CHOX1

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

Throughout this module, we will use articles to discuss several evolutionary topics relevant to Evo-Devo approaches: the concept of homology, the origin and evolution of repetitive structures, the genetic basis of development, and the links between genomic evolution and morphological evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them at the level of major modern groups as well as at the population level.

View the full page

Phylogeny is a search for evolutionary clues. The goal of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods for representing evolutionary histories as trees, and the concept of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the core of this course unit. Distance-based methods highlight the difficulties in distinguishing between homology and homoplasy, as well as the need to construct models of character evolution. The cladistic approach based on maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogenetic nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

Probabilistic approaches are introduced and then explored in greater depth. The long-branch attraction artifact leads to the introduction of probabilistic reasoning. The maximum likelihood method allows us to address the calculation of likelihood, the estimation of model parameters using optimality criteria, the construction of various models of character evolution, and the comparison of models. Bayesian inference, in turn, introduces the distinction between density-based and optimality-based approaches. It then covers the a priori use of probability densities, the estimation of posterior distributions of model parameters based on the data, their approximation using Markov chains with Monte Carlo and Metropolis coupling (MCMCMC), the initialization and convergence phases, and the calculation and interpretation of posterior probabilities for trees and clades. The importance of evolutionary models for DNA, RNA, and protein sequences and their refinement is emphasized.

View the full page