A Roadmap from the Genomics Revolution to a New Era in Public Plant Breeding
Yunbi XuSusanne Dreisigacker, and Jonathan H. Crouch
Applied Biotechnology CenterInternational Maize and Wheat Improvement Center (CIMMYT)
Mexico
Introduction
Seed DNA-based MAS
Selective Genotyping and Pooled DNA
Analysis
Large-Scale Association Mapping in Wheat
Using Multi-Environment Trial Data
Genetics and Genomics of Drought Tolerance
in Maize
Marker-Assisted Selection in Wheat
Future Prospects
Goff and Salmeron 2004 Scientific American 291(2) 42-49
Maize
CHALLENGES
Yield gap to be filled by modern plant breeding
ExperimentalStationyield
PotentialFarmyield
Theoreticalpotential
ActualFarmyield
Yield gap 0
Yield gap I
Yield gap II
For scientists to conceiveand breed potential varieties
Nontransferable technologyEnvironmental differences
{Biological• Variety• Weeds• Pests• Problem soils• Water• Soil fertilitySocioeconomic• Costs• Credit• Tradition• Knowledge• Input• Instructions
17.1
5
G A P
t/ha
(modified from Chaudhary 2000)
Xu. 2009. Molecular Plant Breeding, CABI Publisher
Biotechnology product development process with projected time: 7-12 years
Discovery Market Introduction
Definition of the trait• Choice of genes• Source of genes
Decision and actions here can have a long-term and late consequences
• Stringent agronomic performance and eff icacy criteria
• Greater than 90* of all events are eliminated
• Based in part on methods used to evaluate conventioanl varieties through traditional breeding
Detailed risk assessment for regulatory review
• Food
• Feed
• Fuel
• Environmental
• Product performance
• Investigate complaints
• Support of academic research into applications
Appropriate product stewardship
Selections of line(s) with appropriate characteristics
ProductConceptProductConcept
GeneDiscovery
GeneDiscovery
Line Selection ProductionProduction MarketMarket Post
MarketPost
MarketTransformation
or MASTransformation
or MASGH & fieldEvaluationGH & fieldEvaluation
VarietyDevelopment
VarietyDevelopment
Avai
labl
e te
chno
logy
and
fu
rthe
r im
prov
emen
t
Steps involved in crop biotechnology product developmenet using transformation or marker -assisted selection (MAS). The whole process from discovery to market introduction takes about 7 to 12 years
Road from Basic Genomics Research to Impacts
Bumpy
Long
Windy
Wrong turns Unexpected blockades
Long
Cost-Effective and High Throughput Genotyping Systems
Wrong turns
Genotype by Environment Interaction
Unexpected blockades
Powerful Bioinformatics and Decision Support Tools
Windy
Genetic Architecture of Complex Traits
Bumpy
Molecular Marker Development and Validation
Bottlenecks in Marker-Assisted Selection
Reducing Costs & Increasing Scale and Efficiency of MAS
• Single-Seed Based DNA Genotyping and MAS System
• Precision and High Throughput Global Phenotyping
• Utilization of Genetic and Breeding Materials
• Selective Genotyping and Phenotyping from Large Base Populations
• Integrated Genetic Diversity Analysis, Genetic Mapping and MAS
• Developing Breeding Strategies for Simultaneous Improvement of Multiple Traits
Xu and Crouch 2008 Crop Science 48:391-407
Seed DNA-based Genotyping
Seed
Seed
Molecular biologyGenetic analysis PCRMolecular markersMicroarray… …
DNA
Seedling
Leaf tissue
Planting
Leaf- and seed-DNA approaches
① Soaking ② Sampling ③ Grinding
⑥ Tracking backand planting
④ DNA extraction⑤ PCR and genotyping
Seed DNA-based Genotyping in Maize
Gao et al 2008. Molecular Breeding 22:477–494
