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Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.)
Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), w...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641074/ https://www.ncbi.nlm.nih.gov/pubmed/37965378 http://dx.doi.org/10.1007/s11032-023-01423-y |
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author | Zhang, Yuanyuan Zhang, Mengchen Ye, Junhua Xu, Qun Feng, Yue Xu, Siliang Hu, Dongxiu Wei, Xinghua Hu, Peisong Yang, Yaolong |
author_facet | Zhang, Yuanyuan Zhang, Mengchen Ye, Junhua Xu, Qun Feng, Yue Xu, Siliang Hu, Dongxiu Wei, Xinghua Hu, Peisong Yang, Yaolong |
author_sort | Zhang, Yuanyuan |
collection | PubMed |
description | Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11032-023-01423-y. |
format | Online Article Text |
id | pubmed-10641074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-106410742023-11-14 Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) Zhang, Yuanyuan Zhang, Mengchen Ye, Junhua Xu, Qun Feng, Yue Xu, Siliang Hu, Dongxiu Wei, Xinghua Hu, Peisong Yang, Yaolong Mol Breed Article Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11032-023-01423-y. Springer Netherlands 2023-11-13 /pmc/articles/PMC10641074/ /pubmed/37965378 http://dx.doi.org/10.1007/s11032-023-01423-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Yuanyuan Zhang, Mengchen Ye, Junhua Xu, Qun Feng, Yue Xu, Siliang Hu, Dongxiu Wei, Xinghua Hu, Peisong Yang, Yaolong Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) |
title | Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) |
title_full | Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) |
title_fullStr | Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) |
title_full_unstemmed | Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) |
title_short | Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.) |
title_sort | integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (oryza sativa l.) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641074/ https://www.ncbi.nlm.nih.gov/pubmed/37965378 http://dx.doi.org/10.1007/s11032-023-01423-y |
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