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 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

CropsPartners# SNPsStatus MaizeCIMMYT1250Available for genotyping Cowpea University of California Riverside - Jeff Ehlers1122Available for genotyping ChickpeasICRISAT - Rajeev Varshney2005Available for genotyping PigeonpeasICRISAT - Rajeev Varshney1616Available for genotyping RiceIRRI - Michael Thomson et al.805Available for genotyping CassavaIITA - Morag Ferguson, P. Rabinowicz1740Available for genotyping Sorghum EMBRAPA - Jurandir Magalhaes CIRAD - Jean-Francois Rami1578Available by June 10, 2011 Common bean 1500Available by end of 2011 Wheat 1500Available by end of 2011 Available SNP Markers for Genotyping

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

Summary of improvements funded in first two years of implementation

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 cM Develop a population representing the maximum range of genetic variation Generate a population of 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 Population development F1 F2 F3 F3:4 F3:5 ( if needed) Single seed descent 300 F3 progenies 300 progenies Multilocation phenotyping 1 st Recombination cycleABCDEFGH F1 F2 F3 2 nd Recombination cycle 3 rd Recombination cycle Multilocation phenotyping F3:4 Recombinatior Population development 10 plants/family (A-H), 4 sets of 8 families/cross Bi-parental population QTL detection Genotyping