Aris-Brosou Stéphane, Bielawski Joseph P
Department of Biology, University of Ottawa, 30 Marie Curie, Ottawa, ON, Canada K1N 6N5.
Gene. 2006 Aug 15;378:58-64. doi: 10.1016/j.gene.2006.04.024. Epub 2006 May 22.
A popular approach to examine the roles of mutation and selection in the evolution of genomes has been to consider the relationship between codon bias and synonymous rates of molecular evolution. A significant relationship between these two quantities is taken to indicate the action of weak selection on substitutions among synonymous codons. The neutral theory predicts that the rate of evolution is inversely related to the level of functional constraint. Therefore, selection against the use of non-preferred codons among those coding for the same amino acid should result in lower rates of synonymous substitution as compared with sites not subject to such selection pressures. However, reliably measuring the extent of such a relationship is problematic, as estimates of synonymous rates are sensitive to our assumptions about the process of molecular evolution. Previous studies showed the importance of accounting for unequal codon frequencies, in particular when synonymous codon usage is highly biased. Yet, unequal codon frequencies can be modeled in different ways, making different assumptions about the mutation process. Here we conduct a simulation study to evaluate two different ways of modeling uneven codon frequencies and show that both model parameterizations can have a dramatic impact on rate estimates and affect biological conclusions about genome evolution. We reanalyze three large data sets to demonstrate the relevance of our results to empirical data analysis.
一种研究突变和选择在基因组进化中作用的常用方法是考虑密码子偏好性与分子进化同义速率之间的关系。这两个量之间的显著关系被视为弱选择对同义密码子间替换作用的体现。中性理论预测进化速率与功能限制水平呈负相关。因此，与不受此类选择压力影响的位点相比，在编码相同氨基酸的密码子中，针对非最优密码子使用的选择应导致较低的同义替换率。然而，可靠地衡量这种关系的程度存在问题，因为同义速率的估计对我们关于分子进化过程的假设很敏感。先前的研究表明考虑密码子频率不均等的重要性，特别是当同义密码子使用高度偏倚时。然而，密码子频率不均等可以用不同方式建模，对突变过程做出不同假设。在这里，我们进行了一项模拟研究，以评估对不均等密码子频率建模的两种不同方式，并表明两种模型参数化都可能对速率估计产生巨大影响，并影响关于基因组进化的生物学结论。我们重新分析了三个大型数据集，以证明我们的结果与实证数据分析的相关性。
