CHOX1

Evo-devo is an evolutionary approach to developmental genetics. This discipline seeks to shed light on the changes in developmental mechanisms that explain current and past morphological diversity, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

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Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

Probabilistic approaches are presented and explored in depth. The artifact of attraction to long branches leads to the introduction of probabilistic reasoning. The maximum likelihood method allows us to address likelihood calculation, model parameter estimation by optimality, the construction of different character evolution models, and model comparison. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the estimation of the posterior distributions of model parameters based on the data, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of the posterior probabilities of trees and clades. The importance of DNA, RNA, and protein sequence evolution models and their improvement is emphasized.

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