从系谱树的形状预测进化
Predicting evolution from the shape of genealogical trees
摘要：
如果有一个无性种群的基因组序列样本，我们能预测它的进化未来吗？在这里，我们证明了重建的系谱树的分支模式包含有关采样序列的相对适应度的信息，并且该信息可以用于预测成功的菌株。我们的方法是基于这样的假设，即进化过程中积累的小效应突变，不需要物种特异性的输入，并可以应用于任何无性种群下持续的选择压力。我们使用季节性流感A/H3 N2病毒的历史数据证明了它的性能。我们预测了即将到来的流感季节的祖先谱系，在30%的病例中表现接近最佳，并在19年中的16年中做出了有价值的预测。除了提供预测工具之外，我们做出信息性预测的能力意味着在流行的A/H3 N2流感病毒之间存在持续的适应性变化。
英文摘要：
Given a sample of genome sequences from an asexual population, can one predict its evolutionary future? Here we demonstrate that the branching patterns of reconstructed genealogical trees contains information about the relative fitness of the sampled sequences and that this information can be used to predict successful strains. Our approach is based on the assumption that evolution proceeds by accumulation of small effect mutations, does not require species specific input and can be applied to any asexual population under persistent selection pressure. We demonstrate its performance using historical data on seasonal influenza A/H3N2 virus. We predict the progenitor lineage of the upcoming influenza season with near optimal performance in 30% of cases and make informative predictions in 16 out of 19 years. Beyond providing a tool for prediction, our ability to make informative predictions implies persistent fitness variation among circulating influenza A/H3N2 viruses.
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。
