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Improving genomic prediction in pigs by integrating multi-population data and prior knowledge.

Junliang Wang1, Yujin Lu1,2, Wenjing Zhang1

  • 1State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.

BMC Genomics
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Summary

Optimizing genomic selection (GS) in pigs requires careful reference population selection. Integrating biological knowledge with advanced models like GFBLUP improves prediction accuracy for economically important traits.

Keywords:
Genomic selectionJoint evaluationPrior biological knowledgeReference populationYorkshire pig

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genomic selection (GS) is crucial for enhancing economically important traits in pigs.
  • GS accuracy is highly dependent on reference population size and composition.
  • Optimizing multi-population genomic evaluations requires integrating prior biological knowledge and advanced models.

Purpose of the Study:

  • To explore strategies for optimizing multi-population genomic evaluations in pigs.
  • To assess population similarities using phenotypic distribution, linkage disequilibrium (LD) consistency, heritability, and genetic variance.
  • To evaluate the performance of different genomic prediction models for joint reference populations.

Main Methods:

  • Population similarities were assessed using phenotypic distribution, LD consistency, heritability, and genetic variance.
  • Three genomic prediction models were applied: GBLUP, bivariate GBLUP, and GFBLUP.
  • GFBLUP model integrated meta-Genome-Wide Association Study (m-GWAS) priors.

Main Results:

  • Differences in phenotypic means and genetic variance between populations significantly impacted joint evaluation prediction accuracy, especially for fat thickness.
  • The GFBLUP model demonstrated improved prediction accuracy when genetic contributions between target and reference populations were similar.
  • Population similarity metrics influenced the effectiveness of joint genomic evaluations.

Conclusions:

  • Careful selection of reference populations is vital for accurate genomic evaluations in pigs.
  • Integrating biological priors, such as m-GWAS results, enhances genomic prediction models.
  • The GFBLUP model offers a promising approach for optimizing multi-population genomic selection strategies in pig breeding.