Gene-based prediction of heading time to target real-time and future climate adaptation AGRICULTURE...

Gene-based prediction of heading time to target real-time and future climate adaptation

AGRICULTURE

Scott ChapmanSenior Principal Research Scientist (Crop Adaptation)

22 Sep 2015

Gene-based prediction of heading time | Scott Chapman

Modelling Wheat Phenology• Intro• Why is it important?

• Current climates• Future climates

• Models and prediction• Controls of phenology

• Temperature– Development– Vernalisation

• Photoperiod• Measuring and predicting phenology

• Experiments, parameterisation, validation• Results


Zheng B, Chenu K, Dreccer MF, Chapman SC (2012) Global Change Biology 18, 2899-2914.Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?

Zheng B, Biddulph B, Li D, Kuchel H and Chapman SC (2013) J. Exp. Bot. 64 3747-3761Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments

Zheng B, Chenu K, Chapman SC (2015) Global Change Biology (in press) Velocity of temperature and flowering time in wheat – assisting breeders to keep pace with climate change


In Australia, flowering time is critical....

Waiting for rainfall Sowing FloweringVernalizationPhotoperiod

Earliness per se

Frost Heat Drought

Which is the best cultivar? Now and in future

Minimize

risks


Simulated flowering time and yield over 50y

• Imagine if we could grow• all ‘potential genotypes’• all sowings March to July• Consider drought, ignore frost, heat...

• In Wagga Wagga: • Optimum flowering in early Oct• Best sowing (with diff varieties) is

mid-April to mid-May

• Fortunately, we can ‘imagine’


But frost and heat stresses vary with geography

• Last frost day is latest in edges of HRZ, then in major areas of WA, mallee and central NSW

• Short period of ‘safety’ between frost to heat in main production areas of WA, central NSW







• Experiments, parameterisation, validation• Some results


Prediction of heading date using daily weather

• Flowering time determined by • Vernalisation requirement (VRN genes)• Photoperiod requirement (PPD)• earliness per se (EPS)

• Predict with known genes or QTL• Gene based model (e.g. White & Hoogenboom 1996)

• QTL based model (e.g. Yin et al. 2000)

• Gene network model (e.g. Chapman et al. 2003)

• Gene circuit model (e.g. Salazer et al. 2009)


Climate effects on phenology

Crop

System control

Soil

SWIM

ManagerReportClock

SoilWat

SoilNSoilPHSoilP

ResidueEconomicsFertiliz

Irrigate

Canopy Met

ErosionOther Crops

MaizeSorghumLegume

Wheat

New Module

Manure

Management

ENGINE

Weather

Transpiration

Evaporation

Uptake

Rainfall

Runoff

Infiltration

Drainage

Radiation

APSIM Cropping Systems model


APSIM – Genotype inputs• Thermal time to floral initiation

• Function of crown temperature

• Rp – sensitivity to photoperiod (0 to 5)• Function of day length

• Rv – sensitivity to vernalisation (0 to 5)• Function of accumulated vernalisation


Measuring wheat phenology- Field experiments, parameterisation, validation

• Agronomy• South Perth 2008 and 2009 sown 11th June• 210 lines and 50% heading time recorded

• Treatments• V2 - Pre-imbibed seed vernalization for 8 weeks at 4oC• P2 - Extended day-length to midnight

Treatment name Vernalization Photoperiod

V1P1 Natural Natural

V2P1 Pre-vernalization Natural

V1P2 Natural Extended

V2P2 Pre-vernalization Extended


The gene-based model in APSIM• APSIM - Three line-specific parameters to predict heading time

• and for sensitivities of vernalization and photoperiod • for target thermal time from floral initiation to flowering

• APSIM-G – three gene and one line-specific parameters to predict heading time

Zheng et al (2013) J Exp Bot


A heading time model based on VRN1 and PPD1 alleles, and a single parameter per genotype

