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检测非线性基因型-表型图谱中的高阶上位性

Detecting High-Order Epistasis in Nonlinear Genotype-Phenotype Maps.

作者信息

Sailer Zachary R, Harms Michael J

机构信息

Institute of Molecular Biology, University of Oregon, Eugene, Oregon 97403.

Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403.

出版信息

Genetics. 2017 Mar;205(3):1079-1088. doi: 10.1534/genetics.116.195214. Epub 2017 Jan 18.

Abstract

High-order epistasis has been observed in many genotype-phenotype maps. These multi-way interactions between mutations may be useful for dissecting complex traits and could have profound implications for evolution. Alternatively, they could be a statistical artifact. High-order epistasis models assume the effects of mutations should add, when they could in fact multiply or combine in some other nonlinear way. A mismatch in the "scale" of the epistasis model and the scale of the underlying map would lead to spurious epistasis. In this article, we develop an approach to estimate the nonlinear scales of arbitrary genotype-phenotype maps. We can then linearize these maps and extract high-order epistasis. We investigated seven experimental genotype-phenotype maps for which high-order epistasis had been reported previously. We find that five of the seven maps exhibited nonlinear scales. Interestingly, even after accounting for nonlinearity, we found statistically significant high-order epistasis in all seven maps. The contributions of high-order epistasis to the total variation ranged from 2.2 to 31.0%, with an average across maps of 12.7%. Our results provide strong evidence for extensive high-order epistasis, even after nonlinear scale is taken into account. Further, we describe a simple method to estimate and account for nonlinearity in genotype-phenotype maps.

摘要

在许多基因型-表型图谱中都观察到了高阶上位性。突变之间的这些多向相互作用可能有助于剖析复杂性状,并可能对进化产生深远影响。或者,它们可能是一种统计假象。高阶上位性模型假设突变的效应应该相加,而实际上它们可能相乘或以其他非线性方式组合。上位性模型的“尺度”与基础图谱的尺度不匹配会导致虚假上位性。在本文中,我们开发了一种方法来估计任意基因型-表型图谱的非线性尺度。然后我们可以将这些图谱线性化并提取高阶上位性。我们研究了七个先前已报道存在高阶上位性的实验性基因型-表型图谱。我们发现七个图谱中有五个呈现出非线性尺度。有趣的是,即使考虑了非线性,我们在所有七个图谱中都发现了具有统计学意义的高阶上位性。高阶上位性对总变异的贡献范围为2.2%至31.0%,各图谱的平均贡献为12.7%。我们的结果提供了强有力的证据,表明即使考虑了非线性尺度,广泛存在高阶上位性。此外,我们描述了一种简单的方法来估计和考虑基因型-表型图谱中的非线性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6986/5340324/a74a68d1e84a/1079fig1.jpg

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