title: Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens.
abstract: 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.
authors.forename: Tianfei
authors.initials: T
authors.lastname: Liu
authors.forename: Hao
authors.initials: H
authors.lastname: Qu
authors.forename: Chenglong
authors.initials: C
authors.lastname: Luo
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China. shudm@263.net.
authors.forename: Dingming
authors.initials: D
authors.lastname: Shu
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.forename: Mogens Sandø
authors.initials: MS
authors.lastname: Lund
authors.forename: Guosheng
authors.initials: G
authors.lastname: Su
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001835:Body Weight / Q000235:genetics; D001947:Breeding; D002645:Chickens / Q000235:genetics* / Q000254:growth & development; D023281:Genomics / Q000379:methods*; D016014:Linear Models; D008460:Meat; D008957:Models, Genetic; D010641:Phenotype; D015203:Reproducibility of Results