breeding services
DESCRIPTION
Breeding services. Xavier Delannay. Agenda. Use cases Users / developers interaction Marker services Breeding planning services Phenotyping site improvement GIS support GRSS MARS implementation at GCP. 14 use cases as first phase users of IBP. Users / Developers Interaction. - PowerPoint PPT PresentationTRANSCRIPT
Breeding services
Xavier Delannay
Agenda
Use cases Users / developers interaction Marker services Breeding planning services Phenotyping site improvement GIS support GRSS MARS implementation at GCP
14 use cases as first phase users of IBP
Beans S. Beebe Africa: Ethiopia, Kenya, Tanzania, MalawiCassava E. Okogbenin AfricaChickpeas P. Gaur Africa: Ethiopia, Kenya
Asia: IndiaCowpeas J. Ehlers Africa: Burkina Faso, Mozambique, SenegalMaize (DT Maize) G. Atlin Africa: Angola, Ethiopia, Kenya, Malawi ,
Mozambique, Tanzania, Uganda, Zambia , Maize (AMDROUT) B. Vivek Asia: China, India. ThailandRice (STRASA) A. Kumar Sub-Saharan Africa
South AsiaRice (Green Super Rice) Z. Li Africa and AsiaRice (RI) MN Ndjiondjop West Africa - Nigeria, Burkina Faso, MaliSorghum J-F Rami Africa: Ethiopia, MaliWheat (RI, India) V. Prabhu IndiaWheat (RI, China) R. Jing ChinaWheat (ACIAR) R. Trethowan Asia: IndiaWheat (rust) S. Dreisigacker Asia: China
Africa: Ethiopia, Kenya
Users / Developers Interaction
User committee set in place at Hyderabad launch meeting Difficulty to interact among scientists widely dispersed across
time zones (from California to Australia) Attempts to set up subcommittees not successful
Everyone very busy Best solution may be ad hoc teams regrouping
developers and interested users Field book tested at TL1 meeting in Madrid Optimas interaction
Critical at this meeting for users to give their inputs to developers
IBP marker services In 2009, a new marker services concept was put in place that
uses established high-throughput genotyping services providers to support the projected rapid growth of genotyping needs Transition from low throughput, low capacity, public SSR
genotyping labs to high throughput, high capacity, commercial SNP genotyping services
6-10X reduction in genotyping costs Identification of breeder-friendly SNP platforms that can meet the
flexible needs of MAB applications Ability to ship leaf samples from around the world (no local DNA
extraction needed) Fast turnover to meet tight timelines for MAS and MABC projects Ability to integrate into the LIMS and informatics tools of the MBP
IBP marker services Chunlin He replaced Humberto Gomez in October 2010 as lead of the
marker services and the GSS GSS consists of genotyping projects funded by the GCP to expose NARS
researchers to molecular breeding and help get them started with MB Needs managed by Theme 4, implementation by Marker Services
Marker Services provides access to genotyping services to interested researchers to help in their MB projects
The new marker services concept based on high-throughput SNP genotyping was implemented in 2010 Decision to focus on a single SNP genotyping provider (KBioscience, UK) SNP conversion to KBioscience platform well underway
GCP funds the conversion of the first set of SNPs Assays available to customers after that (average cost 12 cents/datapoint) Good set of genotypes fingerprinted as part of conversion process, good basis to
build on to understand germplasm relationships and provide foundation for wide MB use
SSR genotyping support still being provided by current labs as needed ICRISAT BecA DNA Landmarks
Crops Partners # SNPs Status
Maize CIMMYT 1250 Available for genotyping
CowpeaUniversity of California Riverside - Jeff Ehlers 1122 Available for genotyping
Chickpeas ICRISAT - Rajeev Varshney 2005 Available for genotyping
Pigeonpeas ICRISAT - Rajeev Varshney 1616 Available for genotyping
Rice IRRI - Michael Thomson et al. 805 Available for genotyping
Cassava IITA - Morag Ferguson, P. Rabinowicz 1740 Available for genotyping
Sorghum EMBRAPA - Jurandir MagalhaesCIRAD - Jean-Francois Rami 1578 Available by June 10, 2011
Common bean 1500 Available by end of 2011
Wheat 1500 Available by end of 2011
Available SNP Markers for Genotypinghttp://ibp.generationcp.org/confluence/display/MBP/Activity+3.1.2
Breeding Planning Services
Breeding schemes available on IBP wiki MAS MABC MARS
