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Guo P, Zhu B, Xu L, Niu H, Wang Z, Guan L, Liang Y, Ni H, Guo Y, Chen Y, Zhang L, Gao X, Gao H, Li J. Genomic prediction with parallel computing for slaughter traits in Chinese Simmental beef cattle using high-density genotypes. PLoS One 2017; 12:e0179885. [PMID: 28723906 PMCID: PMC5516975 DOI: 10.1371/journal.pone.0179885] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 06/06/2017] [Indexed: 11/18/2022] Open
Abstract
Genomic selection has been widely used for complex quantitative trait in farm animals. Estimations of breeding values for slaughter traits are most important to beef cattle industry, and it is worthwhile to investigate prediction accuracies of genomic selection for these traits. In this study, we assessed genomic predictive abilities for average daily gain weight (ADG), live weight (LW), carcass weight (CW), dressing percentage (DP), lean meat percentage (LMP) and retail meat weight (RMW) using Illumina Bovine 770K SNP Beadchip in Chinese Simmental cattle. To evaluate the abilities of prediction, marker effects were estimated using genomic BLUP (GBLUP) and three parallel Bayesian models, including multiple chains parallel BayesA, BayesB and BayesCπ (PBayesA, PBayesB and PBayesCπ). Training set and validation set were divided by random allocation, and the predictive accuracies were evaluated using 5-fold cross validations. We found the accuracies of genomic predictions ranged from 0.195±0.084 (GBLUP for LMP) to 0.424±0.147 (PBayesB for CW). The average accuracies across traits were 0.327±0.085 (GBLUP), 0.335±0.063 (PBayesA), 0.347±0.093 (PBayesB) and 0.334±0.077 (PBayesCπ), respectively. Notably, parallel Bayesian models were more accurate than GBLUP across six traits. Our study suggested that genomic selections with multiple chains parallel Bayesian models are feasible for slaughter traits in Chinese Simmental cattle. The estimations of direct genomic breeding values using parallel Bayesian methods can offer important insights into improving prediction accuracy at young ages and may also help to identify superior candidates in breeding programs.
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Affiliation(s)
- Peng Guo
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin, China
| | - Bo Zhu
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingyang Xu
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
- * E-mail: (LYX); (JYL)
| | - Hong Niu
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zezhao Wang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Long Guan
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yonghu Liang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hemin Ni
- Animal Science and Technology College, Beijing University of Agriculture, Beijing, China
| | - Yong Guo
- Animal Science and Technology College, Beijing University of Agriculture, Beijing, China
| | - Yan Chen
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lupei Zhang
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xue Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Huijiang Gao
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Junya Li
- Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
- * E-mail: (LYX); (JYL)
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Tang G, Liu J, Xue J, Yang R, Liu T, Zeng Z, Jiang A, Jiang Y, Li M, Zhu L, Bai L, Shuai S, Wang Z, Li X. Optimizing selection strategies of genomic selection in swine breeding program based on a dataset simulated. Livest Sci 2014. [DOI: 10.1016/j.livsci.2014.04.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Davis SR, Macdonald KA, Waghorn GC, Spelman RJ. Residual feed intake of lactating Holstein-Friesian cows predicted from high-density genotypes and phenotyping of growing heifers. J Dairy Sci 2014; 97:1436-45. [PMID: 24472127 DOI: 10.3168/jds.2013-7205] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Accepted: 12/05/2013] [Indexed: 01/12/2023]
Abstract
A genomic prediction for residual feed intake (RFI) developed in growing dairy heifers (RFIgro) was used to predict and test breeding values for RFI in lactating cows (RFIlac) from an independent, industry population. A selection of 3,359 cows, in their third or fourth lactation during the study, of above average genetic merit for milk production, and identified as at least 15/16ths Holstein-Friesian breed, were selected for genotyping from commercial dairy herds. Genotyping was carried out using the bovine SNP50 BeadChip (Illumina Inc., San Diego, CA) on DNA extracted from ear-punch tissue. After quality control criteria were applied, genotypes were imputed to the 624,930 single nucleotide polymorphisms used in the growth study. Using these data, genomically estimated breeding values (GEBV) for RFIgro were calculated in the selected cow population based on a genomic prediction for RFIgro estimated in an independent group of growing heifers. Cows were ranked by GEBV and the top and bottom 310 identified for possible purchase. Purchased cows (n=214) were relocated to research facilities and intake and body weight (BW) measurements were undertaken in 99 "high" and 98 "low" RFIgro animals in 4 consecutive groups [beginning at d 61 ± 1.0 standard error (SE), 91 ± 0.5 SE, 145 ± 1.3 SE, and 191 ± 1.5 SE d in milk, respectively] to measure RFI during lactation (RFIlac). Each group of ~50 cows (~25 high and ~25 low RFIgro) was in a feed intake facility for 35 d, fed pasture-alfalfa cubes ad libitum, milked twice daily, and weighed every 2 to 3 d. Milk composition was determined 3 times weekly. Body weight change and BW at trial mid-point were estimated by regression of pre- and posttrial BW measurements. Residual feed intake in lactating cows was estimated from a linear model including BW, BW change, and milk component yield (as MJ/d); RFIlac differed consistently between the high and low selection classes, with the overall means for RFIlac being +0.32 and -0.31 kg of dry matter (DM) per day for the high and low classes, respectively. Further, we found evidence of sire differences for RFIlac, with one sire, in particular, being highly represented in the low RFIgro class, having a mean RFIlac of -0.83 kg of DM per day in 47 daughters. In conclusion, genomic prediction of RFIgro based on RFI measured during growth will discriminate for RFIlac in an independent group of lactating cows.
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Affiliation(s)
- S R Davis
- LIC, Private Bag 3016, Hamilton 3240, New Zealand.
| | - K A Macdonald
- DairyNZ, Private Bag 3221, Hamilton 3240, New Zealand
| | - G C Waghorn
- DairyNZ, Private Bag 3221, Hamilton 3240, New Zealand
| | - R J Spelman
- LIC, Private Bag 3016, Hamilton 3240, New Zealand
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