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Related Experiment Videos

The G matrix under fluctuating correlational mutation and selection.

Liam J Revell1

  • 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA. lrevell@fas.harvard.edu

Evolution; International Journal of Organic Evolution
|August 9, 2007
PubMed
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Quantitative genetics uses the G matrix for evolutionary inference. This study uses simulations to show G matrix stability is not always required for consistent evolutionary responses, even when mutation and selection fluctuate.

Area of Science:

  • Evolutionary biology
  • Quantitative genetics
  • Genetics

Background:

  • The additive genetic variance-covariance matrix (G matrix) is crucial for evolutionary inference in quantitative genetics.
  • Its evolutionary stability is essential for reliable predictions of phenotypic differentiation.
  • Individual-based computer simulations offer a novel approach to study G matrix dynamics.

Purpose of the Study:

  • To evaluate the evolutionary stability of the G matrix using the multivariate response-to-selection equation.
  • To explore G matrix dynamics under various conditions of correlational mutation and selection.
  • To investigate G matrix stability when mutation, selection, and population size fluctuate over time.

Main Methods:

  • Utilized individual-based computer simulations to analyze G matrix dynamics.

Related Experiment Videos

  • Employed the multivariate response-to-selection equation to assess G matrix stability.
  • Measured similarity by correlating response-to-selection vectors under random selection gradients.

    • Main Results:

    • Correlational mutation and selection stabilize the G matrix's eigenstructure.
    • G matrix instability did not consistently decrease the predictability of evolutionary responses.
    • Fluctuating mutation, selection, and population size can lead to G matrix eigenstructure instability.

    Conclusions:

    • G matrix stability is not always a prerequisite for consistent evolutionary responses.
    • Assessing G matrix stability via eigenanalysis versus response to selection yields different outcomes.
    • Predictability of selection response can be highest when G matrix eigenstructure is least stable.