2015. jason wallace. applying high throughput genomics to crops for the developing world
TRANSCRIPT
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Applying High-Throughput Genomics to Crops for the Developing World
Jason Wallace Cornell University
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The big picture: Global food security
Photo credit: NASA
• Food security means reliable access to food of sufficient quality and quantity to lead an active and healthy life1
• 842 million people worldwide are food insecure2
• Increasing food security is one of the surest ways to improve health, educational attainment, and political stability
1 Paraphrased from FAO, Declaration of the World Summit on Food Security, 2009 2 FAO, The State of Food Insecurity in the World, 2013
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Major constraints on food security
Environmental variability
Projected surface temperature change3
Negative side-effects
Erosion Pollution NOAA
Deforestation Rhett Butler
Changing consumption habits
Fat & oil Fish
Dairy Meat Fruits
Cereals Vegetables
1.0 2.0 3.0
Consumption (Billion tonnes/year) 2
1 UN Department of Economic and Social Affairs, World Population Prospects: The 2012 Revision. 3NOAA GFDL Climate Research Highlights Image Gallery 2Kearney 2010, Phil Trans Roy Soc B 365
Increasing population
4
Po
pu
lati
on
(b
illio
ns)
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~9 billion by 2050
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2
2010 2030 2050
Today
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Reaching the goal Improved
crops Government
Policies
Agronomic Practices
Infrastructure development
Technology Development
Agroecology
Consumer habits
Market Incentives
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Co
st/m
ega
bas
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$1
$0.1
$10
$100
$1K
$10K
Year 2000 2005 2010 2015
The golden age of crop genetics
• Modern sequencing is opening the floodgates to genetic analysis
0
10
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30
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Ge
no
me
s seq
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Total plant genomes sequenced2
Moore’s Law Cost of sequencing1
Sequencing trends over time
2 Michael & Jackson 2013, The Plant Genome 6 1 Wetterstrand KA. DNA Sequencing Costs, available at: www.genome.gov
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Case studies outline Barnyard Millet
Diversity Analysis Pearl Millet
Genetic Map Creation Maize
Trait Mapping
Shramajeevi Agri Films
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Case studies outline Barnyard Millet
Diversity Analysis Pearl Millet
Genetic Map Creation Maize
Trait Mapping
Shramajeevi Agri Films
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Case Study 1: Barnyard millet diversity
Shramajeevi Agri Films
Barnyard Millet (Echinochloa spp.)
• Barnyard millet (Echinochloa spp.) is an important alternative crop in southern and eastern Asia
• Two species: E. colona (India) and E. crus-galli (Japan)
• Also grown as a forage crop in the US and Japan (“billion-dollar grass”)
• Goal: Characterize the newly created core collection at ICRISAT using genome-wide marker data
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Genotyping-by-sequencing GBS • Created for high-throughput, semi-automated
genotyping
Sequencing adaptor Barcode
Sticky ends
Genomic DNA
Images: Qiagen, Illumina, Elshire et al 2011, PLoS ONE
Restriction digest
Sequence Ligate adaptors
Isolate DNA
Pool & amplify
Sample plants
• Advantages • One step SNP discovery + genotyping
• Simple protocol; no reference required
• Large numbers of SNPs found cheaply
• Broadly applicable
• Drawbacks • False SNPs from
sequencing errors
• Missing data from stochastic sampling
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Cleaning up the data
• Have ~20,000 SNPs after basic filtering
• Problem: Both barnyard millet species are hexaploid -> false SNPs due to paralogs
Minor Allele Frequency
Re
lati
ve a
bu
nd
ance
Minor Allele Frequency
Re
lati
ve a
bu
nd
ance
Combined pop. E. colona E. crus-galli
Differentially segregating alleles
Filter by “heterozygosity”
Site Frequency Spectrum (raw) Site Frequency Spectrum (filtered)
Wallace et al. 2015, Plant Genome (in press)
Ideal
Paralogs
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Phylogenetics
• Phylogeny splits the two species as expected
• Population structure within species closely matches phylogeny and geography
E. colona E. crus-galli
Potential hybrids
Wallace et al. 2015, Plant Genome (in press)
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Outline Barnyard Millet
Diversity Analysis Pearl Millet
Genetic Map Creation Maize
Trait Mapping
Shramajeevi Agri Films
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Genetic Maps for Pearl Millet • Staple crop for India and Sub-saharan Africa
• Large (2.3 GB), diverse genome
