遗传相互作用的强度与突变效应的比例很小
The strength of genetic interactions scales weakly with the mutational effects
摘要：
基因相互作用渗透到生物学的各个方面，从决定进化路径可及性的进化理论，到导致复杂遗传疾病的医学。直到最近，关于上位相互作用的研究一直基于少数突变，充其量只能提供有关遗传相互作用频率和典型强度的轶事证据。在这项研究中，我们分析了公开的酵母遗传相互作用数据库（DRYGIN），其中包含超过500万个双基因敲除突变体的生长率。我们讨论了上位性的几何定义，揭示了一个简单的和令人惊讶的弱比例法则的遗传相互作用的特征强度作为一个功能的突变相结合的影响。然后，我们利用这种缩放来量化自然发生的适应度景观的粗糙度。最后，我们将展示所观察到的粗糙度与Fisher的上位性几何模型预测的不同，并讨论其对演化动力学的影响。尽管特定基因之间的上位性相互作用在很大程度上仍然是不可预测的，但相互作用集合的统计特性可以显示出明显的差异，并且可以用简单的数学定律来描述。通过利用现代高通量技术产生的大量数据，现在可以彻底测试遗传相互作用的理论模型的预测，并在现实的适应度景观上建立有根据的进化计算模型。
英文摘要：
Genetic interactions pervade every aspect of biology, from evolutionary theory where they determine the accessibility of evolutionary paths, to medicine where they contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study we analyze the publicly available Data Repository of Yeast Genetic INteractions (DRYGIN), which contains the growth rates of over five million double gene knockout mutants. We discuss a geometric definition of epistasis which reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilize this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher's geometric model of epistasis and discuss its consequences on the evolutionary dynamics. Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.
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