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GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore
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GRM 2013: Genome-Wide Selection Update -- RK Varshney and A Rathore

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  • 1. Rajeev K. Varshney and Abhishek Rathore Email: r.k.varshney@cgiar.org a.rathore@cgiar.org Genome-Wide Selection Update GCP General Research Meeting Session IX 30th September 2013
  • 2. ISMU 1.0 Challenges in SNP Detection • Mostly Command-line based Linux Tools • Multiple steps involved • Difficult pre-processing & cleaning of raw data • Specialized skills required to process the job • Developing genotyping assays (GoldenGate and KASPar) • Very few user-friendly software
  • 3. Solution: ISMU 1.0 Pipeline Features: – Multicore Architecture – One stop shop for SNP detection – Graphical User Interface – Automated Cleaning of Data – Integration of various popular alignment tools – Customized operation of tools for advanced users – Available in Online and Standalone versions – Easy Installation – Works on CentOS, RHEL & Fedora – Visualization of SNP and Alignment (TABLE/FLAPJACK)
  • 4. Raw Reads Reference ISMU V1.0 Assemble& Align Raw Reads Mine SNPs Generate Marker Matrix Visualizein TABLET and FLAPJACK Export in FLAT Files • Assemble & Align Raw Reads • Mine SNPs • Generate Marker Matrix • Automated Visualize in TABLET and FLAPJACK • Developing genotyping assays • Export in FLAT Files ISMU V1.0
  • 5. ISMU 1.0 Standalone Edition Selection of Alignment Tool & SNP Approach
  • 6. ISMU 1.0 Standalone Edition Results
  • 7. Locus Forward Polymorphism Reverse TC00001_1272 CGCTCAAGAGAACCAGTGTTGGAATGGTGGCGGCGATGGCTGTATTTCCA A/T GAAAAGTAAGGGACTAGAAG TC00075_852 T GAGATGTTCCTATCACCAATGCAAATATCAGGGCAAATGCACTAACATA C/T TTGAGTAAATTTCCCATCTT TC00118_13765 AATTAAGTTAGTAATGACTGGACGAAACCAAGAAATAACTACTTACGTGC T/G AAATTATAGAAGGTCTCCTG TC00130_2668 GTTGTTGATCGAAAGAAAATTTAATTTCTTGTTCGACTGATCACCTTGCT G/A GGTTCCAACTATTCTAAAGT TC00191_3430 TTAATGAATTTGCTTCATCGTCCAAGGTTTACCATTTAGGTGGGTAGAGC T/C ACAGAAATTAAGTATCTGGT TC00212_866 CCCATGTCAATCATCCCAATTTTCTTGCATAAATTATCCTTAAATGGATA G/T CTTTACGTATGATGCTGATC TC00295_2234 AGCCAGTGGAAGCTCCACCAGCAGCAGTAGCAGAAGTTCCAATTGAGACT C/T CTGAAGCTTAGACCAATGGA TC00329_2112 GAGGCGTGAAAAGAAAAAGGCAAAGGAGGAGAGGGAGAAGCAAATAAGGG A/C TGCTGAGGAAAGACTACTGG TC00336_3122 CTGAAATGGAGTGTTTTTATACAAGTTGTAAATAGTGATGTTTTGTACAT C/T TTTCTGGAAGATGATTCATG [HEADING] Customer_Name Company_Name Email_Address Platform_Type GGGT Format_Type Gene; Region; Sequence; Identity; ExistingDesign; or Score [select one] Design_iteration prelim Species Number_of_Assays [DATA] Locus_name,Target_Type,Sequence,Chromosome,Coordinate,Genome_Build_Version,Source,Source_Version,Sequence_Orienta TC00001_1272,SNP,TACTTCATCCCGCTCAAGAGAACCAGTGTTGGAATGGTGGCGGCGATGGCTGTATTTCCA[A/T]GAAAAGTAAGGGACTAGAAGGGCAGAGTGGA 72,0,0,0,Forward,Plus TC00075_852,SNP,TTGTCGACATTGAGATGTTCCTATCACCAATGCAAATATCAGGGCAAATGCACTAACATA[C/T]TTGAGTAAATTTCCCATCTTCATTTGCACAAA ,0,0,0,Forward,Plus TC00118_13765,SNP,ATCTAAAAATAATTAAGTTAGTAATGACTGGACGAAACCAAGAAATAACTACTTACGTGC[T/G]AAATTATAGAAGGTCTCCTGTAAGATCCAA 3765,0,0,0,Forward,Plus TC00130_2668,SNP,TGCGGTCATTGTTGTTGATCGAAAGAAAATTTAATTTCTTGTTCGACTGATCACCTTGCT[G/A]GGTTCCAACTATTCTAAAGTAATACAGGCAT 68,0,0,0,Forward,Plus KASPar ILLUMINA
  • 8.  MABC, MARS and GS approaches seem to most promising for crop improvement  Need to have genomic resources and cost- effective genotyping platforms  Breeders-friendly pipelines and decision support tools required for prediction of phenotype Novel breeding approaches for developing countries MBDT MBDT OptiMAS GS ?
