Genomic Selection & Precision Phenotyping

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


March 27, 2014


Trends in Population, Production & Acres

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1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Po

pu

lati

on

(b

il)

MH

, M

MT

Acreage

Production

Demand

Population

Source – USDA and UN Population Database

Slide: Dalton Henry

656 696

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04/05 05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14

MM

T

Production Use

March 27, 2014

2


Mark Tester and Peter Langridge (2010) Breeding Technologies to Increase

Crop Production in a Changing World, Science 12:327 pp. 818-822


(Accelerating) The Breeding Cycle

March 27, 2014

3

Crossing

Evaluation Selection



The breeder’s (favorite) equation:

March 27, 2014

4

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance

Crossing



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



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’


Dr. Borlaug’s favorite

equation…

March 27, 2014

6

Rt =irsAy


years per cycle


genetic variance




equation…

March 27, 2014

7

Rt =irsAy


years per cycle


genetic variance


Selection Intensity

Large F2 populations

Big screening nurseries

Many crosses / populations



equation…

March 27, 2014

8

Rt =irsAy


years per cycle


genetic variance


Selection Accuracy

Replicated testing

International trials

Separate genetics from noise



equation…

March 27, 2014

9

Rt =irsAy


years per cycle


genetic variance


Genetic Variance

Bring in new genes not present in

current program



equation…

March 27, 2014

10

Rt =irsAy


years per cycle


genetic variance


Shuttle program effectively cut

the breeding cycle time in half



Dec 2, 2013

11

NOT NEW CONCEPTS….just new tools!


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


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)


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


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


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


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***


Feed the Future Innovation Lab for

Applied Wheat Genomics

Dec 2, 2013

18

www.wheatgenetics.org/research/innovation-lab


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, 2014 Borlaug Global Summit, Cd. Obregon, Mexico

19

Rt =irsAy


years per cycle


genetic variance


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


HTP: A multi-disciplinary approach

Dec 2, 2013

21

Plant Breeding

& Genetics Physiology

Engineering Bioinformatics

HTP


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)


HTP: Platform configuration

Dec 2, 2013

23

GreenSeeker CropCircle SONAR

IRT

GPS GPS

sensors

computer

LabView program

10 Hz sampling

Real-time feedback

Flat file output


HTP: Multiple sensor orientation

Dec 2, 2013

24

-3908.040 -3908.035 -3908.030 -3908.025

9637.1

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9637

.135

963

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-data$Right_Longitude

data

$R

ight_

La

titu

de

Right GPS

Left GPS

NDVI

0.1

0.3

0.5

0.7

0.9


NDVI – raw data

Dec 2, 2013

25

-96.6135 -96.6130 -96.6125

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.12

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288

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.12

90

NDVI - 2012.05.10

Longitude (DD.dddd)

Latitu

de

(D

D.d

dd

d)


Assigning data

to field entries

Dec 2, 2013

26

-9636.82 -9636.80 -9636.78 -9636.76 -9636.74

390

7.7

039

07

.72

390

7.7

4

NDVI - 2012.05.10

Longitude

Latitu

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

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39

07

.724

NDVI - 2012.05.10

-data.2$long[!is.na(data.2$pass)]

data

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-9636.800 -9636.804 -9636.808

3907

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3907

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3907

.724

NDVI - 2012.05.10

Longitude

Lat

itu

de

Raw data

Define plot

boundaries

Trim data

Assign to plots


HTP: Plant Height

Dec 2, 2013

27

38.85605 38.85610 38.85615

-100

-90

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SONAR MEASUREMENT - PLANT HEIGHT

Latitude (DD.ddddd)

SO

NA

R (

cm

)

Single pass down one column

Centimeter level

precision in plant

height measurements


Dec 2, 2013

28

X2013.05.31

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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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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

80

85

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95

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Height_Jacob

Phenotyper: Increased accuracy

Plant Height


Dec 2, 2013

29

-9636.82 -9636.80 -9636.78 -9636.76 -9636.74

390

7.7

039

07

.71

39

07.7

23

907

.73

390

7.7

4

NDVI - 2012.05.03

-data.1$long

-9636.82 -9636.80 -9636.78 -9636.76 -9636.74

39

07

.70

390

7.7

13

90

7.7

23

90

7.7

339

07

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NDVI - 2012.05.10

-data.2$long

-9636.82 -9636.80 -9636.78 -9636.76 -9636.74

39

07

.70

390

7.7

13

90

7.7

239

07

.73

39

07

.74

NDVI - 2012.05.15

NDVI: Multi-temporal

measurements

Rapid assessment enables

repeated measurements

over time


NDVI: Multi-temporal measurements

Dec 2, 2013

30

DATE

ND

VI

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


Phenocorn:

Global Deployment

Low(er) cost

Dec 2, 2013

31

GPS

IRT

GreenSeeker

Bipedal

Mobile

Unit


HTP Platform: Unmanned Aerial Vehicles

Dec 2, 2013

32

+ Not too expensive

+ flexible deployment

+ Image whole field


High-throughput

phenotyping

A tool to enable:

More precise assessment

Evaluation of larger populations

Multi-temporal assessment

Assessment of intractable traits

March 27, 2014 Borlaug Global Summit, Cd. Obregon, Mexico

33

Rt =irsAy


years per cycle


genetic variance


Precision also comes from reducing errors

March 27, 2014

34

Rt =irsAy


years per cycle


genetic variance

Crossing



Seed

Increase

Harvest /

Cleaning

Package

Seed Planting

Field

Notes

Harvest /

Weigh

Record

Data Analysis /

Selection

1% errors / mix ups 8% random

data


Apps for managing materials & data

Reduce errors

Increase speed

Reduce fatigue

Dec 2, 2013

35

http://wheatgenetics.org/research/technology


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.


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


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

Date post:	20-Aug-2015
Category:	Science
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