Developing a genome-wide selection model for genetic improvement of residual feed intake and carcass merit in a beef cattle breeding program  

作　　者：ZENG ZhiYao TANG Guo Qing MA JiDeng PLASTOW Graham MOORE Stephen LAI SongJia LI XueWei WANG ZhiQuan 

机构地区：[1]College of Animal Science and Technology,Sichuan Agricultural University,Yaan 625014,China [2]Department of Agricultural,Food and Nutritional Science,University of Alberta,Edmonton,AB T6G 2C8,Canada [3]College of Animal Science and Technology,Heilongjiang August First Land Reclamation University,Daqing 163319,China

出　　处：《Chinese Science Bulletin》2012年第21期2741-2746,共6页

基　　金：supported by the Alberta Livestock and Meat Agency(ALMA)(G550000101);ALMA/Alberta Agricultural Research Institute(AARI)(G550000127);National Natural Science Foundation of China(30771534) who are greatly acknowledged

摘　　要：Residual feed intake (RFI) and carcass merit (CM) are both complex traits emerging as critical targets for beef genetic improvement.RFI and CM traits are difficult and expensive to measure and genetic improvement for these traits through traditional selection methods is not very effective.Therefore,genome-wide selection using DNA markers may be a potential alternative for genetic improvement of these traits.In this study,the efficiency of a genome-wide selection model for genetic improvement of RFI and CM was assessed.The Illumina Bovine50K bead chip was used to genotype 922 beef cattle from the Kinsella Beef Research Ranch of the University of Alberta.A Bayes model and multiple marker regression using a stepwise method were used to conduct the association test.The number of significant SNP markers for carcass weight (CWT),carcass back fat (BF),carcass rib eye area (REA),carcass grade fat (GDF),lean meat yield (LMY),and residual feed intake (RFI) were 75,54,67,57,44 and 50,respectively.Bi-variate analysis of marker scores and phenotypes for all traits were made using DMU Software.The genetic parameter for each trait was estimated.The genetic correlations of marker score and phenotype for CWT,BF,REA,GDF,LMY and RFI were 0.75,0.69,0.87,0.77,0.78,and 0.85,respectively.The average prediction accuracies of phenotypic EBV for the six traits were increased by 0.05,0.16,0.24,0.23,0.17 and 0.19,respectively.The results of this study indicated that the two-trait marker-assisted evaluation model used was a suitable alternative of genetic evaluation for these traits in beef cattle.Residual feed intake （RFI） and carcass merit （CM） are both complex traits emerging as critical targets for beef genetic improvement.RFI and CM traits are difficult and expensive to measure and genetic improvement for these traits through traditional selection methods is not very effective.Therefore,genome-wide selection using DNA markers may be a potential alternative for genetic improvement of these traits.In this study,the efficiency of a genome-wide selection model for genetic improvement of RFI and CM was assessed.The Illumina Bovine50K bead chip was used to genotype 922 beef cattle from the Kinsella Beef Research Ranch of the University of Alberta.A Bayes model and multiple marker regression using a stepwise method were used to conduct the association test.The number of significant SNP markers for carcass weight （CWT）,carcass back fat （BF）,carcass rib eye area （REA）,carcass grade fat （GDF）,lean meat yield （LMY）,and residual feed intake （RFI） were 75,54,67,57,44 and 50,respectively.Bi-variate analysis of marker scores and phenotypes for all traits were made using DMU Software.The genetic parameter for each trait was estimated.The genetic correlations of marker score and phenotype for CWT,BF,REA,GDF,LMY and RFI were 0.75,0.69,0.87,0.77,0.78,and 0.85,respectively.The average prediction accuracies of phenotypic EBV for the six traits were increased by 0.05,0.16,0.24,0.23,0.17 and 0.19,respectively.The results of this study indicated that the two-trait marker-assisted evaluation model used was a suitable alternative of genetic evaluation for these traits in beef cattle.

关 键 词：胴体品质 遗传改良 全基因组 选择模型 采食量 育种计划 肉牛 剩余 

分 类 号：S823[农业科学—畜牧学]