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Mating allocations in Holstein combining genomic information and linear programming optimization at the herd level.

C Bengtsson1, H Stålhammar2, J R Thomasen2

  • 1VikingGenetics, VikingGenetics Sweden AB, 53294 Skara, Sweden; Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 75007 Uppsala, Sweden.

Journal of Dairy Science
|April 7, 2023
PubMed
Summary

Optimizing Holstein cattle mating allocation using genomic data improved polled offspring rates and beta-casein traits. Careful consideration of genetic traits is crucial for effective genomic mating programs.

Keywords:
Nordic total meritmating allocationpollednessβ-casein (A2A2)

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Area of Science:

  • Animal Breeding and Genetics
  • Genomic Selection
  • Quantitative Genetics

Background:

  • Holstein cattle breeding programs aim to enhance economic traits.
  • Genomic information offers new possibilities for optimizing mating allocation.
  • Balancing multiple genetic traits is a challenge in artificial insemination programs.

Purpose of the Study:

  • To explore mating allocation strategies in Holstein cattle using genomic data.
  • To optimize economic scores considering genetic level, relationships, costs, defects, polledness, and beta-casein.
  • To evaluate the impact of different mating criteria on genetic trait frequencies.

Main Methods:

  • Utilized genomic information from 24,333 Holstein females.
  • Employed linear programming to optimize economic scores within herds.
  • Considered genetic level, relationships, semen cost, genetic defects, polledness, and beta-casein.

Main Results:

  • Reduced genetic relationships and eliminated genetic defects with minimal impact on total merit index.
  • Increased polled offspring frequency from 13.5% to 22.5% and A2A2 beta-casein from 66.7% to 75.0% in one generation.
  • Using only polled bulls significantly reduced the overall genetic level; polled allele carriers were less likely to be A2A2 and more likely to carry HH1 defect.

Conclusions:

  • Genomic mating allocation can effectively improve specific genetic traits like polledness and beta-casein.
  • Balancing multiple monogenic traits in economic scores requires careful monitoring to avoid negative trade-offs.
  • Continuous monitoring of comparison criteria is recommended for modern genomic mating programs.