1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 21 22 23 2415
Seed DNA-based genotyping in maizeusing 24 randomly selected SSR markers
From left to right for each markerSarcosyl, sarcosyl+CTAB, CTAB, Leaf DNA
Gao et al 2008. Molecular Breeding 22:477–494
Greenhouse tests: The sampled seed germinated quickly with slightly weak seedlings
CutCut and treatedwith fungicide Control
7 days
14 days
LS SS Control
a
SSControl LSSS
b
LS- large sample size; SS- Small sample sizeControl: no sampling
Seedling establishment for seeds with endosperm sampled for DNA extraction
compared with controls
Gao et al 2008. Molecular Breeding 22:477–494
Conclusion from maize trials
No labeling or tracking is needed
Almost all DNA extraction protocols work well
DNA quality is functionally comparable to leaf DNA
DNA extraction can be high-throughput
30mg endosperm can yield 3-10ug DNA 200-400 agarose-gel PCR-based markersSeveral million chip-based SNP markers
Selective Genotyping and Pooled DAN Analysis
Xu 2009 Molecular Plant Breeding. CABI Publisher
Selective Genotyping and Pooled DNA Analysis
Qualitative traits
Linked
Linked
Unlinked
R plants S plants
A
Quantitative traits
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Selection
DNA Pools
GenotypingLinked
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Why Selective Genotyping and Pooled DNA Analysis
Unlimited number of markers available Two high-dimensionvariables
Large numbers of plants to be genotyped
Cost is still too high
Example: Select two contrasting extremes each with 30 plants from a population with 1000 plants
For selective genotyping 60/1000 = 6%, compared to the entire population genotyping
For Pooled DNA analysis2/1000 = 0.2%, compared to the entire population genotyping2/60 = 3.3% compared to the selective genotyping
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LOD=6.0
LODPower LO
DLO
DcM
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LODPower
QTL effect= 10%Population size =200, Tail size=15Marker density=15 cM
LOD=3.94Power =67%
QTL effect= 10%Population size =500, Tail size=30Marker density=1 cM
LOD=10.37Power =98%
Two Typical Selective Genotyping Strategies
Sun et al (2009) Molecular Breeding
20% 15% 10% 5% 3% 1%153050100
20% 15% 10% 5% 3% 1%153050100
20% 15% 10% 5% 3% 1%153050100
20% 15% 10% 5% 3% 1%153050100
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QTL effect
Tail
size
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QTL effect
Tail
size
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N = 200 N = 500
N = 1000 N = 3000
Selective Genotyping: QTL Effects and Population/Tail Sizes
Xu 2009 Molecular Plant Breeding, CABI Publisher
0
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A. Two interacting QTL with a1=a2=aa= 0.2236
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SIM
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Sun et al. 2009 Mol Breed
Why Can Pooled DNA Analysis Be Used for Genetic Mapping with Illumina GoldenGate Assay ?
POOL 1 POOL 2
5:5 5:5
4:66:4
10:0 0:10
No linkage
Partiallinkage
Completelinkage
Scored asHeterozygotes
Scored asHomozygotes
Inaccuratescoring
Replacement of entire population genotyping
Yes, if the total and tail population sizes are large enough andmarker-density is high.Selective genotyping can be used for effective genetic mapping of QTL with relatively small effects, QTL with epistasis, and linked QTL. Selective genotyping can be used for fine mapping to narrow downthe target region to less than 1cM or even few candidate genes.
