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Chen Y, Wang Z, Qu X, Song B, Tang Y, Li B, Cao G, Yi G. An intronic SNP affects skeletal muscle development by regulating the expression of TP63. Front Vet Sci 2024; 11:1396766. [PMID: 38933706 PMCID: PMC11199888 DOI: 10.3389/fvets.2024.1396766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
Background Porcine skeletal muscle development is pivotal for improving meat production. TP63, a transcription factor, regulates vital cellular processes, yet its role in skeletal muscle proliferation is unclear. Methods The effects of TP63 on skeletal muscle cell viability and proliferation were investigated using both mouse and porcine skeletal muscle myoblasts. Selective sweep analysis in Western pigs identified TP63 as a potential candidate gene for skeletal muscle development. The correlation between TP63 overexpression and cell proliferation was assessed using quantitative real-time PCR (RT-qPCR) and 5-ethynyl-2'-deoxyuridine (EDU). Results The study revealed a positive correlation between TP63 overexpression and skeletal muscle cell proliferation. Bioinformatics analysis predicted an interaction between MEF2A, another transcription factor, and the mutation site of TP63. Experimental validation through dual-luciferase assays confirmed that a candidate enhancer SNP could influence MEF2A binding, subsequently regulating TP63 expression and promoting skeletal muscle cell proliferation. Conclusion These findings offer experimental evidence for further exploration of skeletal muscle development mechanisms and the advancement of genetic breeding strategies aimed at improving meat production traits.
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Affiliation(s)
- Yufen Chen
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- College of Animal Science, Shanxi Agricultural University, Jinzhong, China
| | - Zhen Wang
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Xiaolu Qu
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Bangmin Song
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yueting Tang
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Bugao Li
- College of Animal Science, Shanxi Agricultural University, Jinzhong, China
| | - Guoqing Cao
- College of Animal Science, Shanxi Agricultural University, Jinzhong, China
| | - Guoqiang Yi
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan, China
- Bama Yao Autonomous County Rural Revitalization Research Institute, Bama, China
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Teng J, Zhai T, Zhang X, Zhao C, Wang W, Tang H, Wang D, Shang Y, Ning C, Zhang Q. Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information. Brief Bioinform 2024; 25:bbae276. [PMID: 38856170 PMCID: PMC11163384 DOI: 10.1093/bib/bbae276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/05/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024] Open
Abstract
In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).
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Affiliation(s)
- Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
- Shandong Futeng Food Co. Ltd., Zaozhuang 277500, Shandong, China
| | - Tingting Zhai
- National Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Wenwen Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Hui Tang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Yingli Shang
- College of Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Chao Ning
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
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Yin C, Shi H, Zhou P, Wang Y, Tao X, Yin Z, Zhang X, Liu Y. Genomic Prediction of Growth Traits in Yorkshire Pigs of Different Reference Group Sizes Using Different Estimated Breeding Value Models. Animals (Basel) 2024; 14:1098. [PMID: 38612337 PMCID: PMC11010886 DOI: 10.3390/ani14071098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 03/31/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
The need for sufficient reference population data poses a significant challenge in breeding programs aimed at improving pig farming on a small to medium scale. To overcome this hurdle, investigating the advantages of combing reference populations of varying sizes is crucial for enhancing the accuracy of the genomic estimated breeding value (GEBV). Genomic selection (GS) in populations with limited reference data can be optimized by combining populations of the same breed or related breeds. This study focused on understanding the effect of combing different reference group sizes on the accuracy of GS for determining the growth effectiveness and percentage of lean meat in Yorkshire pigs. Specifically, our study investigated two important traits: the age at 100 kg live weight (AGE100) and the backfat thickness at 100 kg live weight (BF100). This research assessed the efficiency of genomic prediction (GP) using different GEBV models across three Yorkshire populations with varying genetic backgrounds. The GeneSeek 50K GGP porcine high-density array was used for genotyping. A total of 2295 Yorkshire pigs were included, representing three Yorkshire pig populations with different genetic backgrounds-295 from Danish (small) lines from Huaibei City, Anhui Province, 500 from Canadian (medium) lines from Lixin County, Anhui Province, and 1500 from American (large) lines from Shanghai. To evaluate the impact of different population combination scenarios on the GS accuracy, three approaches were explored: (1) combining all three populations for prediction, (2) combining two populations to predict the third, and (3) predicting each population independently. Five GEBV models, including three Bayesian models (BayesA, BayesB, and BayesC), the genomic best linear unbiased prediction (GBLUP) model, and single-step GBLUP (ssGBLUP) were implemented through 20 repetitions of five-fold cross-validation (CV). The results indicate that predicting one target population using the other two populations yielded the highest accuracy, providing a novel approach for improving the genomic selection accuracy in Yorkshire pigs. In this study, it was found that using different populations of the same breed to predict small- and medium-sized herds might be effective in improving the GEBV. This investigation highlights the significance of incorporating population combinations in genetic models for predicting the breeding value, particularly for pig farmers confronted with resource limitations.
