Comparison of genomic predictions for carcass and reproduction traits in Berkshire, Duroc and Yorkshire populations in Korea
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objective: A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms. Methods: The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs. Results: The accuracies under the GBLUP model for BFT and CWT ranged from 0.33-0.72 and 0.33-0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies. Conclusion: The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.
Abstract
Objective: A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms. Methods: The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs. Results: The accuracies under the GBLUP model for BFT and CWT ranged from 0.33-0.72 and 0.33-0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies. Conclusion: The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.
참고문헌 (28)
- J J Arranz, "Comparison of protein markers and microsatellites in differentiation of cattle populations", Animal Genetics, 27(6), pp.415~419 (1996), Wiley[DOI]
- Leif Andersson, "Genetic dissection of phenotypic diversity in farm animals", Nature Reviews Genetics, 2(2), pp.130~138 (2001), Springer Science and Business Media LLC[DOI]
- M.E. Goddard, "Genomic selection", Journal of Animal Breeding and Genetics, 124(6), pp.323~330 (2007), Wiley[DOI]
- Dekkers JCM, "limitations, and opportunities for marker-assisted selection in livestock. “Marker Assisted Selection (MAS) in Crops, Livestock, Forestry and Fish: Current Status and the Way Forward”", FAO Invited Book, ~ (2007)
- Meuwissen TH, "Prediction of total genetic value using genome-wide dense marker maps", Genetics, 157, pp.1819~1829 (2001)
- Ben J Hayes, "Accuracy of genomic breeding values in multi-breed dairy cattle populations", Genetics Selection Evolution, 41(1) (2009), Springer Science and Business Media LLC[DOI]
- Tianfei Liu, "Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens", BMC Genetics, 15(1), pp.110~ (2014), Springer Science and Business Media LLC[DOI]
- S. Hong Lee, "Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship", PLOS ONE, 12(12), pp.e0189775~ (2017), Public Library of Science (PLoS)[DOI]
- S. Hong Lee, "Using information of relatives in genomic prediction to apply effective stratified medicine", Scientific Reports, 7(1) (2017), Springer Science and Business Media LLC[DOI]
- B.J. Hayes, "Invited review: Genomic selection in dairy cattle: Progress and challenges", Journal of Dairy Science, 92(2), pp.433~443 (2009), American Dairy Science Association[DOI]
- P.M. VanRaden, "Invited Review: Reliability of genomic predictions for North American Holstein bulls", Journal of Dairy Science, 92(1), pp.16~24 (2009), American Dairy Science Association[DOI]
- Mariana Piatto Berton, "Growing-finishing performance and carcass yield of pigs reared in a climate-controlled and uncontrolled environment", International Journal of Biometeorology, 59(8), pp.955~960 (2015), Springer Science and Business Media LLC[DOI]
- Shaun Purcell, "PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses", The American Journal of Human Genetics, 81(3), pp.559~575 (2007), Elsevier BV[DOI]
- Brian L. Browning, "A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals", The American Journal of Human Genetics, 84(2), pp.210~223 (2009), Elsevier BV[DOI]
- Gilmour AR, "ASReml User Guide Release 3.0" (2009), VSN International Ltd
- Jian Yang, "GCTA: A Tool for Genome-wide Complex Trait Analysis", The American Journal of Human Genetics, 88(1), pp.76~82 (2011), Elsevier BV[DOI]
- Yvonne M. Badke, "Accuracy of Estimation of Genomic Breeding Values in Pigs Using Low-Density Genotypes and Imputation", G3: Genes|Genomes|Genetics, 4(4), pp.623~631 (2014), Genetics Society of America[DOI]
- I. H. Hwang, "Identification of Muscle Proteins Related to Objective Meat Quality in Korean Native Black Pig", Animal Bioscience, 17(11), pp.1599~1607 (2004), 아세아·태평양축산학회
- C. R. Schwab, "Results from six generations of selection for intramuscular fat in Duroc swine using real-time ultrasound. I. Direct and correlated phenotypic responses to selection1", Journal of Animal Science, 87(9), pp.2774~2780 (2009), Oxford University Press (OUP)[DOI]
- S. Baby, "Evaluation of Genome Based Estimated Breeding Values for Meat Quality in a Berkshire Population Using High Density Single Nucleotide Polymorphism Chips", Animal Bioscience, 27(11), pp.1540~1547 (2014), 아세아·태평양축산학회[DOI]
- D.L Kuhlers, "Genetic selection for real-time ultrasound loin eye area in a closed line of Landrace pigs", Livestock Production Science, 72(3), pp.225~231 (2001), Elsevier BV[DOI]
- E. C. Akanno, "Opportunities for genome-wide selection for pig breeding in developing countries1", Journal of Animal Science, 91(10), pp.4617~4627 (2013), Oxford University Press (OUP)[DOI]
- Cleveland MA, "Prediction of genomic breeding values in a commercial pig population", Proceedings of the 9th World Congress Genetics Applied to Livestock Production (2010)
- Cleveland MA, "Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation", J Anim Sci, 91, pp.3583~3592 (2013)
- David Habier, "The impact of genetic relationship information on genomic breeding values in German Holstein cattle", Genetics Selection Evolution, 42(1), pp.5~ (2010), Springer Science and Business Media LLC[DOI]
- Mike Goddard, "Genomic selection: prediction of accuracy and maximisation of long term response", Genetica, 136(2), pp.245~257 (2009), Springer Science and Business Media LLC[DOI]
- Uimari P, "Reliability of genomic selection of reproduction traits in Finnish Yorkshire pig breed", Proceedings of the 10th World Congress Genetics Applied to Livestock Production (2014)
- Forni S, "Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information", Genet Sel Evol, 43, pp.1~ (2011)[DOI]