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arXiv 1607.07474q-bio.PE
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Ewens抽样公式在任意适应度景观中的推广

Generalization of the Ewens sampling formula to arbitrary fitness landscapes

Pavel Khromov, Constantin D. Malliaris, Alexandre V. Morozov

摘要:

在考虑转录区域、调控模块和其他感兴趣的基因组位点的进化时,我们经常面临等位基因状态的数量大大超过群体大小的情况。在这个极限下,种群最终会达到突变-选择-漂移平衡的稳定状态。虽然新的等位基因继续通过突变进行探索,但群体的统计数据,特别是在从群体中采集的样品中看到特定等位基因构型的概率,并不随时间而改变。在没有选择的情况下,等位基因构型的概率由Ewens抽样公式给出,该公式广泛用于群体遗传学以检测偏离中性的情况。在这里,我们将这个公式推广到任意的,可能上位的,适应度景观。虽然我们的方法是一般性的,我们专注于类的景观,其中等位基因被分组为两个,三个或几个健身状态。这类景观产生的采样概率在计算上更容易处理,并且可以形成从序列数据推断选择签名的基础。我们证明,对于相当大范围的突变率和选择系数,稳态等位基因多样性不是中性的。因此,它可以用来推断选择系数,以及其他关键的进化参数,使用进化群体的高通量测序收集基因座多态性的数据。我们还发现,我们的理论仍然足够准确,即使假设,如无限的等位基因限制和“完全连接”的假设,其中每个等位基因可以突变成任何其他等位基因是放松的。因此,我们的框架建立了一个理论基础,推断选择签名从上位适应度景观进化产生的序列样本。

英文摘要:

In considering evolution of transcribed regions, regulatory modules, and other genomic loci of interest, we are often faced with a situation in which the number of allelic states greatly exceeds the population size. In this limit, the population eventually adopts a steady state characterized by mutation-selection-drift balance. Although new alleles continue to be explored through mutation, the statistics of the population, and in particular the probabilities of seeing specific allelic configurations in samples taken from a population, do not change with time. In the absence of selection, probabilities of allelic configurations are given by the Ewens sampling formula, widely used in population genetics to detect deviations from neutrality. Here we develop an extension of this formula to arbitrary, possibly epistatic, fitness landscapes. Although our approach is general, we focus on the class of landscapes in which alleles are grouped into two, three, or several fitness states. This class of landscapes yields sampling probabilities that are computationally more tractable, and can form a basis for the inference of selection signatures from sequence data. We demonstrate that, for a sizeable range of mutation rates and selection coefficients, the steady-state allelic diversity is not neutral. Therefore, it may be used to infer selection coefficients, as well as other key evolutionary parameters, using high-throughput sequencing of evolving populations to collect data on locus polymorphisms. We also find that our theory remains sufficiently accurate even if the assumptions such as the infinite allele limit and the "full connectivity" assumption in which each allele can mutate into any other allele are relaxed. Thus, our framework establishes a theoretical foundation for inferring selection signatures from samples of sequences produced by evolution on epistatic fitness landscapes.

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