1. Peach Molecular Breeding at the INRA
Avignon and links to a new European
initiative to increase fruit breeding efficiency
FP7 KBBE FruitBreedomics
Patrick LAMBERT
UR1052 UGAFL INRA Avignon
France
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2. Peach breeding programmes at INRA Avignon
Two main targets
Resistance to pests and diseases
Fruit Quality
Powdery mildew
Monilinia
PPV
Green peach aphid
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3. Peach Breeding for resistance to pests and diseases
A main objective
Context
• Sanitary protection of the orchard: a strong concern
• fruit orchards : 21 % of the insecticide market
• Green aphid, powdery mildew, plum pox virus, monilinia :
> resistances are available and exploitable
Motivations:
• Contribute to lower chemicals to protect the environment and the health of all the
actors > producer and consumer
• help in the adoption of alternative methods of phytoprotection to support the
European fruit production and the fruit producers by the release of resistant and
innovative varieties
Stakes:
• Development of new tools to obtain improved material via Marker Assisted Breeding
• an innovative collaborative project at the international level begun in 2011
FP7 KBBE FruitBreedomics
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4. But….how to reach these objectives?
Several sources of resistance are available
but … The agronomical level is low
Successive crosses are needed to get a commercial
agronomical value
Selection process slowed and time consuming
But, molecular markers linked to the loci of
interest could help to speed up the released of
novel resistant varieties
Implementation of Marker Assisted Breeding (MAB)
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5. Few sources of resistance available
Wild
species
related
Pamirskij5
to
peach
and
Rootstocks
peach
Rubira
Prunus davidiana (clone P 1908) : resistant to powdery mildew, green peach
aphid, monilinia and PPV … but the agronomical level is very low
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6. Selecting novel varieties or elite cultivars with improved
resistance > several strategies
Objectives
novel varieties resistant to Powdery mildew, Green peach aphid
Plum pox virus and Monilinia
For short term :
by combining several strong monogenic resistance factors in
selected improved cultivars
Powdery mildew + Myzus persicae
For long term :
Building lasting resistance by combining these monogenic resistances
with polygenic resistance factors from:
- Prunus davidiana (Powdery mildew, Myzus, Monilinia and sharka)
- old varieties and genetic resources (cv Bolinha for Monilinia…)
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7. Overview of the types of resistance carried by the
resistance sources used for breeding
t y
ili
b
ria Weeping Flower peach: Rm1
va
S 2678
ic
Myzus
et
en
Rubira: Rm2
G
S 2605
Monogenic
Malo Konare: Vr1
S 5392
Powdery mildew
Resistances Pamirskij 5: Vr2
Myzus S 6146
MP.SD-3.1 , MP.SD-5.1
Polygenic PPV P. davidiana:
PM.SD-6 , PM.SD8
cl. P1908
PPV.RD-2.1, 2.2, Backcross
Powdery mildew 7.1, 7.2 …
Mono/Poly Pest or disease Origin QTLs/locus (gene) Improved varieties
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8. A pluridisciplinary approach for improving resistance
> genetic to functionnal
Evaluation of collections and genetic resources
Resistance scoring
Study of the aphid feeding behaviour
Classic biological tests by electropenetrography ( EPG)
On leaves and/or fruits Analysis of the organic volatile
compounds
Inoculations / controlled infestations
Biochemical analyses (polyphenols)
scoring scales in classes (0 - 4)
. Associated with genetic studies
Consumers
Fine analysis of
Research
Finding Resistance mechanisms
sources Breeders
Developing tests Fruit grovers
for selection at early Integrating molecular
stages markers
• Genotyping and mapping of segregating
Sowing at a young stage in greenhouse populations
Validation under insect-proof tunnel •QTL and candidate gene mapping
Andor in orchard at a grown-up stage •Marker assisted Breeding (MAB)
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9. Example of the resistance to Myzus persicae
(1) behaviour on the host tree
100
Antibiosis
Aphids on the leaves (%)
80
(colonisation but
P 1908
no reproduction)
60
Malo konare
40 Summergrand
Antixenosis
20 Rubira
(no colonisation) Major
WFP Gene
0
0 24h 48h 72h 96h 120h 144h 168
Time (hour)
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10. (2) Study of the feeding behaviour of Myzus persicae
by using EPG method
Feeding behaviour
generator plant
Signal amplification
Electrode
(copper)
electrode
(Copper)
V signal
Diagramme of the electric ciruitry
Green peach aphid linked with a (mod. Tjallingii)
golden wire
Graph derived from the electricall variations obtained by using the EPG method.
