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Journal of Animal Science Abstract - Animal Genetics

Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population1


This article in JAS

  1. Vol. 94 No. 9, p. 3613-3623
    Received: Feb 22, 2016
    Accepted: May 26, 2016
    Published: August 18, 2016

    2 Corresponding author(s): lgalb@fcav.unesp.br

  1. R. M. O. Silva 2*,
  2. B. O. Fragomeni,
  3. D. A. L. Lourenco,
  4. A. F. B. Magalhães*,
  5. N. Irano*,
  6. R. Carvalheiro*,
  7. R. C. Canesin,
  8. M. E. Z. Mercadante,
  9. A. A. Boligon§,
  10. F. S. Baldi*,
  11. I. Misztal and
  12. L. G. Albuquerque*
  1. * Faculdade de Ciências Agrárias de Veterinárias, UNESP – Univ Estadual Paulista, Department of Animal Science, Jaboticabal, São Paulo, Brazil, 14884-900
     University of Georgia, Department of Animal and Dairy Science, Athens 30602-2771
     Centro APTA Bovinos de Corte, Animal Science Institute, Sertaozinho, São Paulo, Brazil, 13460-000
    § Department of Animal Science, Federal University of Pelotas, Pelotas, RS, Brazil, CEP 96160-000


Animal feeding is the most important economic component of beef production systems. Selection for feed efficiency has not been effective mainly due to difficult and high costs to obtain the phenotypes. The application of genomic selection using SNP can decrease the cost of animal evaluation as well as the generation interval. The objective of this study was to compare methods for genomic evaluation of feed efficiency traits using different cross-validation layouts in an experimental beef cattle population genotyped for a high-density SNP panel (BovineHD BeadChip assay 700k, Illumina Inc., San Diego, CA). After quality control, a total of 437,197 SNP genotypes were available for 761 Nelore animals from the Institute of Animal Science, Sertãozinho, São Paulo, Brazil. The studied traits were residual feed intake, feed conversion ratio, ADG, and DMI. Methods of analysis were traditional BLUP, single-step genomic BLUP (ssGBLUP), genomic BLUP (GBLUP), and a Bayesian regression method (BayesCπ). Direct genomic values (DGV) from the last 2 methods were compared directly or in an index that combines DGV with parent average. Three cross-validation approaches were used to validate the models: 1) YOUNG, in which the partition into training and testing sets was based on year of birth and testing animals were born after 2010; 2) UNREL, in which the data set was split into 3 less related subsets and the validation was done in each subset a time; and 3) RANDOM, in which the data set was randomly divided into 4 subsets (considering the contemporary groups) and the validation was done in each subset at a time. On average, the RANDOM design provided the most accurate predictions. Average accuracies ranged from 0.10 to 0.58 using BLUP, from 0.09 to 0.48 using GBLUP, from 0.06 to 0.49 using BayesCπ, and from 0.22 to 0.49 using ssGBLUP. The most accurate and consistent predictions were obtained using ssGBLUP for all analyzed traits. The ssGBLUP seems to be more suitable to obtain genomic predictions for feed efficiency traits on an experimental population of genotyped animals.

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