applying high-throughput genomics to crops for the...

Applying High-Throughput Genomics to Crops for the Developing World

Jason Wallace Cornell University

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

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

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~9 billion by 2050

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2010 2030 2050

Today

Reaching the goal Improved

crops Government

Policies

Agronomic Practices

Infrastructure development

Technology Development

Agroecology

Consumer habits

Market Incentives

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

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Ge

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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

Case studies outline Barnyard Millet

Diversity Analysis Pearl Millet

Genetic Map Creation Maize

Trait Mapping

Shramajeevi Agri Films

Case studies outline Barnyard Millet

Diversity Analysis Pearl Millet

Genetic Map Creation Maize

Trait Mapping

Shramajeevi Agri Films

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

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

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

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Minor Allele Frequency

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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

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)

Outline Barnyard Millet

Diversity Analysis Pearl Millet

Genetic Map Creation Maize

Trait Mapping

Shramajeevi Agri Films

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

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

Summary statistics

Comparison of Genotyping Depths

# ge

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(lo

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Call depth (= # reads)

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102

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SNP counts

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20k

40k

60k

48k

75k 76k 80k

Fewer SNPs = less diversity

Tifton Patancheru Sadore

Best read depth

Making individual maps

1. Call SNPs

SNPs

1. Call SNPs

2. Group via hierarchical clustering

Making individual maps

1. Call SNPs

2. Group via hierarchical clustering

3. Merge linkage groups

Making individual maps

1. Call SNPs

2. Group via hierarchical clustering

3. Merge linkage groups

4. Order markers

Making individual maps

1. Call SNPs

2. Group via hierarchical clustering

3. Merge linkage groups

4. Order markers

5. Cleanup

Making individual maps

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

Outline Barnyard Millet

Diversity Analysis Pearl Millet

Genetic Map Creation Maize

Trait Mapping

Shramajeevi Agri Films

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

• 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

Both methods get similar results

Traditional GWAS (-log10 p-value)

Bayesian GWAS (cumulative Bayes factor)

• Mappings in both methods are roughly equivalent

Preliminary trait-mapping results

ZCN8

VGT1 ZmRAP2.7

? ?

GIGZ1A?

0 MB 100 MB 150 MB 50 MB

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Association for Days to Anthesis (well-watered) on Chromosome 8

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

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

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

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

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)