effects of physiological gxe on selection · water productivity ... • timing (of everything !) 15...

Interpreting effects of physiological GxE on marker and genomic selection

PLANT INDUSTRY

Scott ChapmanSenior Principal Research Scientist, CSIRO Plant IndustryAdjunct Professor, The University of Queensland

7 Sep 2012 EUCARPIA meeting

Incorporating genetics of traits into physiological models – QTL networks in maize

• Chenu et al PC&E 2009• Chenu et al Genetics 2009• van Eeuwijk et al COPB 2010

2 | Interpreting effects of physiological GxE | Scott Chapman

Understanding and breeding on GxE landscapes

Podlich and Cooper (1998) Bioinform.Chapman et al (2002) AJARChapman et al (2003) Agron. J. Wang et al (2004) Crop Sci.Cooper et al (2005) AJARHammer et al (2005) AJARHammer et al (2006) Trends in Plant Sci.Wang et al (2007) Crop Sci.Chapman (2008) EuphyticaWang et al (2009) TAG

Interpreting effects of physiological GxE | Scott Chapman

http://www.uq.edu.au/lcafs/qugene/

3 |

Gene to Phenotype Modelling‐ value of physiological knowledge (but with fake QTL !)

Cycle of selection0 2 4 6 8 10 12

Yiel

d in

TPE

(kg

ha-1

)

3800

4000

4200

4400

4600

4800

5000

5200

Marker selectionWeighted marker selectionPhysiologically weighted marker selection

G PUnexplainedExplained

Fullydescribed

Contextdependent

Chapman et al 2003; Hammer et al 2005 AJAR


Sorghum production and breeding in Australian dryland environments

Interpreting effects of physiological GxE | Scott Chapman5 |

Water productivity and G x E x M

• Sorghum ~ 1.5M ha, 3M t per year• 0.5 to 6 t/ha• Minimum tillage, rotation with wheat

• Water Productivity• Stored water + in‐season rainfall• Managing dynamics of– Canopy development– Root exploration

• Timing (of everything !)


Breeding challenges‐ complex E x M• Small(ish) market• State pre‐breeding program• 5 companies

• Large geographic region• 1000 km N‐S; 300km E‐W

• Extreme variability• Soils 80 to 300mm water capacity• In‐season rainfall 0 to 700mm

• Management• Sow Sep to Feb• Pop density of 25k to 75k /ha• Row spacing of 0.75 to 3m


Chapman et al 2000 AJAR

Breeding challenges‐ large GxE

• ca. 700 final stage trials• Within seasons• Error ca. 0.2 t2/ha• G ~ 0.05 to 0.5• GxL ~ 0.05 to 1.0 • GxL ~ 70% due to lack of genetic correlation

• G/(G+GxL) ~ 0.1 to 1• Across seasons• G ~ GxL ~ GxY ~ 0.2 • GxLxY ~ 10 times G• G/(G+GxE) ~ 0.2



Dynamics of water supply/demand‐ how does this generate GxE?Genotype Environment

RootAngle: 30 RootAngle: 35 RootAngle: 40 RootAngle: 45 RootAngle: 50

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

MaxTR

ate: 0.6M

axTRate: 0.7

MaxTR

ate: 0.8M

axTRate: 0.9

MaxTR

ate: 1.0

130

137.

514

515

2.5

160

167.

517

518

2.5

190

130

137.

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515

2.5

160

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518

2.5

190

130

137.

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2.5

160

167.

517

518

2.5

190

130

137.

514

515

2.5

160

167.

517

518

2.5

190

130

137.

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515

2.5

160

167.

517

518

2.5

190

TTEJ_INIT

Tille

rMod

Yield

2000

2500

3000

3500

Year=2001,Site=EMERALD_BE120,Pop=5,Skip=solid,Sowing=medium,SowWater=medium

DALBY_Vert180 EMERALD_BE120 GOONDIWINDI_GC120

2000

4000

6000

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10000

2000

4000

6000

8000

10000

2000

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solidsingle

double

1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010Year

Yie

ld (k

g/ha

)

Population

5

7.5


Interpreting effects of physiological GxE on marker and genomic selection

1.A generic modelling framework• complex phenotypes as emergent properties of biological models

2.Characterising GEM landscapes• Simulating effects of traits on water productivity in sorghum

3.Exploring GEM landscapes• Breeding for adaptation, selecting for different environments or traits


