Genomic Tools for Oat Improvement

1. Genomic Tools for Oat Improvement Mark E. Sorrells Cornell Department of Plant Breeding & Genetics

2. Presentation Overview • Background (Other crops already presented) • What are Genomic Tools and how are they used • Funding opportunities for Oat Improvement • Genomic selection for oat improvement

3. Background • For crops with adequate research support, genomic tools have evolved with technological innovation over time. • The availability of genomic tools is affected by: • Public and private funding, • Complexity of the genome, • Importance of the crop domestically and internationally, and • Expertise and research focus of dedicated researchers • Specialty (minor, orphan) crops are less competitive for public funding because of: • A lack of genomic tools • Limited fundamental knowledge about the biology of the species • Difficulty in transferring knowledge to and from model species

4. Useful Resources for Oat Genomics Research • Highly collaborative and open community of researchers • Abundant, inexpensive molecular markers (always need more) • Comparative genome maps (low resolution, old technology) • High density molecular marker & QTL maps (Need more) • Large EST collection (Currently only 7,632 ESTs in GenBank) • BAC libraries (1 or 2?) • Physical map of genome (none) • Full length cDNAs (none?)

5. Useful Resources for Oat Genomics Research (cont.) • High quality phenotypic data collected in target environments (USDA Uniform Nurseries) • Rich collection of germplasm (Oat has 21,292 Accessions) • Microarray development (none) • Transformation system (available) • Doubled haploid system (Maize pollinator or anther culture?) • Online curated database (GrainGenes)

6. Research Activities Benefiting from Molecular Markers • Knowledge of genome structure & function • Genomic relationships of primary germplasm resources • Location of important genes affecting traits of interest • Marker assisted breeding • QTL mapping studies • Physical map construction • Facilitate genome sequencing • Gene cloning • Fingerprinting

7. Funding Strategies for Developing Oat Genomic Tools (U.S.) • Pool available public resources (International DArT consortium) • Limited to community resources not locked up by institutions • Most public researchers have very little flexibility with funds • Does not require justification to an agency • Benefits everyone • Lobby legislators to provide more opportunities for funding oat research • Historically, legislators are reluctant to support projects requiring new funding • Limited to states with powerful legislators in key positions • Often requires a crisis to generate interest • Difficult to convince legislators to invest in a relatively minor crop • Can be long term • Develop a USDA Coordinated Agriculture Project (CAP) • Extremely competitive • Only funds one crop per year • 4 to 5 year funding cycle • Builds strong collaboration within the community of researchers • Tight integration with all stakeholders

8. Funding Strategies for Developing Oat Genomic Tools (cont.) • Identify fundamental research topics that might interest NSF • Challenging for a crop with limited genomic resources • Benefits few researchers if funded • Applied research topics are not competitive • Likely to generate novel and sometimes useful fundamental information • Could open new areas for research • Oat researchers, buyers and processors could establish a public/private research consortium • Challenging to build a united effort with common goals • Intellectual property issues often complicate research activities and slow progress • Benefits to industry are long term and diffuse • Can provide a stable, longer term funding resource • Can benefit the entire oat community • May help stabilize oat production • Likely to generate novel, high value, germplasm and varieties

9. How can we use genomic tools? • Germplasm resources • Identify novel germplasm • Improve sampling for phenotyping • Develop core collections of various types • Gene & marker discovery • Reduce mapping costs • Enhance resolution • Characterize the value of alleles for important traits • Molecular Breeding • Marker assisted breeding • Genomic selection • Comparative mapping for transfer of information from other species • Cloning genes producing novel phenotypes in oat

10. Association Breeding for Oat Improvement Breeding Progress depends on: • Genetic variation for important traits • Development of genotypes with new or improved attributes due to superior combinations of alleles at multiple loci • Accurate selection of rare genotypes that possess the new improved characteristics

