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Predicting response to selection on a quantitative trait: a comparison between models for mixed-mating populations.

J K Kelly1, S Williamson

  • 1Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA. jkk@eagle.cc.ukans.edu

Journal of Theoretical Biology
|October 12, 2000
PubMed
Summary

The structured linear model (SLM) more accurately predicts selection response in mixed mating populations than the genotypic covariance model (GCM), especially with inbreeding depression. This research clarifies genetic metrics and simulation accuracy for evolutionary predictions.

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Area of Science:

  • Quantitative genetics
  • Evolutionary biology
  • Population genetics

Background:

  • Predicting response to selection in mixed mating populations is crucial for understanding evolution.
  • Two models, the genotypic covariance model (GCM) and the structured linear model (SLM), exist but use different genetic metrics.
  • The relative accuracy of these models under various selfing rates and inbreeding depression is not fully understood.

Purpose of the Study:

  • To algebraically relate the genetic metrics used in the GCM and SLM.
  • To compare the predictive accuracy of the GCM and SLM using stochastic simulations across a range of selfing rates.
  • To investigate the impact of inbreeding depression and dominance on model accuracy.

Main Methods:

  • Reformulating the genotypic covariance model (GCM) using the Wright-Kempthorne equation to establish algebraic relationships between genetic metrics.

Related Experiment Videos

  • Employing stochastic simulations to model quantitative trait inheritance in populations with varying selfing rates.
  • Analyzing model predictions under scenarios with and without inbreeding depression and dominance.

    • Main Results:

    • The structured linear model (SLM) demonstrates superior accuracy compared to the genotypic covariance model (GCM) across simulated conditions.
    • Significant discrepancies arise with inbreeding depression for fitness, where the SLM's predictions are more reliable.
    • Under strong inbreeding depression and high selfing rates, the GCM can predict evolutionary trajectories opposite to observed outcomes.
    • Applying random mating models to partially selfing populations yields inaccurate predictions when quantitative trait loci exhibit dominance.

    Conclusions:

    • The structured linear model (SLM) provides a more robust framework for predicting selection response in mixed mating systems.
    • Inbreeding depression and dominance significantly impact the accuracy of evolutionary predictions, highlighting the limitations of simpler models like the GCM.
    • Accurate prediction of selection response in natural populations requires models that appropriately account for selfing, inbreeding depression, and genetic architecture.