多总体联合样本频谱的有效计算
Efficient computation of the joint sample frequency spectra for multiple populations
摘要：
在群体遗传学的广泛研究中使用了样本频率谱（SFS），这是一种描述DNA序列样本中多态位点突变等位基因分布的汇总统计量。特别是近年来，人们对分析多个群体的联合SFS数据以推断复杂人口统计历史的参数，包括变量总体规模、群体分裂时间、迁移率、混合比例等越来越感兴趣。当涉及多个群体并且样本量很大时，现有的基于IFS的推断方法遭受数值不稳定性和高计算复杂性的困扰。在本文中，我们提出了新的分析公式和算法，使有效的计算的预期联合SFS的多个人口相关的一个复杂的人口模型与任意总体规模的历史（包括分段指数增长性）。我们的研究结果是在一个新的软件包，称为MOMI（莫兰模型推理）。通过一个涉及数十个种群的实证研究，我们证明了我们的数值稳定性和计算复杂性的改善。
英文摘要：
A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences. In particular, recently there has been growing interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. Although much methodological progress has been made, existing SFS-based inference methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this paper, we present new analytic formulas and algorithms that enable efficient computation of the expected joint SFS for multiple populations related by a complex demographic model with arbitrary population size histories (including piecewise exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study involving tens of populations, we demonstrate our improvements to numerical stability and computational complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
