Next-Generation Genetic and Genomic Information for

World Food SecurityJack K. Okamuro

National Program Leader for Plant Biology, Crop Productoin & Protection, USDA-ARS

ARS Administrator’s Council MeetingDecember 5, 2012

Beltsville, MD

Challengeo Food Security & Sustainabilityo Climate Change & Adaptabilityo Renewable & Sustainable Energy

Productiono Nutrition & Food Safety

Revolutiono Unleash natural diversity for crop

improvement using next generation genetic and genomic technologies

o Expanded “open access " to global genomic and genetic information, tools and data

o Globalization of genetic and genomic resources for global food security

Model

Barley5%

Maize34%

Millet1%

Oats1%

Rice28%

Rye1%

Sorghum2%

Trit-icale1%

Wheat27%

Barley1%

Maize79%

Mil-let0%

Oats0%

Rice3%

Rye0%

Sorghum2%

Wheat15%

Maize represents 79% of US grain production and 34% of global grain production; 30% of calories for more than 4.5 billion people in 94 developing countries

o Over 10,000 years of adaptation to diverse environments

o Genetic manipulation of flowering allows rapid access to diversity evolved elsewhere

Diversity

Evaluate Natural VariationMathematically Model Genotype to Phenotype

Predict Phenotype

Facilitates Rapid Breeding Progress

Application

Modified from Ed Buckler

Utilize next generation genomic technologies to accelerate and engineer simple and complex traits

USDA-NASS; Troyer 2006 Crop Sci. 46:528–543; Duvick 2005 Maydica 50:193-202

1865 1885 1905 1925 1945 1965 1985 20050

20

40

60

80

100

120

140

160

180

Open pollinated

double cross

single cross

modern

Year

Aver

age

corn

yie

ld (

bu/a

c)

8-fold increase in yield over 80 years

Progress

AccelerationDNA sequencing drives the revolution o Next generation $15/$4,000

genotype/genome sequence

o Genotyping by sequencing provides effective SNP coverage

o GBS reveals genome-wide variation in genome structure (RDV)

Log2 ratios of RDV across Chr6

Map, analyze, model target traitsNested Association Mapping (NAM)o Crossed and sequenced 25 diverse maize lines to

capture a substantial portion of world’s breeding diversity

o Derived 5000 inbred lines from the crosseso Grew millions of plants, multiple locations/seasonso Largest genetic dissection system ever

Tx303

Mo18W

MS71 Hp301

CML333CML247

P39

CML228

Ki11

M37W

CML103

NC350

Oh43

Ky21

CML52

Oh7B

M162W

CML69

Tzi8

Ki3

NC358

CML322 CML277

IL14H B97CML52B73

F1

RIL2 RIL199 RIL200RIL1

…

B73

F1

RIL2 RIL199 RIL200RIL1

…

P39

McM

ulle

n et

al 2

009

Scie

nce

Modified from Ed Buckler

Trait models

o NAM data enables researchers to predict traits based on genotype.

o Develop new models that incorporate weighted loci

-150

-100

-50

0

50

100

150Effects Estimated for Days to Silk

QTL

12h

Significant QTL24h 36h

Increase Flowering Time

Decrease Flowering TimeNu

mbe

r of A

llele

s

•Flowering is controlled by more than 50 genes, each with small effects

Genotype-based trait prediction NAM based models enable

Determine the genetic basis for complex traitsExample: Altered leaf morphology allowed increased planting density. Newer hybrids have upright leaves (Duvick 2005)

Applications

Trait models

Upper Leaf Angle Leaf Length Leaf Width

93% of significant alleles display <18mm effect

96% of significant alleles display <2.5° effect

95% of significant alleles:display <3mm effect

-200-150-100

-500

50100150200

3 6 9 12 15 18 21 24

Freq

uenc

y of A

llele

Allelic Effect (mm)

Significant alleles

-200-150-100

-500

50100150200

0.5 1.5 2.5 3.5 4.5

Freq

uenc

y of A

llele

Allelic Effect (mm)