1. APSIM ‐ A generic modelling framework

2. Characterising GEM landscapes

3. Exploring GEM landscapes with breeding


Drought patternCrop

System control

Soil

SWIM

ManagerReportClock

SoilWat

SoilNSoilPHSoilP

ResidueEconomicsFertiliz

Irrigate

Canopy Met

ErosionOther Crops

Maize

Sorghum

Legume

Wheat

New Module

Manure

Management

ENGINE

Weather

Transpiration

Evaporation

Uptake

Rainfall

Runoff

Infiltration

Drainage

Radiation

APSIM – integrate interaction of plant & environment


Predicts crop yield, given a physiological model driven by weather, soil inputs and parameters that drive traits and are related to gene network controls

Development Growth

Physiol Maturity

Initiation

Anthesis

Emergence

T, W&N

T, W&N

T, PP

Grain Yield

Grain Number Grain Size & N

BiomassRADN

TE T RUE Rint

vpd

kl LAISLNRoots k

TN

LNo

LNo

A A >A

APSIM ‐ A Deterministic Physiological Framework‐ daily timestep, 300+ state variables

Hammer et al JExpBot 2010


Biomass at Maturity

y = 0.8816x + 116.35R2 = 0.8649

0

500

1000

1500

2000

2500

0 500 1000 1500 2000 2500Observed

Pred

icte

d

Grain Yield at Maturity

y = 0.9081x + 78.134R2 = 0.7957

0

200

400

600

800

1000

0 200 400 600 800 1000Observed

Pred

icte

d

Phenology

40

60

80

100

120

140

160

40 60 80 100 120 140 160Observed

Pred

icte

d

Days to Flower

Days to Marturity

Biomass at Flowering

y = 0.7605x + 102.55R2 = 0.7274

0

200

400

600

800

1000

0 200 400 600 800 1000Observed

Pred

icte

d

Crop model validation

• Iterations of experiments & model design

• Aim to capture functional controls


Water productivity – supply and demand

• Water Demand• Maturity• Tillering• Leaf transpiration rate• Management

• Water Supply• Rainfall• Soil storage• Root exploration• Management

• Timing (of everything !)


Genotypic traits affecting water productivity

• Input traits• TTEJ_INIT – thermal time from end juvenile to FI at least 40 QTL• TillerMod – ‘tillering propensity’ 10 to 20 QTL• RootAngle – angle of nodal roots, affects root depth 5 to 10 QTL• MaxTRate – reduced transpiration with high VPD QTL unknown

• Output traits• Yield• Flowering date, leaf area, biomass, water use


Development Growth

Physiol Maturity

Initiation

Anthesis

Emergence

T, W&N

T, W&N

T, PP

Grain Yield


BiomassRADN

TE T RUE Rint

vpd

kl LAISLNRoots k

TN

LNo

LNo

A A >A

Genotypic traits affecting water productivity


TTEJ_INIT

TillerMod

MaxTRate

RootAngle


GEM landscape for sorghum

• Genotype or Trait bins (9 x 9 x 5 x 5) = 3645• TTEJ_INIT (130 to 190 Cd – ca. 2‐3 weeks)• TillerMod (9 values – 0 to 4 tillers)• MaxTRate (0.6 to 1.0 mm/h)• RootAngle (30 to 50 deg from vertical – deep vs flat)

• Environment (3 x 110) = 330• Site/soil depth (Emerald 120 mm, Dalby 180, Goondiwindi 120)• Year (1890 to 2010)

• Management (3 x 3 x 3 x 3) = 81• Sowing time (early, medium, late)• Starting soil water (low, medium, high)• Density (2.5, 5.0, 7.5 plants/m2)• Row spacing (solid, skip, double skip)

• ~ 100 million phenotypes in 27k environments• ~ 42 days on one PC; 2 days on 10k processors


RootAngle: 30 RootAngle: 35 RootAngle: 40 RootAngle: 45 RootAngle: 50

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

-2.0-1.5-1.0-0.5+0

+0.5+1.0+1.5+2.0

MaxTR

ate: 0.6M

axTRate: 0.7

MaxTR

ate: 0.8M

axTRate: 0.9

MaxTR

ate: 1.0

130

137.

514

515

2.5

160

167.

517

518

2.5

190

130

137.

514

515

2.5

160

167.

517

518

2.5

190

130

137.

514

515

2.5

160

167.

517

518

2.5

190

130

137.

514

515

2.5

160

167.

517

518

2.5

190

130

137.

514

515

2.5

160

167.

