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An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci
Studying the genetic basis of variation in plant metabolism has been greatly facilitated by genomic and metabolic profiling advances. In this study, we use metabolomics and growth measurements to map QTL in rice, a major staple crop. Previous rice metabolism studies have largely focused on identifyi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4954952/ https://www.ncbi.nlm.nih.gov/pubmed/27440503 http://dx.doi.org/10.1038/srep30143 |
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author | Li, Baohua Zhang, Yuanyuan Mohammadi, Seyed Abolghasem Huai, Dongxin Zhou, Yongming Kliebenstein, Daniel J. |
author_facet | Li, Baohua Zhang, Yuanyuan Mohammadi, Seyed Abolghasem Huai, Dongxin Zhou, Yongming Kliebenstein, Daniel J. |
author_sort | Li, Baohua |
collection | PubMed |
description | Studying the genetic basis of variation in plant metabolism has been greatly facilitated by genomic and metabolic profiling advances. In this study, we use metabolomics and growth measurements to map QTL in rice, a major staple crop. Previous rice metabolism studies have largely focused on identifying genes controlling major effect loci. To complement these studies, we conducted a replicated metabolomics analysis on a japonica (Lemont) by indica (Teqing) rice recombinant inbred line population and focused on the genetic variation for primary metabolism. Using independent replicated studies, we show that in contrast to other rice studies, the heritability of primary metabolism is similar to Arabidopsis. The vast majority of metabolic QTLs had small to moderate effects with significant polygenic epistasis. Two metabolomics QTL hotspots had opposing effects on carbon and nitrogen rich metabolites suggesting that they may influence carbon and nitrogen partitioning, with one locus co-localizing with SUSIBA2 (WRKY78). Comparing QTLs for metabolomic and a variety of growth related traits identified few overlaps. Interestingly, the rice population displayed fewer loci controlling stochastic variation for metabolism than was found in Arabidopsis. Thus, it is possible that domestication has differentially impacted stochastic metabolite variation more than average metabolite variation. |
format | Online Article Text |
id | pubmed-4954952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-49549522016-07-26 An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci Li, Baohua Zhang, Yuanyuan Mohammadi, Seyed Abolghasem Huai, Dongxin Zhou, Yongming Kliebenstein, Daniel J. Sci Rep Article Studying the genetic basis of variation in plant metabolism has been greatly facilitated by genomic and metabolic profiling advances. In this study, we use metabolomics and growth measurements to map QTL in rice, a major staple crop. Previous rice metabolism studies have largely focused on identifying genes controlling major effect loci. To complement these studies, we conducted a replicated metabolomics analysis on a japonica (Lemont) by indica (Teqing) rice recombinant inbred line population and focused on the genetic variation for primary metabolism. Using independent replicated studies, we show that in contrast to other rice studies, the heritability of primary metabolism is similar to Arabidopsis. The vast majority of metabolic QTLs had small to moderate effects with significant polygenic epistasis. Two metabolomics QTL hotspots had opposing effects on carbon and nitrogen rich metabolites suggesting that they may influence carbon and nitrogen partitioning, with one locus co-localizing with SUSIBA2 (WRKY78). Comparing QTLs for metabolomic and a variety of growth related traits identified few overlaps. Interestingly, the rice population displayed fewer loci controlling stochastic variation for metabolism than was found in Arabidopsis. Thus, it is possible that domestication has differentially impacted stochastic metabolite variation more than average metabolite variation. Nature Publishing Group 2016-07-21 /pmc/articles/PMC4954952/ /pubmed/27440503 http://dx.doi.org/10.1038/srep30143 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Li, Baohua Zhang, Yuanyuan Mohammadi, Seyed Abolghasem Huai, Dongxin Zhou, Yongming Kliebenstein, Daniel J. An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci |
title | An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci |
title_full | An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci |
title_fullStr | An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci |
title_full_unstemmed | An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci |
title_short | An Integrative Genetic Study of Rice Metabolism, Growth and Stochastic Variation Reveals Potential C/N Partitioning Loci |
title_sort | integrative genetic study of rice metabolism, growth and stochastic variation reveals potential c/n partitioning loci |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4954952/ https://www.ncbi.nlm.nih.gov/pubmed/27440503 http://dx.doi.org/10.1038/srep30143 |
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