从古代基因组中深入估计自然选择的强度和时间
Guillaume Laval1, Etienne Patin1, Lluis Quintana-Murci1,2
1Human Evolutionary Genetics Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France.
Molecular ecology resources
|August 31, 2024
概括
卷积神经网络 (CNN) 通过直接使用古老的DNA基因型来改善自然选择分析,比传统的等位基因频率方法提供更精确的选择强度和时间估计. 这种方法提升了对人类进化和疾病史的理解.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学 计算生物学
- 进化基因组学 进化基因组学
背景情况:
- 估计自然选择的影响历史上依赖于过去的等位基因频率,但这种方法产生不精确的选择强度 (S) 和时间 (T) 度量.
- 在特定的时间段中,古老样本的稀缺性进一步复杂化了基于等位基频率的精确选择分析.
研究的目的:
- 开发一种新的方法,绕过时代定义的等位基因频率计算,用于精细的选择参数估计.
- 实施卷积神经网络 (CNN),通过时间直接分析古代基因型,以检测自然选择.
主要方法:
- 利用计算机模拟来比较基于基因型的CNN与使用等位基因频率轨迹的近似贝叶斯计算 (ABC).
- 将开发的CNN方法应用于实证的古代和现代欧洲基因组数据,以分析选择事件.
主要成果:
- 基于基因型的CNN在准确性方面始终超过了ABC方法,无论选择模型或古代基因型是否可用.
- 对欧洲基因组的分析显示,在不同地区的新石器后时期,选择事件的发生率更高.
- 对众多变体的选择参数 (s和T) 的精细估计,包括众所周知的阳性选择和疾病风险变体的案例.
结论:
- 在古代基因组中,CNN提供了一种强大而更精确的方法来检测自然选择的足迹.
- 这些发现支持了最近与欧洲人口对病原体的宿主防御相关的积极和消极选择.
- 这种方法对于理解人类疾病 (如结核病) 的进化历史至关重要.
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