Epistasis in a model of molecular signal transduction.
Epistasis in a model of molecular signal transduction.
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DOI：
10.1371/journal.pcbi.1001134
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发表时间：
2011-05
影响因子：
4.3
通讯作者：
Shraiman B
中科院分区：
文献类型：
--
作者：
Pumir A;Shraiman B
生物功能通常涉及复杂的相互作用的分子网络，具有许多反馈和调节回路。当系统的一个或几个部分被修改时，系统的特性如何受到影响是一个基本的问题，对我们研究和理解生物过程和治疗疾病的方式有许多影响。这个问题可以用基因型与表型的关系来重新表述：一个基因座的遗传变异的影响在多大程度上取决于所有其他基因座的遗传变异？上位性的系统定量测量-在不同基因座的等位基因的效果从加性的偏差-对一个给定的数量性状仍然是一个重大的挑战。在这里，我们采取一种补充的方法，从理论上研究不同的多个参数的影响，在一个验证模型的分子信号转导。为了与基因型/表型映射相连接，我们将模型的参数解释为不同的基因座，这些参数的离散选择为等位基因，这使我们能够系统地检查信号输出（数量性状）对可能的等位基因组合的依赖性。我们表明，相当普遍的数量性状的行为近似加性（弱上位性）时，等位基因对应的参数的小的变化;上位性出现的结果，等位基因之间的差异很大。当上位性相对较强时，它集中在基因座的稀疏子集和低阶（例如成对）相互作用中。我们发现，专注于表现出强烈的加性效应的位点之间的相互作用是一种有效的方式来确定大多数上位性。我们的模型研究定义了解释实验数据的理论框架，并为中等复杂的生物回路预期的遗传相互作用结构提供了统计预测。遗传表型特性通常由复杂的途径定义，因此取决于影响不同基因的多种多态性。绘制这种遗传变异的表型结果是我们理解疾病易感性的核心，也是理解进化动力学的基础。同时发生的多个遗传多态性的影响与单独发生的相同多态性的影响有何关系？人们通常认为个体效应不受干扰地增加，但在许多情况下已经观察到多态性之间的相互作用。相互作用塑造表型分布的程度取决于生物系统内在相互作用的性质和群体中多态性的分布。在这里，我们接近系统方面的问题，通过使用一个适度复杂的生物分子途径-无脊椎动物的光转导-的定量建模，提供多个参数变化的非加性效应的统计特征。我们发现，相互作用与多态性的小子集，并表明，集中研究的一组强加性多态性也占总相互作用的一个显着的部分：一个发现相关的全基因组关联分析。
Biological functions typically involve complex interacting molecular networks, with numerous feedback and regulation loops. How the properties of the system are affected when one, or several of its parts are modified is a question of fundamental interest, with numerous implications for the way we study and understand biological processes and treat diseases. This question can be rephrased in terms of relating genotypes to phenotypes: to what extent does the effect of a genetic variation at one locus depend on genetic variation at all other loci? Systematic quantitative measurements of epistasis – the deviation from additivity in the effect of alleles at different loci – on a given quantitative trait remain a major challenge. Here, we take a complementary approach of studying theoretically the effect of varying multiple parameters in a validated model of molecular signal transduction. To connect with the genotype/phenotype mapping we interpret parameters of the model as different loci with discrete choices of these parameters as alleles, which allows us to systematically examine the dependence of the signaling output – a quantitative trait – on the set of possible allelic combinations. We show quite generally that quantitative traits behave approximately additively (weak epistasis) when alleles correspond to small changes of parameters; epistasis appears as a result of large differences between alleles. When epistasis is relatively strong, it is concentrated in a sparse subset of loci and in low order (e.g. pair-wise) interactions. We find that focusing on interaction between loci that exhibit strong additive effects is an efficient way of identifying most of the epistasis. Our model study defines a theoretical framework for interpretation of experimental data and provides statistical predictions for the structure of genetic interaction expected for moderately complex biological circuits. Heritable phenotypic properties are often defined by complex pathways and therefore dependent on multiple polymorphisms affecting different genes. Mapping phenotypic consequences of such genetic variation is central to our understanding of disease susceptibility and is fundamental to understanding evolutionary dynamics. How does the effect of multiple genetic polymorphisms occurring together relate to the effect of same polymorphisms in isolation? It is often assumed that individual effects add without interference, yet interactions between polymorphisms have been observed in numerous contexts. The extent to which interactions shape phenotype distributions depends on the nature of interaction intrinsic to the biological system and on the distribution of polymorphisms in the population. Here we approach the systems aspect of the problem by using quantitative modeling of a moderately complex bio-molecular pathway - invertebrate phototransduction - to provide a statistical characterization of non-additive effects of multiple parameter changes. We find that interaction is associated with small subsets of polymorphisms and demonstrate that focusing the study on the set of strong additive polymorphisms accounts also for a significant fraction of total interaction: a finding relevant to the genome-wide-association analysis.
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影响因子：
3.9
作者：
Rand, D. A.
通讯作者：
Rand, D. A.
影响因子：
56.9
作者：
Maerkl, Sebastian J.;Quake, Stephen R.
通讯作者：
Quake, Stephen R.
DOI：
10.1073/pnas.0711884105
发表时间：
2008-07-29
影响因子：
11.1
作者：
Pumir, Alain;Graves, Jennifer;Shraiman, Boris I.
通讯作者：
Shraiman, Boris I.
影响因子：
2
作者：
Rand, DA;Shulgin, BV;Millar, AJ
通讯作者：
Millar, AJ
影响因子：
16.2
作者：
Hardie, RC;Martin, F;Raghu, P
通讯作者：
Raghu, P