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