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Optimum multistage genomic selection in dairy cattle.

V Börner1, F Teuscher, N Reinsch

  • 1Leibniz Institute for Farm Animal Biology, Research Unit Genetics and Biometry, 18196 Dummerstorf, Germany.

Journal of Dairy Science
|March 31, 2012
PubMed
Summary

Optimizing dairy cattle breeding involves using genomic selection strategies with varying single nucleotide polymorphism (SNP) chip densities. The study identifies optimal investment allocation to maximize genetic gain, highlighting the sensitivity of breeding schemes to cost and accuracy changes.

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Area of Science:

  • Animal Breeding and Genetics
  • Genomics
  • Quantitative Genetics

Background:

  • Genomic selection strategies utilize single nucleotide polymorphism (SNP) genotypes to estimate breeding values (GEBV).
  • Different SNP chip densities and imputation algorithms offer varying accuracies and costs for GEBV.
  • Optimizing investment allocation across selection paths is crucial for maximizing annual genetic gain (ΔG(a)), especially under financial constraints.

Purpose of the Study:

  • To identify optimal multistage breeding plans that maximize genetic gain per year (ΔG(a)).
  • To evaluate the sensitivity of ΔG(a) to variations in the cost and accuracy of GEBV derived from high-density (GEBV(HD)) and low-density (GEBV(LD)) SNP genotypes.
  • To determine the most effective allocation of financial resources in dairy cattle breeding programs.

Main Methods:

  • Deterministic methods were employed to identify optimal breeding plans.
  • Selection strategies incorporated GEBV derived from both GEBV(HD) and GEBV(LD).
  • Costs and accuracies of GEBV were systematically varied to assess their impact on ΔG(a).

Main Results:

  • Optimal strategies predominantly use GEBV(LD) for identifying future bull dams, while sire selection generates the majority of ΔG(a).
  • The dam-to-sire path, with lower selection intensity, showed higher sensitivity of ΔG(a) to GEBV(LD) cost and accuracy changes.
  • Male selection's genetic gain was primarily influenced by GEBV(HD) accuracy, with minimal impact from cost variations.

Conclusions:

  • Changes in GEBV(LD) cost and accuracy exert the most significant pressure on breeding scheme structure for maintaining high ΔG(a).
  • Genomic selection of bull dams, while costly, yielded the lowest genetic gain.
  • Strategic investment in genomic selection is essential for efficient and cost-effective dairy cattle improvement.