25 fruit breedomics-f. laurens
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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
To fill in the gap between Genetics/Genomics and breeding
AIM
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
7 14
17
13
12
10
9 8
6
5
2
11
Israël NZ
China
South Africa
1
+ close links with :
Rosbreed (WSU), SLU (Sweeden)
Partners
25
WP5 Trait knowledge
WP1 Breeding WP2 Pre-Breeding European Breeding Platform
Diversity and QTL mapping WP3 PBA
WP4 LD/GWA
WP6 SNP chips WP7 bioinfo Tools
WP
8 D
issem
inatio
n W
P9
. Man
age
me
nt
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. T
roill
ard
(IT
) J. Bo
nan
i (IRTA
)
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
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
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
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
Limit of the past mapping studies
CH04c06zCH02b07
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%
EvolF
s_D
2-D
1.D
3-D
1.D
4-D
1_R
²=18.5
4%
EvolF
f_D
2-D
1.D
3-D
1.D
4-D
1_R
²=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
EvolF
f_D
2-D
1_R
²=26%
EvolF
s_D
2-D
1_R
²=13.7
9%
W1_D4_R²=2.75%
Dp_D2.D3.D4_R²=9.11%
LG13
CH01g05
CH04c07
MDAJ761MADS-7
15
20
25
30
35
40
Fs_D4_R²=2.65%
Ff_D4_R²=3.34%
W1_D
4_R
2=
2.1
1%
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%
Evo
lFf_
D4-D
1_R
²=5.2
6%
Evo
lFs_
D4-D
1_R
²=3.8
5%
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
CH05e06CH04g09Z
Hi04d02
GD103
CH02b12y
CH04e03
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
EvolF
s_D
2-D
1.D
3-D
1_R
²=6.9
0% EvolF
s_D
2-D
1.D
3-D
1_R
²=13.7
9%
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
Pedigree Based Analysis
Allelic diversity
Association Genetics
CULTIVARS MAPPING 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
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
WP5 Trait knowledge
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)
02
04
06
08
01
00
25-apr 16-may 30-may 20-jun 4-jul 18-jul maturity
Infe
ctio
n p
rob
ab
ility (
%)
* ** *
SG
ZE
Infection probability for 2 cultivars along fruit growth
I II III
mg/d
m²
01
23
4
Oleanolic acid
mg/d
m²
02
46
810
Ursolic acid
Aire d
e p
ic/d
m²/
10^5
05
10
15
20
25
30
Pic52.5_312
Jours après f loraison
Aire d
e p
ic/d
m²/
10^5
05
10
15
Pic64.6_307
40 60 80 100 120 140 160
05
10
15
20
25
30
Jours après f loraison
Aire d
e p
ic/d
m²/
10^5
Pic65.7_312
40 60 80 100 120 140 160
05
10
15
20
25
Aire d
e p
ic/d
m²/
10^5
p-coumaroyl derivative
SG
ZE
days after bloom
Evolution of fruit surface compounds along fruit growth
Tools to assess fruit quality
0 20 40 60 800
2
4
6
8
10
12
14
16
18
20
22
24
26
40
45
50
55
60
65
70
0 20 40 60 800
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
Firm
nes
s (K
g)
Firmness (after 21 dd at 0°C)
gene discovery by transcriptomics
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'
PhiPS2 Trmmol
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
Fa
cto
r 2 : 1
7.6
6%
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
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
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
European Breeding Platform
Questionnaires, meetings
Breeder stakeholders
Outputs
Research (genetics) Tools
Knowledge on the main agronomic traits
Breeding Tools
Plant material Information on
germplasm Methodology,
protocols
Costs Efficiency
Fruit chain Tools Tests
To predict/control/trace (Fruit Quality,…)
Curators Information on the germplasm (duplication, genetic
relationship, structure, …)
Efficiency
Longer term
Acknowledgement to
the FruitBreedomics consortium
Go on www.fruitbreedomics.com to get the last news of the project
francois.laurens@angers.inra.fr
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