将离散时间的赖特-费舍尔模型扩展到生物库规模的数据集
Jeffrey P Spence1, Tony Zeng1, Hakhamanesh Mostafavi1
1Department of Genetics, Stanford University.
bioRxiv : the preprint server for biology
|June 9, 2023
概括
这项研究引入了种群遗传学的新算法,有效地近似了离散时间赖特-费舍尔模型. 这种新方法可以在大量人群中进行准确的遗传推断,从而提高我们对等位基因频率演变的理解.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学是一种计算生物学.
- 统计遗传学 统计遗传学
背景情况:
- 离散时间赖特·费舍尔 (DTWF) 模型对于种群遗传学至关重要,它描述了因漂移,突变和选择而导致的等位基因频率变化.
- 现有的DTWF模型概率计算方法在现代外体序列测序中常见的大样本大小方面存在困难.
- 对于DTWF模型的扩散近似在样本大小或强选择的情况下失败.
研究的目的:
- 在DTWF模型下开发一个可扩展的算法来计算概率.
- 为大群体近似DTWF模型,以可证明的边界误差.
- 为了在生物银行规模上实现精确的人口遗传推断.
主要方法:
- 开发了一种新的算法,利用二项式和超几何分布的稀疏性和低等级近似.
- 启用了对DTWF过渡矩阵的线性时间矩阵向量乘法.
- 证明了近似误差的理论界限.
主要成果:
- 该算法实现了与人口大小相关的线性时间复杂性.
- 证明了对数十亿个人的高精度和可扩展性.
- 表明,增加超出当前外基因组测序队列的样本大小为选择系数估计提供了有限的额外信息,除了极端适应性效应.
结论:
- 新的算法可以在前所未有的规模上进行严格的人口遗传推断.
- 超出当前外基组测序队列的样本大小的增加为估计选择系数提供了减少的回报,除了具有极端适应性效应的变体.
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