The rice defensome database: Centralizing and unifying R-genes, resistance QTL mapping and defence gene expression for rice blast
Along years, the rice community has produced an incredible amount of information on the rice blast system: R-genes and resistance QTLs were mapped while there has been a recent increase in the information relative to gene expression upon infection. There is a need for integrating all these informations. Since 2002, the availability of the rice genome provides a new frame for integrating genetic and molecular information into a unified physical map. Some efforts were made in the past to anchor genetic information to the rice physical map (Wisser et al, 2005). We have extensively reviewed more than 60 publications on R-gene mapping, more than 20 published and unpublished studies on QTL mapping and more than 60 publications on gene expression in infected rice plants. This allowed the positioning of 85 R-genes, 343 QTLs and more than 5000 defence-related genes. The density of defence genes along chromosomes varies greatly, highlighting chromosome regions rich in defence genes. A metaQTL analysis was done to reduce the number of regions of the genome potentially useful for breeding blast resistance, leading to the identification of 160 blast resistance metaQTLs. Examination of physical distances indicated that more than 16 R-genes are found in intervals of less than 500 kb and that many QTLs represent very small intervals in which defence gene expression is found. This database was inserted into the OrygenesDB web site (Droc et al, 2007) and all associated tools. This database offers new opportunities for developing markers useful for marker-assisted selection and pinpoints some regions of the genome that could be targeted using candidate gene strategies. (Texte intégral)
| Main Authors: | , , , , , , , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
CIRAD
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| Subjects: | F30 - Génétique et amélioration des plantes, C30 - Documentation et information, Oryza, Oryza sativa, banque de données, génie génétique, marqueur génétique, gène, locus des caractères quantitatifs, résistance aux maladies, carte génétique, http://aims.fao.org/aos/agrovoc/c_5435, http://aims.fao.org/aos/agrovoc/c_5438, http://aims.fao.org/aos/agrovoc/c_24833, http://aims.fao.org/aos/agrovoc/c_15974, http://aims.fao.org/aos/agrovoc/c_24030, http://aims.fao.org/aos/agrovoc/c_3214, http://aims.fao.org/aos/agrovoc/c_37974, http://aims.fao.org/aos/agrovoc/c_2328, http://aims.fao.org/aos/agrovoc/c_24002, |
| Online Access: | http://agritrop.cirad.fr/543097/ |
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| Summary: | Along years, the rice community has produced an incredible amount of information on the rice blast system: R-genes and resistance QTLs were mapped while there has been a recent increase in the information relative to gene expression upon infection. There is a need for integrating all these informations. Since 2002, the availability of the rice genome provides a new frame for integrating genetic and molecular information into a unified physical map. Some efforts were made in the past to anchor genetic information to the rice physical map (Wisser et al, 2005). We have extensively reviewed more than 60 publications on R-gene mapping, more than 20 published and unpublished studies on QTL mapping and more than 60 publications on gene expression in infected rice plants. This allowed the positioning of 85 R-genes, 343 QTLs and more than 5000 defence-related genes. The density of defence genes along chromosomes varies greatly, highlighting chromosome regions rich in defence genes. A metaQTL analysis was done to reduce the number of regions of the genome potentially useful for breeding blast resistance, leading to the identification of 160 blast resistance metaQTLs. Examination of physical distances indicated that more than 16 R-genes are found in intervals of less than 500 kb and that many QTLs represent very small intervals in which defence gene expression is found. This database was inserted into the OrygenesDB web site (Droc et al, 2007) and all associated tools. This database offers new opportunities for developing markers useful for marker-assisted selection and pinpoints some regions of the genome that could be targeted using candidate gene strategies. (Texte intégral) |
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