Significant alleles

-250

-150

-50

50

150

250

0.5 1 1.5 2 2.5 3 3.5 4

Freq

uenc

y of A

llele

Allelic Effect (°)

Significant alleles

500

600

700

800

900

1000

650 750 850 950

Obs

erve

d

Predicted

R2=0.84

55

65

75

85

95

105

115

60 70 80 90 100 110O

bser

ved

Predicted

R2=0.81

2535455565758595

40 50 60 70 80 90

Obs

erve

d

Predicted

R2=0.78

Models accurately predict complex traits if the right relatives are measured. Focus on high value traits.

Pos alleles

Neg alleles

Hybrid vigor

Jun Cao and Patrick S. Schnable

Hybrid

Hybrid-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.50.0100

0.0200

0.0300

0.0400

0.0500

0.0600

1/Recombination

Residual Hets

Inde

x of

Hyb

rid V

igor

In

dex

of R

ecom

bina

tion

Genomic Position

o Bad mutations occur all the timeo Genomic mixing (recombination) is necessary to remove theseo Regions with low recombination benefit from being in a hybrid state

(i.e. cover for each other)

Conclusionso Trait variation is predictableo Common adaptive alleles selected by breeders are rare

variants in wild populations; eo Environment determines the frequency and fitness of

polymorphisms. o High impact of the adoption of genomic technologies for

crop improvement

One team of many

www.panzea.org

Important challenge

o IWGPG NPGI Workshop, PAG Saturday, 12 January 2013• What tools and resources are needed that are not

currently available?• What tools and resources are needed that will enable

translation of basic research for agriculture? For basic research in plant genomics?

• What information and resource repository needs are not currently being met?

• What opportunities do you see for leveraging investments through international coordination?

o ARS Big Data Workshop, February 2013o G8 Open Data Research Collaboration Platform Workshop,

April 2013

How to accelerate and expand the adoption of next generation genomic technologies for crop improvement?Target developing economy countries

GRIN-Global

panzea

ARS provides open access system for global crop information system crop researc

Globalize open data accessCollaboratorsASPBCIMMYtCold Spring Harbor LabCornell UniversityEnsemblEuropean Bioinf InstGenome InstituteiPlant CollaborativeICRISATIRRIJCVIKEGGKnowledgebaseMIPSMonsantoOryzabasePhytozomePlant Ontology ConsortiumPLAZASyngentaTAIR

EndUsers

ComputationalUsers

TeraGridXSEDE

Multi-level UserAccess

From Eric Lyons

Expand open access to community tools & services through the iPlant Collaborative

Globalization

2012 New User Map

Deliver

G-8 countries agreed to share relevant agricultural data available from G-8 countries with African partnerso WORKSHOP. To convene an international

conference on Open Data for Agricultureo GLOBAL PLATFORMS. To develop options for the

establishment of a global platform to make reliable agricultural and related information available to African farmers, researchers and policymakers, taking into account existing agricultural data systems.

o PILOT. Explore options for establishing a pilot to make genetic and genomics data openly available; integrate genetics and genomics data with geo-spatial, agro-ecological, weather, and other relevant data to make practical and useful information available to African farmers,

G8/G20 Alliance for Open Data for Agriculture

World Hunger and CIMMYT’s Presence in Maize

Collaboration with IITA

Global partnerships CIMMYT

Innovation

SoyFACE Global Change Research Facility

Three-dimensional root architecture phenotyping

Field-Based Phenotyping

New technologies neededA new generation of plant breeders, bioinformaticists, programmers, IT specialistsLong term data storage & curation

Challengeo Food Security & Sustainabilityo Climate Change & Adaptabilityo Renewable & Sustainable Energy

Productiono Nutrition & Food Safety

Acknowledgementso Maize Diversity Project Team o ARS Database Teams (Albany, Ames,

Ithaca, Cold Spring Harboro IWGPGo ARS National Programs