Evolutionary quantitative genetics of nonlinear developmental systems

• Published in: Evolution Vol. 69; no. 8; pp. 2050 - 2066
• Main Author: Morrissey, Michael B.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.08.2015; Society for the Study of Evolution; Oxford University Press
• Subjects: Biological Evolution; Biological variation; Correlation analysis; Covariance matrices; Development; Ecological competition; epistasis; Epistasis, Genetic; Evolution; Evolutionary genetics; extended selection gradients; Genetic Fitness; Genetic Pleiotropy; Genetic variance; Genetic Variation; Genetics; Genotype & phenotype; Models, Genetic; Phenotype; Phenotypes; phenotypic landscape; Phenotypic traits; Quantitative genetics; Quantitative traits; Selection, Genetic; Stabilizing selection; Development; quantitative genetics; epistasis; extended selection gradients; stabilizing selection; phenotypic landscape
• ISSN: 0014-3820; 1558-5646; 1558-5646
• DOI: 10.1111/evo.12728

In quantitative genetics, the effects of developmental relationships among traits on microevolution are generally represented by the contribution of pleiotropy to additive genetic covariances. Pleiotropic additive genetic covariances arise only from the average effects of alleles on multiple traits, and therefore the evolutionary importance of nonlinearities in development is generally neglected in quantitative genetic views on evolution. However, nonlinearities in relationships among traits at the level of whole organisms are undeniably important to biology in general, and therefore critical to understanding evolution. I outline a system for characterizing key quantitative parameters in nonlinear developmental systems, which yields expressions for quantities such as trait means and phenotypic and genetic covariance matrices. I then develop a system for quantitative prediction of evolution in nonlinear developmental systems. I apply the system to generating a new hypothesis for why direct stabilizing selection is rarely observed. Other uses will include separation of purely correlative from direct and indirect causal effects in studying mechanisms of selection, generation of predictions of medium-term evolutionary trajectories rather than immediate predictions of evolutionary change over single generation time-steps, and the development of efficient and biologically motivated models for separating additive from epistatic genetic variances and covariances.