Recommendation
Large QTL (15% or higher): Population size = 200+; tail size =20+
Medium QTL (3-10%): Population size =500-1000; tail size=50+
Small QTL (0.2-3%): Population size =3000-5000; tail size=100+
Innovative Uses of Selective Genotyping and Pooled DNA Analysis
“All-in-one plate” - Genetic Mapping of All Target Traits in One Step
Availability of trait-specific genetic and breeding materials:Inbreds/cultivars with extreme phenotypesEternal/fixed segregating populations (e.g., recombinant inbred lines), doubled haploids, near isogenic lines, introgression linesGenetic stocks (e.g. single segment substitution lines) and mutant libraries
Availability of phenotypic data: Multiple environmental trials (MET) Availability of pooled DNA genotyping system
=>>>One 384-well plate could be designed to cover 192 traits/populations=>>>Almost all major gene/QTL controlled agronomic traits for a crop species
Innovative Uses of Selective Genotyping and Pooled DNA Analysis
Materials collected so far
1. 8 RIL populations, each with 60 to 150 lines2. 5 BIL populations3. 3 F2 populations4. 800 Inbreds selected from over 2000 of various sources
What traits are covered:
1. Drought tolerance (multiple sources)2. Disease resistance (5)3. Insect resistance (4)4. Nitrogen response5. Grain quality and nutrition (10)6. Lodging resistance7. Physiological traits (5)8. Agronomical traits (5+)
Representing 2200 extreme entries or over 6000 entries of entire populations
“All-In-One Plate” Project in CIMMYT
Large-Scale Association Mapping In Wheat Using Multi-Environment Trial Data And
Ex-situ Germplasm Collections
The International Wheat Improvement NetworkCIMMYT's improved germplasm is dispatched through nurseries targeted to specific agro-ecological environments to a network of researchers
Data from these trials are returned to CIMMYT, catalogued, analyzed
ESWYT: Elite spring wheat yield trial
30-50 lines distributed each year from 1979 to present to partners in over 40 National Agricultural Research Systems
Full pedigrees and selection histories are known and phenotypic data cover yield, agronomic, pathological and quality data
Wheat Phenome Atlas
ESWYT Phenome Atlas:25 cycles from 1979/1980 to 2004/2005685 lines1445 trials across 400 locationsPhenotypic data for 21 traits:8 agronomic traits (including yield)3 rusts (leaf, stripe, and stem rust)10 other foliar diseasesGenotypic data: 1447 DArT
Expansion to the entire set of SAWYT
Connect phenotype and genotype “data islands” in time and space usingnew biometrical tools, to better understand interactions among genes that influence complex traits
Patterns of genetic variationDetermination of population structure based on phenotypic, pedigree and marker dataEnvironments and GEIEnvironmental classificationLD analyses LD in different subsetsChanges in allele frequencies over time and locationsUnderstanding of linkage blocks and selectionHaplotypes at known genese.g translocations, introgressionsMarker-trait associationsAssociations using diverse modelsFamily-based associationsGenotyping a set of parents
Crossa et al., 2007 Genetics 177:1889-1913Dreisigacker et al., 2009 in preparation
Wheat Phenome Atlas
Many significantly associated markers per trait
● Stem rust resistance: 63 DArT (colocalized with many reported including 1B.1R with major effect)
● Leaf rust resistance: 87 DArT (colocalized with > 50 reported)
● Yellow rust resistance: 122 DArT (colocalized with many reported genes with minor effect)
● Powdery mildew resistance: 61 DArT, no direct selection for resistance to PM has been done at CIMMYT. Several were associated to genes transferring resistance to PM
● Grain yield: 213 DArT (corresponding to 7 QTL already published, QTL correlated with regions observed to be under continuous selection)
Next step: Marker validation and conversion for MAS – e.g. new stem rust QTL
wPt9690c 0.00wPt2573c 1.20wPt0357c 4.50wPt5931c 5.20wPt8833c 5.20wPt7599c 7.20wPt0959c 14.60
P34/M483-77 29.80wPt2991 34.50wPt7662 36.80
P33/M59-148 37.20wPt1089 39.00DuPw217 39.20wPt9532 39.40wPt1922 39.60wPt3130 39.60wPt4283 39.60wPt7150 39.60wPt9990 39.60wPt6994 39.70wPt4520 39.80wPt8239 40.20wPt4720 40.50wPt1547 40.60wPt6282 41.40wPt3116 41.40wPt3304 41.40wPt5188 41.40wPt1852 41.90wPt7777 41.90wPt6988 42.90wPt8015 44.00wPt4706 46.50wPt7954 47.60wPt0259 58.10
P34/M48-158 59.20wPt3376 60.00wPt5256 61.90wPt1241 62.60wPt8814 63.10wPt3605 63.50wPt2786 64.30wPt2175 65.00wPt7745 65.50wPt4858 66.10stm5212 66.10
P46/M48-423 66.90gwm132 69.70wPt3309 70.60wPt5333 70.60wPt2218 74.40wPt9594 75.00
P46/M62-107 75.60P45/M60-265 80.20
wPt1700 84.00P34/M48-84 86.40
wPt3733 87.50P34/M48-331 88.40
gwm518.2 88.50
YR(3), SR, YR
GY(3), GYAA(1), SR
GY
GY(2), SR
GY(2), SR
PM(9), GY
GY(5), GYAA(3), SR
GY(5), GYAA(3), SR
GY(3), SRGY(5), GYAA(1), SR
PM(12), GY
GY(2), SRGY(3), SR
GY(3), GYAA(4), GY
6BS ESWYT20_24
potential QTL for stem rust resistance
1. QTL validation
....GATGCACATGAAACGGGAGCGCCGGGTGGCCGCCGTGAACAAGTTCAGAGAGAAGAGAAAAGAGAGCAGGAAGAGGCAGTTCGTGGGCAGCCGCCACCGCCGGCTGCCGTTGAGAGATAACCTCCCGCCACACACCTAGctatacctagtacctactatttagac....
