integration of physiological breeding and genomic selection for...
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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
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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
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Accelerating the breeding cycle
3/2/2015
Crossing
Evaluation Selection
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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
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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
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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
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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
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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
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Genomic Selection & Precision Phenotyping
3/2/2015
NOT NEW CONCEPTS….just new tools!
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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
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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)
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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.
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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
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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
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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
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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
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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
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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
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Effect of stage 1 accuracy: Parent selection
0.0
0
.2
0.4
0
.6
0.8
1
.0
Pro
po
rtio
n s
ele
cte
d
Stage 1 selection accuracy
0.0 0.2 0.4 0.6
Stage 1 Stage 2
Stage 3
Jessica Rutkoski
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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
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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
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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
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Feed the Future Innovation Lab for Applied Wheat Genomics
3/2/2015 www.wheatgenetics.org/research/innovation-lab
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Four-parameter logistic model
f x, b,c,d,e( )( ) = c+d - c
1+ exp b log x( ) - log e( )( ){ }
Senescence Model
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JBP PUS LDH
Senescence model for individual lines
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“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
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May 7, 2014 27
-9636.82 -9636.80 -9636.78 -9636.76 -9636.74
39
07
.70
39
07
.72
39
07
.74
NDVI - 2012.05.10
Longitude
La
titu
de
-9636.800 -9636.804 -9636.808
3907
.720
3907
.722
3907
.724
NDVI - 2012.05.10
Longitude
Lat
itu
de
-9636.800 -9636.804 -9636.808
390
7.7
20
390
7.7
22
39
07
.724
NDVI - 2012.05.10
-data.2$long[!is.na(data.2$pass)]
data
.2$
lat[
!is.n
a(d
ata
.2$
pa
ss)]
-9636.800 -9636.804 -9636.808
3907
.720
3907
.722
3907
.724
NDVI - 2012.05.10
Longitude
Lat
itu
de
Raw data
Define plot boundaries
Trim data Assign to plots
Geo-referenced Data
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Phenocart
3/2/2015
Phenocart design, Obregon Mexico
NDVI map - BISA, Ludhiana
BISA, Ludhiana, India - Feb 2015
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HTP via UAV
3/2/2015
3DR IRIS+ | NDVI converted Cannon S100
NDVI image - BISA, Ludhiana, INDIA, Jan 2015
NDVI map - BISA, Jabalapur
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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
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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 ★