Laboratoire de Physique, Ecole Normale Supérieure de Lyon, Lyon, France.
PLoS Comput Biol. 2011 May;7(5):e1001134. doi: 10.1371/journal.pcbi.1001134. Epub 2011 May 12.
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.
生物功能通常涉及复杂的相互作用分子网络，具有众多的反馈和调节环。当系统的一个或几个部分被修改时，系统的性质会受到怎样的影响，这是一个具有根本意义的问题，对我们研究和理解生物过程以及治疗疾病的方式有很多影响。这个问题可以用从基因型到表型的关系来重新表述：一个基因座的遗传变异对表型的影响在多大程度上取决于所有其他基因座的遗传变异？系统地定量测量上位性——不同基因座的等位基因效应的加性偏差——对于给定的数量性状仍然是一个主要挑战。在这里，我们采取一种互补的方法，在已验证的分子信号转导模型中研究多个参数变化的理论效应。为了与基因型/表型映射联系起来，我们将模型的参数解释为具有离散参数选择的不同基因座，这允许我们系统地检查信号输出（数量性状）对可能的等位基因组合集的依赖性。我们相当普遍地表明，当等位基因对应于参数的小变化时，数量性状表现出近似加性（弱上位性）；上位性是由于等位基因之间存在很大差异而出现的。当上位性相对较强时，它集中在少数几个基因座和低阶（例如，两两）相互作用中。我们发现，关注表现出强加性效应的基因座之间的相互作用是识别大部分上位性的有效方法。我们的模型研究为实验数据的解释提供了一个理论框架，并为中等复杂的生物电路的遗传相互作用的结构提供了统计预测。
