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Improving reliability of genomic predictions for Jersey sires using bootstrap aggregation sampling.

Ashley A Mikshowsky1, Daniel Gianola2, Kent A Weigel1

  • 1Department of Dairy Science, University of Wisconsin, Madison 53706.

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|March 14, 2016
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Summary

Genomic selection in dairy bulls uses genomic predicted transmitting ability (GPTA) for decisions. A new method, bagging genomic BLUP, was tested to improve reliability, but it only slightly improved identifying bulls with performance deviations.

Keywords:
bootstrap samplingdairy cattlegenomic predictionreliability

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Dairy Science

Background:

  • Genomic selection is crucial in the dairy industry, using genomic predicted transmitting ability (GPTA) for bull evaluation.
  • Current reliability (REL) values have limitations in identifying bulls with significant deviations between GPTA and actual daughter performance.
  • Daughter yield deviations (DYD) are used to validate GPTA accuracy later in a bull's life.

Purpose of the Study:

  • To evaluate the effectiveness of bootstrap aggregation sampling (bagging) with genomic BLUP (GBLUP) for predicting young Jersey bulls' GPTA.
  • To determine if bagging GBLUP provides a more reliable alternative to published REL values for assessing prediction accuracy.
  • To assess if bagging GBLUP can identify bulls with potential future performance discrepancies.

Main Methods:

  • Applied bagging GBLUP to predict GPTA for protein yield, somatic cell score, and daughter pregnancy rate in young Jersey bulls.
  • Utilized bootstrap samples from a reference population of older Jersey bulls with DYD data.
  • Correlated bagged GBLUP predictions with subsequent DYD data.

Main Results:

  • Bagging GBLUP did not improve predictive correlations compared to standard GBLUP.
  • The method allowed for the computation of bootstrap predictive reliabilities.
  • Standard deviations of bagged GBLUP predictions showed a weak improvement in identifying bulls with future performance deviations for protein yield only.

Conclusions:

  • Bagging GBLUP is not a superior alternative to standard GBLUP for predictive accuracy in this context.
  • Bootstrap predictive reliabilities offer a potential diagnostic tool for genome-enabled prediction systems.
  • The approach showed limited success in identifying bulls with significant GPTA-DYD discrepancies, particularly for somatic cell score and daughter pregnancy rate.