Mark E. Sorrells

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

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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

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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

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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

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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

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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

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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
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Implementation of Genomic Selection

• Discovery dataset -Large number of markers on
  moderate sized population that has been
  phenotyped (Discovery or Training Popn)
• 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

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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
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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

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SummaryAssociation 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

• USDA Cooperative State Research, Education and
  Extension Service

The Quaker Oat Company for many years of support