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Breeding by Design for Functional Rice with Genome Editing Technologies
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Published on: January 3, 2025

Genomic breeding value prediction: methods and procedures.

M P L Calus1

  • 1Animal Sciences Group, Animal Breeding and Genomics Centre, Wageningen University and Research Centre, 8200 AB Lelystad, The Netherlands.

Animal : an International Journal of Animal Bioscience
|March 27, 2012
PubMed
Summary
This summary is machine-generated.

Genomic selection significantly improves animal breeding by using DNA markers to predict breeding values more accurately than traditional methods. This approach enhances genetic gain in livestock populations.

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Area of Science:

  • Animal genetics and breeding
  • Quantitative genetics
  • Bioinformatics

Background:

  • Genomic selection represents a major advancement in animal breeding, surpassing traditional pedigree indexes.
  • It leverages dense marker maps to predict breeding values with higher accuracy, reducing reliance on extensive phenotyping.

Purpose of the Study:

  • To explain the principles and applications of genomic selection in animal breeding.
  • To highlight the factors influencing the accuracy of genomic predictions and estimated breeding values.

Main Methods:

  • Utilizes dense marker maps to infer linkage disequilibrium (LD) between markers and quantitative trait loci (QTL).
  • Employs reference populations with known phenotypes and genotypes to estimate marker effects.
  • Various statistical models are used to estimate marker effects, often focusing on additive effects.

Main Results:

  • Genomic selection can increase prediction accuracies by up to 0.31 compared to pedigree indexes.
  • Breeding values for young candidates can be predicted with accuracies up to 0.85.
  • Accuracy is highly dependent on reference population size, marker density, heritability, and animal relationships.

Conclusions:

  • Genomic selection offers a powerful tool for accelerating genetic improvement in animal populations.
  • Frequent re-estimation of marker effects is crucial for maintaining prediction accuracy due to LD breakdown across generations.
  • Models that effectively utilize persistent LD information are vital, especially when re-estimation is infrequent.