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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...

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Autores principales: Shao, Jing, Hao, Yangfan, Wang, Lanfen, Xie, Yuxin, Zhang, Hongwei, Bai, Jiangping, Wu, Jing, Fu, Junjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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