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FruitBreedomics KOM 29 03-2011 7 WP5 presentation
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    FruitBreedomics KOM 29 03-2011 7 WP5 presentation FruitBreedomics KOM 29 03-2011 7 WP5 presentation Presentation Transcript

    • WP5: Enhancing the knowledge of genetics underlying novel traits and providing phenotyping methods WP leader Bénédicte Quilot-Turion, INRA Avignon, FRANCE
    • Objectives
      • Develop tools for novel trait determination
      • Investigate novel and complex traits using ‘omics’ approaches and modelling
      • Make novel trait analysis high throughput and applicable for the research community and the breeding industry
    • Approach / Methodology
      • Selection of cultivars to cover a broad spectrum of the trait variation
      • Experiments generating background knowledge on the trait (needed to set up assays and instrumental tests)
      • For some complex traits modelling will be used to help identifying the key factors controlling the traits
      • As much as possible, interaction will be exercised between research on apple and peach
      • Protocols for high throughput quantification of the trait will be developed to allow large scale phenotyping. Some of them will be applied in other WPs
      • They will be spread among the consortium members for standardization and adoption by the research community and industry
    • Tasks
      • Task 5.1 Methods to improve resistance to storage disease Monilinia in peach
      • Development of a reliable infection test to evaluate Monilinia resistance (year 1)
      • Methodology to measure fruit physical and biochemical characteristics potentially linked to Monilinia resistance (year 1)
      • On a few contrasted cultivars for Monilinia :
      • infection tests will be performed together with fruit characteristics measurements at different growth stages, in order to identify fruit factors involved in resistance (year 2)
      • Survey of Monilinia airborne spores in orchards and record of climatic factors (year 1, year 2)
      • Information will be then included in an existing fruit growth model currently able to predict both the skin conductance and the appearance of micro-cracks on the skin in relation to environment (climate, cultivation practices) (year 3)
      • Knowledge on the genetic control of the key mechanisms influencing Monilinia infection (results from WP3) will be examined and as much as possible included in the model (year 4)
    • Tasks Task T5.2: Tools to assess fruit quality - New tools for improved texture determination Instrumental tools and reference analysis to characterise the sub-traits of apple and peach texture. Apple _ non targeted approaches: microarrays (PFR-DLO) and a full genome oligo array (INRA and FEM) Peach _ gene expression of indicator genes downstream of ethylene perception - New tools to determine apple’s sensitivity to storage New storage simulation tests will be developed with the aim to develop a tool to select genotypes which are suited for prolonged storage - New tools to determine apple fibre content The use of near infra-red (NIR) analyses will be explored to make available an easy tool to determine fiber content of apple
    • Tasks Task T5.3: Tests to select traits important for climate change adaptation - Tests to predict chilling and heat requirement Predictive methods will be developed to characterize the genetic variability of chilling and heat requirement for apple and peach (year 1 - year 2). Improved chilling models will be used in order to quantify CR, based on the results obtained with the two complementary tests. - Tests to evaluate responses to water scarcity Evaluation of drought resistance in France and Italy on the same genotypes (year 1 - year 3) of apple and peach, on trees subjected to drought stress conditions, respectively in field and in pots. Identification of the parameter(s) most related to drought susceptibility Via common apple cultivars, results will be linked to the high-throughput assessment of drought resistance performed in WP4 on the apple core-collection.
    • Expected results / outcomes ML5.1 Tools and methods compared to characterize traits and sub-traits M24 ML5.2 Validation of tools on contrasted cultivars M36 ML5.3 Standardised tools and methods available for large-scale phenotyping M48 ML5.4 Knowledge of the genetics underlying novel and complex traits M54
    • Deliverables
      • D5.1
      • List of the tools and protocols developed for phenotyping new (sub)-traits M12
      • D5.2
      • Identification of effective tools or protocols to characterise (sub)-traits M24
      • D5.3
      • Identification of the sub-traits linked to the novel traits M36
      • D5.4
      • Relation of gene expression, SNP and genetic control (QTL) of traits M48
      • D5.5
      • Scientific paper on fruit characteristics involved in Monilinia resistance M48
      • D5.6
      • Standardised tools and protocols to be used for large-scale phenotyping M54
      • D5.7
      • Integrative peach simulation model to analyse Monilinia infections at orchard M54
    • Interactions planned with other WPs
      • WP3 -> WP5: information from the QTL analysis on Monilinia. Genetic information on texture
      • WP7 -> WP5: release of the database
      • WP8 -> WP5 : specifications for research and development
      • WP5 -> WP1: phenotypic tests for new traits to be included in the selection process; data on few genitors/new traits
      • WP5 -> WP2: fine instrumental assessment of texture traits; Monilinia testing
      • WP5 -> WP3: new tests to assess texture attributes. Phenotypic assessment of abiotic stresses (chilling requirement) and Monilinia. New candidate genes for texture
      • WP5 -> WP4: tests to assess for phenology, texture, drought, Monilinia
      • WP5 -> WP7: upload of data; specification for the database
      • WP5 -> WP8: dissemination ; to all stakeholders: phenotypic traits for new traits; with RosBREED, data on texture
    • Partners involved : 9 task topic leader sub-task other contributors nb of partners 5.1 Storage disease B. Quilot   INRA-Av 2   UNIMI 5.2 Fruit quality J. Mes texture DLO 8 INRA-Ang INRA-Nantes FEM-IASMA PFR (New Zealand) IRTA UNIMI ARO (Volcani) storage DLO 2 INRA-Ang fiber INRA-Nantes 1 5.3 Climate adaptation E. Costes CR INRA-Mtp 3 INRA-Bdx ARC (South Africa) water UNIBO 3 INRA-Mtp INRA-Bdx