Furuse Yuki
Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan
Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan.
mSystems. 2021 Feb 23;6(1):e01151-20. doi: 10.1128/mSystems.01151-20.
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 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. In biology, research on evolution is important to understand the significance of genetic mutation. When there is a significantly beneficial mutation, a population of species with the mutation prospers and predominates, in a process called "selective sweep." However, there are few methods that can find such a mutation causing selective sweep from genetic data. We here introduce a novel method to detect such mutations. Applying the method to the genomes of ebolavirus, influenza viruses, and the novel coronavirus, we detected known biologically significant mutations and identified mutations the importance of which is previously unrecognized. The method can deepen our understanding of molecular and evolutionary biology.
基因突变在进化中起着核心作用。对于一个显著有益的突变，一次突变事件就足以使物种通过所谓的“单系选择性清除”过程繁荣并占据主导地位。然而，现有的依靠计算突变事件数量来检测选择的方法无法在选择性清除中找到这样的突变。我们在此介绍一种在单氨基酸/核苷酸水平检测可能导致单系选择性清除进化的突变的方法。该方法使用群体遗传检验统计量 Tajima's D 来识别与选择性清除相关的遗传特征。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综合征冠状病毒 2，以识别已知的具有生物学意义的突变以及与潜在选择性清除相关的未被识别的突变。该方法可以检测有益突变，可能会导致发现与这些突变相关的先前未知的生物学功能和机制。在生物学中，进化研究对于理解基因突变的意义很重要。当存在一个显著有益的突变时，具有该突变的物种群体就会繁荣并占据主导地位，这一过程称为“选择性清除”。然而，很少有方法能够从遗传数据中找到导致选择性清除的这样一个突变。我们在此介绍一种新颖的方法来检测此类突变。将该方法应用于埃博拉病毒、流感病毒和新型冠状病毒的基因组，我们检测到了已知的具有生物学意义的突变，并识别出了其重要性先前未被认识到的突变。该方法可以加深我们对分子生物学和进化生物学的理解。
