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Journal of Animal Science Abstract - Animal Genetics and Genomics

Including crossbred pigs in the genomic relationship matrix through utilization of both linkage disequilibrium and linkage analysis1


This article in JAS

  1. Vol. 95 No. 12, p. 5197-5207
    Received: May 08, 2017
    Accepted: Sept 22, 2017
    Published: November 28, 2017

    2 Corresponding author(s):

  1. M. W. Iversen 2*†,
  2. Ø. Nordbø*‡,
  3. E. Gjerlaug-Enger*,
  4. E. Grindflek*,
  5. M.S. Lopes§# and
  6. T. H. E. Meuwissen
  1. * Topigs Norsvin, Storhamargata 44, 2317 Hamar, Norway
     Norwegian University of Life Sciences, Postboks 5003 NMBU, 1432 Ås, Norway
     GENO SA, Storhamargata 44, 2317 Hamar, Norway
    § Topigs Norsvin Research Center, Beuningen 6641 SZ, the Netherlands
    # Topigs Norsvin, Curitiba 80420-210, Brazil


In pig breeding, the final product is a crossbred (CB) animal, while selection is performed at the purebred (PB) level using mainly PB data. However, incorporating CB data in genetic evaluations is expected to result in greater genetic progress at the CB level. Currently, there is no optimal way to include CB genotypes into the genomic relationship matrix. This is because, in single-step genomic BLUP, which is the most commonly used method, genomic and pedigree relationships must refer to the same base. This may not be the case when several breeds and CB are included. An alternative to overcome this issue may be to use a genomic relationship matrix (G matrix) that accounts for both linkage disequilibrium (LD) and linkage analysis (LA), called GLDLA. The objectives of this study were to further develop the GLDLA matrix approach to utilize both PB and CB genotypes simultaneously, to investigate its performance, and the general added value of including CB genotypes in genomic evaluations. Data were available on Dutch Landrace, Large White, and the F1 cross of those breeds. In total, 7 different G matrix compositions (PB alone, PB together, each PB with the CB, all genotypes across breeds, and GLDLA) were tested on 3 maternal traits: total number born (TNB), live born (LB), and gestation length (GL). Results show that GLDLA gave the greatest prediction accuracy of all the relationship matrices tested for PB prediction, but not for CB prediction. Including CB genotypes in general increased prediction accuracy for all breeds. However, in some cases, these increases in prediction accuracy were not significant (at P < 0.05). To conclude, CB genotypes increased prediction accuracy for some of the traits and breeds, but not for all. The GLDLA matrix had significantly greater prediction accuracy in PB than the other G matrix with both PB and CB genotypes, except in one case. While for CB, the G matrix with genotypes across all breeds gave the greatest accuracy, though this was not significantly different from GLDLA. Computation time was high for GLDLA, and research will be needed to reduce its computational costs to make it feasible for use in routine evaluations. The main conclusion is that inclusion of CB genotypes is beneficial for both PB and CB animals.

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