Beisel Craig J, Rokyta Darin R, Wichman Holly A, Joyce Paul
Initiative for Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, ID 83844, USA.
Genetics. 2007 Aug;176(4):2441-9. doi: 10.1534/genetics.106.068585. Epub 2007 Jun 11.
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.
在对进化遗传学进行建模时，通常假定突变效应是根据连续概率分布来分配的，并且已经使用了多种分布，其合理性程度各不相同。对于具有有益效应的突变，目前最受青睐的分布是指数分布，部分原因是它可以根据极值理论得到合理的解释，因为有益突变的适应度应该处于适应度分布的最右端尾部。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三种可能的极限形式之一，另外两种大致对应于具有右截断尾部的分布和具有重尾的分布。我们描述了一个用于分析有益突变适应度效应的似然比框架，重点是检验分布为指数分布的原假设。我们还描述了如何处理遗漏最小效应突变的情况，这些突变通常很难通过实验识别。这项技术使得能够将该检验应用于功能获得性突变，在这种情况下，祖先基因型在选择条件下无法生长。我们还描述了如何跨实验汇总数据，因为我们预计在任何特定实验中可能的有益突变很少。
