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