Literature DB >> 32146884

Universal rules for the interaction of selection and transmission in evolution.

Sean H Rice1.   

Abstract

The Price equation shows that evolutionary change can be written in terms of two fundamental variables: the fitness of parents (or ancestors) and the phenotypes of their offspring (descendants). Its power lies in the fact that it requires no simplifying assumptions other than a closed population, but realizing the full potential of Price's result requires that we flesh out the mathematical representation of both fitness and offspring phenotype. Specifically, both need to be treated as stochastic variables that are themselves functions of parental phenotype. Here, I show how new mathematical tools allow us to do this without introducing any simplifying assumptions. Combining this representation of fitness and phenotype with the stochastic Price equation reveals fundamental rules underlying multivariate evolution and the evolution of inheritance. Finally, I show how the change in the entire phenotype distribution of a population, not simply the mean phenotype, can be written as a single compact equation from which the Price equation and related results can be derived as special cases. This article is part of the theme issue 'Fifty years of the Price equation'.

Entities:  

Keywords:  Price equation; orthogonal polynomials; stochasticity

Mesh:

Year:  2020        PMID: 32146884      PMCID: PMC7133511          DOI: 10.1098/rstb.2019.0353

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  17 in total

Review 1.  Environmental quality and evolutionary potential: lessons from wild populations.

Authors:  Anne Charmantier; Dany Garant
Journal:  Proc Biol Sci       Date:  2005-07-22       Impact factor: 5.349

2.  The relation between multilocus population genetics and social evolution theory.

Authors:  Andy Gardner; Stuart A West; Nicholas H Barton
Journal:  Am Nat       Date:  2006-12-22       Impact factor: 3.926

3.  Artificial selection on metabolic rates and related traits in rodents.

Authors:  Marek Konarzewski; Aneta Ksiazek; Iwona B Lapo
Journal:  Integr Comp Biol       Date:  2005-06       Impact factor: 3.326

4.  The place of development in mathematical evolutionary theory.

Authors:  Sean H Rice
Journal:  J Exp Zool B Mol Dev Evol       Date:  2011-09-06       Impact factor: 2.656

5.  THE MEASUREMENT OF SELECTION ON CORRELATED CHARACTERS.

Authors:  Russell Lande; Stevan J Arnold
Journal:  Evolution       Date:  1983-11       Impact factor: 3.694

6.  Analysis of the inheritance, selection and evolution of growth trajectories.

Authors:  M Kirkpatrick; D Lofsvold; M Bulmer
Journal:  Genetics       Date:  1990-04       Impact factor: 4.562

7.  The effect of linkage on limits to artificial selection.

Authors:  W G Hill; A Robertson
Journal:  Genet Res       Date:  1966-12       Impact factor: 1.588

8.  Speeding up microevolution: the effects of increasing temperature on selection and genetic variance in a wild bird population.

Authors:  Arild Husby; Marcel E Visser; Loeske E B Kruuk
Journal:  PLoS Biol       Date:  2011-02-01       Impact factor: 8.029

9.  Evolution with stochastic fitness and stochastic migration.

Authors:  Sean H Rice; Anthony Papadopoulos
Journal:  PLoS One       Date:  2009-10-09       Impact factor: 3.240

10.  A stochastic version of the Price equation reveals the interplay of deterministic and stochastic processes in evolution.

Authors:  Sean H Rice
Journal:  BMC Evol Biol       Date:  2008-09-25       Impact factor: 3.260

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  1 in total

1.  Fifty years of the Price equation.

Authors:  Jussi Lehtonen; Samir Okasha; Heikki Helanterä
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-03-09       Impact factor: 6.237

  1 in total

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