Sexual Dimorphism Increases Evolvability in a Genetic Regulatory Network
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The majority of work on genetic regulatory networks has focused on environmental and mutational robustness, and much less attention has been paid to the conditions under which a network may produce an evolvable phenotype. Sexually dimorphic characters often show rapid rates of change over short evolutionary time scales and while this is thought to be due to the strength of sexual selection acting on the trait, a dimorphic character with an underlying pleiotropic architecture may also influence the evolution of the regulatory network that controls the character and affect evolvability. As evolvability indicates a capacity for phenotypic change and mutational robustness refers to a capacity for phenotypic stasis, increases in evolvability may show a negative relationship with mutational robustness. I tested this with a computational model of a genetic regulatory network and found that, contrary to expectation, sexually dimorphic characters exhibited both higher mutational robustness and higher evolvability. Decomposition of the results revealed that linkage disequilibrium within sex and linkage disequilibrium between sexes, two of the three primary components of additive genetic variance and evolvability in quantitative genetics models, contributed to the differences in evolvability between sexually dimorphic and monomorphic populations. These results indicate that producing two pleiotropically linked characters did not constrain either the production of a robust phenotype or adaptive potential. Instead, the genetic system evolved to maximize both quantities.
KeywordsGenetic regulatory network Mutational robustness Dimorphic trait Epistasis Pleiotropy Linkage disequilibrium
I would like to thank both my advisors, Thomas F. Hansen and David Houle, for helpful discussions during the development of this research. The manuscript, particularly the figures, benefited from comments from Jason Pienaar and two anonymous reviewers provided insightful suggestions. I would also like to thank the Center for Ecological and Evolutionary Synthesis at the University of Oslo for hosting my research visit. This work was supported by a Norwegian Research Council Leiv Eiriksson Mobility Grant, and a U.S. National Science Foundation Biological Informatics Postdoctoral Fellowship.
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