Observed days to heading

Sim

ula

ted

da

ys to

he

ad

ing

60

80

100

120

140

N = 340

RMSE = 2.9 d

y = 21.7 + 0.77x

R2 = 0.91

V1P1

N = 339

RMSE = 3.2 d

y = 15.66 + 0.81x

R2 = 0.74

V1P2

60

80

100

120

140

60 80 100 120 140

N = 340

RMSE = 3.7 d

y = 25.8 + 0.71x

R2 = 0.83

V2P1

60 80 100 120 140

N = 340

RMSE = 2.8 d

y = -5.92 + 1.07x

R2 = 0.73

V2P2

Target thermal time from FI to FL

Fre

qu

en

cy

0

10

20

30

40

400 600 800 1000

Zheng et al. 2013 J Exp Bot


Validation using independent data...• 79 sites (38.1o S to 17.2o S; 114.7o E to 152.3o E)• Sowing: 30th Mar to 11th Sep (range of 165 d)• Heading: 30th May to 24th Nov (range of 179 d)• Years: 2005 to 2011• No bias in sowing date of latitude response• 172 lines of South Perth trials

Observed days to heading

Sim

ula

ted

da

ys to

he

ad

ing

60

80

100

120

140

160

60 80 100 120 140 160

N = 4475

RMSE = 4.3 d

y = 0.28 + 0.98x

R2 = 0.96

TOSYIE

NVTNAT

PHIAGT

Zheng et al. 2013 J Exp Bot


Median heading times across Australian wheatbelt for 1st June sowing (1960-2009)

• Example for Vrn-A1v, Vrn-B1a, Vrn-D1a, Ppd-D1b and earliness of 800 oCd (Ellison)

• Gradually later from north to south and from inland to coast

http://croptsrv-cdc.it.csiro.au/flowering/


Median heading times across Australian wheatbelt for 1st June sowing (1960-2009)

• Example for Vrn-A1v, Vrn-B1a, Vrn-D1a, Ppd-D1b and earliness of 800 oCd

• Gradually later from north to south and from inland to coast

http://croptsrv-cdc.it.csiro.au/flowering/


Tools to summarise temperature environmentOptimum sowing and flowering cf. frost and heat

Zheng et al 2012 Global Change Biology


Over last 50y, temperature increased by 0.05 to 0.2C per decade...


Over last 50y, flowering advanced...

...by 0.5 to 1d per decade...


Changes in Janz flowering time over last 50y...

Zheng et al 2015 GCB


...and in the future?• Temperature increase

(Preston et al. 2006)• 0.4–2.0°C above 1990 levels by the

year 2030• 2-4°C by 2070

• Increase of climate variability (IPCC, 2007)• More periods of extreme high

temperature?• Fewer frost events? Possibly not !

Spring 2030 Ozclim1 to 2°C increase

Spring 2050Larger increase in SE region


Changes in Janz flowering time over last 50y...

...in future easternbelt will be limitedto late April sowing..


Methods and traits• Association genetics for:

• heading date• Earliness components: heading date measured in field experiment with

factorial combinations of pre-vernalization / extended photoperiod (2 years)• ASPIM phenology parameters TT, Rp, Rv (optimized)

Vernalization Photoperiod Sources of variations

No No All three components

Pre-vernalization No Photoperiod sensitivity

No Extended Vernalization requirement

Pre-vernalization Extended Earliness per se

• 158 genotypes / 5675 SNPs + 6 major genes (Ppd-D1, Vrn1, Rht1)


Predictions of heading date using APSIM with QTL-based parameters – Western Australia

Optimized parameters QTL-based parameters

80 90 100 110 120

80

90

100

110

120

Observ

ed h

eadin

g d

ate

Predicted heading date

RMSE = 1.6R² = 0.98

n = 125

WA

80 90 100 110 120

80

90

100

110

120

Observ

ed h

eadin

g d

ate


RMSE = 3.7R² = 0.77

n = 125

WA

Average genotype values for the considered region


Predictions of heading date using APSIM with QTL-based parameters – southern Australia

