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
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
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

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
H , 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. % Advantage of GS over MARS #QTL Heritability 0.2 0.4 0.8 130 121 118
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
#QTL Heritability

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