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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN.

B M Lopez1, H S Kang1, T H Kim1

  • 1Department of Animal Science and Technology, Sunchon National University, Suncheon 540-742, Korea.

Asian-Australasian Journal of Animal Sciences
|March 9, 2016
PubMed
Summary
This summary is machine-generated.

Genomic selection strategies in swine breeding significantly improve genetic gain and accuracy compared to conventional methods. While genomic selection increases costs, it offers higher profitability, especially with strategies like GS1 and GS2.

Keywords:
Breeding ProgramGenomic SelectionSwineZPLAN+

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Area of Science:

  • Animal Science
  • Genetics
  • Quantitative Genetics

Background:

  • Conventional breeding programs in swine nucleus farms rely on progeny testing.
  • Genomic selection offers potential for increased accuracy and genetic gain.
  • Evaluating new genomic strategies against established methods is crucial for optimizing breeding programs.

Purpose of the Study:

  • To compare the effectiveness of a conventional selection program with novel genomic selection strategies in swine.
  • To assess genetic gain, cost, return, and profit associated with different selection approaches.
  • To determine the optimal genomic-enhanced breeding value (GEBV) implementation for swine nucleus farms.

Main Methods:

  • Utilized ZPLAN+ software to model and compare four selection strategies: conventional selection (CS), genomic selection (GS1), genomic selection with performance testing (GS2), and a mixed genomic and progeny test approach (GS3).
  • Evaluated selection accuracy, annual monetary genetic gain, discounted costs, and discounted profit per animal for each strategy.
  • Assessed the impact of genotyping costs on the economic viability of genomic selection.

Main Results:

  • Genomic selection strategies (GS1, GS2, GS3) demonstrated higher selection accuracy for young boars (26-31% increase) and greater annual monetary genetic gain (10-12% increase) compared to conventional selection (CS).
  • Genomic selection strategies incurred higher costs (35-89% increase) but resulted in increased profit per animal for GS1 (8% higher) and GS2 (2% higher) compared to CS.
  • GS1 proved to be the most profitable genomic breeding scenario, outperforming GS2 and GS3.

Conclusions:

  • Genomic selection schemes, particularly GS1 and GS2, offer superior monetary genetic gain and profitability over conventional selection in swine breeding.
  • The integration of genomic information, especially in strategies like GS1 (purely genomic) and GS2 (genomic plus performance testing), enhances breeding program efficiency.
  • While initial investment is higher, genomic selection provides a clear advantage for optimizing genetic improvement and economic returns in swine nucleus farms.