2. PCR-based marker development from DArT sequences
3. Verification of nearby SSR markers
significant DART
Step 1: sampling global genetic resources to create a core sample based on passport information
Various collections
Data collection
Composite Set (10%, up to 3000)
Step 3. Association mapping approaches genes/alleles tagged for marker-assisted breeding
Anonymous markers
Phenotyping Genotyping
Functional markers
Exploitation of germplasm collections for allele mining
Step 2: genotying the complosite set to select a reference sample for integrated characterisation and evaluation efforts
Genotyping, Sampling
Reference sampleor mini core
Slide reference: J.C. Glazman
Drought phenotyping of three wheat reference samples
Genetics and Genomics of Drought Tolerance in Maize
Multidisciplinary approach to improve drought tolerance in tropical maize
Phenotype GenotypeField evaluation
(segregating populations)
Drought consensus map(QTL, gene, Expression data)
Morphological and physiological data(Heritability)
(Phenotypic correlations)(Genotypic correlations)
Linkage maps
Candidate genesESTs
QTL data
Profiling experiment(RT-PCR/Northern/microarry)
Drought genes(ESTs)
Sample harvest for(RNA extraction)(DNA extraction)
MAS
New elite germplasm
DNA microarraySelected SNPs
Allele sequencingat selected genes
(Association genetics)
Preselection tool to predictdrought tolerance in new germplasm
MARS
Summary of the QTL analysis
● Four crosses / six segregating populations● F2/3, F3/4 and RIL families / hybrids● Tlaltizapan, Zimbabwe, Kenya
-P1xP2, (F3 families) 2 stress and 2 well-watered trails- P1xCM247 (F3 families) 3 stress and 1 ww trials- H16xK6 (F4 families) 8 stress and 1 ww trials- CM444xSCMalwi (F3 families) 10 stress and 3 ww trials- P1xP2 (RILs from C1) 7 stress and 3 ww trials- CM444xSCMalwi 6 (RIls, from C4) 8 stress and 3 ww trialsTotal: 43 water-stressed and 13 well-watered trials
● About 1000 QTL identified through individual analysis● About 600 QTL identified from combined analysis
Jean-Marcel Ribaut et al
From plant phenotype to gene expression
GY ENO0.77 0.42 0. 82
EW0D SW0D
3 QTL 2QTL 3QTL
Droughttolerance
Geneexpression
Sucrose(carbohydrates)
ABA
Proline(Stress response)
-0.64
-0.57
Functional genomics to go
to the genes
Yieldcomponents
Secondarytraits
Physiologicalparameters
-0.51
A very large number of the QTL for carbohydrate regulation map in the QTL-rich regions identified on the consensus map
Metabolites assayed:- ABA and metabolites: ABA-glucose ester, phaseic acid- proline- carbohydrates: glucose, sucrose, starch
Tissues sampled:- leaf at 2 and 4 weeks after irrigation stopped- ear tip and silk at 0 and 7 d after anthesis
5000 tissue samples each year (2005-2006); assayed in duplicate1200 samples in 2007-2008; assayed in duplicate
Significant SNP associations for metabolite traits in 384 tropical inbreds tested under drought (flowering stage)
in 2005-2006 at Mexico TL. Specific for ABA in ear and silk
Specific for CHO in silk
Marker-Assisted Selection in Wheat
Breeding Objectives: e.g. Rust Resistance
Globally effective resistance to rusts:Leaf rust:Occurs worldwide wherever wheat is grown. It is most important where dews are frequent during the jointing through flowering stages and temperatures are mild, 15-25 C.Hotspots in DW: Morocco, Chile, Ethiopia, MexicoStem rust:Wide spread. Race Ug99 is currently spreading across Africa, Asia and most recently into Middle East and is causing major concern and an increase of food riots and civil unrest, notably in West and Central Africa among the worst-hit countries.
