The strength of genetic interactions scales weakly with mutational effects.
The strength of genetic interactions scales weakly with mutational effects.
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DOI：
10.1186/gb-2013-14-7-r76
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发表时间：
2013-07-26
期刊：
影响因子：
12.3
通讯作者：
Gore J
中科院分区：
文献类型：
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作者：
Velenich A;Gore J
遗传相互作用遍及生物学的各个方面，从决定进化路径可及性的进化理论，到可能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都是基于少数突变，最多只能提供关于基因相互作用频率和典型强度的轶事证据。在这项研究中，我们分析了一个公开可用的数据集，其中包含了超过500万个酵母双敲除突变体的生长速度。我们讨论了上位性的几何定义，揭示了遗传相互作用的特征强度作为突变组合效应的函数的简单而令人惊讶的弱标度定律。然后，我们利用这种缩放来量化自然发生的健身景观的粗糙度。最后，我们展示了观察到的粗糙度与Fisher的占位几何模型所预测的粗糙度有何不同，并讨论了进化动力学的后果。虽然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规律，并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，现在有可能彻底测试遗传相互作用理论模型的预测，并在现实的适应性景观中建立明智的进化计算模型。
Genetic interactions pervade every aspect of biology, from evolutionary theory, where they determine the accessibility of evolutionary paths, to medicine, where they can 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 a publicly available dataset that contains the growth rates of over five million double knockout mutants of the yeast Saccharomyces cerevisiae. We discuss a geometric definition of epistasis that 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 utilized 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 the consequences for 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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DOI：
10.1073/pnas.0712255105
发表时间：
2008-03-04
影响因子：
11.1
作者：
Mani, Ramamurthy;Onge, Robert P. St.;Roth, Frederick P.
通讯作者：
Roth, Frederick P.
影响因子：
64.8
作者：
Poelwijk, Frank J.;Kiviet, Daniel J.;Tans, Sander J.
通讯作者：
Tans, Sander J.
影响因子：
3.3
作者：
Orr, HA
通讯作者：
Orr, HA
在基因组量表上对酵母中适应性和遗传相互作用的定量分析。
影响因子：
48
作者：
Baryshnikova, Anastasia;Costanzo, Michael;Kim, Yungil;Ding, Huiming;Koh, Judice;Toufighi, Kiana;Youn, Ji-Young;Ou, Jiongwen;San Luis, Bryan-Joseph;Bandyopadhyay, Sunayan;Hibbs, Matthew;Hess, David;Gingras, Anne-Claude;Bader, Gary D.;Troyanskaya, Olga G.;Brown, Grant W.;Andrews, Brenda;Boone, Charles;Myers, Chad L.
通讯作者：
Myers, Chad L.
DOI：
10.1038/nrg2452
发表时间：
2008-11
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
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