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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843929/ https://www.ncbi.nlm.nih.gov/pubmed/35178070 http://dx.doi.org/10.3389/fgene.2021.769849 |
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author | Wang, Ye Diao, Chenguang Kang, Huimin Hao, Wenjie Mrode, Raphael Chen, Junhai Liu, Jianfeng Zhou, Lei |
author_facet | Wang, Ye Diao, Chenguang Kang, Huimin Hao, Wenjie Mrode, Raphael Chen, Junhai Liu, Jianfeng Zhou, Lei |
author_sort | Wang, Ye |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8843929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88439292022-02-16 A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs Wang, Ye Diao, Chenguang Kang, Huimin Hao, Wenjie Mrode, Raphael Chen, Junhai Liu, Jianfeng Zhou, Lei Front Genet 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. Frontiers Media S.A. 2022-02-01 /pmc/articles/PMC8843929/ /pubmed/35178070 http://dx.doi.org/10.3389/fgene.2021.769849 Text en Copyright © 2022 Wang, Diao, Kang, Hao, Mrode, Chen, Liu and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Ye Diao, Chenguang Kang, Huimin Hao, Wenjie Mrode, Raphael Chen, Junhai Liu, Jianfeng Zhou, Lei 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 | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843929/ https://www.ncbi.nlm.nih.gov/pubmed/35178070 http://dx.doi.org/10.3389/fgene.2021.769849 |
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