# Presentation: 758503

## Mark E. Sorrells
Cornell Department of Plant Breeding & Genetics

- Mark E. Sorrells
- Cornell Department of Plant Breeding & Genetics

_**Genomic Tools for Oat Improvement**_

## 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

## 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

## 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?)

## 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)

## 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

## 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

## 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

## 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

## 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

## Association Breeding for Oat Improvement

- Primary Goals:
- Allele discovery
- Allele validation
- Parental & progeny selection

## 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

## Strategies for Molecular Breeding

- 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

- 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

## Germplasm

- New Populations

- New Synthetics, Lines, Varieties

- Elite Synthetics, Lines, Varieties

- Hybridization

- Selection
- (Intermating)

- Evaluation Trials

- Genotypic &
- Phenotypic data

- Parental
- Selection

- Marker Assisted Selection

*Characterize Allelic Value*
* **& Validate QTL/Marker*
*Allele Associations*

- Application of Association Analysis in a Breeding Program

- Elite germplasm
- feeds back into
- hybridization
- nursery

*Genomic*
*Selection*

-  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

## 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

## 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

## Genomic Selection & Marker Assisted Recurrent Selection Schemes for Maize Inbred Development

- Bernardo & Yu 2008

- Used computer simulation to compare Genomic Selection to Marker Assisted Recurrent Selection
- Varied number of QTL and h2

- Off-season nurseries

- Training Population to develop prediction equations

- Simulations:
- QTL - 20, 40, & 100
- H2 - 0.2, 0.5, 0.8

## 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_
-           0.2   0.4   0.8
-     130   121   118
-     136   132   135
- 100    143   128   130

## 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

## Acknowledgements

- USDA Cooperative State Research, Education and Extension Service

- The Quaker Oat Company for many years of support