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Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content
INTRODUCTION: Genomic selection (GS) is a potential breeding approach for soybean improvement. METHODS: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9793221/ https://www.ncbi.nlm.nih.gov/pubmed/36582644 http://dx.doi.org/10.3389/fpls.2022.1064623 |
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author | Sun, Bo Guo, Rui Liu, Zhi Shi, Xiaolei Yang, Qing Shi, Jiayao Zhang, Mengchen Yang, Chunyan Zhao, Shugang Zhang, Jie He, Jianhan Zhang, Jiaoping Su, Jianhui Song, Qijian Yan, Long |
author_facet | Sun, Bo Guo, Rui Liu, Zhi Shi, Xiaolei Yang, Qing Shi, Jiayao Zhang, Mengchen Yang, Chunyan Zhao, Shugang Zhang, Jie He, Jianhan Zhang, Jiaoping Su, Jianhui Song, Qijian Yan, Long |
author_sort | Sun, Bo |
collection | PubMed |
description | INTRODUCTION: Genomic selection (GS) is a potential breeding approach for soybean improvement. METHODS: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. RESULTS: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log(10)(P) greater than 1 were included. DISCUSSION: This information will help improve GS efficiency and facilitate the application of GS. |
format | Online Article Text |
id | pubmed-9793221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97932212022-12-28 Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content Sun, Bo Guo, Rui Liu, Zhi Shi, Xiaolei Yang, Qing Shi, Jiayao Zhang, Mengchen Yang, Chunyan Zhao, Shugang Zhang, Jie He, Jianhan Zhang, Jiaoping Su, Jianhui Song, Qijian Yan, Long Front Plant Sci Plant Science INTRODUCTION: Genomic selection (GS) is a potential breeding approach for soybean improvement. METHODS: In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. RESULTS: Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log(10)(P) greater than 1 were included. DISCUSSION: This information will help improve GS efficiency and facilitate the application of GS. Frontiers Media S.A. 2022-12-13 /pmc/articles/PMC9793221/ /pubmed/36582644 http://dx.doi.org/10.3389/fpls.2022.1064623 Text en Copyright © 2022 Sun, Guo, Liu, Shi, Yang, Shi, Zhang, Yang, Zhao, Zhang, He, Zhang, Su, Song and Yan 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 | Plant Science Sun, Bo Guo, Rui Liu, Zhi Shi, Xiaolei Yang, Qing Shi, Jiayao Zhang, Mengchen Yang, Chunyan Zhao, Shugang Zhang, Jie He, Jianhan Zhang, Jiaoping Su, Jianhui Song, Qijian Yan, Long Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
title | Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
title_full | Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
title_fullStr | Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
title_full_unstemmed | Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
title_short | Genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
title_sort | genetic variation and marker−trait association affect the genomic selection prediction accuracy of soybean protein and oil content |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9793221/ https://www.ncbi.nlm.nih.gov/pubmed/36582644 http://dx.doi.org/10.3389/fpls.2022.1064623 |
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