶斯计算方法来估计人口基因组学模型中分离选择的强度
Martyna Lukaszewicz1,2,3, Ousseini Issaka Salia1,4,2,3,5, Paul A Hohenlohe1,4,2,3
1Institute for Interdisciplinary Data Sciences (IIDS), University of Idaho, Moscow, ID, United States of America.
Journal of Computational Mathematics and Data Science
|April 15, 2024
概括
大致贝叶斯计算 (ABC) 能够在复杂的进化模型中进行统计推理. 本研究开发了一个模拟模型来评估ABC.
科学领域:
- 进化生物学是进化的生物学.
- 计算遗传学的计算遗传学
- 人口遗传学 人口遗传学
背景情况:
- 大型进化模型中的统计估计是计算密集的,阻碍了准确的概率计算.
- 单核酸多态化 (SNP) 数据减少了遗传数据的大小,同时保留了关键信息.
- 大致贝叶斯计算 (ABC) 提供了一种基于模拟的方法,可以绕过统计推理中的直接概率评估.
研究的目的:
- 开发和评估一种机械模型,以模拟具有可变参数的前进时间分离选择.
- 调查ABC的计算可行性和准确性,以推断选择强度和位置位置.
- 评估不同汇总统计数据的性能,以估计在不同选择场景下进行的选择.
主要方法:
- 开发了一种机械的前进时间模拟模型,包含可变迁移,繁殖模式和迁移选择周期.
- 采用近似贝叶斯计算 (ABC) 来对模型参数进行统计推理.
- 在总结数据上实施了异常扫描,以扩大选择位置的参数空间.
- 基于人口差异化和链接不平衡 (LD) 的评估总结统计.
主要成果:
- 证明了ABC的计算可行性,用于在复杂模型中推断选择参数.
- 评估了选择位置位置和强度的估计质量.
- 确定了重组率作为估计分歧选择中的混因素的影响.
- 确定了人口差异化与选择估计中的基于LD的总结统计数据的相对表现.
结论:
- ABC是一种可行的计算方法,用于在大型进化模型中的统计推理.
- 开发的模拟模型和总结统计数据提供了关于在分歧选择下估计选择的见解.
- 重组率显著影响不同的选择强度和LD模式的估计.
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