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Affiliation(s)
- Chang Yin
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Haoran Shi
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Peng Zhou
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Yuwei Wang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Xuzhe Tao
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China; (Z.Y.); (X.Z.)
| | - Xiaodong Zhang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China; (Z.Y.); (X.Z.)
| | - Yang Liu
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China; (C.Y.); (H.S.); (P.Z.); (Y.W.); (X.T.)
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Cai W, Hu J, Fan W, Xu Y, Tang J, Xie M, Zhang Y, Guo Z, Zhou Z, Hou S. Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population. Evol Appl 2024; 17:e13638. [PMID: 38333555 PMCID: PMC10848588 DOI: 10.1111/eva.13638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 09/02/2023] [Accepted: 12/07/2023] [Indexed: 02/10/2024] Open
Abstract
Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.
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Affiliation(s)
- Wentao Cai
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Jian Hu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Wenlei Fan
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
- College of Animal Science and TechnologyQingdao Agricultural UniversityQingdaoChina
| | - Yaxi Xu
- College of Animal Science and TechnologyBeijing University of AgricultureBeijingChina
| | - Jing Tang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Ming Xie
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Yunsheng Zhang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Zhanbao Guo
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Zhengkui Zhou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
| | - Shuisheng Hou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijingChina
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Wei C, Chang C, Zhang W, Ren D, Cai X, Zhou T, Shi S, Wu X, Si J, Yuan X, Li J, Zhang Z. Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs. Animals (Basel) 2023; 13:3746. [PMID: 38136785 PMCID: PMC10740834 DOI: 10.3390/ani13243746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.
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Affiliation(s)
- Chen Wei
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Chengjie Chang
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Wenjing Zhang
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Duanyang Ren
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Xiaodian Cai
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Tianru Zhou
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Shaolei Shi
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Xibo Wu
- Guangxi State Farms Yongxin Animal Husbandry Group Co., Ltd., Nanning 530022, China; (X.W.); (J.S.)
| | - Jinglei Si
- Guangxi State Farms Yongxin Animal Husbandry Group Co., Ltd., Nanning 530022, China; (X.W.); (J.S.)
| | - Xiaolong Yuan
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Jiaqi Li
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Zhe Zhang
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
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Zhao W, Zhang Z, Wang Z, Ma P, Pan Y, Wang Q, Zhang Z. Factors affecting the accuracy of genomic prediction in joint pig populations. Animal 2023; 17:100980. [PMID: 37797495 DOI: 10.1016/j.animal.2023.100980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 08/28/2023] [Accepted: 08/31/2023] [Indexed: 10/07/2023] Open
Abstract
Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.
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Affiliation(s)
- Wei Zhao
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiaotong University, 800# Dongchuan Road, Shang, East 200240, China
| | - Zhenyang Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, 866# Yuhangtang Road, Hangzhou, East 310058, China
| | - Zhen Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, 866# Yuhangtang Road, Hangzhou, East 310058, China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiaotong University, 800# Dongchuan Road, Shang, East 200240, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, 866# Yuhangtang Road, Hangzhou, East 310058, China; Hainan Institute, Zhejiang University, Yongyou Industrial Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, 866# Yuhangtang Road, Hangzhou, East 310058, China; Hainan Institute, Zhejiang University, Yongyou Industrial Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, 866# Yuhangtang Road, Hangzhou, East 310058, China.
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