517

518

2.5

190

TTEJ_INIT

Tille

rMod

Yield

2000

2500

3000

3500

Year=2001,Site=EMERALD_BE120,Pop=5,Skip=solid,Sowing=medium,SowWater=medium


2000

4000

6000

8000

10000

2000

4000

6000

8000

10000

2000

4000

6000

8000

10000

solidsingle

double

1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010Year

Yie

ld (k

g/ha

)

Population

5

7.5

Environment x Management effects ‐– site, year, soil water, population, row spacing



2000

4000

6000

8000

10000

2000

4000

6000

8000

10000

2000

4000

6000

8000

10000

solidsingle

double

1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010Year

Yie

ld (k

g/ha

)

Population

5

7.5

Genotype effects, e.g. early maturity– 1990 – 2010, Dalby


DALBY_Vert180

4000

5000

6000

7000

8000

9000

10000

1995 2000 2005 2010Year

Yie

ld (k

g/ha

) TTEJ_INIT130

160

190


DALBY_Vert180

4000

5000

6000

7000

8000

9000

10000

1995 2000 2005 2010Year

Yie

ld (k

g/ha

) TTEJ_INIT130

160

190


Genotype effects, e.g. early maturity– 1990 – 2010, Dalby

Growing the whole breeding program(in one environment)

• Displays two traits• Yield as heatmap• Expand to four trait dimensions


Thermal time to floral initiation(increasing time to maturity)

Tilleringpropensity

Maxyield

Minyield

DALBY_Vert180

EMERALD_BE120

GOONDIWINDI_GC120

2000

4000

6000

8000

10000

2000

4000

6000

8000

10000

2000

4000

6000

8000

10000

1900 1920 1940 1960 1980 2000Year

Yie

ld (k

g/ha

)

TTEJ_INIT130

160

190

Growing the whole breeding programOne of 27 000 environments ‐ Emerald 2004


Statistical characteristics of landscapes


Emerald 2001‐2010


• Flips 2001, 2004, 2005, 2010

Scatter Plot Matrix

200130003500

3000

200025002000

200255006000

5500

450050004500

2003600070006000

45005500500

200460006500

6000

500055005000

200530003500

3000

200025002000

200670008000

7000

500060005000

20076000700080006000

400050006000000

20086000700080006000

400050006000000

200970008000

7000

500060005000

201055006000

5500

450050004500

Constrained landscape has similar Vg to ‘real world’ breeding population

• Variance for• all traits• maturity in central 30%• tillering in central 30%• Maturity and tillering constrained


2002 2004 2006 2008 2010

0.0

0.5

1.0

1.5

2.0

Year

Gen

otyp

ic v

aria

nce


Scatter Plot Matrix

200130003500

3000

20002500000

2002550060005500

50005500

5000

2003600065006000

50005500000

2004600065006000

55006000

5500

2005250030002500

20002500000

200660006500

6000

500055005000

200760006500

6000

500055005000

200860006500

6000

500055005000

20096000650070006000

500055006000

000

2010550060005500

50005500

5000

Correlations between years‐ changes with trait physiology


0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

TillerMod = 0.0 (midpoint) TillerMod = +2.0

‘yo‐yo’ effects in GxE

• System describes G effects on water use patterns• Water demand – maturity, tillering, transpiration rate• Water supply ‐ root angle/depth

• Has broad characteristics of ‘real’ landscape

• How can we build breeding strategies to explore landscapes• e.g. early‐generation screening of traits, later generation screening of yield; imputing responses in different environments


Genotype

Trait genetics

APSIM

Manager

BiologicalModules

Surface Residue

EnvironmentalModules

ErosionB

ErosionA

Other N moduleorSoilN

CropC

CropB

CropA

PastureC

PastureB

PastureA

SwimorSoilwat

Economics Climate

APSIM

Simulate Crop Improvement

Strategies

Experiments –physiology and

genetics

Trait dissection and functional physiology

Cooper et al. 2002, In Silico Biol.

Phenotype

Software and Database Tools

A Research framework to capture physiological responses as part of breeding simulations


ENGINE

QUG

GESG-E System

POPGermplasm

POPGermplasm

QMPParameters

QU-LINE

QU-MASS

QU-PEDRRS

G-P Plugin

Output1

Output2

Output...

Outputn

QU-RRS

QU-MAS

QU-LINEQU-LINE

QU-RRS

QU-MAS

QU‐Gene

• Models populations of genotypes undergoing recombination, crossing and selection

• Engine to generate starting population and modules to simulate alternative breeding methods

• Podlich and Cooper 1998 Bioinformatics

Contact for licence:[email protected]

http://www.uq.edu.au/lcafs/qugene/


A simple experiment‐ GxE interactions under perfect marker selection

• Environments – 10 years, 1 location (Emerald)• Genetic trait architecture• Two chromosomes, eight QTL, four traits• Starting population of 100 lines with average favourable allele freq of 20%• H2 = 1.0 (perfect marker selection)

• Recurrent selection for 5 cycles (5 runs)• Selection among 50 F3s, 10% selection pressure

• Selection experiments• Yield in one environment (ET1 = Emerald 2001)• Yield in 10 environments (ET2 = Emerald 2001‐2010)• Selection by input or output traits