11. Association Breeding for Oat Improvement Primary Goals: • Allele discovery • Allele validation • Parental & progeny selection

12. Association Analysis as a Breeding Strategy Issues: • Breeding programs are dynamic, complex genetic entities that require frequent evaluation of marker / phenotype relationships. • Accurate detection and estimation of QTL effects required • Pre-existing marker alleles may be linked to undesirable QTL alleles • Population structure can cause a high frequency of false positive associations between markers and QTL • Linkage disequilibrium is unknown and highly variable among populations

13. Strategies for Molecular Breeding • Marker Assisted Selection • Only significant markers are used for selection, usually qualitative traits • Genomic Selection (Meuwissen, Hayes & Goddard 2001) • Requires genome-wide markers that are used to estimate a breeding value for each individual • Marker/QTL effects are estimated and updated only after a generation is phenotyped • Association Breeding (Breseghello & Sorrells 2006) • Uses conventional hybridization/MAS/Testing for significant markers but allows for updating breeding values for alleles • Phenotyping and association analysis are used as often as necessary for allele discovery and validation

14. Application of Association Analysis in a Breeding Program Germplasm Parental Selection Hybridization Genomic Selection Elite germplasm feeds back into hybridization nursery New Populations Marker Assisted Selection Selection (Intermating) Characterize Allelic Value & Validate QTL/Marker Allele Associations New Synthetics, Lines, Varieties Evaluation Trials Elite Synthetics, Lines, Varieties Genotypic & Phenotypic data • MAS identifies desired segregates up front so selection pressure can be increased for other traits • Association breeding facilitates allele discovery and evaluation • Genomic selection reduces cycle time by reducing frequency of phenotyping

15. Genomic Selection Methodology • Genome-wide markers are used to explain all or nearly all of the genetic variance of the trait • One or more markers are assumed to be in LD with each QTL affecting the trait • A genomic estimated breeding value for each individual is obtained by summing the effects for that genotype • Genetic relationships and population structure are taken into account by the prediction equation • Multiple generations of selection can be imposed without phenotyping Goddard & Hayes 2007

16. Implementation of Genomic Selection • Discovery dataset -Large number of markers on moderate sized population that has been phenotyped (Discovery or Training Pop’n) • Derive prediction equations for predicting breeding values using random regression BLUP or Bayesian analysis. • Validate prediction equation using independent population and all or selected markers to reduce bias in estimates (Validation population) • A selection population is genotyped (no phenotyping) and the prediction equation is used to calculate genomic breeding values (Multiple generations of recurrent selection) • Update prediction equation periodically with phenotyping

17. Genomic Selection & Marker Assisted Recurrent Selection Schemes for Maize Inbred Development Bernardo & Yu 2008 Simulations: QTL - 20, 40, & 100 H2 - 0.2, 0.5, 0.8 Training Population to develop prediction equations Used computer simulation to compare Genomic Selection to Marker Assisted Recurrent Selection Varied number of QTL and h2 Off-season nurseries

18. Genomic Selection & Marker Assisted Recurrent Selection Schemes for Maize Inbred Development Bernardo & Yu 2008 Results of simulations: Response to genomic selection was 18-43% higher than MARS across different population sizes, numbers of QTL and heritabilities. Advantage of GS over MARS was greatest for low h2 and many QTL. % Advantage of GS over MARS #QTLHeritability 0.2 0.4 0.8 130 121 118 136 132 135 100 143 128 130

19. Summary:Association Breeding and Genomic Selection • Allelic values of previously identified alleles can be dynamically updated based on advanced trial data as desired • New alleles can be identified and characterized to determine their value • Predicted breeding values will improve with more markers; however, the oat DArT markers provide an excellent start and supplemental markers can focus on specific QTL regions and candidate genes • Most important advantages are reductions in the length of the selection cycle and phenotyping cost

20. USDA Cooperative State Research, Education and Extension Service Acknowledgements The Quaker Oat Company for many years of support