Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens

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• 作者：Tianfei Liu (1) (2) (3)
  Hao Qu (1) (3)
  Chenglong Luo (1) (3)
  Dingming Shu (1) (3)
  Jie Wang (1) (3)
  Mogens Sand酶 Lund (2)
  Guosheng Su (2)
  
  1. Institute of Animal Science ; Guangdong Academy of Agricultural Sciences ; Guangzhou ; 510640 ; China
  2. Department of Molecular Biology and Genetics ; Center for Quantitative Genetics and Genomics ; Aarhus University ; DK-8830 ; Tjele ; Denmark
  3. State Key Laboratory of Livestock and Poultry Breeding ; Guangzhou ; 510640 ; China
  
• 关键词：Accuracy ; Breeding value ; Cross ; validation ; Genomic prediction
• 刊名：BMC Genetics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：240 KB
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• 刊物主题：Life Sciences, general; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics; Genetics and Population Dynamics;
• 出版者：BioMed Central
• ISSN：1471-2156

文摘

Background Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage. Results Genomic prediction was assessed using a 4-fold cross-validation procedure for two validation scenarios. In the first scenario, each test data set comprised two half-sib families (family sample) and the rest represented the reference data. In the second scenario, the whole data were randomly divided into four subsets (random sample). In each fold of validation, one subset was used as the test data and the others as the reference data in each single validation. Genomic breeding values were predicted using a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions. The accuracy of genomic estimated breeding value (GEBV) was measured as the correlation between GEBV and the corrected phenotypic value. Using the three models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits in the family sample scenario, and were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits in the random sample scenario. The differences in the prediction accuracies between the three models were very small; the Bayesian mixture model was slightly more accurate. According to the results from the random sample scenario, the accuracy of GEBV was 0.197 higher than the conventional pedigree index, averaged over the five traits. Conclusions The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection. Genomic selection for growth and carcass traits in broiler chickens is promising.