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Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice
Improvement of the breeding efficiencies of heterotic crops adaptive to different conditions can mitigate the food shortage crisis due to overpopulation and climate change. To date, diverse molecular markers have been used to guide field phenotypic selection, whereas accurate predictions of complex...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Life Science Alliance LLC
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918511/ https://www.ncbi.nlm.nih.gov/pubmed/31836628 http://dx.doi.org/10.26508/lsa.201900551 |
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author | Dan, Zhiwu Chen, Yunping Zhao, Weibo Wang, Qiong Huang, Wenchao |
author_facet | Dan, Zhiwu Chen, Yunping Zhao, Weibo Wang, Qiong Huang, Wenchao |
author_sort | Dan, Zhiwu |
collection | PubMed |
description | Improvement of the breeding efficiencies of heterotic crops adaptive to different conditions can mitigate the food shortage crisis due to overpopulation and climate change. To date, diverse molecular markers have been used to guide field phenotypic selection, whereas accurate predictions of complex heterotic traits are rarely reported. Here, we present a practical metabolome-based strategy for predicting yield heterosis in rice. The dissection of population structure based on untargeted metabolite profiles as the initial critical step in multivariate modeling performed better than the screening of predictive variables. Then the assessment of each predictive variable’s contribution to predictive models according to all latent factors was more precise than the conventional first one. Metabolites belonging to specific pathways were closely associated with yield heterosis, and the up-regulation of galactose metabolism promoted robust yield heterosis in hybrids under different growth conditions. Our study demonstrates that metabolome-based predictive models with correctly dissected population structure and screened predictive variables can facilitate accurate predictions of yield heterosis and have great potential for establishing molecular marker–based precision breeding programs. |
format | Online Article Text |
id | pubmed-6918511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Life Science Alliance LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-69185112019-12-19 Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice Dan, Zhiwu Chen, Yunping Zhao, Weibo Wang, Qiong Huang, Wenchao Life Sci Alliance Research Articles Improvement of the breeding efficiencies of heterotic crops adaptive to different conditions can mitigate the food shortage crisis due to overpopulation and climate change. To date, diverse molecular markers have been used to guide field phenotypic selection, whereas accurate predictions of complex heterotic traits are rarely reported. Here, we present a practical metabolome-based strategy for predicting yield heterosis in rice. The dissection of population structure based on untargeted metabolite profiles as the initial critical step in multivariate modeling performed better than the screening of predictive variables. Then the assessment of each predictive variable’s contribution to predictive models according to all latent factors was more precise than the conventional first one. Metabolites belonging to specific pathways were closely associated with yield heterosis, and the up-regulation of galactose metabolism promoted robust yield heterosis in hybrids under different growth conditions. Our study demonstrates that metabolome-based predictive models with correctly dissected population structure and screened predictive variables can facilitate accurate predictions of yield heterosis and have great potential for establishing molecular marker–based precision breeding programs. Life Science Alliance LLC 2019-12-13 /pmc/articles/PMC6918511/ /pubmed/31836628 http://dx.doi.org/10.26508/lsa.201900551 Text en © 2019 Dan et al. https://creativecommons.org/licenses/by/4.0/This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Articles Dan, Zhiwu Chen, Yunping Zhao, Weibo Wang, Qiong Huang, Wenchao Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
title | Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
title_full | Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
title_fullStr | Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
title_full_unstemmed | Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
title_short | Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
title_sort | metabolome-based prediction of yield heterosis contributes to the breeding of elite rice |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918511/ https://www.ncbi.nlm.nih.gov/pubmed/31836628 http://dx.doi.org/10.26508/lsa.201900551 |
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