Breeding Objectives: e.g. Rust Resistance
Globally effective resistance to rusts:Leaf rust:
Use of major genes effective in hot spotsPyramiding on top of these, molecularly marked major genes (Lr19, Lr47…)Use of minor gene-based resistance, some molecularly marked (Lr34, Lr46)
Stem rust:Use identified sources of resistance Pyramid effective major genes, transfer from BW to DW (Sr25, Sr22, Sr26)Use of minor gene-based resistance, some molecularly marked (Sr2)
MAS
MAS
MAS
MAS
Sampling of leave tissue
MAS Outline
Field selection based on MAS
Fragment analyses
Bo1
Lr34
DNA extraction
PCR
Rusts: Lr19/Sr25, Lr34, Lr35/Sr39, Lr37/Yr17/Sr38, Lr47, Sr2, Sr22, Sr24, Sr26, Sr36, Lr14a, Lr46additional Fungi: Fhb1, Fhb2, 5A-QTL, H, StbViruses: Bdv2 Soil borne diseases: Cre1, Cre3, Cre5, Rlnn, 2.49 Quality: Pina,b, 1B/1R, 1A/R translocation, Gpc-B1, GBSS, Glu-D1, Glu-1Bx, pre-harvest sprouting, PsyPrecocity: Ppd-D1, Ppd-B1, Vrn-A1, Vrn-B1, Vrn-D1, Rht1, Rht2 Other: Bo1, Ph1
MAS for wheatRoutine application of approx. 30 markers: Major genes or major QTL of quantitative traits:
CIMMYT Wheat - MAS: Throughput vs. costs Increasing deployment of MAS in CIMMYT wheat breeding programsMAS for major genes needs now to be combined with whole genome selectionMajor drawback in wheat is the lack of high throughput genotyping platforms
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Marker-Assisted Breeding Platform in Maize
I. Major gene introgression (target genes only)2 -10 markers for each trait
Single trait introgressionMultiple trait introgression
A few markers for hundreds of plantsTaqman genotyping system
II. Marker-assisted backcrossing (target genes plus background)2 -10 markers for each trait 100-200 markers for background selectionA few hundreds of markers for hundreds of plantsIllumina genotyping system
III. Whole genome or genomewide assay500 to several thousands or millions of markers for hundreds or thousands of plants/linesIllumina genotyping system
Information CollectionInformation Integration
Data standardizationDevelopment of generic databasesUse of controlled vocabularies/ontologiesInteroperable query systemRedundant data condensingDatabase integrationTool-based information integration
Information retrieval and miningInformation management systems
Breeding Informatics
Germplasm management, evaluation, and enhancementBreeding population management and improvement
Building up heterotic patternsPrediction of hybrid performance Marker-assisted inbred and synthetic creation
Genetic map constructionMarker-trait association identification and validationMarker-assisted selection methodologies and implementationGenotype by environment interaction analysisIntellectual property right and plant variety protectionBreeding design through simulation and modeling
Decision Support Tools
GenotypeSequencesMarkersMapsGenealogy
PhenotypeYieldQualityAgronomyStress response
EnvironmentWater FertilizerSoilTemperaturePrecipitationGISDay length
Data Tools Output
Gene functional analysis
Genetic diversity
Germplasm evaluation
Germpalsm classification
Variety identification
Genetic mapping
Marker-trait association
Marker-assisted selection
GXE interaction
Environmental classification
Variety stability/adaptability
LIMS and Analytical Tools for Genetic Improvement
BLASTN/X…MapmakerMultiQTLGeneFlowQTL CartographerSAS/JAMPStructureGeneMapperPowerMarkerArlequinBiPlotCMTV……..
