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Genomic selection of agronomic traits in hybrid rice using an NCII population
BACKGROUND: Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945574/ https://www.ncbi.nlm.nih.gov/pubmed/29748895 http://dx.doi.org/10.1186/s12284-018-0223-4 |
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author | Xu, Yang Wang, Xin Ding, Xiaowen Zheng, Xingfei Yang, Zefeng Xu, Chenwu Hu, Zhongli |
author_facet | Xu, Yang Wang, Xin Ding, Xiaowen Zheng, Xingfei Yang, Zefeng Xu, Chenwu Hu, Zhongli |
author_sort | Xu, Yang |
collection | PubMed |
description | BACKGROUND: Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. RESULTS: From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. CONCLUSIONS: This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12284-018-0223-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5945574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-59455742018-05-14 Genomic selection of agronomic traits in hybrid rice using an NCII population Xu, Yang Wang, Xin Ding, Xiaowen Zheng, Xingfei Yang, Zefeng Xu, Chenwu Hu, Zhongli Rice (N Y) Original Article BACKGROUND: Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. RESULTS: From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. CONCLUSIONS: This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12284-018-0223-4) contains supplementary material, which is available to authorized users. Springer US 2018-05-10 /pmc/articles/PMC5945574/ /pubmed/29748895 http://dx.doi.org/10.1186/s12284-018-0223-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Xu, Yang Wang, Xin Ding, Xiaowen Zheng, Xingfei Yang, Zefeng Xu, Chenwu Hu, Zhongli Genomic selection of agronomic traits in hybrid rice using an NCII population |
title | Genomic selection of agronomic traits in hybrid rice using an NCII population |
title_full | Genomic selection of agronomic traits in hybrid rice using an NCII population |
title_fullStr | Genomic selection of agronomic traits in hybrid rice using an NCII population |
title_full_unstemmed | Genomic selection of agronomic traits in hybrid rice using an NCII population |
title_short | Genomic selection of agronomic traits in hybrid rice using an NCII population |
title_sort | genomic selection of agronomic traits in hybrid rice using an ncii population |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945574/ https://www.ncbi.nlm.nih.gov/pubmed/29748895 http://dx.doi.org/10.1186/s12284-018-0223-4 |
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