1] School of Animal and Microbial Sciences, University of Reading, Whiteknights, UK [2] Instituto Gulbenkian de Ciencia, Oeiras, Portugal.
1] Departamento de Bioquímica, Genética e Inmunología, Universidad de Vigo, Vigo, Spain [2] Centre for Molecular Biology 'Severo Ochoa', Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain.
Heredity (Edinb). 2014 Mar;112(3):255-64. doi: 10.1038/hdy.2013.101. Epub 2013 Oct 23.
The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.
当某些过程未被考虑时，分子进化中的参数估计可能会产生偏差。例如，当忽略重组时，使用密码子替代模型对分子水平的选择进行估计可能会产生向上的偏差。在这里，我们使用近似贝叶斯计算 (ABC) 从编码序列联合估计重组、分子适应和替代率。我们描述了一种基于回归的策略，用于为编码数据选择摘要统计量的子集，并表明这种方法可以准确推断重组，允许内密码子重组断点、分子适应和密码子替代率。我们证明了我们的 ABC 方法在各种进化场景下都可以优于其他分析方法。我们还表明，尽管密码子替代模型的选择很重要，但我们的推断对于中度模型失配具有稳健性。此外，我们证明我们的方法可以准确选择最适合数据的进化模型，为无法使用完全似然方法时提供了一种替代方法。最后，我们将我们的 ABC 方法应用于从 24 个已发表的人类免疫缺陷病毒 1 编码数据集共同估计重组、替代和分子适应率。
