Statistical epistasis is a generic feature of gene regulatory networks
Statistical epistasis is a generic feature of gene regulatory networks
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DOI：
10.1534/genetics.106.058859
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发表时间：
2007-01-01
期刊：
影响因子：
3.3
通讯作者：
Carlborg, Orjan
中科院分区：
文献类型：
--
作者：
Gjuvsland, Arne B.;Hayes, Ben J.;Carlborg, Orjan
基因间的功能依赖性是数量性状基因网络的一个重要特征。然而，最近的研究表明，可以归因于统计上位性的遗传变异的比例从几乎为零到非常高不等。因此，它是根本的，以及工具的重要性，以更好地了解是否不同的功能依赖模式之间的多态性基因引起不同的统计相互作用模式或没有。在这里，我们解决这个问题相结合的定量遗传模型方法与基因型-表型模型能够翻译等位基因变异和监管原则转化为表型变异的基因表达水平。我们发现，基因调控网络与反馈基序和无反馈基序可以表现出广泛的可能的统计遗传结构方面的两种类型的效果解释表型方差和数量的表观位点的基础上观察到的表型效应。虽然所有的图案都能够窝藏显着的相互作用，积极的反馈会产生更高的数额和更多类型的统计上位性。研究结果还表明，在遗传模型中纳入统计相互作用项将增加检测其他QTL的机会，以及在广泛的监管制度的遗传位点之间的功能依赖性。本文说明了如何统计遗传学方法可以卓有成效地结合非线性系统动力学，阐明生物学问题超出了每种方法的孤立。
Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.