We construct a reliable estimation of evolutionary parameters within the Wright-Fisher model, which describes changes in allele frequencies due to selection and genetic drift, from time-series data. Such data exists for biological populations, for example via artificial evolution experiments, and for the cultural evolution of behavior, such as linguistic corpora that document historical usage of different words with similar meanings. Our method of analysis builds on a Beta-with-Spikes approximation to the distribution of allele frequencies predicted by the Wright-Fisher model. We introduce a self-contained scheme for estimating the parameters in the approximation, and demonstrate its robustness with synthetic data, especially in the strong-selection and near-extinction regimes where previous approaches fail. We further apply to allele frequency data for baker's yeast (Saccharomyces cerevisiae), finding a significant signal of selection in cases where independent evidence supports such a conclusion. We further demonstrate the possibility of detecting time-points at which evolutionary parameters change in the context of a historical spelling reform in the Spanish language.

翻译：我们构建了一种基于Wright-Fisher模型（该模型描述由选择与遗传漂变导致的等位基因频率变化）对进化参数进行可靠估计的方法，该方法适用于时间序列数据。此类数据存在于生物群体中（例如通过人工进化实验获得），也存在于文化行为的进化中（例如记录具有相似含义的不同词语历史使用情况的语料库）。我们的分析方法基于Wright-Fisher模型预测的等位基因频率分布的Beta-Spike近似。我们引入了一个自包含的近似参数估计方案，并通过合成数据证明了其鲁棒性，特别是在之前方法失效的强选择和近灭绝场景中。进一步地，我们将其应用于面包酵母（Saccharomyces cerevisiae）的等位基因频率数据，在独立证据支持相关结论的情况下检测到了显著的选择信号。我们还展示了在西班牙语历史拼写改革的背景下，检测进化参数变化时间点的可能性。