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Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure
Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Geno...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144439/ https://www.ncbi.nlm.nih.gov/pubmed/35631723 http://dx.doi.org/10.3390/plants11101298 |
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author | Shao, Jing Hao, Yangfan Wang, Lanfen Xie, Yuxin Zhang, Hongwei Bai, Jiangping Wu, Jing Fu, Junjie |
author_facet | Shao, Jing Hao, Yangfan Wang, Lanfen Xie, Yuxin Zhang, Hongwei Bai, Jiangping Wu, Jing Fu, Junjie |
author_sort | Shao, Jing |
collection | PubMed |
description | Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59–0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources. |
format | Online Article Text |
id | pubmed-9144439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91444392022-05-29 Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure Shao, Jing Hao, Yangfan Wang, Lanfen Xie, Yuxin Zhang, Hongwei Bai, Jiangping Wu, Jing Fu, Junjie Plants (Basel) Article Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59–0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources. MDPI 2022-05-12 /pmc/articles/PMC9144439/ /pubmed/35631723 http://dx.doi.org/10.3390/plants11101298 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shao, Jing Hao, Yangfan Wang, Lanfen Xie, Yuxin Zhang, Hongwei Bai, Jiangping Wu, Jing Fu, Junjie Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure |
title | Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure |
title_full | Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure |
title_fullStr | Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure |
title_full_unstemmed | Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure |
title_short | Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure |
title_sort | development of a model for genomic prediction of multiple traits in common bean germplasm, based on population structure |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144439/ https://www.ncbi.nlm.nih.gov/pubmed/35631723 http://dx.doi.org/10.3390/plants11101298 |
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