Optimized parameters QTL-based parameters

80 90 100 110 120 130

80

90

100

110

120

130

Observ

ed h

eadin

g d

ate


RMSE = 3.5R² = 0.9

n = 123

SA

70 80 90 100 110 120 130

80

90

100

110

120

130

Observ

ed h

eadin

g d

ate


RMSE = 3.6R² = 0.83

n = 123

SA

Average genotype values for the considered region


Current state of play• What we can do

• Summarise results nationally or any site– For conventional sowings (mid April

through June)

• Predict new genotypes– Using

– Known genes– VRN & PP screen

– Cannot estimate earliness-per-se from genes

• What we would like to do

• Predict for earlier sowing dates• Produce maps/local information• Capture GxExM, Augment NVT

• Predict– Using iso-lines to improve

prediction– alternative alleles of major and

minor genes– Capture earliness-per-se genes

Gene-based prediction of heading time to target real-time and future climate adaptation

AGRICULTURE

Scott Chapman, Bangyou Zheng, James Hunt CSIROBen Biddulph DAFWAKarine Chenu QAAFI, The University of QueenslandMatthieu Bogard, Arvalis Toulouse22 Sep 2015


Roles for Models in breeding Parental pool

Selection of best

Novel germplasm

IndustryoutputNew

cultivars

Crossing

Climate, Management

NewTraits and methods

Thermal time pre- and post-flowering (oCd)

-1500 -1000 -500 0 500

Wa

ter

sup

ply

/de

ma

nd

ra

tio

0.0

0.2

0.4

0.6

0.8

1.0

Env. type 1Env. type 2Env. type 3Env. type 4

Environment characterisation- Drought patterns to interpret GxE

Biom

ass

L

AI

Cov

er

Zado

ks

In-season ‘phenotyping’

WA

NT

QLD

SA NSW

VIC

RomaDalbyMoree

DubboLoxton

Ceduna

Emerald

BirchipCummins

GunnedahMerredinEsperanceWandering

Geraldton

Port PirieWagga Wagga

Longerenong

Wongan Hills

Cairns

Sydney

Brisbane

MelbourneCanberra

Darwin

Genetic trait value

Prediction of Adaptation


Thermal time pre- and post-flowering (oCd)

-1500 -1000 -500 0 500

Wa

ter

sup

ply

/de

ma

nd

ra

tio

0.0

0.2

0.4

0.6

0.8

1.0

Env. type 1Env. type 2Env. type 3Env. type 4

Some general points on phenotyping and modelling• Modeling adds value to breeding, but has to be relevant to what breeders do

(molecular biologists had to learn this too....)

Environment characterisation• Models used as ‘virtual checks’ or to provide ‘environment indices’

to assist in the analysis of phenotypes

Prediction of trait value• Specific experiments for ‘global’ coefficients,

e.g. flowering time responses, transpiration efficiency etc• Evaluation of effects of ‘known phenotypic range’ in simulations

Phenotype assessment• Global coefficients + local ‘grid-search’ coefficients to ‘fit’

the observed data and estimate ‘virtual phenotypes

Genetic trait value• Experiments on GxExM landscapes to allow exploration of impacts

of these factors on breeding efficiency and opportunity


Thermal time – earliness per se


Photoperiod Sensitivity – penalise development

Rp varies from 0 to 5

... Reduces daily thermal time....


Vernalisation Sensitivity – penalise development

• Vernalise

• De-vernalise

... Reduces thermal time


APSIM – Genotype ‘parameters’• TT - Thermal time target

• EARLINESS PER SE – thermal time target

• Fp - Photoperiod function• PHOTOPERIOD SENSITIVITY affected by values of Rp

• Fv – Vernalisation function• VERNILISATION SENSITIVITY affected by values of Rv

• So, sensitivity to photoperiod and vernalisation SLOWS progress to the target....

Gene-based prediction of heading time to target real-time and future climate adaptation AGRICULTURE...

Documents

Transcript of Gene-based prediction of heading time to target real-time and future climate adaptation AGRICULTURE...