A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs
Residual feed intake (RFI) is considered as a measurement of feed efficiency, which is greatly related to the growth performance in pigs. Daily feeding records can be obtained from automatic feeders. In general, RFI is usually calculated from the total measurement records during the whole test perio...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Frontiers Media
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/117946 |
| _version_ | 1855518765523927040 |
|---|---|
| author | Wang, Y. Diao, C. Kang, H. Hao, W. Mrode, Raphael A. Chen, J. Liu, J. Zhou, L. |
| author_browse | Chen, J. Diao, C. Hao, W. Kang, H. Liu, J. Mrode, Raphael A. Wang, Y. Zhou, L. |
| author_facet | Wang, Y. Diao, C. Kang, H. Hao, W. Mrode, Raphael A. Chen, J. Liu, J. Zhou, L. |
| author_sort | Wang, Y. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Residual feed intake (RFI) is considered as a measurement of feed efficiency, which is greatly related to the growth performance in pigs. Daily feeding records can be obtained from automatic feeders. In general, RFI is usually calculated from the total measurement records during the whole test period. This measurement cannot reflect genetic changes in different growth periods during the test. A random regression model (RRM) provides a method to model such type of longitudinal data. To improve the accuracy of genetic prediction for RFI, the RRM and regular animal models were applied in this study, and their prediction performances were compared. Both traditional pedigree-based relationship matrix (A matrix) and pedigree and genomic information-based relationship matrix (H matrix) were applied for these two models. The results showed that, the prediction accuracy of the RRM was higher than that of the animal model, increasing 24.2% with A matrix and 40.9% with H matrix. Furthermore, genomic information constantly improved the accuracy of evaluation under each evaluation model. In conclusion, longitudinal traits such as RFI can describe feed efficiency better, and the RRM with both pedigree and genetic information was superior to the animal model. These results provide a feasible method of genomic prediction using longitudinal data in animal breeding. |
| format | Journal Article |
| id | CGSpace117946 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1179462025-12-08T10:29:22Z A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs Wang, Y. Diao, C. Kang, H. Hao, W. Mrode, Raphael A. Chen, J. Liu, J. Zhou, L. animal feeding livestock genomes genomics agriculture swine feed intake genetics Residual feed intake (RFI) is considered as a measurement of feed efficiency, which is greatly related to the growth performance in pigs. Daily feeding records can be obtained from automatic feeders. In general, RFI is usually calculated from the total measurement records during the whole test period. This measurement cannot reflect genetic changes in different growth periods during the test. A random regression model (RRM) provides a method to model such type of longitudinal data. To improve the accuracy of genetic prediction for RFI, the RRM and regular animal models were applied in this study, and their prediction performances were compared. Both traditional pedigree-based relationship matrix (A matrix) and pedigree and genomic information-based relationship matrix (H matrix) were applied for these two models. The results showed that, the prediction accuracy of the RRM was higher than that of the animal model, increasing 24.2% with A matrix and 40.9% with H matrix. Furthermore, genomic information constantly improved the accuracy of evaluation under each evaluation model. In conclusion, longitudinal traits such as RFI can describe feed efficiency better, and the RRM with both pedigree and genetic information was superior to the animal model. These results provide a feasible method of genomic prediction using longitudinal data in animal breeding. 2022-02-01 2022-02-03T20:19:53Z 2022-02-03T20:19:53Z Journal Article https://hdl.handle.net/10568/117946 en Open Access Frontiers Media Wang, Y., Diao, C., Kang, H., Hao, W., Mrode, Raphael A., Chen, J., Liu, J. and Zhou, L. 2022. A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs. Frontiers in Genetics 12: 769849. |
| spellingShingle | animal feeding livestock genomes genomics agriculture swine feed intake genetics Wang, Y. Diao, C. Kang, H. Hao, W. Mrode, Raphael A. Chen, J. Liu, J. Zhou, L. A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| title | A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| title_full | A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| title_fullStr | A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| title_full_unstemmed | A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| title_short | A random regression model based on a single-step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| title_sort | random regression model based on a single step method for improving the genomic prediction accuracy of residual feed intake in pigs |
| topic | animal feeding livestock genomes genomics agriculture swine feed intake genetics |
| url | https://hdl.handle.net/10568/117946 |
| work_keys_str_mv | AT wangy arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT diaoc arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT kangh arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT haow arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT mroderaphaela arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT chenj arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT liuj arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT zhoul arandomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT wangy randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT diaoc randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT kangh randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT haow randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT mroderaphaela randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT chenj randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT liuj randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs AT zhoul randomregressionmodelbasedonasinglestepmethodforimprovingthegenomicpredictionaccuracyofresidualfeedintakeinpigs |