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, Stanford, CA 94305, USA.
Genetics
|September 19, 2023
概括
一个新的可扩展算法接近于离散时间的赖特-费舍尔模型,使得数以百万计的人口遗传学分析成为可能. 这种方法提高了从大型遗传数据集中估计选择系数的准确性.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 离散时间的赖特-费舍尔 (DTWF) 模型对于理解由于遗传漂移,突变和选择而导致的等位基因频率演变至关重要.
- 目前用于DTWF概率的计算方法受到可扩展性的限制,在外体测序中常见的大样本大小上失败.
- 扩散近似,虽然可以通过计算处理,但在大样本或强选择的情况下变得不准确.
研究的目的:
- 开发一个可扩展的算法,用可证明的边界误差来近似DTWF模型.
- 为了使生物库规模数据集 (数百万个体) 的精确人口遗传学推断成为可能.
- 评估增加样本大小对估计选择系数的影响,特别是对于功能丧失变体.
主要方法:
- 利用了两个关键的DTWF属性:大约过渡概率的稀疏性和相似的等位基因频率的过渡分布的接近性.
- 开发了一种近似的矩阵向量乘法技术,实现线性时间复杂性.
- 将这些原理扩展到超几何分布,以进行高效的次采样概率计算.
主要成果:
- 这种新的算法准确地近似了DTWF模型,并扩展到数以千万计的人口大小.
- 理论和实践演示证实了近似的高精度.
- 分析表明,样本大小超出目前的大型外基因组测序队列,为估计选择系数提供减少的回报,除了具有极端适应性效应的基因.
结论:
- 开发的可扩展算法克服了人口遗传学中以前的计算限制.
- 这有助于严格的,大规模的推断,包括生物银行级别的分析.
- 研究结果表明,目前的大型外基因组测序工作接近大多数基因的选择系数估计回报率下降的点.
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