Modélisation des biais mutationnels et rôle de la sélection sur l’usage des codons

The acquisition of genomic data continues to grow, as does the appetite to interpret them. But determining the processes that shaped the evolution of coding sequences (and their relative importance) is a scientific challenge that requires the development of statistical models of evolution that increasingly take into account heterogeneities in mutation and selection processes.  Identifying selection is a task that typically requires comparing two models: a null model that does not allow for an adaptive evolutionary regime and an alternative model that allows it. When a test between these two models rejects the null, we consider to have detected the presence of adaptive evolution. The task is all the more difficult as the signal is weak and confounded with various heterogeneities neglected by the models.  The detection of selection on codon usage is controversial, particularly in Vertebrates. There are several reasons for this controversy: (1) there is a sociological bias in seeing selection as the main driver of evolution, to such an extent that heterogeneities relating to mutation processes are historically neglected; (2) according to the principles of population genetics, the small effective size of vertebrate populations limits the power of selection over synonymous mutations conferring a minimal advantage; (3) On the other hand, selection on the use of codons could be very localized along the coding sequences, at specific sites, subject to selective constraints related to DNA patterns used by the splicing machinery, for example.  Phylogenetic mutation-selection models are the preferred tools to address these issues, as they explicitly model mutation processes and selective constraints. All the heterogeneities neglected by the mutation-selection models of Yang and Nielsen [2008] can generate false positives, ranging from 20% (site-specific amino acid preference) to 100% (hypermutability of transitions in CpG context)[Laurin-Lemay et al., 2018b]. In particular, the hypermutability of transitions in the CpG context alone can explain the selection on codon usage detected by Yang and Nielsen [2008].  However, modelling phenomena that take into account data interdependencies (e.g., hypermutability of the CpG context) greatly increases the complexity of the likelihood function. On the other hand, today’s sophisticated models require high-dimensional parameter vectors to model the heterogeneity of the processes studied, in our case selective constraints on the protein.   Approximate Bayesian Computation (ABC) is used to bypass the calculation of the likelihood function. This approach differs from the Markov Chain Monte Carlo (MCMC) sampling commonly used to approximate the posterior distribution. We explored the idea of combining these approaches for a specific problem involving high-dimensional parameters and new parameters taking into account dependencies between sites. Under certain conditions, when the high dimensionality parameters are weakly correlated to the new parameters of interest, it is possible to infer the high dimensionality parameters with the MCMC method, and then the parameters of interest using the ABC. This new approach is called Conditional Approximate Bayesian Computation (CABC) [Laurin-Lemay et al., 2018a]. We were able to verify the effectiveness of the CABC method in a case study, namely the hypermutability of transitions in the CpG context within Eutheria [Laurin-Lemay et al.,2018a]. We find that 100% of the 137 genes tested have significant hypermutability of transitions. We have also shown that models incorporating hypermutability of transitions in CpG contexts predict a codon usage closer to that of the genes studied. This suggests that a significant part of codon usage can be explained by mutational processes alone.   Finally, we explore several avenues of research emanating from our methodological developments: the application of hypermutability detection of transitions in CpG contexts to the Vertebrate scale; the expansion of the model to recognize contexts other than only CpG (e.g., hypermutability of transitions and transversions in CpG and TpA context); and methodological perspectives to improve the performance of the CABC approach.

Keyphrases: molecular evolution, approximate bayesian computation, phenomenological and mechanistic modeling, codon usage, vertebrates evolution, coding sequence evolution