使用卷积神经网络推断凝聚时间和变异年龄
Juba Nait Saada1, Zoi Tsangalidou1, Miriam Stricker1
1Department of Statistics, University of Oxford, Oxford, UK.
Molecular biology and evolution
|September 22, 2023
概括
我们开发了CoalNN,这是一种新的深度学习方法,用于准确估计基因组变异的年龄和最近共同祖先 (TMRCA) 的时间. 这种方法通过提供对人类人口统计历史和选择压力的精确见解来增强人口遗传分析.
科学领域:
- 人口遗传学 人口遗传学
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- 准确推断时间到最近的共同祖先 (TMRCA) 和基因组变异年龄对于人口遗传学至关重要.
- 现有的基于模型的方法在某些场景中存在局限性.
研究的目的:
- 开发一种新的,准确的,可适应的方法来推断双对TMRCAs和等位基因年龄.
- 将这种方法应用于大规模的基因组数据,以深入了解人口历史和选择.
主要方法:
- 开发了CoalNN,一种使用模拟训练的卷积神经网络的无概率方法.
- 利用转移学习来调整模型以适应不同的人口参数.
- 将CoalNN应用于1000个基因组项目的2,504个样本,分析了约8000万个变异.
主要成果:
- 在模拟中,CoalNN与基于模型的现有方法匹配或超过TMRCA和等位基年龄预测的准确性.
- 在26个人口中推断的变异年龄显示出显著的差异,反映了人口历史和负面选择.
- 产生负选择特征的全基因组注释,改善复杂特征的遗传分析.
结论:
- 无概率的模拟训练模型对于在大型基因组数据集中推断基因谱系属性是有效的.
- CoalNN提供了有关人口人口统计和进化过程的宝贵见解.
- 开发的注释增强了对遗传性和选择对复杂特征的影响的研究.
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