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2015. Jason Wallace. Applying high throughput genomics to crops for the developing world
- 1. Applying High-Throughput Genomics toCrops for the Developing World Jason Wallace Cornell University
- 2. 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
- 3. Major constraints onfood 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 Gallery2Kearney 2010, Phil Trans Roy Soc B 365 Increasing population 4 Population(billions)1 6 8 ~9 billion by 2050 10 12 2 2010 2030 2050 Today
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- 5. Cost/megabase $1 $0.1 $10 $100 $1K $10K Year 2000 2005 20102015 The golden age of crop genetics • Modern sequencing is opening the floodgates to genetic analysis 0 10 20 30 40 50 60 Genomessequenced Total plant genomes sequenced2 Moore’s Law Cost of sequencing1 Sequencing trends over time 2 Michael & Jackson 2013, The Plant Genome 61 Wetterstrand KA. DNA Sequencing Costs, available at: www.genome.gov
- 6. Case studies outline BarnyardMillet Diversity Analysis Pearl Millet Genetic Map Creation Maize Trait Mapping Shramajeevi Agri Films
- 7. Case studies outline BarnyardMillet Diversity Analysis Pearl Millet Genetic Map Creation Maize Trait Mapping Shramajeevi Agri Films
- 8. Case Study 1: Barnyardmillet 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
- 9. Genotyping-by-sequencing GBS • Createdfor high-throughput, semi-automated genotyping Sequencing adaptor Barcode Sticky ends Genomic DNA Images: Qiagen, Illumina, Elshire et al 2011, PLoS Restriction digest SequenceLigate 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
- 10. Cleaning up thedata • Have ~20,000 SNPs after basic filtering • Problem: Both barnyard millet species are hexaploid -> false SNPs due to paralogs Minor Allele Frequency Relativeabundance Minor Allele Frequency Relativeabundance 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
- 11. Phylogenetics • Phylogeny splitsthe 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)
- 12. Outline Barnyard Millet Diversity Analysis PearlMillet Genetic Map Creation Maize Trait Mapping Shramajeevi Agri Films
- 13. Genetic Maps forPearl 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
- 14. Mapping Populations • 3biparental 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
- 15. Summary statistics Comparison ofGenotyping Depths #genotypes(logscale) Call depth (= # reads) 100 102 104 106 108 SNP counts 0 20k 40k 60k 48k 75k 76k80k Fewer SNPs = less diversity Tifton Patancheru Sadore Best read depth
- 16. Making individual maps 1.Call SNPs SNPs
- 17. 1. Call SNPs 2.Group via hierarchical clustering Making individual maps
- 18. 1. Call SNPs 2.Group via hierarchical clustering 3. Merge linkage groups Making individual maps
- 19. 1. Call SNPs 2.Group via hierarchical clustering 3. Merge linkage groups 4. Order markers Making individual maps
- 20. 1. Call SNPs 2.Group via hierarchical clustering 3. Merge linkage groups 4. Order markers 5. Cleanup Making individual maps
- 21. Merge maps forfinal assembly • 4824 contigs assembled into 1.68 GB reference • 92.8% of sequence data • 60% have putative orientations • Not perfect, but pretty good
- 22. Outline Barnyard Millet Diversity Analysis PearlMillet Genetic Map Creation Maize Trait Mapping Shramajeevi Agri Films
- 23. Case Study 3: TraitMappingintheCIMMYTWEMA 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 PC1PC2 PC3
- 24. • Two approachesto mapping traits in WEMA Trait mapping Env 3 Env 4Env 2Env 1 Unified Posterior Probabilities Bayesian GWASTraditional Joint GWAS merge
- 25. Both methods getsimilar results Traditional GWAS (-log10 p-value) Bayesian GWAS (cumulative Bayes factor) • Mappings in both methods are roughly equivalent
- 26. Preliminary trait-mapping results ZCN8 VGT1 ZmRAP2.7 ? ? GIGZ1A? 0MB 100 MB 150 MB50 MB ? -log10p-value Association for Days to Anthesis (well-watered) on Chromosome 8
- 27. 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
- 28. Future Need 1: High-throughputphenotyping 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
- 29. Future Need 2: Datainfrastructure • 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
- 30. Make crosses Phenotype yi =m + Smzijujdj + ei (Re)train model Predict via modelGenotype Standard breeding cycle Selection cycle (faster, less expensive) Training cycle (slower, expensive) Future Need 3: Faster breeding methods Genomic Selection scheme
- 31. 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)