Instituto Gulbenkian de Ciência, Oeiras, Portugal; The authors contributed equally to this work.
Evolution. 2014 Jan;68(1):150-62. doi: 10.1111/evo.12234. Epub 2013 Sep 2.
Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear--a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of -0.01, and an effective number of traits nine in mutS(-) E. coli. This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models.
解析决定适应速度的因素是进化生物学中的一个主要问题。一个关键参数是新突变对适合度的影响，而这不可避免地取决于环境和遗传背景。突变的命运还取决于种群大小，它决定了突变经历漂变的程度。在这里，我们同时操纵种群大小和基因型组成，并跟踪 23 个不同的大肠杆菌基因型的适应过程。这些基因型之前在强烈的遗传漂变下积累了突变，涵盖了大量的适合度变异。我们发现了一个简单的规则：净适合度变化与新突变出现的基因型的适合度呈负相关——这是上位性的标志。我们发现，费希尔的几何模型可以解释观察到的适合度变化模式，并使用近似贝叶斯计算推断出最适合数据的模型参数。我们估计了带有大置信区间的适应度改变突变的基因组突变率为 0.01/代，平均突变效应为-0.01，mutS(-)大肠杆菌的有效性状数为 9。这个框架可以扩展到用数据来检验更广泛范围的模型，并测试不同类别的适合度景观模型。
