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This article in JAS

  1. Vol. 94 No. 3, p. 909-919
     
    Received: Aug 28, 2015
    Accepted: Dec 20, 2015
    Published: February 5, 2016


    2 Corresponding author(s): danilino@uga.edu
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doi:10.2527/jas.2015-9748

Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices1

  1. D. A. L. Lourenco 2*,
  2. S. Tsuruta*,
  3. B. O. Fragomeni*,
  4. C. Y. Chen,
  5. W. O. Herring and
  6. I. Misztal*
  1. * Department of Animal and Dairy Science, University of Georgia, Athens 30602
     Genus PIC, Hendersonville, TN 37075

Abstract

Combining purebreed and crossbreed information is beneficial for genetic evaluation of some livestock species. Genetic evaluations can use relationships based on genomic information, relying on allele frequencies that are breed specific. Single-step genomic BLUP (ssGBLUP) does not account for different allele frequencies, which could limit the genetic gain in crossbreed evaluations. In this study, we tested the performance of different breed-specific genomic relationship matrices (GB) in ssGBLUP for crossbreed evaluations; we also tested the importance of genotyping crossbred animals. Genotypes were available for purebreeds (AA and BB) and crossbreeds (F1) in simulated and real pig populations. The number of genotyped animals was, on average, 4,315 for the simulated population and 15,798 for the real population. Cross-validation was performed on 1,200 and 3,117 F1 animals in the simulated and real populations, respectively. Simulated scenarios were under no artificial selection, mass selection, or BLUP selection. Two genomic relationship matrices were constructed based on breed-specific allele frequencies: 1) GB1, a genomic relationship matrix centered by breed-specific allele frequencies, and 2) GB2, a genomic relationship matrix centered and scaled by breed-specific allele frequencies. All G (the across-breed genomic relationship matrix), GB1, and GB2 were also tuned to account for selective genotyping. Using breed-specific allele frequencies reduced the number of negative relationships between 2 purebreeds, pulling the average closer to 0, as in the pedigree-based relationship matrix. For simulated populations that included mass selection, genomic EBV (GEBV) in F1, when using GB1 and GB2, were, on average, 10% more accurate than G; however, after tuning to account for selective genotyping, G provided the same accuracy as for breed-specific genomic relationship matrices. For the real population, accuracies for litter size in F1 were 0.62 for G, GB1, and GB2, and tuning had no impact on accuracy, except for GB2, which was 1 percentage point less accurate. Accuracy of GEBV for number of stillborns in F1 was 0.5 for all tested genomic relationship matrices with no changes after tuning. We observed that genotyping F1 increased accuracies of GEBV for the same animals by up to 39% compared with having genotypes for only AA and BB. In crossbreed evaluations, accounting for breed-specific allele frequencies promoted changes in G that were not influential enough to improve accuracy of GEBV. Therefore, the best performance of ssGBLUP for crossbreed evaluations requires genotypes for pure- and crossbreeds and no breed-specific adjustments in the realized relationship matrix.

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