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NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites
Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056766/ https://www.ncbi.nlm.nih.gov/pubmed/30065739 http://dx.doi.org/10.3389/fpls.2018.01022 |
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author | Ellis, Noel Hattori, Chie Cheema, Jitender Donarski, James Charlton, Adrian Dickinson, Michael Venditti, Giampaolo Kaló, Péter Szabó, Zoltán Kiss, György B. Domoney, Claire |
author_facet | Ellis, Noel Hattori, Chie Cheema, Jitender Donarski, James Charlton, Adrian Dickinson, Michael Venditti, Giampaolo Kaló, Péter Szabó, Zoltán Kiss, György B. Domoney, Claire |
author_sort | Ellis, Noel |
collection | PubMed |
description | Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value. |
format | Online Article Text |
id | pubmed-6056766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60567662018-07-31 NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites Ellis, Noel Hattori, Chie Cheema, Jitender Donarski, James Charlton, Adrian Dickinson, Michael Venditti, Giampaolo Kaló, Péter Szabó, Zoltán Kiss, György B. Domoney, Claire Front Plant Sci Plant Science Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value. Frontiers Media S.A. 2018-07-17 /pmc/articles/PMC6056766/ /pubmed/30065739 http://dx.doi.org/10.3389/fpls.2018.01022 Text en Copyright © 2018 Ellis, Hattori, Cheema, Donarski, Charlton, Dickinson, Venditti, Kaló, Szabó, Kiss and Domoney. http://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 Ellis, Noel Hattori, Chie Cheema, Jitender Donarski, James Charlton, Adrian Dickinson, Michael Venditti, Giampaolo Kaló, Péter Szabó, Zoltán Kiss, György B. Domoney, Claire NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites |
title | NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites |
title_full | NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites |
title_fullStr | NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites |
title_full_unstemmed | NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites |
title_short | NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites |
title_sort | nmr metabolomics defining genetic variation in pea seed metabolites |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056766/ https://www.ncbi.nlm.nih.gov/pubmed/30065739 http://dx.doi.org/10.3389/fpls.2018.01022 |
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