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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A modelling framework for the analysis of artificial-selection time series.

Arnaud Le Rouzic1, David Houle1, Thomas F Hansen1

  • 1Center for Ecology and Evolutionary Synthesis, Department of Biology, University of Oslo, Norway.

Genetics Research
|April 9, 2011
PubMed
Summary

Artificial selection experiments reveal genetic architecture dynamics. Statistical models analyze selection response time series, aiding breeders and evolutionary biologists in understanding trait evolution.

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Area of Science:

  • Evolutionary Biology
  • Quantitative Genetics
  • Animal Breeding

Background:

  • Artificial selection experiments provide crucial empirical data for understanding genetic architecture.
  • Selected traits can diverge significantly, even beyond interspecific differences, offering insights into evolutionary potential.
  • Analyzing selection response dynamics can reveal underlying genetic mechanisms.

Purpose of the Study:

  • To propose a statistical framework for analyzing the dynamics of selection-response time series.
  • To demonstrate the utility of both phenomenological and mechanistic models in interpreting artificial selection data.
  • To provide a practical tool for breeders, geneticists, and evolutionary biologists.

Main Methods:

  • Development of a statistical framework to model the temporal dynamics of selection response.
  • Application of phenomenological models (agnostic to genetic mechanisms) and mechanistic models (incorporating mutation, epistasis, canalization).
  • Implementation of the framework in a software package for practical analysis.

Main Results:

  • The proposed framework effectively describes the dynamics of selection-response time series.
  • Both phenomenological and mechanistic models provide valuable interpretations of artificial selection data.
  • The software package facilitates the analysis of complex selection experiments.

Conclusions:

  • Statistical modeling of selection response is essential for understanding genetic architecture.
  • The developed framework and software offer a robust approach for analyzing artificial selection experiments.
  • This work aids in predicting and managing evolutionary responses to selection in biological systems.