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Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Frequency-dependent Selection01:21

Frequency-dependent Selection

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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Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.For one, natural selection can only act upon existing genetic variation. Hypothetically, redtusks may enhance elephant survival by deterring ivory-seeking poachers. However, if there are no gene variants—or alleles—for redtusks, natural selection cannot increase the prevalence of...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

A dynamic deterministic model to optimize a multiple-trait selection scheme.

A D Costard1, Z G Vitezica, C R Moreno

  • 1l'Institut National de la Recherche Agronomique, SAGA Station d'Amélioration Génétique Animale, Castanet-Tolosan, France. Anne.Devalle@toulouse.inra.fr

Journal of Animal Science
|November 26, 2008
PubMed
Summary
This summary is machine-generated.

A new mathematical model optimizes livestock breeding by balancing selection for disease resistance (monogenic trait) and production traits (polygenic variation). This approach maximizes desired genotypes while minimizing genetic gain loss in quantitative traits.

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Last Updated: Jun 27, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Published on: December 9, 2012

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Animal genetics
  • Quantitative genetics
  • Mathematical modeling

Background:

  • Simultaneous selection on multiple traits is complex in breeding programs.
  • Balancing selection for a specific gene (monogenic) and overall genetic merit (polygenic) is challenging.

Purpose of the Study:

  • To develop a mathematical model for optimizing simultaneous selection on a monogenic and a polygenic trait.
  • To maximize the frequency of desired genotypes for a monogenic trait while minimizing genetic gain loss for a polygenic trait.

Main Methods:

  • Developed a deterministic model incorporating polygenic variation and a monogenic trait (e.g., disease resistance).
  • Utilized a genetic algorithm to solve the optimization problem for complex breeding schemes.
  • Modeled overlapping generations, sex-specific selection, and assortative mating.

Main Results:

  • The model allows global optimization of selection schemes.
  • Demonstrated the ability to maximize desired monogenic genotypes and minimize genetic gain loss in polygenic traits.
  • Successfully applied the model to dairy sheep selection incorporating scrapie resistance (Prp gene).

Conclusions:

  • The developed mathematical approach provides a flexible framework for optimizing complex breeding programs.
  • This method can be adapted for various genetic models and selection strategies.
  • Facilitates integrating disease resistance genes into existing livestock selection schemes effectively.