Testing the extreme value domain of attraction for distributions of beneficial fitness effects
Testing the extreme value domain of attraction for distributions of beneficial fitness effects
复制标题
DOI：
10.1534/genetics.106.068585
复制
发表时间：
2007-08-01
期刊：
影响因子：
3.3
通讯作者：
Joyce, Paul
中科院分区：
文献类型：
--
作者：
Beisel, Craig J.;Rokyta, Darin R.;Joyce, Paul
在进化遗传学建模中，通常假设突变效应是根据连续概率分布分配的，并且多个分布已经被不同程度地使用。对于具有有益效果的突变，目前倾向于指数分布，部分原因是它可以用极值理论来证明，因为有益突变应该在适应度分布的最右端具有适应度。虽然诉诸极值理论似乎是合理的，但指数分布只是尾部分布的三种可能的极限形式之一，其他两种大致对应于右截尾分布和厚尾分布。我们描述了一个似然比框架，用于分析有益突变的适应性效应，重点是检验分布是指数分布的零假设。我们还描述了如何解释丢失的最小效应突变，这往往是很难确定的实验。这种技术使得可以将测试应用于功能获得性突变，其中祖先基因型在选择性条件下无法生长。我们还描述了如何在实验中汇集数据，因为我们预计在任何特定的实验中几乎没有可能的有益突变。
In modeling evolutionary genetics, it is often assumed that mutational effects are assigned according to a continuous probability distribution, and multiple distributions have been used with varying degrees of justification. For mutations with beneficial effects, the distribution currently favored is the exponential distribution, in part because it can be justified in terms of extreme value theory, since beneficial mutations should have fitnesses in the extreme right tail of the fitness distribution. While the appeal to extreme value theory seems justified, the exponential distribution is but one of three possible limiting forms for tail distributions, with the other two loosely corresponding to distributions with right-truncated tails and those with heavy tails. We describe a likelihood-ratio framework for analyzing the fitness effects of beneficial mutations, focusing on testing the null hypothesis that the distribution is exponential. We also describe how to account for missing the smallest-effect mutations, which are often difficult to identify experimentally. This technique makes it possible to apply the test to gain-of-function mutations, where the ancestral genotype is unable to grow under the selective conditions. We also describe how to pool data across experiments, since we expect few possible beneficial mutations in any particular experiment.