Department of Biology, Stanford University, Stanford, CA 94305, USA.
Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
Genetics. 2024 Nov 6;228(3). doi: 10.1093/genetics/iyae145.
Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here, we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination and discuss their implications for the rates of horizontal gene transfer in bacteria.
重组通过将现有变体重新组合到新的遗传背景上来打破遗传连锁。这些动态传统上通过检查等位基因之间的相关性以及它们如何随重组率的函数衰减来量化。然而，这些相关性的幅度受到其他进化力量（如自然选择和遗传漂变）的强烈影响，因此很难梳理出重组的影响。在这里，我们引入了一种分析替代统计学的理论框架，这些统计学用于衡量重组产生的同系物。我们推导出了预测这些统计数据如何依赖于重组和反复突变率、负选择和遗传漂变的强度以及突变等位基因的当前频率的解析表达式。我们发现，同系物的程度可以强烈依赖于这个频率尺度，这反映了这些突变发生的潜在时间尺度。我们展示了如何利用这些缩放特性来隔离重组的影响，并讨论它们对细菌中水平基因转移率的影响。
