Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico. http://www.borlaug100.org

1. KANSAS STATE
U N I V E R S I T Y
Jesse Poland
Wheat Genetics Resource Center
Applied Wheat Genomics Innovation Lab
Kansas State University, USA
Genomic Selection & Precision Phenotyping
March 27, 2014
1
Borlaug Global Summit, Cd. Obregon, Mexico
Borlaug Global Summit, Cd. Obregon, Mexico
March 27, 2014

2. KANSAS STATE
U N I V E R S I T Y
Trends in Population, Production & Acres
0
1
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0
100
200
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400
500
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700
800
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1,000
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050
Population(bil)
MH,MMT
Acreage
Production
Demand
Population
Source – USDA and UN Population Database
Slide: Dalton Henry
March 27, 2014
2
Borlaug Global Summit, Cd. Obregon, Mexico
Mark Tester and Peter Langridge (2010) Breeding Technologies to Increase
Crop Production in a Changing World, Science 12:327 pp. 818-822

3. KANSAS STATE
U N I V E R S I T Y
(Accelerating) The Breeding Cycle
March 27, 2014
3
Crossing
Evaluation Selection
Borlaug Global Summit, Cd. Obregon, Mexico

4. KANSAS STATE
U N I V E R S I T Y
The breeder’s (favorite) equation:
March 27, 2014
4
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Crossing
Evaluation Selection
Borlaug Global Summit, Cd. Obregon, Mexico
Selection Intensity
 Increase (to a limit)
 Need bigger populations
Selection Accuracy
 Increase
 More precise measurements
 Reduce Errors
 Correct for environment
Genetic Variance (Diversity)
 Increase
 Mixed bag (not all good)
 A must have
Years per Cycle
 Decrease!
 Constant ‘rate’ of return

5. KANSAS STATE
U N I V E R S I T Y
Genomic Selection & Precision Phenotyping
Dec 2, 2013
5
NOT NEW CONCEPTS!
Phenotype is what we eat!
Phenotype results from complex process of
genetics and environment.
We can improve the environment (i.e. Agronomy) and
we can improve the genetics (i.e. Breeding)
Use field testing to ‘observe’ the underlying
genetics
- (GBS) ‘genotyping-by-seeing’

6. KANSAS STATE
U N I V E R S I T Y
Dr. Borlaug’s favorite
equation…
March 27, 2014
6
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Borlaug Global Summit, Cd. Obregon, Mexico

7. KANSAS STATE
U N I V E R S I T Y
Dr. Borlaug’s favorite
equation…
March 27, 2014
7
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Borlaug Global Summit, Cd. Obregon, Mexico
Selection Intensity
 Large F2 populations
 Big screening nurseries
 Many crosses / populations

8. KANSAS STATE
U N I V E R S I T Y
Dr. Borlaug’s favorite
equation…
March 27, 2014
8
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Borlaug Global Summit, Cd. Obregon, Mexico
Selection Accuracy
 Replicated testing
 International trials
Separate genetics from noise

9. KANSAS STATE
U N I V E R S I T Y
Dr. Borlaug’s favorite
equation…
March 27, 2014
9
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Borlaug Global Summit, Cd. Obregon, Mexico
Genetic Variance
 Bring in new genes not present in
current program

10. KANSAS STATE
U N I V E R S I T Y
Dr. Borlaug’s favorite
equation…
March 27, 2014
10
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Borlaug Global Summit, Cd. Obregon, Mexico
Shuttle program effectively cut
the breeding cycle time in half

11. KANSAS STATE
U N I V E R S I T Y
Genomic Selection & Precision Phenotyping
Dec 2, 2013
11
NOT NEW CONCEPTS….just new tools!

12. KANSAS STATE
U N I V E R S I T Y
Genomic Selection
1) Training Population (genotypes + phenotypes)
2) Selection Candidates (genotypes)
Dec 2, 2013
12
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

13. KANSAS STATE
U N I V E R S I T Y
Why use sequencing for genotyping?
+ Amazing developments in sequencing output
+ Very good for wheat where polyploidy and duplications cause
problems with hybridization/PCR assays
+ Polymorphism discovery simultaneous with genotyping
+ No ascertainment bias
+ Low per sample cost
- Complex bioinformatics
- Requires paradigm shift in
molecular markers
Dec 2, 2013
13
Genotyping-by-sequencing (GBS)

14. KANSAS STATE
U N I V E R S I T Y
Genotyping-by-sequencing (GBS)
“massively parallel sequencing”
- next-gen sequencing (Illumina)
“multiplex” = using DNA barcode
- unique DNA sequence synthesized on the
adapter
- pool 48-384 samples together
“reduced-representation”
- capture only the portion of the genome
flanking restriction sites
- methylation-sensitive restriction enzymes
- Target rare, low-copy sites in genome
- PstI (CTGCAG), MspI (CCGG)
14
“…massively parallel sequencing of multiplexed reduced-representation genomic libraries.”
Elshire, R. J., J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto, E. S. Buckler and S. E. Mitchell
(2011). "A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity
Species." PloS one 6(5): e19379.
Dec 2, 2013

