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Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp

BACKGROUND: Hybrid poplars species are candidates for biomass production but breeding efforts are needed to combine productivity and water use efficiency in improved cultivars. The understanding of the genetic architecture of growth in poplar by a Quantitative Trait Loci (QTL) approach can help us t...

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Autores principales: Monclus, Romain, Leplé, Jean-Charles, Bastien, Catherine, Bert, Pierre-François, Villar, Marc, Marron, Nicolas, Brignolas, Franck, Jorge, Véronique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3520807/
https://www.ncbi.nlm.nih.gov/pubmed/23013168
http://dx.doi.org/10.1186/1471-2229-12-173
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author Monclus, Romain
Leplé, Jean-Charles
Bastien, Catherine
Bert, Pierre-François
Villar, Marc
Marron, Nicolas
Brignolas, Franck
Jorge, Véronique
author_facet Monclus, Romain
Leplé, Jean-Charles
Bastien, Catherine
Bert, Pierre-François
Villar, Marc
Marron, Nicolas
Brignolas, Franck
Jorge, Véronique
author_sort Monclus, Romain
collection PubMed
description BACKGROUND: Hybrid poplars species are candidates for biomass production but breeding efforts are needed to combine productivity and water use efficiency in improved cultivars. The understanding of the genetic architecture of growth in poplar by a Quantitative Trait Loci (QTL) approach can help us to elucidate the molecular basis of such integrative traits but identifying candidate genes underlying these QTLs remains difficult. Nevertheless, the increase of genomic information together with the accessibility to a reference genome sequence (Populus trichocarpa Nisqually-1) allow to bridge QTL information on genetic maps and physical location of candidate genes on the genome. The objective of the study is to identify QTLs controlling productivity, architecture and leaf traits in a P. deltoides x P. trichocarpa F1 progeny and to identify candidate genes underlying QTLs based on the anchoring of genetic maps on the genome and the gene ontology information linked to genome annotation. The strategy to explore genome annotation was to use Gene Ontology enrichment tools to test if some functional categories are statistically over-represented in QTL regions. RESULTS: Four leaf traits and 7 growth traits were measured on 330 F1 P. deltoides x P. trichocarpa progeny. A total of 77 QTLs controlling 11 traits were identified explaining from 1.8 to 17.2% of the variation of traits. For 58 QTLs, confidence intervals could be projected on the genome. An extended functional annotation was built based on data retrieved from the plant genome database Phytozome and from an inference of function using homology between Populus and the model plant Arabidopsis. Genes located within QTL confidence intervals were retrieved and enrichments in gene ontology (GO) terms were determined using different methods. Significant enrichments were found for all traits. Particularly relevant biological processes GO terms were identified for QTLs controlling number of sylleptic branches: intervals were enriched in GO terms of biological process like ‘ripening’ and ‘adventitious roots development’. CONCLUSION: Beyond the simple identification of QTLs, this study is the first to use a global approach of GO terms enrichment analysis to fully explore gene function under QTLs confidence intervals in plants. This global approach may lead to identification of new candidate genes for traits of interest.
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spelling pubmed-35208072012-12-14 Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp Monclus, Romain Leplé, Jean-Charles Bastien, Catherine Bert, Pierre-François Villar, Marc Marron, Nicolas Brignolas, Franck Jorge, Véronique BMC Plant Biol Research Article BACKGROUND: Hybrid poplars species are candidates for biomass production but breeding efforts are needed to combine productivity and water use efficiency in improved cultivars. The understanding of the genetic architecture of growth in poplar by a Quantitative Trait Loci (QTL) approach can help us to elucidate the molecular basis of such integrative traits but identifying candidate genes underlying these QTLs remains difficult. Nevertheless, the increase of genomic information together with the accessibility to a reference genome sequence (Populus trichocarpa Nisqually-1) allow to bridge QTL information on genetic maps and physical location of candidate genes on the genome. The objective of the study is to identify QTLs controlling productivity, architecture and leaf traits in a P. deltoides x P. trichocarpa F1 progeny and to identify candidate genes underlying QTLs based on the anchoring of genetic maps on the genome and the gene ontology information linked to genome annotation. The strategy to explore genome annotation was to use Gene Ontology enrichment tools to test if some functional categories are statistically over-represented in QTL regions. RESULTS: Four leaf traits and 7 growth traits were measured on 330 F1 P. deltoides x P. trichocarpa progeny. A total of 77 QTLs controlling 11 traits were identified explaining from 1.8 to 17.2% of the variation of traits. For 58 QTLs, confidence intervals could be projected on the genome. An extended functional annotation was built based on data retrieved from the plant genome database Phytozome and from an inference of function using homology between Populus and the model plant Arabidopsis. Genes located within QTL confidence intervals were retrieved and enrichments in gene ontology (GO) terms were determined using different methods. Significant enrichments were found for all traits. Particularly relevant biological processes GO terms were identified for QTLs controlling number of sylleptic branches: intervals were enriched in GO terms of biological process like ‘ripening’ and ‘adventitious roots development’. CONCLUSION: Beyond the simple identification of QTLs, this study is the first to use a global approach of GO terms enrichment analysis to fully explore gene function under QTLs confidence intervals in plants. This global approach may lead to identification of new candidate genes for traits of interest. BioMed Central 2012-09-26 /pmc/articles/PMC3520807/ /pubmed/23013168 http://dx.doi.org/10.1186/1471-2229-12-173 Text en Copyright ©2012 Monclus et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Monclus, Romain
Leplé, Jean-Charles
Bastien, Catherine
Bert, Pierre-François
Villar, Marc
Marron, Nicolas
Brignolas, Franck
Jorge, Véronique
Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp
title Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp
title_full Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp
title_fullStr Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp
title_full_unstemmed Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp
title_short Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp
title_sort integrating genome annotation and qtl position to identify candidate genes for productivity, architecture and water-use efficiency in populus spp
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3520807/
https://www.ncbi.nlm.nih.gov/pubmed/23013168
http://dx.doi.org/10.1186/1471-2229-12-173
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