About Us | Help Videos | Contact Us | Subscriptions

Journal of Animal Science Abstract - Animal Genetics

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus1


This article in JAS

  1. Vol. 93 No. 6, p. 2653-2662
    unlockOPEN ACCESS
    Received: Dec 20, 2014
    Accepted: Mar 28, 2015
    Published: June 9, 2015

    2 Corresponding author(s): danilino@uga.edu

  1. D. A. L. Lourenco 2*,
  2. S. Tsuruta*,
  3. B. O. Fragomeni*,
  4. Y. Masuda*,
  5. I. Aguilar,
  6. A. Legarra,
  7. J. K. Bertrand*,
  8. T. S. Amen§,
  9. L. Wang§,
  10. D. W. Moser§ and
  11. I. Misztal*
  1. * Department of Animal and Dairy Science, University of Georgia, Athens 30602
     Instituto Nacional de Investigacion Agropecuaria, Canelones, Uruguay 90200
     Institut National de la Recherche Agronomique, UMR1388 GenPhySE, Castanet Tolosan, France 31326
    § Angus Genetics Inc., St. Joseph, MO 64506


Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE–BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © 2015. American Society of Animal Science