• Reference genome in process
Pearl Millet (Pennisetum glaucum)
• Goal: Assemble genetic maps to anchor scaffolds into pseudochromosomes
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Mapping Populations • 3 biparental populations used for genetic mapping:
• 841 x 863 (“Patancheru”)
• ~ 100 RILs from ICRISAT-Patancheru
• Tift 99B x Tift 454 (“Tifton”)
• ~ 180 RILs from Som Punnuri, Ft. Valley State University, USA
• Wild x Domestic F2s (“Sadore”)
• ~ 300 F2 plants from Boubacar Kountche, ICRISAT-Niamey
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Summary statistics
Comparison of Genotyping Depths
# ge
no
typ
es
(lo
g sc
ale
)
Call depth (= # reads)
100
102
104
106
108
SNP counts
0
20k
40k
60k
48k
75k 76k 80k
Fewer SNPs = less diversity
Tifton Patancheru Sadore
Best read depth
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Making individual maps
1. Call SNPs
SNPs
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1. Call SNPs
2. Group via hierarchical clustering
Making individual maps
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1. Call SNPs
2. Group via hierarchical clustering
3. Merge linkage groups
Making individual maps
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1. Call SNPs
2. Group via hierarchical clustering
3. Merge linkage groups
4. Order markers
Making individual maps
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1. Call SNPs
2. Group via hierarchical clustering
3. Merge linkage groups
4. Order markers
5. Cleanup
Making individual maps
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Merge maps for final assembly
• 4824 contigs assembled into 1.68 GB reference
• 92.8% of sequence data
• 60% have putative orientations
• Not perfect, but pretty good
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Outline Barnyard Millet
Diversity Analysis Pearl Millet
Genetic Map Creation Maize
Trait Mapping
Shramajeevi Agri Films
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Case Study 3: Trait Mapping in the CIMMYT WEMA Populations
• WEMA = Water-Efficient Maize for Africa
• ~20 biparental families, ~200 lines each
• Goal: Use data from across families to map trait loci with high resolution
3D PCA plot of the WEMA families
PC1 PC2
PC3
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• Two approaches to mapping traits in WEMA
Trait mapping
Env 3 Env 4 Env 2 Env 1
Unified Posterior Probabilities
Bayesian GWAS Traditional Joint GWAS
merge
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Both methods get similar results
Traditional GWAS (-log10 p-value)
Bayesian GWAS (cumulative Bayes factor)
• Mappings in both methods are roughly equivalent
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Preliminary trait-mapping results
ZCN8
VGT1 ZmRAP2.7
? ?
GIGZ1A?
0 MB 100 MB 150 MB 50 MB
?
-lo
g10
p-v
alu
e
Association for Days to Anthesis (well-watered) on Chromosome 8
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Conclusions
Photo credit: NASA
• Genomic technology can rapidly characterize almost any crop
• These genetic tools help breed crops faster and better
• Genotyping is basically solved; the bottlenecks are now phenotyping and selection
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Future Need 1: High-throughput phenotyping
Photo credits: CIMMYT & Michael Gore
• Genotyping frequently cheaper than dirt (field space)
• Phenotyping is now the limiting factor
Manual recording Rapid phenotyping High-throughput phenotyping
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Future Need 2: Data infrastructure
• Both genotyping and phenotyping threaten to drown us in data.
• Data is only useful if it is usable
• Need to develop solutions so genotypes, phenotypes, and germplasm are integrated and linked
SERVER FARM IMAGE
Torkild Retvedt
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Make crosses
Phenotype
yi = m + Smzijujdj + ei
(Re)train model
Predict via model Genotype
Standard breeding cycle
Selection cycle (faster, less expensive)
Training cycle (slower, expensive)
Future Need 3: Faster breeding methods
Genomic Selection scheme
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Acknowledgements
The Buckler Lab
Collaborators
• C. Tom Hash (ICRISAT-Niamey)
• Boubacar Kountche (ICRISAT-Niamey)
• Som Punnuri (Fort Valley State University)
• Hari Upadhyaya (ICRISAT-Patancheru)
• Rajeev Varshney (ICRISAT-Patancheru)
• Xin Liu (BGI)
• Xuecai Zhang (CIMMYT-Mexico)
• The Institute for Genomic Diversity (Cornell)
• The Maize Diversity Project
• The Pearl Millet Genome Sequencing Consortium
Funding
• National Science Foundation (NSF)
• Plant Genome Research Program
• Basic Research to Enable Agricultural Development (BREAD)
• The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)
• The International Maize and Wheat Improvement Center (CIMMYT)
• The United States Agency for International Development (USAID)
• The United States Department of Agriculture Agricultural Research Service (USDA-ARS)