next-generation genetic and genomic information for world food security
DESCRIPTION
Next-Generation Genetic and Genomic Information for World Food Security. Jack K. Okamuro National Program Leader for Plant Biology, Crop Productoin & Protection, USDA-ARS. ARS Administrator’s Council Meeting December 5, 2012 Beltsville, MD. Challenge. Food Security & Sustainability - PowerPoint PPT PresentationTRANSCRIPT
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