Robert-Cedergren Center for Bioinformatics and Genomics, Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.
Department of Biology, Institute of Biochemistry, and School of Mathematics and Statistics, Carleton University, Ottawa, ON, Canada.
Mol Biol Evol. 2018 Nov 1;35(11):2819-2834. doi: 10.1093/molbev/msy173.
A key question in molecular evolutionary biology concerns the relative roles of mutation and selection in shaping genomic data. Moreover, features of mutation and selection are heterogeneous along the genome and over time. Mechanistic codon substitution models based on the mutation-selection framework are promising approaches to separating these effects. In practice, however, several complications arise, since accounting for such heterogeneities often implies handling models of high dimensionality (e.g., amino acid preferences), or leads to across-site dependence (e.g., CpG hypermutability), making the likelihood function intractable. Approximate Bayesian Computation (ABC) could address this latter issue. Here, we propose a new approach, named Conditional ABC (CABC), which combines the sampling efficiency of MCMC and the flexibility of ABC. To illustrate the potential of the CABC approach, we apply it to the study of mammalian CpG hypermutability based on a new mutation-level parameter implying dependence across adjacent sites, combined with site-specific purifying selection on amino-acids captured by a Dirichlet process. Our proof-of-concept of the CABC methodology opens new modeling perspectives. Our application of the method reveals a high level of heterogeneity of CpG hypermutability across loci and mild heterogeneity across taxonomic groups; and finally, we show that CpG hypermutability is an important evolutionary factor in rendering relative synonymous codon usage. All source code is available as a GitHub repository (https://github.com/Simonll/LikelihoodFreePhylogenetics.git).
分子进化生物学中的一个关键问题是突变和选择在塑造基因组数据方面的相对作用。此外，突变和选择的特征在基因组上和随时间推移都是不均匀的。基于突变-选择框架的机制密码子替代模型是分离这些影响的有前途的方法。然而，在实践中，出现了几个复杂的问题，因为考虑到这种不均匀性通常意味着要处理高维性的模型（例如，氨基酸偏好），或者导致跨位点依赖（例如，CpG 超突变），使得似然函数难以处理。近似贝叶斯计算 (ABC) 可以解决这个后一个问题。在这里，我们提出了一种新的方法，称为条件 ABC (CABC)，它结合了 MCMC 的抽样效率和 ABC 的灵活性。为了说明 CABC 方法的潜力，我们将其应用于基于新的突变水平参数的哺乳动物 CpG 超突变性研究，该参数暗示了相邻位点之间的依赖性，同时结合了由狄利克雷过程捕获的对氨基酸的特定位置的纯化选择。我们对 CABC 方法的概念验证开辟了新的建模视角。我们对该方法的应用揭示了在不同基因座之间 CpG 超突变性的高度不均匀性，以及在分类群之间的轻微不均匀性；最后，我们表明 CpG 超突变性是导致相对同义密码子使用的一个重要进化因素。所有的源代码都可以在 GitHub 存储库中获得（https://github.com/Simonll/LikelihoodFreePhylogenetics.git）。
