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25 fruit breedomics-f. laurens

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  • 1. Laurens F., Aranzana M.J., Arus P., Bonany J., Corelli L. Patocchi A., Peil, A., Quilot B., Stella A., Troillard V., Velasco R., van de Weg E, … An integrated approach for increasing breeding efficiency in apple and peach 1 March 2011- 31 August 2015 EU-FP7 large collaborative project
  • 2. To fill in the gap between Genetics/Genomics and breeding AIM
  • 3. 15 Research 1 – INRA 2 – ARO (IL) 5 – CRA-W 6 – CRA 8 – ETHZ 9 – EVD 10 – FEM 12 – IRTA 13 – JKI 15 – PTP 16 – RBIPH 18 – DLO 19 – UMIL 20 – UNIBO 21 – READING 22 – ARC (ZA) 23 – PFR (NZ) 24 – ZJU (CN) 25 – KUL 26 – RCL SMEs 3 – ASF 4 – B3F 7 – DNV 14 – NOVADI 17 – RDG Management 11 – IT 22 23 24 16 18 19 20 21 3 4 714 17 13 12 10 98 6 5 2 11 IsraëlNZ China South Africa 1 + close links with : Rosbreed (WSU), SLU (Sweeden) Partners 25
  • 4. WP5 Trait knowledge WP1 Breeding WP2 Pre-BreedingEuropean Breeding Platform Diversity and QTL mapping WP3 PBA WP4 LD/GWA WP6 SNP chips WP7 bioinfoTools WP8Dissemination WP9.Management Structure A. Patocchi (EVD) A. Peil (JKI) E. van de Weg (DLO) M.J. Aranzana (IRTA) B. Quilot (INRA) A. Stella (PTP)R. Velasco (FEM) V.Troillard(IT) J.Bonani(IRTA)
  • 5. WP1 Breeding Test the efficiency of the use of molecular markers in current breeding programmes For apple: - B3F - Novadi - UNIBO - EVD  Costs and Efficiency  time  space  quality For peach: - ASF - DNV - RDG - INRA - UNIMIL - IRTA Scientific support: - DLO - INRA - FEM - UNIBO - EVD
  • 6. Test the efficiency of the use of molecular markers in current breeding programmes Molecular Assisted Breeding Genome Wide Selection WP1 Breeding  Costs and Efficiency  time  space  quality
  • 7. WP2 Pre-Breeding  Creation and evaluation of progenies pyramiding resistance and fruit traits  Creation of new early apple flowering lines carrying resistance genes AIM: To prepare new genitors (progenitors) for the future breeding programmes  Selection time A. Peil: New traits in advanced breeding populations of apple and peach
  • 8. Diversity and QTL mapping WP3 PBA 3. QTL Fine mapping 2. QTL new traits 5. Wider QTL mining 4. Genet. div . in EU Breeding 6. QTL validation 1. Adaptation Flex WP4 LD/GA 1. Phen & genet variability 2. Core collection 3. QTL mapping by GWA Pedigree Based Analysis Genome Wide Association
  • 9. Limit of the past mapping studies CH04c06z CH02b07 CH02c11 CH03d11 CH04g09y CH02b03-2 MS06g03 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Fs_D2.D3.D4_R²=18.4%Fs_D2.D3.D4_R²=6.07% Ff_D1.D2.D3.D4_R²=16%Ff_D1.D2.D3.D4_R²=6% Stif_D2.D3.D4_R²=18.44%Stif_D2_R²=6.45% W1_D1.D2.D3.D4_R²=34% EvolFs_D2-D1.D3-D1.D4-D1_R²=18.54% EvolFf_D2-D1.D3-D1.D4-D1_R²=21% Dp_D3.D4_R²=9.93% LG10 CH05h05 Hi04g05 CH02g01 GD147 NH009b CH05f04 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 EvolFf_D2-D1_R²=26% EvolFs_D2-D1_R²=13.79%W1_D4_R²=2.75% Dp_D2.D3.D4_R²=9.11% LG13 CH01g05 CH04c07 MDAJ761 MADS-7 15 20 25 30 35 40 Fs_D4_R²=2.65% Ff_D4_R²=3.34% W1_D4_R2=2.11% LG14 NZ02b01 Hi03g06p CH01d08 CH02d11 CH02c09 Hi023Stif2y 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 Fs_D1.D2.D3.D4_R²=10.59% Ff_D1.D2.D3.D4_R²=9% W1_D1.D2.D3.D4_R²=5.58% Stif_D1.D2.D3.D4_R²=13.54% EvolFf_D4-D1_R²=5.26% EvolFs_D4-D1_R²=3.85% LG15 Hi02c07 CH-VFs CH05g08 25 30 35 40 45 50 55 60 65 70 75 Stif_D1.D2.D3.D4_R²=9.4% Fs_D1.D2.D3.D4_R²=14.95% W1_D1.D2.D3.D4_R²=12.95% Ff_D1.D2.D3.D4_R²=14.57% Dp_D2.D3.D4_R²=10.52% LG1 Hi22Fs2 CN493139x Hi04a08 CH03a09 CH05e06 CH04g09Z Hi04d02 GD103 CH02b12y CH04e03 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 EvolFs_D2-D1.D3-D1_R²=6.90% EvolFs_D2-D1.D3-D1_R²=13.79% LG5 -Low marker density (SSR)  gaps  weak precision on the QTL mapping Lack of information on the allelic diversity -Lack of cheap and high throughput genotyping tools
  • 10. Pedigree Based Analysis Allelic diversity Association Genetics CULTIVARSMAPPING POPULATIONS RallsJan Delicious Winesap RomBeauty Jonathan M_PRI668-100 GoldenDel F2_26829-2-2 RedWinter Wagenerap Prima Cox F_X-4598 Anta34.16 F_X-4355 Jefferies PRI830-101 Clochard ReiDuMans GranSmith F_Ill_#2 O53T136 Fuji Crandall PRI14-126 PRI14-152 Idared KidsOrRed X-4598 Z185 X-2599 Chantecler Ill_#2 Rubinette X-6823 TN_R10A8 PRI668-100 X-3177 PRI612-1 Gala X-4355 X-3188 PRI672-3 X-6417 X-2771 X-3263 RedWinterX3177 Florina X-6681 X-6799 Coop-17 X-4638 X-3143 Galarina X-6564 X-6820 Baujade X-3259 X-6679 Dorianne X-6808 X-3318 X-6398 X-6683 X-3305 12_I01 I_CC03 12_K01 12_L01 12_O03 I_BB02 I_J01 12_F01 12_J01 I_W01 I_M01 12_N01 12_P01 Apple 24 progenies 20K SNP chip Peach 18 progenies 9K SNP chip Apple 400SNP chip/GBS? 4 CC Peach 9K SNP chip 4 CC Fine Genetic Mapping