Goal to develop macros to allow calculation of costs of different breeding scenarios
Importance of Phenotyping Services
The GCP and the Gates Foundation are funding extensive efforts for the implementation of MB into breeding
Good sets of marker tools are now available for low cost, high quality genotyping
The generation of quality phenotypic data is a critical component of a successful implementation of molecular breeding in developing countries Need to get accurate and precise information on trait-marker linkages for
effective predictive use of markers in breeding (MAS) Precise phenotyping needed to accurately identify genomic regions of
interest for recombination in segregating progenies (MARS) Quality multilocation trials needed to assess GXE effects and help in
assessment of potential usefulness of new QTLs
Local Phenotyping Capacity: An Issue
In many NARS, phenotyping capacities are not sufficiently developed to face the challenges of uniform screening conditions and controlled stress environments Constraints in:
facilities and human capacity documentation and data management
Competition for good land and resources
There is a need to characterize phenotypic sites for: Climate data Soil conditions
There is a need to better integrate multi-location phenotypic data Shared genotypes and protocols, quality of data collection
Strategy for GCP Phenotyping Network
Shift with CI concept from a primary focus on a few centralized sites (mostly CG-managed) to the use of multiple decentralized sites (mostly managed by NARS)
Implementation strategy Complete the characterization of local sites by GIS team Identify sites in need of infrastructure improvements Establish prioritized list of needs for each year of MBP plan Use combination of MBP, TL1 and CI funds to help improve
capacity of key sites ($700K for each of first two years, lower amounts after that)
Dr. Hannibal Muhtar was hired as a consultant to help in the evaluation and the establishment of the infrastructure improvements for the African sites
Summary of phenotyping sites
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Africa (32 sites):Benin 2Burkina Faso 2 1Ethiopia 1 1Ghana 2Kenya 1 1 2 2Malawi 1 2Mali 2 1 5Mozambique 1Niger 1 1Nigeria 2 1 1Senegal 1 1Tanzania 1 1 1Zimbabwe 1
Asia (24 sites):China 2 4India 5 1 3 2 4Indonesia 1Philippines 1Thailand 1Vietnam 1
Americas (9 sites):Brazil 1 2Colombia 4 1USA 1
Total 8 7 8 5 8 10 8 10 8
Summary of improvements funded in first two years of implementation
Project Location Infrastructure improvements funded
Africa:Minjibir, Kano State, Nigeria Irrigation, rainout shelter, weather stationManga, Ghana Irrigation, rainout shelter, weather station
Chickpea Egerton University, Kenya Extension of irrigation facility, weather station, fencingCowpea IIAM station, Chokwe, Mozambique New irrigation pumpGroundnut Naliendele Research Station, Tanzania Complete irrigation system
Badeggi, Nigeria Irrigation, weather station, tensiometersBanfora, Burkina Faso Irrigation, weather station, tensiometersLongorola, Mali Irrigation, weather station, tensiometersSotuba, Mali Irrigation, other site improvementsCinzana, Mali Irrigation, weather station, other site improvements
Sorghum (Al tol) Sadoré (Niamey), Niger Irrigation equipmentChepkoilel, Kenya Irrigation, fencing, greenhouseSega, Kenya Irrigation, fencing
India:Chickpea Regional Agricultural Research Station
(RARS) of ANGRAU, NandyalRainout shelter, upgrading of weather station
Indira Gandhi Agricultural University, Raipur Rainout shelterCentral Rainfed Upland Rice Research Station, Hazaribag Rainout shelter
China:Wheat Four sites Shelter for heating stress
Cassava
Rice
Sorghum (MARS)
Rice
Comparative genomics (maize and sorghum)
GIS Tools (Glenn Hyman)
Improving geographic targeting Planning multi-environment trials Support GxE analysis Support phenotyping Modeling tools for phenotyping Information package for MBP trial sites
Genetic Resources Supply Service (GRSS)
Validation of germplasm reference sets of 19 crops continues; unanticipated delays have been experienced
Genotyping and analyses of data completed for all crops except for cassava Differences observed between the original and validation dataset, further testing ongoing.
