Why and how genetic canalization evolves in gene regulatory networks.
Genetic canalization reflects the capacity of an organism's phenotype to remain unchanged in spite of mutations. As selection on genetic canalization is weak and indirect, whether or not genetic canalization can reasonably evolve in complex genetic architectures is still an open question. In this paper, we use a quantitative model of gene regulatory network to describe the conditions in which substantial canalization is expected to emerge in a stable environment.



Through an individual-based simulation framework, we confirmed that most parameters associated with the network topology (complexity and size of the network) have less influence than mutational parameters (rate and size of mutations) on the evolution of genetic canalization. We also established that selecting for extreme phenotypic optima (nil or full gene expression) leads to much higher canalization levels than selecting for intermediate expression levels. Overall, constrained networks evolve less canalization than networks in which some genes could evolve freely (i.e. without direct stabilizing selection pressure on gene expression).



Taken together, these results lead us to propose a two-fold mechanism involved in the evolution of genetic canalization in gene regulatory networks: the shrinkage of mutational target (useless genes are virtually removed from the network) and redundancy in gene regulation (so that some regulatory factors can be lost without affecting gene expression).
Laboratoire Évolution, Génomes, Comportement, Écologie, CNRS-IRD-Univ. Paris-Sud-Université Paris-Saclay, Gif-sur-Yvette, 91198, France.
Estelle
E
Rünneburger
Laboratoire Évolution, Génomes, Comportement, Écologie, CNRS-IRD-Univ. Paris-Sud-Université Paris-Saclay, Gif-sur-Yvette, 91198, France. arnaud.le-rouzic@egce.cnrs-gif.fr.
Arnaud
A
Le Rouzic
D005075:Biological Evolution; D003198:Computer Simulation; D004777:Environment; D019143:Evolution, Molecular; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D009154:Mutation; D012641:Selection, Genetic
Evolution of epistasis; Genetic architecture; Individual-based simulations; Quantitative genetics