Development Growth

Physiol Maturity

Initiation

Anthesis

Emergence

T, W&N

T, W&N

T, PP

Grain Yield


BiomassRADN

TE T RUE Rint

vpd

kl LAISLNRoots k

TN

LNo

LNo

A A >A

Generating GxE with four traits


TTEJ_INIT

TillerMod

MaxTRate

RootAngle


2 chromosomes, 8 QTL, 4 interacting traits


• Input traits• TTEJ_INIT – thermal time from

end juvenile to FI 8 QTL• TillerMod – ‘tillering propensity’ 3

QTL• RootAngle – angle of nodal roots,

affects root depth 2 QTL• MaxTRate – reduced transpiration

with high VPD 4 QTL

• Output traits• Yield• Flowering date, leaf area,

biomass, water use

Change in trait fitness

• ET1 is negatively correlated with mean of 2001‐2010..

So…• Selection for yield in ET1 decreases yield in ETALL


Yield.ET1 Yield.ETALL LAIA.ET1

LAIA.ETALL TTEJ_INIT.ET1 TillerMod.ET1

MaxTrate.ET1 RootAngle.ET1

2000

3000

4000

5000

6000

2000

3000

4000

5000

6000

2000

3000

4000

5000

6000

1 2 3 4 5 6 1 2 3 4 5 6as.numeric(as.character(cycle))

Yie

ld

environmentET1

ET2

ETALL

Selection for yield‐ 5 cycles in one environment (2001)

• Severe drought environment

• Yield selection• Lowered MaxTRate(saved water)

• Increased RootAngle(explored more soil)


Selection for yield in 2001 ‘environment’ (ET1)Poor progress on 2001 vs 2001‐2010 landscapes‐ need more tillers !


Selection for yield 2001 (ET1) or 2001‐2010 (ET2)ET2 2001‐2010 selection increased tillers and root angle


Change in allele frequency

• 3 tillering QTL favoured at different rates depending on linkage with maturity and nearby traits

• Complex responses related to QTL model and relative favourability of traits at specific alleles


Yield.ET1 Yield.ETALL LAIA.ET1 LAIA.ETALLTEJ_INIT.ETTillerMod.ET1MaxTrate.ET ootAngle.ET

0.00.20.40.60.81.0

0.00.20.40.60.81.0

0.00.20.40.60.81.0

0.00.20.40.60.81.0

0.00.20.40.60.81.0

0.00.20.40.60.81.0

0.00.20.40.60.81.0

0.00.20.40.60.81.0

Q_N

AM

_1_10Q

_NA

M_1_53

Q_N

AM

_1_66Q_N

AM

_1_112Q_N

AM

_1_182Q_N

AM

_2_23Q_N

AM

_2_123Q_N

AM

_2_147

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6as.numeric(as.character(cycle))

valu

e allele

1

2

Genotype

Trait genetics

APSIM

Manager

BiologicalModules

Surface Residue

EnvironmentalModules

ErosionB

ErosionA

Other N moduleorSoilN

CropC

CropB

CropA

PastureC

PastureB

PastureA

SwimorSoilwat

Economics Climate

APSIM

Simulate Crop Improvement

Strategies

Experiments –physiology and

genetics

Trait dissection and functional physiology

Cooper et al. 2002, In Silico Biol.

Phenotype

Software and Database Tools

A Research framework to capture physiological responses as part of breeding simulations


Representing Genotype‐Phenotype landscapes– alternative models

P = G_Fully‐described + G_Context‐dependent + G_Unexplained +

G PUnexplainedExplained

Fully described

ContextDependent

epistasis,pleiotropy,

GxE

(Cooper et al AJAR 2005) Int. Crop Science Congress, Brisbane 200443 | Interpreting effects of physiological GxE | Scott Chapman

Conclusions

• Deterministic model of crop growth• Predicts crop yield, given a physiological model driven by weather, soil inputs and parameters that drive traits and are related to gene network controls

• System generates ‘real‐world’ GxE signals• Similar variances and correlations for yield

• A platform for investigation of questions in• Systems biology of plant‐crop growth and development• Statistical analysis of gene‐to‐phenotype relationships• Role of GxE in marker and genomic selection


Acknowledgements

CSIRO• Bangyou Zheng, Adrian Hathorn

University of Queensland/QAAFI• Erik van Oosterom, Graeme Hammer, David Jordan, Karine Chenu, Vijaya Singh, Zonjian Yang

• Queensland Government• Greg McLean, Al Doherty, Emma Mace

• GRDC andGeneration Challenge Program

|Exploring complexity | Scott Chapman45 | Interpreting effects of physiological GxE | Scott Chapman

effects of physiological gxe on selection · water productivity ... • timing (of everything !) 15...

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