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使用“上位性”软件包从基因型-表型图谱分析高阶上位性

Analyzing High-Order Epistasis from Genotype-Phenotype Maps Using 'Epistasis' Package.

作者信息

Chen Junyi, Wong Ka-Chun

机构信息

Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.

出版信息

Methods Mol Biol. 2021;2212:265-275. doi: 10.1007/978-1-0716-0947-7_16.

Abstract

Epistasis is the phenomenon about the interactions between genes, leading to complex phenotypic effects. The interactions between three or more mutations called "high-order epistasis" aroused significant interests in recent studies. However, there are still debates for analysis of high-order epistasis due to the non-linear model complexity and statistical artifacts. A recent "epistasis" Python package was therefore developed to characterize high-order epistasis by estimating non-linear scaling for mutation effects to extract high-order epistasis using linear models. This method successfully discovered statistically significant high-order epistasis on several real genotype-phenotype maps. We provided a concise and step-by-step guide to apply the "epistasis" by reproducing the high-order epistasis discoveries on real genotype-phenotype data using the latest API of the package.

摘要

上位性是指基因之间相互作用从而产生复杂表型效应的现象。三个或更多突变之间的相互作用被称为“高阶上位性”,在最近的研究中引起了极大关注。然而,由于非线性模型的复杂性和统计假象,对于高阶上位性的分析仍存在争议。因此,最近开发了一个名为“epistasis”的Python软件包,通过估计突变效应的非线性缩放来表征高阶上位性,以便使用线性模型提取高阶上位性。该方法在几个真实的基因型-表型图谱上成功发现了具有统计学意义的高阶上位性。我们通过使用该软件包的最新API在真实的基因型-表型数据上重现高阶上位性发现,提供了一个简洁的逐步指南来应用“epistasis”。

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