Integrated IMS for molecular breeding
ICIS
FUTURE PROSPECTS
RFLP maps with markers every 10cM
PCR-based markers every 1 cM
Whole genome sequence for one or two genotypes
Array-based genotyping using 100K SNPsAll candidate genes + all germplasm collections
SQUENCING THEM ALL (???)
1980s
1990s
2000s
2010s
2020s
Long
Cost-Effective and High Throughput Genotyping Systems
Wrong turns
Genotype by Environment Interaction
Unexpected blockades
Powerful Bioinformatics and Decision Support Tools
Windy
Genetic Architecture of Complex Traits
Bumpy
Molecular Marker Development and Validation
Bottlenecks in Marker-Assisted Selection
Now … … Gene Networks + G-P-E Model
Home
Networks
TrafficWeatherCarDriver
Performance of a Geneat a road network is determined by …
Gene EnhancersDGProgram DirectorPeople around
OfficeLabStreet
Regulators
Acknowledgements
FundingRockefeller FoundationBill and Melinda Gates FoundationEuropean CommunityGeneration Challenge ProgramUnited States Agency for International DevelopmentNational Nature Science Foundation of ChinaState Scholarship Fund of China
SNP markers, genotyping etcMolecular and Functional Diversity in the Maize Genome Ed. Buckler, Mike McMullen, Jim HollandCornell Life Sciences Core Laboratories Center: Peter Schweitzer
Lab and field supportEva Huerta MirandaCarlos Martinez FloresMartha Hernandez Rodríguez Alberto Vergara AlvaMaria Asunción Moreno OrtegaJose Simon Pastrana Marias
Maize Molecular Breeding Groupand colleagues at CIMMYTShibin Gao Stephen MugoZhuanfang Hao Dan MakumbiYanli Lu Jianbing Yan Raman Babu Suketoshi TabaJiankang WangCosmos Magorokosho Bindiganavile S. Vivek
Main Contents· Molecular Plant Breeding Tools: Markers and Maps· Molecular Plant Breeding Tools: Omics and Arrays· Populations in Genetics and Breeding · Plant Genetic Resources: Management, Evaluation and Enhancement · Molecular Dissection of Complex Traits: Theory · Molecular Dissection of Complex Traits: Practice · Marker Assisted Selection: Theory · Marker Assisted Selection: Practice · Genotype by Environment Interaction · Isolation and Functional Analysis of Genes · Gene Transfer and Genetically Modified Plants · Intellectual Property Rights and Plant Variety Protection · Breeding Informatics · Decision Support Tools
HardbackPub Date: November 2009 ISBN: 9781845933920640 pages
Main DescriptionRecent advances in plant genomics and molecular biology have revolutionized our understanding of plant genetics, providing new opportunities for more efficient and controllable plant breeding. Successful techniques require a solid understanding of the underlying molecular biology as well as experience in applied plant breeding. Bridging the gap between developments in biotechnology and its applications in plant improvement, Molecular Plant Breeding provides an integrative overview of issues from basic theories to their applications to crop improvement including molecular marker technology, gene mapping, genetic transformation, quantitative genetics, and breeding methodology.
ReadershipResearchers and students involved in plant breeding and plant biology
18 August 2009 16:00 - 17:00Social Sciences Seminar Room (G210) UWA (Hackett Entrance No. 1, Car Park 3 and 4)
Associate Prof Sven-Erik JacobsenDepartment of Agriculture and EcologyUniversity of Copenhagen, Denmark
Enquiries: (08) 6488 4717 Email: [email protected] Website: ioa.uwa.edu.au
Climate proof cropping systems and the potential for under-utilised species in the Mediterranean environment
