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Changes in genomic predictions when new information is added.

Jorge Hidalgo1, Daniela Lourenco1, Shogo Tsuruta1

  • 1Department of Animal and Dairy Science, University of Georgia, Athens, GA.

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|February 5, 2021
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Summary
This summary is machine-generated.

Genomic evaluations in large beef cattle populations show high stability and accuracy, comparable to traditional methods. Genomic estimated breeding values (GEBV) exhibit less extreme changes than estimated breeding values (EBV) over time.

Keywords:
algorithm for proven and youngfrequency of genomic evaluationssingle-step genomic best linear unbiased predictionstability of genomic predictions

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Area of Science:

  • Animal Genetics and Breeding
  • Quantitative Genetics
  • Genomic Selection

Background:

  • The stability of genomic evaluations is crucial for their practical application in livestock breeding.
  • Large genotyped populations are essential for accurate estimation of chromosome segment effects and reliable breeding values.
  • Previous studies suggest high accuracy and persistence of breeding values when sufficient data is available.

Purpose of the Study:

  • To investigate the stability and changes in estimated breeding values (EBV) and genomic EBV (GEBV) over a one-year period with monthly updates.
  • To compare the magnitude of changes in EBV and GEBV in a large, genotyped beef cattle population (American Angus).
  • To assess the impact of new data, including progeny phenotypes, on the stability of GEBV.

Main Methods:

  • Utilized a large dataset of American Angus cattle with extensive pedigree and genotype information.
  • Estimated EBV using Best Linear Unbiased Prediction (BLUP) and GEBV using single-step genomic BLUP with the Algorithm for Proven and Young (APY).
  • Mimicked monthly evaluations by incrementally adding 2017 data to a 2016 baseline dataset and compared predictions across evaluations.

Main Results:

  • Correlations between monthly EBV and GEBV evaluations remained high (≥0.97 for EBV, ≥0.99 for GEBV) throughout the year.
  • Average absolute changes in EBV were generally smaller than GEBV, but GEBV showed less extreme maximum changes.
  • GEBV changes were minimal for young animals and sires with fewer progeny, and updating the APY core group without new data had a small impact.

Conclusions:

  • Genomic evaluations in large genotyped populations demonstrate comparable stability and persistence to traditional genetic evaluations.
  • GEBV exhibit less extreme fluctuations compared to EBV, indicating enhanced reliability over time.
  • The findings support the robustness and practical utility of genomic selection in beef cattle breeding programs.