A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs
Genomic prediction (GP), which uses genome-wide markers to estimate breeding values, is a crucial tool for accelerating genetic progress in livestock and plant breeding. The accuracy of GP depends on several factors, including the statistical model, marker density, and cross-validation strategy. Thi...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-2615/15/7/966 |
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| author | Xiaojian Chen Yiyi Liu Yuling Zhang Zhanwei Zhuang Jinyan Huang Menghao Luan Xiang Zhao Linsong Dong Jian Ye Ming Yang Enqin Zheng Gengyuan Cai Jie Yang Zhenfang Wu Langqing Liu |
| author_facet | Xiaojian Chen Yiyi Liu Yuling Zhang Zhanwei Zhuang Jinyan Huang Menghao Luan Xiang Zhao Linsong Dong Jian Ye Ming Yang Enqin Zheng Gengyuan Cai Jie Yang Zhenfang Wu Langqing Liu |
| author_sort | Xiaojian Chen |
| collection | DOAJ |
| description | Genomic prediction (GP), which uses genome-wide markers to estimate breeding values, is a crucial tool for accelerating genetic progress in livestock and plant breeding. The accuracy of GP depends on several factors, including the statistical model, marker density, and cross-validation strategy. This study evaluated these factors to optimize GP accuracy for eight economically important carcass and body traits in a Duroc × (Landrace × Yorkshire) (DLY) pig population. This study used 50 K SNP chip data from 1494 DLY pigs, which were imputed to the whole genome sequence (WGS) level. Seven different models were compared, including GBLUP, ssGBLUP, and five Bayesian models. The ssGBLUP model consistently outperformed other models across all traits, with prediction accuracies ranging from 0.371 to 0.502. Further analyses showed that prediction accuracy improved with increasing cross-validation folds and marker density, particularly in the low-density panel. However, the improvement plateaued in medium-to-high-density scenarios. These findings underscore the importance of carefully selecting the model, marker density, and cross-validation strategy to optimize GP accuracy for carcass and body traits in commercial pigs. The insights from this study can guide breeders and researchers in maximizing genetic progress in pig breeding programs. |
| format | Article |
| id | doaj-art-0dc8685939f249c1b800984dbfe4d8a5 |
| institution | OA Journals |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-0dc8685939f249c1b800984dbfe4d8a52025-08-20T02:17:00ZengMDPI AGAnimals2076-26152025-03-0115796610.3390/ani15070966A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial PigsXiaojian Chen0Yiyi Liu1Yuling Zhang2Zhanwei Zhuang3Jinyan Huang4Menghao Luan5Xiang Zhao6Linsong Dong7Jian Ye8Ming Yang9Enqin Zheng10Gengyuan Cai11Jie Yang12Zhenfang Wu13Langqing Liu14National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaGuangdong Zhongxin Breeding Technology Co., Ltd., Guangzhou 510642, ChinaGuangdong Zhongxin Breeding Technology Co., Ltd., Guangzhou 510642, ChinaCollege of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaNational Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, ChinaGenomic prediction (GP), which uses genome-wide markers to estimate breeding values, is a crucial tool for accelerating genetic progress in livestock and plant breeding. The accuracy of GP depends on several factors, including the statistical model, marker density, and cross-validation strategy. This study evaluated these factors to optimize GP accuracy for eight economically important carcass and body traits in a Duroc × (Landrace × Yorkshire) (DLY) pig population. This study used 50 K SNP chip data from 1494 DLY pigs, which were imputed to the whole genome sequence (WGS) level. Seven different models were compared, including GBLUP, ssGBLUP, and five Bayesian models. The ssGBLUP model consistently outperformed other models across all traits, with prediction accuracies ranging from 0.371 to 0.502. Further analyses showed that prediction accuracy improved with increasing cross-validation folds and marker density, particularly in the low-density panel. However, the improvement plateaued in medium-to-high-density scenarios. These findings underscore the importance of carefully selecting the model, marker density, and cross-validation strategy to optimize GP accuracy for carcass and body traits in commercial pigs. The insights from this study can guide breeders and researchers in maximizing genetic progress in pig breeding programs.https://www.mdpi.com/2076-2615/15/7/966ssGBLUPmarker densitygenomic prediction accuracycross-validationcarcass and body traits |
| spellingShingle | Xiaojian Chen Yiyi Liu Yuling Zhang Zhanwei Zhuang Jinyan Huang Menghao Luan Xiang Zhao Linsong Dong Jian Ye Ming Yang Enqin Zheng Gengyuan Cai Jie Yang Zhenfang Wu Langqing Liu A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs Animals ssGBLUP marker density genomic prediction accuracy cross-validation carcass and body traits |
| title | A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs |
| title_full | A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs |
| title_fullStr | A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs |
| title_full_unstemmed | A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs |
| title_short | A Comparative Study of Optimizing Genomic Prediction Accuracy in Commercial Pigs |
| title_sort | comparative study of optimizing genomic prediction accuracy in commercial pigs |
| topic | ssGBLUP marker density genomic prediction accuracy cross-validation carcass and body traits |
| url | https://www.mdpi.com/2076-2615/15/7/966 |
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