This document provides an update on the Integrated Breeding Workflow System (IBWS). It summarizes recent enhancements to analytical pipelines, breeding view, decision support tools, and genotyping services. Plans for further improvements are also outlined, including better integration between components, additional analysis functions, and a streamlined user interface. The overall goal is to continue enhancing functionality based on user feedback to better support crop breeding programs.

1. Integrated Breeding Workflow System
Update
GCP General Research Meeting
September 27 – 30, 2013
Lisbon, Portugal

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. Analytical Tools
 Breeding View – GenStat
 Current pipelines
 Single environment field trial analysis
 GxE analysis
 QTL linkage analysis

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. 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. 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. 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. 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. 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. What is planned for MBDT
 Full integration into IBWS
 Enhancements to UI display (e.g. similar flapjack
format)
 Include unmapped markers

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. 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

14. 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

15. 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

16. SNP markers at GCP

17. 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)

18. 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.

19. 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

20. 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

21. Finally
 Sunday 1-5 PM “Introduction to the IBWS”