  • 9. Breeding Cycle Crossing Field evaluation Line Selection y ir R A t σ = genetic gain over time years per cycle selection intensity selection accuracy genetic variance NEW cheaper to genotype = larger populations for same $$ make selections in ‘off target’ years maintain favorable rare alleles Select years earlier on single plant basis Inbreeding Multi-location, Multi-year testing Seed Increase Based on discussions with several colleagues e.g. Jesse Poland, J-L Jannink, Gary Atlin
  • 10. GS-Models • Usually involves relatively high number of markers • To meet the challenges, statistical methods that can handle high-dimensional data have been developed • However, their respective properties are still not fully understood, • Causing considerable uncertainty about the choice of models for genomic prediction • Factors affecting GS are also not very clear
  • 11. GS ISMU V2 Raw Reads Reference Assemble& Align Raw Reads Mine SNPs Generate Marker Matrix Visualizein TABLET and FLAPJACK Export in FLAT Files GDMS Genotypic Matrix & QTLs Lines selected for further crossing in GS External Genotyping Platforms Called SNPs ISMU V2.0
  • 12. GS-Models • To meet the challenges, statistical methods that can handle high-dimensional data have been developed • However, their respective properties are still not fully understood • Causing considerable uncertainty about the choice of models for genomic prediction • Factors affecting GS are also not very clear
  • 13. Factors Affecting GS-Models • Marker density, genome size and structure • Size of the training population • Historical effective population size • Trait heritability • Relationship between training population & selection candidates • Number of genes and distribution of their effects • Method used for the estimation of marker effects • GxE
  • 14. Validation Studies • Fit available models • Cross Validation • Prepare a matrix of validation scores • Compare over the multiple environments • Select Final model Training set Testing set Cross Validation K(=5) - fold cross-validation
  • 15. ISMU 2.0 Pipeline Analysis Capabilities to ISMU 1.0 • GUI for Genomic Selection • Multicore Support • R and Fortran Libraries for GS • Project Mode Development • IDE Supports • Multiple Method & Traits at once • Platform Support – Windows x64 and x32 – CentOS x64 and Ubuntu x64 – MAC (Under Testing…) In collaboration with J L Jannink, John Hickey and Aaron Lorenz
  • 16. • Data Diagnostics – Graphical Summary – Tabular Summary • Subset Data – Missing % – MAF – PIC • Genomic Selection – RR-BLUP – Kinship Gauss – Bayesian LASSO – BayesB and BayesCπ – Random Forest Regression (RFR) • HTML & PDF Output ISMU 2.0 Pipeline Analysis Capabilities to ISMU 2.0
  • 17. ISMU 2.0
  • 18. ISMU 2.0
  • 19. Browse Data
  • 20. Data in ISMU2.0
  • 21. Calculation of Marker Summary
  • 22. Summary Plots
  • 23. Various Statistics
  • 24. Export to MS-Excel (Windows)
  • 25. GS Methods
  • 26. GS Methods
  • 27. GS Results
  • 28. GS Results
  • 29. Export to PDF
  • 30. Export to High Quality Graphics 300DPI
  • 31. Future Plans • Customized Parameters for GS Scripts • Integrating more Algorithms • Implementation of Cross Validation • Linking with IBWS • Data Import/Export Module • Online Version of ISMU 2.0 • Linking with Agricultural Genomics Network • Making available on more OS • Average GEBVs • Multi-trait GS • Capacity building in NARS Partners – 4th International Workshop on Next Generation Genomics and Integrated Breeding for Crop Improvement, Feb 19th -21st 2014
  • 32. Acknowledgements Many Friends & Collaborators
  • 33. Thanks…
  • 34. Thanks…

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