Literature DB >> 31883344

Is evolution predictable? Quantitative genetics under complex genotype-phenotype maps.

Lisandro Milocco1, Isaac Salazar-Ciudad1,2,3.   

Abstract

A fundamental aim of post-genomic 21st century biology is to understand the genotype-phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation. Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population. The other major field of research concerned with the GPM, developmental evolutionary biology, or evo-devo, has found the GPM to be highly nonlinear and complex. Here, we quantify how the predictions of quantitative genetics are affected by a complex, nonlinear map based on the development of a multicellular organ. We compared the predicted change in mean phenotype for a single generation using the multivariate breeder's equation, with the change observed from the model of development. We found that there are frequent disagreements between predicted and observed responses to selection due to the nonlinear nature of the genotype-phenotype map. Our results are a step toward integrating the fields studying the GPM.
© 2019 The Authors. Evolution © 2019 The Society for the Study of Evolution.

Keywords:  G-matrix; evo-devo; genotype-phenotype map; mathematical modeling; quantitative genetics

Mesh:

Year:  2020        PMID: 31883344     DOI: 10.1111/evo.13907

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  3 in total

1.  A method to predict the response to directional selection using a Kalman filter.

Authors:  Lisandro Milocco; Isaac Salazar-Ciudad
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-06       Impact factor: 12.779

Review 2.  Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map.

Authors:  Luis-Miguel Chevin; Christelle Leung; Arnaud Le Rouzic; Tobias Uller
Journal:  Genetica       Date:  2021-10-06       Impact factor: 1.633

Review 3.  Ecological limits to evolutionary rescue.

Authors:  Christopher A Klausmeier; Matthew M Osmond; Colin T Kremer; Elena Litchman
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-11-02       Impact factor: 6.237

  3 in total

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