  • 11. Diversity and QTL mapping WP3 PBA 3. QTL Fine mapping 2. QTL new traits 5. Wider QTL mining 4. Genet. div . in EU Breeding 6. QTL validation 1. Adaptation Flex WP4 LD/GA 1. Phen & genet variability 2. Core collection 3. QTL mapping by GWA Pedigree Based Analysis Genome Wide Association E. Van de Weg. QTL discovery and perspectives for the use of markers in fruit breeding programs M.J. Aranzana.Genetic variability in apple and peach European variety collections
  • 12. WP5 Trait knowledge
  • 13. Enhancing the knowledge of genetics underlying novel traits and providing phenotyping methods • Protocols for infection tests in lab on contrasted cultivars • Test of artificial infections in orchard • Epidermal thickness measurements (microscopy) • Biochemical analyses (cutins, waxes, surface and epiderm compounds…) Methods to improve resistance to storage disease Monilinia in peach (T5.1) 020406080100 25-apr 16-may 30-may 20-jun 4-jul 18-jul maturity Infectionprobability(%) * ** * SG ZE Infection probability for 2 cultivars along fruit growth I II III mg/dm² 01234 Oleanolic acid mg/dm² 0246810 Ursolic acid Airedepic/dm²/10^5 051015202530 Pic52.5_312 Jours après floraison Airedepic/dm²/10^5 051015 Pic64.6_307 40 60 80 100 120 140 160 051015202530 Jours après floraison Airedepic/dm²/10^5 Pic65.7_312 40 60 80 100 120 140 160 0510152025 Airedepic/dm²/10^5 p-coumaroyl derivative SG ZE days after bloom Evolution of fruit surface compounds along fruit growth
  • 14. Tools to assess fruit quality 0 20 40 60 80 0 2 4 6 8 10 12 14 16 18 20 22 24 26 40 45 50 55 60 65 70 0 20 40 60 80 0 2 4 6 8 10 12 14 16 18 20 22 24 26 40 45 50 55 60 65 70 Acoustic crispness Ethylene sensor -2 0 2 4 6 8 10 12 14 16 18 20 0 100 200 300 400 500 600 700 800 900 Iride(NM) DA 0,83 Dixired (M)DA 0,77 BigTop (SM) DA 0,7 NM SM M find different fruit parameters to discriminate between fruits with different softening behaviours (Melting, Non Melting, Slow Melting, Stony Hard) X-ray computed tomography a a b a b c d a b a b c d a b c b c M SM NM SH c c days Firmness(Kg) Firmness (after 21 dd at 0°C) gene discovery by transcriptomics
  • 15. Tests to select traits important for climate change adaptation : water scarcity A PCA was performed on 13 physiological variables to identify a subset of parameters for phenotyping Projection of the variables on the factor-plane ( 1 x 2) Active Photo Cond Ci Fo' Fm' Fs Fv'/Fm' PhiPS2Trmmol Tleaf LEAF (MPa) STEM (MPa) delta (Mpa) -1.0 -0.5 0.0 0.5 1.0 Factor 1 : 59.69% -1.0 -0.5 0.0 0.5 1.0 Factor2:17.66% The combination of leaf temperature and fluorescence provides a very good compromise between rapid AND effective assessment of drought resistance of a given genotype 17 apple genotypes _ 2 water treatments _ greenhouse
  • 16. to get a better knowledge of the apple breeding programs and understand the needs and requests of apple breeders and the whole fruit chain Contacts with stakeholders
  • 17. Objective: Establish links with all kinds of stakeholders to: 1- collect the needs and requirements of the whole fruit chain 2- provide the breeders with solutions to improve efficiency of their programs (plant material, tools, methodologies, skills, …) 3- release new cultivars following the requirement of the industry and the consumers Contacts with stakeholders 1st steps: 1- Contacts and collaboration with apple and peach breeders (questionnaires, meetings,…) 2- Contacts with other apple (and peach) chain actors 3- Extension to other species
  • 18. European Breeding Platform Questionnaires, meetings Breeder stakeholders
  • 19. Outputs Research (genetics) Tools Knowledge on the main agronomic traits Breeding Tools  Plant material  Information on germplasm  Methodology, protocols  Costs Fruit chain  Tools  Tests To predict/control/trace (Fruit Quality,…) Curators Information on the germplasm (duplication, genetic relationship, structure, …)  Efficiency Longer term
  • 20. Acknowledgement to the FruitBreedomics consortium
  • 21. Go on www.fruitbreedomics.com to get the last news of the project francois.laurens@angers.inra.fr