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Related Concept Videos

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Published on: January 16, 2019

Additive genetic variability and the Bayesian alphabet.

Daniel Gianola1, Gustavo de los Campos, William G Hill

  • 1Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA. gianola@ansci.wisc.edu

Genetics
|July 22, 2009
PubMed
Summary
This summary is machine-generated.

Genomic-assisted selection uses molecular markers for predicting traits in breeding. This review critically examines statistical concepts, Bayesian models, and their application in genetic evaluation for animals and crops.

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Area of Science:

  • Quantitative genetics
  • Genomic selection
  • Statistical modeling in breeding

Background:

  • Genomic-assisted selection leverages molecular markers for trait prediction.
  • This paradigm is transforming animal and plant breeding strategies.
  • Understanding the underlying statistical concepts is crucial for effective implementation.

Purpose of the Study:

  • To critically review theoretical and statistical concepts in genomic-assisted genetic evaluation.
  • To examine the relationship between marker effects variance and additive genetic variance.
  • To explore Bayesian models for marker-assisted selection and their theoretical implications.

Main Methods:

  • Review of statistical models for genomic prediction.
  • Examination of Bayesian variance of marker effects.
  • Analysis of marker genotypes and resemblance between relatives.
  • Exploration of linkages between marker-based and infinitesimal models.

Main Results:

  • Established relationships between Bayesian variance of marker effects and additive genetic variance.
  • Explored connections between marker genotypes and relatedness.
  • Reviewed marker-based models against the infinitesimal model.
  • Investigated theoretical aspects of Bayesian models and prior sensitivity.

Conclusions:

  • Genomic-assisted selection offers a powerful paradigm for genetic improvement.
  • Theoretical insights into Bayesian models are essential for robust marker-assisted selection.
  • Further research can refine Bayesian regression procedures for enhanced accuracy.