Genetic mutations play a central role in evolution. For a significantly beneficial mutation, a one-time mutation event suffices for the species to prosper and predominate through the process called “monophyletic selective sweep.” However, existing methods that rely on counting the number of mutation events to detect selection are unable to find such a mutation in selective sweep. We here introduce a method to detect mutations at the single amino acid/nucleotide level that could be responsible for monophyletic selective sweep evolution. The method identifies a genetic signature associated with selective sweep using the population genetic test statistic Tajima’s D. We applied the algorithm to ebolavirus, influenza A virus, and severe acute respiratory syndrome coronavirus 2 to identify known biologically significant mutations and unrecognized mutations associated with potential selective sweep. The method can detect beneficial mutations, possibly leading to discovery of previously unknown biological functions and mechanisms related to those mutations.

中文翻译：

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识别与单系选择性扫描相关的潜在有益遗传突变和病毒遗传数据的概念验证研究

基因突变在进化中起着核心作用。对于显着有益的突变，一次性突变事件足以通过称为“单系选择性扫描”的过程使物种繁荣并占主导地位。然而，现有的依靠计数突变事件数量来检测选择的方法无法在选择性扫描中找到这样的突变。我们在这里介绍了一种在单个氨基酸/核苷酸水平上检测突变的方法，该方法可能负责单系选择性扫描进化。该方法使用群体遗传测试统计 Tajima's D识别与选择性扫描相关的遗传特征. 我们将该算法应用于埃博拉病毒、甲型流感病毒和严重急性呼吸系统综合症冠状病毒 2，以识别已知的具有生物学意义的突变和与潜在选择性扫描相关的未识别突变。该方法可以检测有益的突变，可能导致发现以前未知的与这些突变相关的生物学功能和机制。