title: Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens.
abstract: Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.|State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.
authors.forename: Tianfei
authors.initials: T
authors.lastname: Liu
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.|State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.
authors.forename: Chenglong
authors.initials: C
authors.lastname: Luo
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.|State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.|Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangzhou, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Ma
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.|State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.
authors.forename: Dingming
authors.initials: D
authors.lastname: Shu
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Denmark.
authors.forename: Mogens Sandø
authors.initials: MS
authors.lastname: Lund
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Denmark.
authors.forename: Guosheng
authors.initials: G
authors.lastname: Su
authors.affiliation: Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.|State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Qu
mesh_terms: D000821:Animal Feed; D000818:Animals; D001835:Body Weight / Q000235:genetics*; D001947:Breeding; D002645:Chickens / Q000235:genetics* / Q000254:growth & development; D004435:Eating / Q000235:genetics*; D005260:Female; D023281:Genomics / Q000379:methods*; D008460:Meat / Q000032:analysis*; D008957:Models, Genetic; D015233:Models, Statistical