A genetic algorithm approach to detecting lineage-specific variation in selection pressure
A genetic algorithm approach to detecting lineage-specific variation in selection pressure
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DOI：
10.1093/molbev/msi031
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发表时间：
2005-03-01
影响因子：
10.7
通讯作者：
Frost, SDW
中科院分区：
文献类型：
--
作者：
Pond, SLK;Frost, SDW
非同义(DN)与同义(DS)替换率的比率omega提供了蛋白质水平上的选择衡量标准。已经开发的模型允许omega在不同的谱系中有所不同。然而，这些模型要求先验地指定差异选择起作用的谱系。我们提出了一种遗传算法方法，将系统发育中的谱系分配给固定数量的不同类别的To，从而允许可变的选择压力，而不需要特定谱系的先验规范。这种方法可以识别出比单一比率模型更好的拟合度，以及比(在信息论意义上)完全局部模型更好的拟合度，在完全局部模型中，假设所有谱系在不同的w值下进化，但使用的参数要少得多。通过对合理解释数据的模型进行平均，我们可以评估我们的结论对模型估计中的不确定性的稳健性。我们的方法还可以用于将先验指定分支类的模型的结果与范围广泛的可信模型进行比较。我们举例说明了我们在灵长类溶菌酶序列上的方法，并将它们与以前应用于相同数据集的方法进行了比较。
The ratio of nonsynonymous (dN) to synonymous (dS) substitution rates, omega, provides a measure of selection at the protein level. Models have been developed that allow omega to vary among lineages. However, these models require the lineages in which differential selection has acted to be specified a priori. We propose a genetic algorithm approach to assign lineages in a phylogeny to a fixed number of different classes of to, thus allowing variable selection pressure without a priori specification of particular lineages. This approach can identify models with a better fit than a single-ratio model, and with fits that are better than (in an information theoretic sense) a fully local model, in which all lineages are assumed to evolve under different values of w, but with far fewer parameters. By averaging over models which explain the data reasonably well, we can assess the robustness of our conclusions to uncertainty in model estimation. Our approach can also be used to compare results from models in which branch classes are specified a priori with a wide range of credible models. We illustrate our methods on primate lysozyme sequences and compare them with previous methods applied to the same data sets.