Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability.
Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability.
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DOI：
10.1371/journal.pcbi.1004773
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发表时间：
2016-03
影响因子：
4.3
通讯作者：
Louis AA
中科院分区：
文献类型：
--
作者：
Greenbury SF;Schaper S;Ahnert SE;Louis AA
基因型-表型（GP）图谱中的突变邻域被广泛认为比随机机会预期的更有可能共享特征。这种遗传相关性应该会对进化动力产生强烈的影响。我们探索和量化这些直觉，通过比较三个GP地图模型的RNA二级结构，HP模型的蛋白质三级结构，和Polyomino模型的蛋白质四级结构，一个简单的随机空模型，保持映射到每个表型的基因型的数量，但随机分配基因型。在这些GP图中的基因型的突变邻域比在随机空模型中更可能包含映射到相同表型的基因型。这种中性相关性可以通过对突变的鲁棒性来量化，突变的鲁棒性可以比空模型的鲁棒性大许多个数量级，并且至关重要的是，高于形成突变连接基因型的大型中性网络的临界阈值，这增强了探索表型新奇的能力。因此，中性相关性增加了进化性。我们还研究了非中性相关性：与空模型相比，i）如果一个特定的ii）如果在基因型的1-突变邻域中发现一次（非中性）表型，则在该邻域中多次发现该表型的机会大于预期; iii）如果基因型映射到折叠或自组装表型，则其非中性邻居不太可能是潜在有害的非折叠或非组装表型。类型i）和类型ii）的非中性相关性降低了通过中性探索发现新表型的速度，因此可能会降低可进化性，而类型iii）的非中性相关性可能会促进进化探索，从而增加可进化性。进化动力来自于作用于基因型的突变和作用于表型的自然选择的相互作用。因此，了解基因型-表型（GP）图谱的结构对于理解进化过程至关重要。我们要解决一个关于结构的简单问题：基因型是否正相关？也就是说，基因型的突变邻居是否比随机概率更有可能映射到相似的表型？约翰·梅纳德·史密斯（John Maynard Smith）和其他人认为，直觉的答案是肯定的。在这里，我们量化这些直观的比较模型GP地图的RNA二级结构，蛋白质三级结构和蛋白质四级结构的随机GP地图。我们发现了强中性相关性：点突变比随机机会预期的数量级更有可能将映射到相同表型的基因型联系起来，这极大地增加了通过生成中性网络进行进化创新的潜力。如果GP地图像随机地图一样不相关，进化甚至可能不可能。我们还发现了非中性突变的相关性：突变邻域的多样性低于随机机会的预期。这种局部异质性减缓了新的表型变异的发现速度，但非中性相关性也通过降低突变为有害的非折叠或非组装表型的概率来增强进化性。
Mutational neighbourhoods in genotype-phenotype (GP) maps are widely believed to be more likely to share characteristics than expected from random chance. Such genetic correlations should strongly influence evolutionary dynamics. We explore and quantify these intuitions by comparing three GP maps—a model for RNA secondary structure, the HP model for protein tertiary structure, and the Polyomino model for protein quaternary structure—to a simple random null model that maintains the number of genotypes mapping to each phenotype, but assigns genotypes randomly. The mutational neighbourhood of a genotype in these GP maps is much more likely to contain genotypes mapping to the same phenotype than in the random null model. Such neutral correlations can be quantified by the robustness to mutations, which can be many orders of magnitude larger than that of the null model, and crucially, above the critical threshold for the formation of large neutral networks of mutationally connected genotypes which enhance the capacity for the exploration of phenotypic novelty. Thus neutral correlations increase evolvability. We also study non-neutral correlations: Compared to the null model, i) If a particular (non-neutral) phenotype is found once in the 1-mutation neighbourhood of a genotype, then the chance of finding that phenotype multiple times in this neighbourhood is larger than expected; ii) If two genotypes are connected by a single neutral mutation, then their respective non-neutral 1-mutation neighbourhoods are more likely to be similar; iii) If a genotype maps to a folding or self-assembling phenotype, then its non-neutral neighbours are less likely to be a potentially deleterious non-folding or non-assembling phenotype. Non-neutral correlations of type i) and ii) reduce the rate at which new phenotypes can be found by neutral exploration, and so may diminish evolvability, while non-neutral correlations of type iii) may instead facilitate evolutionary exploration and so increase evolvability. Evolutionary dynamics arise from the interplay of mutations acting on genotypes and natural selection acting on phenotypes. Understanding the structure of the genotype-phenotype (GP) map is therefore critical for understanding evolutionary processes. We address a simple question about structure: Are the genotypes positively correlated? That is, will the mutational neighbours of a genotype be more likely to map to similar phenotypes than expected from random chance? John Maynard Smith and others have argued that the intuitive answer is yes. Here we quantify these intuitions by comparing model GP maps for RNA secondary structure, protein tertiary structure, and protein quaternary structure to a random GP map. We find strong neutral correlations: Point mutations are orders of magnitude more likely than expected by random chance to link genotypes that map to the same phenotype, which vitally increases the potential for evolutionary innovation by generating neutral networks. If GP maps were uncorrelated like the random map, evolution may not even be possible. We also find correlations for non-neutral mutations: Mutational neighbourhoods are less diverse than expected by random chance. Such local heterogeneity slows down the rate at which new phenotypic variation can be found. But non-neutral correlations also enhance evolvability by lowering the probability of mutating to a deleterious non-folding or non-assembling phenotype.