15. KANSAS STATE
U N I V E R S I T Y Dec 2, 2013
15
Population Sequencing (POPSEQ)
Anchoring and ordering of whole-genome assemblies
Ordered whole genome assembly for wheat
 De novo assembly of Synthetic W7984
 Anchoring with SynOpDH
 Reference anchoring of GBS markers
Size 9.144 Gbp
N50 (scaffold) 120,643 / 21.2kbp

16. KANSAS STATE
U N I V E R S I T Y
GS: Prediction of wheat quality
Dec 7, 2013
16
CIMMYT elite breeding lines (n=1,138)
Cycle 45 & 46 International Bread Wheat Screening Nursery (C45IBWSN)
Replicated yield tests
 2009 & 2010
 6 environments
One replication for quality testing
 milling
 dough rheology
 baking tests
Best Linear Unbiased Estimate (BLUE)
Genotyping-by-sequencing
15,330 SNPs (imputed with MVN-EM)(rrBLUP)
Cross-validation (x100)
 Training sets of n=134
 Validation sets of n=30
- thousand kernel weight
- mix time
- pup loaf volume
Sarah Battenfield, KSU

17. KANSAS STATE
U N I V E R S I T Y Dec 7, 2013
17
GS: Prediction of wheat quality
Sarah Battenfield, KSU
TRAIT
PREDICTION
ACCURACY
(r)
Test Weight 0.725***
Grain Hardness 0.513***
Grain Protein 0.630***
Flour Protein 0.604***
Flour SDS 0.666***
Mixograph Mix Time 0.718***
Alveograph W 0.697***
Alveograph P/L 0.476***
Loaf Volume 0.638***

18. KANSAS STATE
U N I V E R S I T Y
Feed the Future Innovation Lab for
Applied Wheat Genomics
Dec 2, 2013
18
www.wheatgenetics.org/research/innovation-lab

19. KANSAS STATE
U N I V E R S I T Y
Genomic Selection
A tool to enable:
 Selection on single plant or seed
 Selection in unobserved environments
 Maintenance of genetic diversity
 Evaluation of larger populations
March 27, 2014Borlaug Global Summit, Cd. Obregon, Mexico
19
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance

20. KANSAS STATE
U N I V E R S I T Y
High-throughput phenotyping
• Automated or semi-automated platforms for
rapid, precision assessment
• High-throughput analysis pipelines
• Decrease the phenotyping burden and increase
efficiency of the breeding program
Dec 2, 2013
20

21. KANSAS STATE
U N I V E R S I T Y
HTP: A multi-disciplinary approach
Dec 2, 2013
21
Plant Breeding
& Genetics Physiology
Engineering
Bioinformatics
HTP

22. KANSAS STATE
U N I V E R S I T Y
HTP: “Geo-referenced proximal sensing”
Dec 2, 2013
22
GPS
Data logger
Sensors
Sensors
- GreenSeeker = NDVI
- IRT = canopy temperature
- SONAR = plant height
Physiologically define
proximal measurements
RTK-GPS
(cm level accuracy)

23. KANSAS STATE
U N I V E R S I T Y
HTP: Platform configuration
Dec 2, 2013
23
GreenSeeker
CropCircle SONAR
IRT
GPSGPS
sensors
computer
LabView program
 10 Hz sampling
 Real-time feedback
 Flat file output

24. KANSAS STATE
U N I V E R S I T Y
HTP: Multiple sensor orientation
Dec 2, 2013
24
-3908.040 -3908.035 -3908.030 -3908.025
9637.1339637.1349637.1359637.1369637.1379637.1389637.1399637.140
-data$Right_Longitude
data$Right_Latitude
Right GPS
Left GPS
NDVI
0.1
0.3
0.5
0.7
0.9

25. KANSAS STATE
U N I V E R S I T Y
NDVI – raw data
Dec 2, 2013
25
-96.6135 -96.6130 -96.6125
39.128439.128639.128839.1290
NDVI - 2012.05.10
Longitude (DD.dddd)
Latitude(DD.dddd)

26. KANSAS STATE
U N I V E R S I T Y
Assigning data
to field entries
Dec 2, 2013
26
-9636.82 -9636.80 -9636.78 -9636.76 -9636.74
3907.703907.723907.74
NDVI - 2012.05.10
Longitude
Latitude
-9636.800 -9636.804 -9636.808
3907.7203907.7223907.724
NDVI - 2012.05.10
Longitude
Latitude
-9636.800 -9636.804 -9636.808
3907.7203907.7223907.724
NDVI - 2012.05.10
-data.2$long[!is.na(data.2$pass)]
data.2$lat[!is.na(data.2$pass)]
-9636.800 -9636.804 -9636.808
3907.7203907.7223907.724
NDVI - 2012.05.10
Longitude
Latitude
Raw data
Define plot
boundaries
Trim data
Assign to plots

27. KANSAS STATE
U N I V E R S I T Y
HTP: Plant Height
Dec 2, 2013
27
38.85605 38.85610 38.85615
-100
-90
-80
-70
-60
-50
-40
SONAR MEASUREMENT - PLANT HEIGHT
Latitude (DD.ddddd)
SONAR(cm)
Single pass down one column
Centimeter level
precision in plant
height measurements

