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Is evolution predictable? Quantitative genetics under complex genotype-phenotype maps.

Lisandro Milocco1, Isaac Salazar-Ciudad1,2,3

  • 1Institute of Biotechnology, University of Helsinki, 00014, Helsinki, Finland.

Evolution; International Journal of Organic Evolution
|December 29, 2019
PubMed
Summary
This summary is machine-generated.

Quantitative genetics

Keywords:
G-matrixevo-devogenotype-phenotype mapmathematical modelingquantitative genetics

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Area of Science:

  • Evolutionary biology
  • Genetics
  • Developmental biology

Background:

  • Understanding the genotype-phenotype map (GPM) is crucial in post-genomic biology.
  • Quantitative genetics uses linear models for GPM approximation, enabling selection response prediction.
  • Developmental evolutionary biology (evo-devo) reveals complex, nonlinear GPMs.

Purpose of the Study:

  • To quantify the impact of nonlinear genotype-phenotype maps on quantitative genetics predictions.
  • To compare predictions from the multivariate breeder's equation with developmental model outcomes.
  • To advance the integration of quantitative genetics and evo-devo.

Main Methods:

  • Modeling the development of a multicellular organ to represent a nonlinear GPM.
  • Utilizing the multivariate breeder's equation to predict phenotypic change under selection.
  • Comparing predicted selection response with observed changes from the developmental model.

Main Results:

  • Frequent disagreements were observed between predicted and actual responses to selection.
  • The nonlinear nature of the genotype-phenotype map significantly affects prediction accuracy.
  • The study highlights limitations of linear models in complex biological systems.

Conclusions:

  • Nonlinear genotype-phenotype maps challenge the predictive power of traditional quantitative genetics.
  • Integrating evo-devo insights is essential for a comprehensive understanding of the GPM.
  • This research bridges quantitative genetics and evo-devo by analyzing GPM nonlinearity.