GRM 2013: Integrated Breeding Workflow System update -- M Sawkins


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GRM 2013: Integrated Breeding Workflow System update -- M Sawkins

  1. 1. Integrated Breeding Workflow System Update GCP General Research Meeting September 27 – 30, 2013 Lisbon, Portugal
  2. 2. IBWS Update  What has happened since the last meeting?  Analytical Pipeline  Breeding View (GenStat and R versions)  Decision support tools  OptiMAS  Molecular Breeding Decision Tool (backcrossing)  GDMS – genotyping data management system  Genotyping support services (GSS)
  3. 3. Analytical Tools  Breeding View – GenStat  Current pipelines  Single environment field trial analysis  GxE analysis  QTL linkage analysis
  4. 4. Breeding View GenStat Pipelines  Breeding View - GenStat Pipelines  Single environment field trial analysis  Analysis for row-column designs including spatial analysis  Run multiple traits simultaneously  Automatic generation of diagnostic plots  Options added to control layout of report  Integration in IBWS
  5. 5. Breeding View GenStat Pipelines  Single trait linkage analysis (QTL)  Run multiple traits simultaneously  Improved output, including graphics (e.g., profile plots)  Additional output file containing QTL results  GxE/multi-site analysis  Includes FW regression, AMMI, GGE biplots and stability coefficients  Run multiple traits simultaneously  Select environments  IBWS integration (input only)
  6. 6. What is planned for Breeding View  Enhancement of existing pipelines  Sequential analysis of a series of environments in SSA pipeline  Implementation of additional pipelines  Field design generation pipeline  Genomic selection pipeline  QTLxE  Update installation program to include Breeding View – R  Same interface, same output files  SSA pipeline already implemented  Initial implementation of GxE pipeline  Enhanced integration in IBWS  Improvements/changes in the reporting of results
  7. 7. OptiMAS  Improvements to OptiMAS algorithms to reduce calculation time and memory consumption  Previous analyses can be reloaded with chosen with previous configuration/preferences  Marker QC step to check for inconsistencies in genotyping  Improvements to the website including online and offline (within software) documentation and tutorial.
  8. 8. What is planned for OptiMAS  Full integration into IBWS  Development of a wizard for automated decision making (selection of crosses).  Computation of diversity score based on the pedigree or markers.  Inclusion of QTL position uncertainty in score computation  Use of allele effects at QTL in order to compute expected gain for different traits and possibility to weight different traits to compute indexes.
  9. 9. Molecular Breeding Decision Tool (MBDT)  Selection of polymorphic markers including those linked to chosen trait and export of list to a text file between parental lines.  Improvements to calculation of % recovery recurrent parent. Export function added to export the list of genotypes.  Be able to view individual chromosomes.  Option added to prioritize recombination (heterozygotes) at a flanking marker over percentage of recovery  Partial integration into the IBWS  Be able to use different marker types (i.e. combination of SSR and SNP markers)
  10. 10. What is planned for MBDT  Full integration into IBWS  Enhancements to UI display (e.g. similar flapjack format)  Include unmapped markers
  11. 11. Genotyping data management system (GDMS)  Select polymorphic markers between two lines (including trait specific markers) and export as genotyping order form  Improvements made in importing genotyping data direct into GDMS  Tools to help in the selection of parental lines  Select set of lines and send to flapjack for visualization, including unmapped markers  Investigate relationships among selected germplasm by calculating similarity matrix direct from GDMS
  12. 12. What is planned for GDMS  Implement the new interface for GDMS  Enhance functionality to help in parental selection  Relatedness; Trait specific markers; visual selection of polymorphic markers  Further integration in the IBWS to improve the uploading of genotyping datasets  Enhance the selection of diagnostic markers and display of results for progeny selection in a MAS experiment  Implement additional QC steps  Ability to edit part of a dataset
  13. 13. Conclusion  Following the first release of the IBWS  Enhancements in functionality of tools have continued and will continue  Better integration (retrieval and saving of data)  Series of user feedback sessions being conducted with breeders  Identify any gaps in current functionality or changes in existing functionality  Refactor the entire IBWS interface to improve the user experience
  14. 14. IBP marker services  The new marker services concept based on high-throughput SNP genotyping was operational in 2011  Decision to focus on a single SNP genotyping provider (KBioscience, UK)  SNP conversion to KBioscience platform with  1,000-2,000 SNPs for each crop of interest  SSR genotyping support still being provided by current labs as needed  BecA  ICRISAT
  15. 15. SNP markers at GCP
  16. 16. GSS Projects at GCP  See poster 8.3 2007 2008 2009 2010 2011 2012 2013 2014 GSS-II (35) GSS-III (16) GSS-Commissioned B (WACCI) (14) SNP Conversion (18) Multi-year course-I (MYC1) (30) Multi-year course-II (MYC2) (36) Forensic (QA/QC) (180)** GSS (open call) *This is an estimated number GSS-I (19) GSS-Commissioned A (NARS&ACCI) (25) Fingerprinting-A (non-TL crops) (8) Fingerprinting-B (TL crops) (11)
  17. 17. Crop #Samples #Data Points Cassava 294 404,100 Chickpeas 520 430,730 Common bean 189 214,595 Cowpea 200 213,249 Groundnut 259 18,855 Maize 144 166,542 Pigeonpea 293 390,288 Rice 528 567,150 Sorghum 646 320,154 Soybean 246 255,254 Wheat 200 356,576 Total 3,337,493 Includes SNP conversion projects, fingerprinting and mapping pops.
  18. 18. Diagnostic markers from within and outside the GCP  Construction of consensus genetic maps for all crops  For use in SNP selection tool  To identify KASPar markers as replacements for existing diagnostic markers  Populating GDMS with marker information  Populating GDMS with fingerprint information  Populating GDMS with QTL/MTA information
  19. 19. Predictive markers outside of GCP  Triticeae-CAP project  One activity to transform existing wheat and barley diagnostic markers to SNP markers (KASPar)  Discussion with Cristobal Uauy (JIC), Susanne Dreisigacker (CIMMYT) and Gina Brown-Guerida (USDA)  Sent the list of predictive markers for wheat  Notified LGCGenomics that GCP should have access to them  Information to be incorporated as part of the IBWS  Barley markers are also available
  20. 20. Finally  Sunday 1-5 PM “Introduction to the IBWS”