Publication Date:
2012-07-14
Description:
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. Content Type Journal Article Category Article Pages 2741-2746 DOI 10.1007/s11434-012-5325-6 Authors ZhiYao Zeng, College of Animal Science and Technology, Sichuan Agricultural University, Yaan, 625014 China GuoQing Tang, College of Animal Science and Technology, Sichuan Agricultural University, Yaan, 625014 China JiDeng Ma, College of Animal Science and Technology, Sichuan Agricultural University, Yaan, 625014 China Graham Plastow, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2C8, Canada Stephen Moore, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2C8, Canada SongJia Lai, College of Animal Science and Technology, Sichuan Agricultural University, Yaan, 625014 China XueWei Li, College of Animal Science and Technology, Sichuan Agricultural University, Yaan, 625014 China ZhiQuan Wang, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2C8, Canada Journal Chinese Science Bulletin Online ISSN 1861-9541 Print ISSN 1001-6538 Journal Volume Volume 57 Journal Issue Volume 57, Number 21
Print ISSN:
1001-6538
Electronic ISSN:
1861-9541
Topics:
Natural Sciences in General
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