The different feeding phases correspond to different intensities.
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2012
11. Segregating populations used for breeding
and genetic mapping
Interspecific peach x P.davidiana
*Summergrand x P.davidiana (SD): F1 77 ind.
*AF (Summergrand x P.davidiana)40: F2 99 ind.
*SD40 x Summergrand (BC1= BC1s+BC1r)
(BC1) x Zephir: BC2 162 ind.
*Rubira x P.davidiana (RxD)² : F2 190 ind.
Pamirskij 5 x P.davidiana (PxD)² : F1 250 ind.
Malo Konare x P.davidiana (MKxD)² : F1 170 ind.
Intraspecific peach
*Pamirskij 5 x Rubira (PxR)²: F1 190 ind.
AF Bolinha x Bolinha (B²): F1>170 ind.
*(Weeping Flower peach x Pamirskij 5 (WP²) F2 100 ind.
* Genetic maps available anchored to the Prunus reference map
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12. Targets for the developement of Marker Assisted
Breeding (MAB) at UGAFL
Position of major genes and QTLs identified from our peach mapping populations
Rmia Vr2
Gr
Rm2 Resistance to pests and diseases
Major gene for Powdery mildew
Major gene for green peach aphid
Fruit Quality and phenolics
Major gene for color
Major gene for nematode Fructose/Glucose content
QTL Powdery mildew P. davidiana Chlorogenic acid
QTL Myzus persicae P. davidiana Dicaffeoyl quinic acid
QTL Plum pox Virus P. davidiana
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13. However, the curent use of markers in selection has
several limites
Many researches, results, QTLs ….BUT
To date just a few used in selection
(simple traits)
Main reasons:
- Low marker density (SSR)
gaps in the genetic maps
weak precision on the QTL mapping
- Lack of information on the allelic diversity
- Lack of information on background and
environmental effects
- So far, lack of cheap and high throughput
genotyping tools
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14. An integrated approach for
increasing breeding efficiency in
apple and peach
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…….
EU-FP7 KBBE large collaborative project
1 March 2011- 31 August 2015
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15. AIMS
To fill in the gap between Genetics/Genomics and
breeding
- By developing and using molecular tools (SNP , Full transcript
chips) through multi-national collaborations
- By improving knowledge about genetics + genomics of major
agronomic traits + allelic diversity
By transferring material + tools + methodologies
to breeders
(within and outside the consortium)
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16. Partners
24 China
22 NZ
21
18
5 13
22 4
16
1
11 8 9
South Africa 3
19 20
14
7
15 22
Close links with 17 12 6
10 Israël
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17. WP1 Breeding European Breeding
1. Breeding strategies Platform WP2 Pre-Breeding
1. Material evaluation
2. Fine mapping of traits Targets
2. Conventional pre breeding
>Monogenic traits
3. Pilot studies 3. Fast prebreeding
>QTLs with strong
4. MAB Pipeline
effects
5. Data Base interface
WP1 leader: A. Patocchi WP2 leader: A. Peil
(EVD) (JKI)
The general objective is to develop more Aims at the establishment of pre-breeding material
efficient breeding programmes applying In a more efficient way by using SNP markers
marker assisted reeding. 10,000 seedlings for each species
WP1 will use the two 9k SNP Illumina Chips Different crosses with different trait
developed in the framework of RosBREED combinations
Aims at the improvement and application of a
fast-breeding system in apple.