28. KANSAS STATE
U N I V E R S I T Y Dec 2, 2013
28
X2013.05.31
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−10−50510
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−10−50510
0.84***
(0.78,0.89)
X2013.06.06
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0.53***
(0.65,0.4)
0.55***
(0.67,0.42)
Height_Jesse
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707580859095100
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7580859095
0.49***
(0.61,0.35)
0.43***
(0.56,0.29)
0.55***
(0.42,0.66)
Height_Jon
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−10 −5 0 5 10
0.47***
(0.60,0.34)
0.41***
(0.54,0.27)
70 75 80 85 90 95 100
0.36***
(0.22,0.5)
0.56***
(0.43,0.67)
80 85 90 95 100
80859095100
Height_Jacob
Phenotyper:
Increased accuracy
Plant Height

29. KANSAS STATE
U N I V E R S I T Y Dec 2, 2013
29
-9636.82 -9636.80 -9636.78 -9636.76 -9636.74
3907.703907.713907.723907.733907.74
NDVI - 2012.05.03
-data.1$long
-9636.82 -9636.80 -9636.78 -9636.76 -9636.74
3907.703907.713907.723907.733907.74
NDVI - 2012.05.10
-data.2$long
-9636.82 -9636.80 -9636.78 -9636.76 -9636.74
3907.703907.713907.723907.733907.74
NDVI - 2012.05.15
NDVI: Multi-temporal
measurements
Rapid assessment enables
repeated measurements
over time

30. KANSAS STATE
U N I V E R S I T Y
NDVI: Multi-temporal measurements
Dec 2, 2013
30
DATE
NDVI
0.0
0.2
0.4
0.6
0.8
5/3/12 5/10/12 5/15/12 5/21/12
Advanced Yield Nursery
Identify dynamic
differences among
genotypes

31. KANSAS STATE
U N I V E R S I T Y
Phenocorn:
 Global Deployment
 Low(er) cost
Dec 2, 2013
31
GPS
IRT
GreenSeeker
Bipedal
Mobile
Unit

32. KANSAS STATE
U N I V E R S I T Y
HTP Platform: Unmanned Aerial Vehicles
Dec 2, 2013
32
+ Not too expensive
+ flexible deployment
+ Image whole field

33. KANSAS STATE
U N I V E R S I T Y
High-throughput
phenotyping
A tool to enable:
 More precise assessment
 Evaluation of larger populations
 Multi-temporal assessment
 Assessment of intractable traits
March 27, 2014Borlaug Global Summit, Cd. Obregon, Mexico
33
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance

34. KANSAS STATE
U N I V E R S I T Y
Precision also comes from reducing errors
March 27, 2014
34
Rt =
irsA
y
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
Crossing
Evaluation Selection
Borlaug Global Summit, Cd. Obregon, Mexico
Seed
Increase
Harvest /
Cleaning
Package
Seed
Planting
Field
Notes
Harvest /
Weigh
Record
Data
Analysis /
Selection
1% errors / mix ups 8% random
data

35. KANSAS STATE
U N I V E R S I T Y
Apps for managing materials & data
 Reduce errors
 Increase speed
 Reduce fatigue
Dec 2, 2013
35
http://wheatgenetics.org/research/technology

36. KANSAS STATE
U N I V E R S I T Y Dec 2, 2013
36
The rate of genetic gain [in plant breeding programs]
can be increased through adoption of simple but
innovative tools for data collection and management.

37. KANSAS STATE
U N I V E R S I T Y
Turbo-charging the breeding program…
Dec 2, 2013
37
To implement Genomic Selection and
High-throughput precision phenotyping:
 Fundamental mechanics of program must
be functioning well
 Data management is robust and efficient
Implications for:
 Funders of crops research
 Students in crops research
 Research Scientists
 Breeders

38. KANSAS STATE
U N I V E R S I T Y
Ravi Singh
David Bonnett
Matthew Reynolds
Yann Manes
Susanne
Dreisigacker
Jose Crossa
Hector Sanchez
Shuangye Wu
Josh Sharon
Ryan Steeves
Jared Crain
Sandra Dunckel
Trevor Rife
Traci Viinanen
Narinder Singh
Daljit Singh
Xu “Kevin” Wang
Erena Edae
Bikram Gill
Bernd Friebe
Sunish Seghal
Jon Raupp
Duane Wilson
Eric Olson
Ed Buckler
Rob Elshire
Jeff Glaubitz
Jean-Luc Jannink
Mark Sorrells
Jeffrey Endelman
Julie Dawson
Jessica Rutkoski
Rebecca Nelson
Mike Gore
Robbie Waugh
Hui Liu
Pedro Andrade-Sanchez
John Heun
Jeffery White
Kelly Thorp
Andrew French
Mike Salvucci
Nils Stein
Martin Mascher
Burkhard Steuernagel
Thomas Nussbaumer
Kevin Price
Nan An
Niaqian Zhang
Jed Barker
Allan Fritz
Sarah Battenfield
Chris Gaynor
Lee DeHaan