A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments.
A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments.
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DOI：
10.1534/genetics.113.156190
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发表时间：
2014-03
期刊：
影响因子：
3.3
通讯作者：
Jensen JD
中科院分区：
文献类型：
--
作者：
Bank C;Hietpas RT;Wong A;Bolon DN;Jensen JD
几十年来，适应在进化过程中的作用一直存在争议。中性论者和选择论者之间长达一个世纪的争论的核心是适应性效应（DFE）的分布，即所有突变的选择效应。描述DFE的尝试是多种多样的，理论家和实验家都一样。新的高通量技术的立场作出重要贡献的实证工作，以表征的DFE，但这种方法的有用性取决于强大的统计方法的可用性，他们的解释。我们在这里提出并讨论了贝叶斯MCMC方法来估计深度测序数据的适应度，并使用它来评估六种不同环境条件下酿酒酵母中Hsp90编码区中相同560个点突变的DFE。使用这些估计，我们比较了从野生型突变覆盖一个，两个和三个核苷酸的步骤，显示多步突变在具有挑战性的环境中具有更大的适应潜力，但在标准环境中也往往是更有害的DFE的差异。所有的意见进行了讨论，从费舍尔的几何模型所产生的期望。
The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)—that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type—showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher’s geometric model.