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18. Diversity and QTL
mapping
WP3 PBA WP4 LD/GWA
1. Adaptation Flex Software
2. QTL new traits
3. QTL Fine mapping
1. Phen & genet variability
4. Genet. div . in EU Breeding
2. Core collection Establish
5. Wider QTL mining
3. QTL mapping by GWA
6. QTL validation
WP3 leader: E. van de Weg WP4 leader: M.J. Aranzana
(DLO) (IRTA)
WP3 WP4
Identification and characterisation of QTL Exploring the phenotypic and genetic
By the pedigree-based analysis approach diversity in collections and exploitation by
association genetics approach
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19. Fine Genetic Mapping
Allelic diversity
Pedigree Based Analysis Association Genetics
Objective
Find an « association » (correlation)
between the variation of a marker and a trait
SNP : Single Nucleotide Polymorphism
Steps:
•Phenotyping
•Genotyping
•Find out associations
markers/phenotypes Rafalski & Morgante, 2004
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20. Fine Genetic Mapping
Allelic diversity
Pedigree Based Analysis Association Genetics
MAPPING POPULATIONS CULTIVARS
RallsJan
De licious Fuji
X-3 318
X-314 3
W inesap PRI668-10 0 X-2771
I_J01
Cranda ll
RomBe auty
X-6 398
Jo nathan PR I14-126 Galarina
X-3177
X-3263
M_PRI668-100 PR I14-152 12_F01
X-656 4 X-6 683
GoldenDel Red WinterX31 77
Idared
PRI612-1
F2_26829-2-2
X-3 305
X-682 0 12_J0 1
RedW inter KidsOrRed
Florina
W agenerap
Gala
X-4 598 12_I01
Prima Baujade
I_ W01
Cox Z185
X-6681
I_ CC03
X-4355
F_X-4598
X-325 9
X-2 599
Anta34 .16
I_M01
1 2_K01
X-6799
F_X-4355 Chantecler
X-3188
X-667 9
Je fferies
Ill_#2 12_L 01
PRI83 0-101 12 _N01
Coop-17
PRI672-3
Clochard Rub inette Dorianne
12_O03
ReiDuMans
X-6 823
12_P0 1
GranSmith X-6417 X-4638
X-680 8
I_BB02
F_Ill_#2 TN_R10A8
O53T1 36
Apple Peach Apple Peach
30 progenies 30 progenies
European COST 873 and ISHS joint meeting
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A 20-50K ISNP chip
LIMENTAT ON 9K SNP chip
9K 14-1719chip 20129K SNP chip
SNP janvier
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AGRICULTURE
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2012
21. WP5 New Trait knowledge
WP5 leader: B. Quilot
(INRA)
1. Biotic stresses: Monilia
2. Fruit quality : texture, fiber
chilling
3. Abiotic stresses:
requirement, water stress
WP5
Aims at enhancing the knowledge of genetics underlying novel traits
And providing phenotyping methods by
developing tools for novel traits determination
Investigate novel complex traits using « omics » approach and modeling
Make novel trait analysis high throughput applicable for the breeding industry
23. WP1 Breeding European Breeding
1. Breeding strategies Platform WP2 Pre-Breeding
1. Material evaluation
2. Fine mapping Traits
2. Conventional pre breeding
3. Pilot studies
3. Fast prebreeding
4. MAB Pipeline
5. DB interface
WP6 SNP chips
PFR
ARC WP6 leader: R. Velasco
Low Medium High
FEM Italy
Low cost MAB
Diversity and QTL
WP3 PBA WP5 Trait
mapping
1. Adaptation Flex knowledge
WP4 LD/GWA
2. QTL new traits
1. Phen & genet vriability 1. Biotic stresses
3. QTL Fine mapping
2. Core collection 2. Fruit quality
