Literature DB >> 12836827

The constancy of the G matrix through species divergence and the effects of quantitative genetic constraints on phenotypic evolution: a case study in crickets.

Mattieu Bégin1, Derek A Roff.   

Abstract

Long-term phenotypic evolution can be modeled using the response-to-selection equation of quantitative genetics, which incorporates information about genetic constraints (the G matrix). However, little is known about the evolution of G and about its long-term importance in constraining phenotypic evolution. We first investigated the degree of conservation of the G matrix across three species of crickets and qualitatively compared the pattern of variation of G to the phylogeny of the group. Second, we investigated the effect of G on phenotypic evolution by comparing the direction of greatest quantitative genetic variation within species (g(max)) to the direction of phenotypic divergence between species (Delta(z)). Each species, Gryllus veletis, G. firmus, and G. pennsylvanicus, was reared in the laboratory using a full-sib breeding design to extract quantitative genetic information. Five morphological traits related to size were measured. G matrices were compared using three statistical approaches: the T method, the Flury hierarchy, and the MANOVA method. Results revealed that the differences between matrices were small and mostly caused by differences in the magnitude of the genetic variation, not by differences in principal component structure. This suggested that the G matrix structure of this group of species was preserved, despite significant phenotypic divergence across species. The small observed differences in G matrices across species were qualitatively consistent with genetic distances, whereas ecological information did not provide a good prediction of G matrix variation. The comparison of g(max) and Delta(z) revealed that the angle between these two vectors was small in two of three species comparisons, whereas the larger angle corresponding to the third species comparison was caused in large part by one of the five traits. This suggests that multivariate phenotypic divergence occurred mostly in a direction predicted by the direction of greatest genetic variation, although it was not possible to demonstrate the causal relationship from G to Delta(z). Overall, this study provided some support for the validity of the predictive power of quantitative genetics over evolutionary time scales.

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Year:  2003        PMID: 12836827     DOI: 10.1111/j.0014-3820.2003.tb00320.x

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


  14 in total

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Journal:  Evolution       Date:  2011-09-25       Impact factor: 3.694

2.  Genetic and environmental contributions to variation and population divergence in a broad-spectrum foliar defence of Eucalyptus tricarpa.

Authors:  Rose L Andrew; Ian R Wallis; Chris E Harwood; William J Foley
Journal:  Ann Bot       Date:  2010-03-12       Impact factor: 4.357

3.  Comparative analysis of the multivariate genetic architecture of morphological traits in three species of Gomphocerine grasshoppers.

Authors:  Anasuya Chakrabarty; Holger Schielzeth
Journal:  Heredity (Edinb)       Date:  2019-10-24       Impact factor: 3.821

4.  Re-creating ancient hybrid species' complex phenotypes from early-generation synthetic hybrids: three examples using wild sunflowers.

Authors:  David M Rosenthal; Loren H Rieseberg; Lisa A Donovan
Journal:  Am Nat       Date:  2005-05-02       Impact factor: 3.926

5.  Neopolyploidy and diversification in Heuchera grossulariifolia.

Authors:  Benjamin P Oswald; Scott L Nuismer
Journal:  Evolution       Date:  2011-01-03       Impact factor: 3.694

6.  Evolution of the Genotype-to-Phenotype Map and the Cost of Pleiotropy in Mammals.

Authors:  Arthur Porto; Ryan Schmelter; John L VandeBerg; Gabriel Marroig; James M Cheverud
Journal:  Genetics       Date:  2016-10-26       Impact factor: 4.562

7.  Connecting QTLS to the g-matrix of evolutionary quantitative genetics.

Authors:  John K Kelly
Journal:  Evolution       Date:  2008-12-12       Impact factor: 3.694

8.  Evolutionary rates for multivariate traits: the role of selection and genetic variation.

Authors:  William Pitchers; Jason B Wolf; Tom Tregenza; John Hunt; Ian Dworkin
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-08-19       Impact factor: 6.237

9.  Differential evolvability along lines of least resistance of upper and lower molars in island house mice.

Authors:  Sabrina Renaud; Sophie Pantalacci; Jean-Christophe Auffray
Journal:  PLoS One       Date:  2011-05-11       Impact factor: 3.240

10.  Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution.

Authors:  Sabrina Renaud; Anne-Béatrice Dufour; Emilie A Hardouin; Ronan Ledevin; Jean-Christophe Auffray
Journal:  PLoS One       Date:  2015-07-20       Impact factor: 3.240

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