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Research Program Genetic Gains (RPGG) Review Meeting 2021: Forward Breeding By Dr Himabindu Kudapa

A scalable and responsive genomic data management system from GOBii. The mission of the Genomics Open-source breeding informatics initiative (GOBii) is to build open-source genomic data management and analysis tools to enable breeders to implement genomic and marker-assisted selection as part of their routine breeding programs.

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Research Program Genetic Gains (RPGG) Review Meeting 2021: Forward Breeding By Dr Himabindu Kudapa

  1. 1. RP Genetic Gains meeting Jan 5-6, 2021 Himabindu Kudapa Forward Breeding
  2. 2. GOBII Data Loader and Extractor
  3. 3. Experimental design Capture Collate/ organize Curate AnalyzeArchive Planting and labeling in field DNA Assay 38474037 38668738 38748270 38867490 MSU7_12_17391484_G/TMSU7_12_17391872_A/GMSU7_2_10003379_C/A Plate 01-A01 G:G G:G T:T T:T T:T A:A A:A Plate 01-A02 G:G G:G T:T T:T T:T A:A C:A Plate 01-A03 G:G G:G T:T T:T T:T A:A C:A Plate 01-A04 A:G A:G C:T G:T T:T A:A C:A Plate 01-A05 A:G A:G C:T G:T T:T A:A C:C Plate 01-A06 A:A A:A C:C G:G T:T A:A A:A Plate 01-A07 A:G A:G C:T G:T T:T A:A C:C Plate 01-A08 A:G A:G C:T G:T T:T A:A C:A Plate 01-A09 A:A A:A C:C G:G T:G A:A A:A Plate 01-A10 G:G G:G T:T T:T T:T A:A C:A Plate 01-A11 A:A A:A C:C G:T T:T A:A A:A Plate 01-A12 A:G A:G C:T G:T T:G A:A C:A Plate 01-B01 A:G A:G C:T G:T T:T A:A C:A Plate 01-B02 G:G G:G T:T T:T T:T A:A C:A Plate 01-B03 A:A A:A C:C G:G T:T A:A C:A Plate 01-B04 G:G G:G T:T T:T T:T A:A C:A Plate 01-B05 A:A A:A C:C G:G T:T A:A C:C Plate 01-B06 A:G A:G C:T G:T T:T A:A C:A Plate 01-B07 A:A A:A C:C G:G T:T A:A C:C Plate 01-B08 A:A A:A C:C G:G T:T A:A C:A Plate 01-B09 A:A A:A C:C G:G T:T A:A A:A Order form Genotyping data BMS - GOBii - HTPG Integration • Notify Intertek of upcoming orders • Assign plate and sample IDs • Provide barcodes • Load raw data into GOBII • Store genotypic data • Facilitate data extraction • Genotypic data in ready-to- analyze formats Connect data to visualization and analysis tools HTPG
  4. 4. Flapjack for MABC Goal: Identify the best individual plants in a backcross population to cross or advance to the next generation Load genotypic data for the BC plants, the donor, recurrent parents and the QTL file Run the Marker-assisted backcrossing analysis Load genotypic data for the F1 progeny and their expected parents Run the Pedigree Verification analysis: F1 (known parents) Pedver for F1 Pedigree Verification Goal: Validate crosses GOBii Tools
  5. 5. GOBii GS-Galaxy analysis pipeline
  6. 6. Genomic selection analysis pipeline 4. Visualize and select based on prediction values 2. Get data by uploading from local or browsing file from a server 5. Save Galaxy Workflow 1. GOBII data extract post to Galaxy 3. Run BGLR calculator by specifying parameters and models
  7. 7. GOBii community, collaboration and training Workshop by the EiB with GOBii and HTPG Uganda (November 8th-10th, 2017) We are building a global community of knowledge through workshops, hackathons and cross-training to transform breeding http://cbsugobii05.tc.cornell.edu/wordpress/
  8. 8. Genomic Prediction in Sorghum VNMKV, Parbhani PDKV, Akola • Training population (~423 lines) encompassing parent material for all important traits and varieties/ lines/ hybrids from NARS (both from India and Africa) which include A and B lines, A2, A3 and A4 hybrids etc. customized • Participation of two AICSIP centres - Parbhani, Akola • Multi-location (3 locations) and 3 seasons data: Phenotyping for morphological and agronomical traits along with key traits for tolerance/resistance • Preliminary data analysis of four major traits (including grain yield) has been used to build-up six genomic prediction models and identified three best models for sorghum breeding GS optimization and sorghum breeding trial at Patancheru
  9. 9. In summary…  Genomic resources available  GOBii will serve as the repository for all genotyping data generated from different projects  Modernization of breeding program- use of decision support tools  Forward breeding to enhance selection intensity with reduced genotyping cost  Enhance user interface for better decision making

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