Integration of physiological breeding and genomic selection for wheat improvement

Jesse Poland Kansas State University

Feb 20, 2015

5th International Conference on

Next Generation Genomics and Integrated Breeding for Crop Improvement

3/2/2015

60% Increase in demand for wheat by 2050

- 20% Potential yield decrease from climate change

2% Rate of gain needed to meet projections

< 1% Current rate of gain

3/2/2015

Accelerating the breeding cycle

3/2/2015

Crossing

Evaluation Selection

Increasing the rate of gain

3/2/2015

 

Rt =irsA

y

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance

Crossing

Evaluation Selection

3/2/2015

 

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance Selection Intensity

Large F2 populations

Big screening nurseries

Many crosses / populations

3/2/2015

 

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance Selection Accuracy

Replicated testing

International trials

Separate genetics from noise

3/2/2015

 

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance Genetic Variance

Bring in new genes not

present in current program

Conserve genetic variance

3/2/2015

 

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance Cycle time

Off season nurseries

Shuttle program effectively cut the

breeding cycle time in half

Genomic Selection & Precision Phenotyping

3/2/2015

NOT NEW CONCEPTS….just new tools!

Genomic Selection

1) Training Population (genotypes + phenotypes)

2) Selection Candidates (genotypes)

3/2/2015 Heffner, E.L., M.E. Sorrells, J.-L. Jannink. 2009. Genomic selection for crop improvement. Crop Sci. 49:1-12. DOI: 10.2135/cropsci2008.08.0512

Inexpensive, high-density genotypes

Accurate phenotypes

Prediction of total genetic value using dense genome-wide markers

GS: Prediction of wheat quality

3/2/2015

Sarah Battenfield, KSU

TRAIT PREDICTION ACCURACY

(r)

Test Weight 0.73***

Grain Hardness 0.51***

Grain Protein 0.63***

Flour Protein 0.60***

Flour SDS 0.67***

Mixograph Mix Time 0.72***

Alveograph W 0.70***

Alveograph P/L 0.48***

Loaf Volume 0.64***

CIMMYT elite breeding lines (n=1,138) Cycle 45 & 46 International Bread Wheat Screening Nursery (C45IBWSN)

Genomic Selection

A tool to enable:

Selection on single plant or seed

Selection in unobserved environments

Maintenance of genetic diversity

Evaluation of larger populations

3/2/2015

 

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance

Technical challenge addressed … logistical challenge remains.

0.42 Yield (Drought)

0.33 Thousand Kernel Weight

0.33 Heading Date

Prediction Accuracy

3/2/2015 Poland, J., et al. (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Gen. 5, 103-113 DOI: 10.3835/plantgenome2012.06.0006

CIMMYT: Current breeding scheme

Selected bulk Select single plants in F4

30,000 F4:F5 lines, seed

increase and visual selection

7000 1st year yield trial

1200 2nd year yield trial

Disseminate selected lines

Bulk breeding

Stage 1

30,000

Stage 2

7,000

Stage 3

1,200

Intermate

Multiple stages and options for selection

Objective: Estimate genetic gain advantage from improved selection accuracy in stage 1 Method: Deterministic simulation

Jessica Rutkoski

Expected gain from selection

2 A

g ic h

Falconer and Mackay, 1996, Hallauer et al. 2012

If selection occurs at multiple stages:

gTotalg

Calculate for each pathway

Weighted average gain (path 1, path 2, path3) Weighted average time (path 1, path 2, path 3)

Jessica Rutkoski

Breeding scheme simulations

For each level of stage 1 accuracy:

Simulate all combinations

• Population size per stage

• Proportion new parents per stage

Estimate genetic gain

for each combination

Find maximum

Jessica Rutkoski

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0 0.2 0.4 0.6 0.8

Stage 1 accuracy

Ge

net

ic g

ain

Effect of stage 1 accuracy: Genetic gain

Jessica Rutkoski

Effect of stage 1 accuracy: Parent selection

0.0

0

.2

0.4

0

.6

0.8

1

.0

Pro

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Stage 1 selection accuracy

0.0 0.2 0.4 0.6

Stage 1 Stage 2

Stage 3

Jessica Rutkoski

Stage 1 selection

3. High-throughput phenotyping + pedigree

More traits to one the way…

2. Genomic prediction

4. High-throughput phenotyping + pedigree + genomic

1. Phenotypic selection

Predictor traits correlation to yield

Green NDVI, grain filling

Red NDVI, grain filling

Green NDVI, vegetative

Days to heading

Grain yield

Canopy temperature, vegetative

Canopy temperature, grain filling

Jessica Rutkoski

Predictor traits successful in animal breeding

Inclusion of correlated traits greatly increased prediction accuracy (genomic & pedigree)

As number of traits increased, the advantage of using

genomic rather than pedigree relationships decreased

Feed the Future Innovation Lab for Applied Wheat Genomics

3/2/2015 www.wheatgenetics.org/research/innovation-lab

Four-parameter logistic model

f x, b,c,d,e( )( ) = c+d - c

1+ exp b log x( ) - log e( )( ){ }

Senescence Model

JBP PUS LDH

Senescence model for individual lines

“Geo-referenced proximal sensing”

May 7, 2014 26

GPS

Sensors

Sensors

- GreenSeeker = NDVI

- IRT = canopy temperature

- SONAR = plant height

Physiologically define proximal measurements

RTK-GPS (cm level accuracy)

GPS GPS

sensors

computer

May 7, 2014 27

-9636.82 -9636.80 -9636.78 -9636.76 -9636.74

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NDVI - 2012.05.10

Longitude

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-9636.800 -9636.804 -9636.808

3907

.720

3907

.722

3907

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NDVI - 2012.05.10

Longitude

Lat

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-9636.800 -9636.804 -9636.808

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NDVI - 2012.05.10

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-9636.800 -9636.804 -9636.808

3907

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NDVI - 2012.05.10

Longitude

Lat

itu

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Raw data

Define plot boundaries

Trim data Assign to plots

Geo-referenced Data

Phenocart

3/2/2015

Phenocart design, Obregon Mexico

NDVI map - BISA, Ludhiana

BISA, Ludhiana, India - Feb 2015

HTP via UAV

3/2/2015

3DR IRIS+ | NDVI converted Cannon S100

NDVI image - BISA, Ludhiana, INDIA, Jan 2015

NDVI map - BISA, Jabalapur

Increasing the rate of gain

3/2/2015

 

Rt =irsA

y

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance

Crossing

Evaluation Selection

May 7, 2014 31 31

Shuangye Wu ★

Josh Sharon

Ryan Steeves

Jared Crain ★

Sandra Dunckel

Trevor Rife

Daljit Singh ★

Narinder Singh

Traci Viinanen

Xu (Kevin) Wang

Lisa Borello

Erena Edae

Atena Haghighattalab

Allan Fritz

Sarah Battenfield

Dale Schinstock

Kyle McGahee

Naiqian Zhang

Jed Barker

Yong (Ike) Wei

www.wheatgenetics.org

Ravi Singh ★

Susanne Dreisigacker

Matthew Reynolds

David Bonnett

Rick Ward

Suchismita Mondal

Ravi Vallaru ★

Uttam Kumar ★

Steve Welch

Nan An★

Mark Sorells

Jessica Rutkoski ★