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This article in JAS

  1. Vol. 89 No. 1, p. 23-28
     
    Received: Apr 09, 2010
    Accepted: Sept 22, 2010
    Published: December 4, 2014


    2 Corresponding author(s): cychen9@uga.edu
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doi:10.2527/jas.2010-3071

Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: An example using broiler chickens

  1. C. Y. Chen 21,
  2. I. Misztal*,
  3. I. Aguilar*†,
  4. S. Tsuruta*,
  5. T. H. E. Meuwissen,
  6. S. E. Aggrey§,
  7. T. Wing# and
  8. W. M. Muir
  1. Department of Animal and Dairy Science, University of Georgia, Athens 30602-2771;
    Instituto Nacional de Investigación Agropecuaria, Las Brujas 90200, Uruguay;
    Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, NO-1432 As, Norway;
    Department of Poultry Science, University of Georgia, Athens 30602-2772;
    Cobb-Vantress Inc., PO Box 1030, Siloam Springs, AR 72761-1030; and
    Department of Animal Science, Purdue University, West Lafayette, IN 47907-1151

ABSTRACT

ABSTRACT

Data of broiler chickens for 2 pure lines across 3 generations were used for genomic evaluation. A complete population (full data set; FDS) consisted of 183,784 and 164,246 broilers for the 2 lines. The genotyped subsets (SUB) consisted of 3,284 and 3,098 broilers with 57,636 SNP. Genotyped animals were preselected based on more than 20 traits with different index applied to each line. Three traits were analyzed: BW at 6 wk (BW6), ultrasound measurement of breast meat (BM), and leg score (LS) coded 1 = no and 2 = yes for leg defect. Some phenotypes were missing for BM. The training population consisted of the first 2 generations including all animals in FDS or only genotyped animals in SUB. The validation data set contained only genotyped animals in the third generation. Genetic evaluations were performed using 3 approaches: 1) phenotypic BLUP, 2) extending BLUP methodologies to utilize pedigree and genomic information in a single step (ssGBLUP), and 3) Bayes A. Whereas BLUP and ssGBLUP utilized all phenotypic data, Bayes A could use only those of the genotyped subset. Heritabilities were 0.17 to 0.20 for BW6, 0.30 to 0.35 for BM, and 0.09 to 0.11 for LS. The average accuracies of the validation population with BLUP for BW6, BM, and LS were 0.46, 0.30, and <0 with SUB and 0.51, 0.34, and 0.28 with FDS. With ssGBLUP, those accuracies were 0.60, 0.34, and 0.06 with SUB and 0.61, 0.40, and 0.37 with FDS, respectively. With Bayes A, the accuracies were 0.60, 0.36, and 0.09 with SUB. With SUB, Bayes A and ssGBLUP had similar accuracies. For traits of high heritability, the accuracy of Bayes A/SUB and ssGBLUP/FDS were similar, and up to 50% better than BLUP/FDS. However, with low heritability, ssGBLUP/FDS was 4 to 6 times more accurate than Bayes A/SUB and 50% better than BLUP/FDS. An optimal genomic evaluation would be multi-trait and involve all traits and records on which selection is based.

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