Validated reference sets and Microsatellite Kits for sorghum and chickpea are now available from ICRISAT and CIRAD
The reference sets will be used in a pilot program to evaluate demand, protocols for maintenance, sustainability and quality assurance
Validation for reference sets of 8 priority crops (including sorghum, chickpea, maize, wheat, rice, cowpea, groundnut, and common bean) are expected to be completed by July 2011
A complete report for all expected by October 2011 A Singer-based ordering portal for the reference sets has been developed by
Bioversity; other sets will be cataloged and accessible through the portal as they become available
GCP MARS concept
MARS concept demonstrated in large seed companies (maize, soybeans) Large-scale testing needed to identify small QTL effects
MARS has great potential for many developing country programs Lower historical intensity of breeding means that large QTL effects should
still be present (low-hanging fruits) Probably fewer QTLs to recombine than for commercial programs
MARS process implemented as proof of concept for GCP crops Beans, cassava, chickpea, cowpea, rice, sorghum, wheat Optimum implementation will vary from crop to crop
Opportunity to test various options during first implementation phase
MARS implementation specifics
Typical MARS program uses crosses made by breeders in their traditional breeding programs Look for good complementarity in parents Select parents of similar maturities to reduce variability in yield testing Fingerprint each parent to identify sets of polymorphic markers spread on average
every 10-20 cM Develop a population representing the maximum range of genetic variation
Generate a population of 200-300 F2- or F3-derived lines No phenotypic selection during population development, except for traits of critical
importance (MAS can be used if desired to select for those traits) Generate enough seed from each F2 or F3 plant to conduct yield trials, for
instance to F2:4 or F3:5 if two generations needed Phenotyping done with bulked final seed for each progeny In hybrid crops such as maize, use testcrosses for yield evaluation
Sample and preserve DNA from each founding F2 or F3 plant for later genotyping, or take bulk samples from later generations Genotyping can be done at any time prior to phenotyping data collection
Phenotypic evaluation of populations
Each population is then field tested in multiple locations appropriate for evaluation Only 1 or 2 reps needed per location, but use as many locations as possible
Goal is to identify QTLs that are significant across multiple environments (limited GxE interaction)
Very important to have quality phenotypic data (use alpha lattice or other improved design)
Use across-location average for each progeny for QTL analysis Measure as many useful traits as possible to take advantage of the
MARS process Testing for abiotic stresses will require two sets of locations
Irrigated vs. non-irrigated for drought tolerance
Mechanics of recurrent selection
Define sets of complementary progenies for recombination Plant out 8-10 seed of each selected progeny Genotype individual plants and select in each progeny the plants with
the best combination of chosen QTLs to recombine Cross selected plants from complementary progenies to combine their
QTLs Do in two or three stages:
A x B and C x D, then intercross F1s Select progenies with best genotypes and redo the cycle until most
QTLs have been recombined It is important to use several independent sets of plants in parallel in
this process to avoid losing too much variability at unselected loci Software will be available from the IBP to facilitate this process
Parent 1 X Parent 2
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F1
F2
F3
F3:4
F3:5 ( if needed)
Single seed descent
300 F3 progenies
300 progenies
Multilocation phenotyping
1st Recombination cycle A B C D E F G H
F1 F1 F1 F1
F1 F1
F1
F2
F3
2nd Recombination cycle
3rd Recombination cycle
Multilocation phenotyping
F3:4
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10 plants/family (A-H), 4 sets of 8 families/cross
Bi-parental population
QTL detection
Genotyping