4. Genet. div . in EU Breeding
3. QTL mapping by GWA 3. Abiotic stresses
5. Wider QTL mining
6. QTL validation
24. WP1 Breeding European Breeding
1. Breeding strategies Platform WP2 Pre-Breeding
1. Material evaluation
2. Fine mapping Traits
2. Conventional pre breeding
3. Pilot studies
3. Fast prebreeding
4. MAB Pipeline
5. DB interface
WP7 bioinfo
WP7 leader: A. Stella
(PTP) FruitBreed Data Base
Genome Database
For Rosaceae (GDR)
Diversity and QTL
WP3 PBA
mapping
1. Adaptation Flex Software WP5 Trait
WP4 LD/GWA knowledge
2. QTL new traits
1. Phen & genet variability
3. QTL Fine mapping 1. Biotic stresses
2. Core collection
4. Genet. div . in EU Breeding 2. Fruit quality
3. QTL mapping by GWA
5. Wider QTL mining 3. Abiotic stresses
6. QTL validation
25. WP1 Breeding European Breeding
1. Breeding strategies Platform WP2 Pre-Breeding
WP9 leader: V. Troillard (IT)
1. Material evaluation
WP9. Management
WP8 leader: J. Bonany (IRTA)
2. Fine mapping Traits
2. Conventional pre breeding
WP8 Dissemination
3. Pilot studies
3. Fast prebreeding
4. MAB Pipeline
5. DB interface
WP6 SNP chips Tools WP7 bioinfo
Low Medium High FruitBreed Data Base
Low cost MAB
Diversity and QTL
WP3 PBA mapping
1. Adaptation Flex Software WP5 Trait
WP4 LD/GWA knowledge
2. QTL new traits
1. Phen & genet variability
3. QTL Fine mapping 1. Biotic stresses
2. Core collection
4. Genet. div . in EU Breeding 2. Fruit quality
3. QTL mapping by GWA
5. Wider QTL mining 3. Abiotic stresses
6. QTL validation
26. WP1 Breeding European Breeding
1. Breeding strategies Platform WP2 Pre-Breeding
WP9 leader: V. Troillard (IT)
1. Material evaluation
WP9. Management
WP8 leader: J. Bonany (IRTA)
2. Fine mapping Traits
2. Conventional pre breeding
WP8 Dissemination
3. Pilot studies
3. Fast prebreeding
4. MAB Pipeline
Breeding stakeholders
5. DB interface
WP6 SNP chips Tools WP7 bioinfo
Low Medium High FruitBreed Data Base
Low cost MAB
Diversity and QTL
WP3 PBA mapping
1. Adaptation Flex Software WP5 Trait
WP4 LD/GWA knowledge
2. QTL new traits
1. Phen & genet variability
3. QTL Fine mapping 1. Biotic stresses
2. Core collection
4. Genet. div . in EU Breeding 2. Fruit quality
3. QTL mapping by GWA
5. Wider QTL mining Germplasm 3. Abiotic stresses
6. QTL validation curators
28. Peach Breeding aknowledgement
T. Pascal 1, P. Lambert 1, B. Quilot, J.L. Poëssel 1, M. Corre1, E. Lecerf 1, M.H. Sauge 2,
JP. Lacroze 2, F. Pfeiffer 1, M. Staudt 3, Y. Rahbé A. 4, Moing 5
1
INRA, Unité de Génétique et d'Amélioration des Fuits et Légumes, BP 94 - 84143 Avignon - Montfavet Cedex
2 INRA, Unité Plante et Système de culture Horticole, Domaine St Paul, Site Agroparc, 84914 Avignon - Montfavet
3
Centre d'Ecologie Fonctionnelle et Evolutive - CNRS, 1919, Route de Mende, 34293 Montpellier Cedex,
4 INSA - INRA UA 203, Laboratoire de Biologie Appliquéee 406, 20 av. A. Einstein, 69621 Villeurbanne
5
INRA, UMR 619 Biologie du Fruit - Plate-forme Metabolome-Fluxome , BP81, 33883 Villenave d’Ornon
Thanks for your attention
ALIMENTATION
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AGRICULTURE
UGAFL - 19 janvier 2012 Zurich
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2012