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The Fates of Mutant Lineages and the Distribution of Fitness Effects of Beneficial Mutations in Laboratory Budding Yeast Populations

机译:实验室萌芽酵母种群中突变谱系的命运和有益突变的适应性分布

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摘要

The outcomes of evolution are determined by which mutations occur and fix. In rapidly adapting microbial populations, this process is particularly hard to predict because lineages with different beneficial mutations often spread simultaneously and interfere with one another’s fixation. Hence to predict the fate of any individual variant, we must know the rate at which new mutations create competing lineages of higher fitness. Here, we directly measured the effect of this interference on the fates of specific adaptive variants in laboratory Saccharomyces cerevisiae populations and used these measurements to infer the distribution of fitness effects of new beneficial mutations. To do so, we seeded marked lineages with different fitness advantages into replicate populations and tracked their subsequent frequencies for hundreds of generations. Our results illustrate the transition between strongly advantageous lineages that decisively sweep to fixation and more moderately advantageous lineages that are often outcompeted by new mutations arising during the course of the experiment. We developed an approximate likelihood framework to compare our data to simulations and found that the effects of these competing beneficial mutations were best approximated by an exponential distribution, rather than one with a single effect size. We then used this inferred distribution of fitness effects to predict the rate of adaptation in a set of independent control populations. Finally, we discuss how our experimental design can serve as a screen for rare, large-effect beneficial mutations.
机译:进化的结果取决于发生和修复的突变。在快速适应微生物种群的过程中,很难预测这一过程,因为具有不同有益突变的谱系通常会同时传播并干扰彼此的固定。因此,要预测任何单个变异的命运,我们必须知道新突变产生更高适应性竞争谱系的速率。在这里,我们直接测量了这种干扰对实验室酿酒酵母种群中特定适应性变异命运的影响,并使用这些测量值推断出新的有益突变的适应性效应分布。为此,我们将具有不同适应性优势的标记世系播种到重复种群中,并跟踪了数百代的后续频率。我们的结果说明了在决定性地扫掠到固定点的优势强的谱系与通常在实验过程中出现的新突变所无法比拟的优势更强的优势谱系之间的过渡。我们开发了一个近似似然框架,以将我们的数据与模拟进行比较,发现这些竞争性有益突变的效果最好通过指数分布而不是单一效果大小来近似。然后,我们使用这种推断的适应性分布来预测一组独立控制群体中的适应率。最后,我们讨论了我们的实验设计如何可以用作稀有,大影响的有益突变的筛选。

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