Jump-Chain Simulation of Markov Substitution Processes Over Phylogenies.

We draw attention to an under-appreciated simulation method for generating artificial data in a phylogenetic context. The approach, which we refer to as jump-chain simulation, can invoke rich models of molecular evolution having intractable likelihood functions. As an example, we simulate data under a context-dependent model allowing for CpG hypermutability and show how such a feature can mislead common codon models used for detecting positive selection. We discuss more generally how this method can serve to elucidate the ways by which currently used models for inference are susceptible to violations of their underlying assumptions. Finally, we show how the method could serve as an inference engine in the Approximate Bayesian Computation framework.

Conditional Approximate Bayesian Computation: A New Approach for Across-Site Dependency in High-Dimensional Mutation-Selection Models.

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).

Multiple Factors Confounding Phylogenetic Detection of Selection on Codon Usage.

Detecting selection on codon usage (CU) is a difficult task, since CU can be shaped by both the mutational process and selective constraints operating at the DNA, RNA, and protein levels. Yang and Nielsen (2008) developed a test (which we call CUYN) for detecting selection on CU using two competing mutation-selection models of codon substitution. The null model assumes that CU is determined by the mutation bias alone, whereas the alternative model assumes that both mutation bias and/or selection act on CU. In applications on mammalian-scale alignments, the CUYN test detects selection on CU for numerous genes. This is surprising, given the small effective population size of mammals, and prompted us to use simulations to evaluate the robustness of the test to model violations. Simulations using a modest level of CpG hypermutability completely mislead the test, with 100% false positives. Surprisingly, a high level of false positives (56.1%) resulted simply from using the HKY mutation-level parameterization within the CUYN test on simulations conducted with a GTR mutation-level parameterization. Finally, by using a crude optimization procedure on a parameter controlling the CpG hypermutability rate, we find that this mutational property could explain a very large part of the observed mammalian CU. Altogether, our work emphasizes the need to evaluate the potential impact of model violations on statistical tests in the field of molecular phylogenetic analysis. The source code of the simulator and the mammalian genes used are available as a GitHub repository (https://github.com/Simonll/LikelihoodFreePhylogenetics.git).