Department of Mathematics and Statistics, University of Otago, Dunedin 9054, New Zealand.
Centre for Computational Evolution, University of Auckland, Auckland 1142, New Zealand.
Syst Biol. 2021 Jan 1;70(1):145-161. doi: 10.1093/sysbio/syaa051.
We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.].
我们描述了一种新的、计算效率高的贝叶斯方法，用于从非连锁的二元标记推断种系发生树和种群动态。使用等位基因频率动态的扩散模型和新颖的数值算法进行似然计算。扩散方法允许分析包含数百或数千个个体的数据集。该方法称为 Snapper，已作为 BEAST2 包的一部分实现。我们进行了模拟实验来评估数值误差、计算要求和恢复已知模型参数的准确性。对大豆 SNP 数据的重新分析表明，在实践中，Snapp 和 Snapper 中实现的模型可能难以区分，我们通过进一步的模拟对此进行了测试。我们使用从 41 个种群的 399 只淡水龟中采样的 SNP 数据集展示了可能的分析规模。[贝叶斯推断；扩散模型；多物种合并；SNP 数据；种系发生树；谱方法。]。
