Applications of simulation tools <ul><li>Research  </li></ul><ul><ul><li>With simulation, true population values are known...
Steps to run QuLine Breeding strategies GE system Population Input information about the GxE system and populations QUGENE...
Software available from QuGene website www.uq.edu.au/lcafs/qugene
Steps in running simulation <ul><li>Define Gene-Environment System </li></ul><ul><ul><li>Gene and QTL positions and effect...
Two examples <ul><li>Cross prediction and gene pyramiding </li></ul><ul><ul><li>Simulating large numbers of crosses and st...
Example 1: Cross-prediction and Gene pyramiding Gene-Environment System <ul><li>Only 4 genes but complex additive and epis...
Example 1: Cross-prediction and Gene pyramiding Starting population <ul><li>Alleleic values set up for all lines in starti...
Example 1: Cross-prediction and Gene pyramiding Breeding program definition <ul><li>Describe every step and selection opti...
Example 1: Cross-prediction and Gene pyramiding Run selection strategies
Example 1: Cross-prediction and Gene pyramiding Compare strategies for different haplotypes
Two examples <ul><li>Cross prediction and gene pyramiding </li></ul><ul><ul><li>Simulating large numbers of crosses and st...
Example 2 Combining QTL GenStat QTL analysis: Six yield QTL in eight Env
Getting data into simulation <ul><li>Uses R code from GenStat QTL batch program </li></ul><ul><li>Builds FlapJack text fil...
Example 2: Combining QTL FlapJack input files <ul><li>*.map </li></ul><ul><li>C1P0  1 0 </li></ul><ul><li>C1P7  1 7.4 </li...
Example 2: Combining QTL FlapJack input files <ul><li>*.qtl </li></ul><ul><li>Name Chromosome Position Pos.Min Pos.Max Tra...
Example 2: Combining QTL Import data into FlapJack
Example 2: Combining QTL Flapjack - Chromosome + QTL view
Loading data from Flapjack formats
Loading data from Flapjack formats
Summary of Gene-Environment System - environments
Summary of Gene-Environment System - Trait heritability
Summary of Gene-Environment System - genome view
Summary of Gene-Environment System - flanking markers
Run Qu-Gene engine to build files for simulation
Program view  of Breeding module Load or create new breeding program <ul><li>Load and edit </li></ul><ul><ul><li>existing ...
Build simulation
<ul><li>Build simulations for selection for yield or F2 selection for QTL </li></ul><ul><li>Selection for yield </li></ul>...
Yield of 50 F8 lines derived from 1000 F2s Selection for yield vs F2 selection for 2 QTL
Yield of 50 F8 lines derived from 1000 F2s Selection for yield vs F2 selection for 2 QTL
Yield of 50 F8 lines derived from 1000 F2s Linkage drag in Chromosome 1
Opportunities <ul><li>Substantial number of options to investigate </li></ul><ul><li>On-going applications in the further ...
Simulation applications for QTL <ul><li>Steps in a tactical simulation: </li></ul><ul><ul><li>Import the data from GenStat...
Future <ul><li>Improved functionality </li></ul><ul><ul><li>Polypoids  </li></ul></ul><ul><ul><li>Sex differentiation  </l...
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Quline

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Phan mem trong chon giong

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Quline

  1. 1. Applications of simulation tools <ul><li>Research </li></ul><ul><ul><li>With simulation, true population values are known for the models used </li></ul></ul><ul><li>Strategic </li></ul><ul><ul><li>Comparison of improvement schemes, across wide range of genetic models, and GE systems </li></ul></ul><ul><li>Tactical </li></ul><ul><ul><li>Given known information – simulate outcomes of parental selection, prediction of cross performance, stop/go decisions in MARS, etc. </li></ul></ul>
  2. 2. Steps to run QuLine Breeding strategies GE system Population Input information about the GxE system and populations QUGENE QuLine *. fit *. var *. fre *. ham *. cro *. his *. rog *.pou *. fix Major outputs from QuLine Crosses after selection Genetic advance Genes fixed Gene frequency Selection history Hamming distance Population details Cross details Variance components
  3. 3. Software available from QuGene website www.uq.edu.au/lcafs/qugene
  4. 4. Steps in running simulation <ul><li>Define Gene-Environment System </li></ul><ul><ul><li>Gene and QTL positions and effects </li></ul></ul><ul><ul><li>Starting population alleles </li></ul></ul><ul><ul><li>Run QuGene to generate starting conditions </li></ul></ul><ul><li>Describe breeding strategies </li></ul><ul><ul><li>Define all steps and options in each strategy </li></ul></ul><ul><ul><li>Crossing block, phenotyping, selection </li></ul></ul><ul><li>Run simulations </li></ul><ul><ul><li>On 1 machine or across network </li></ul></ul><ul><ul><li>Compare results </li></ul></ul><ul><ul><ul><li>Effects on phenotype of selected lines </li></ul></ul></ul><ul><ul><ul><li>Composition of haplotypes at end of process </li></ul></ul></ul>
  5. 5. Two examples <ul><li>Cross prediction and gene pyramiding </li></ul><ul><ul><li>Simulating large numbers of crosses and strategies to find the ‘best’ way to create a specific haplotype </li></ul></ul><ul><li>Simulating QTL experiments, MAS and MARS </li></ul><ul><ul><li>Input data from GenStat analysis </li></ul></ul><ul><ul><li>Crossing strategies to pyramid QTL </li></ul></ul><ul><ul><li>MARs strategies to pyramid QTL </li></ul></ul>
  6. 6. Example 1: Cross-prediction and Gene pyramiding Gene-Environment System <ul><li>Only 4 genes but complex additive and epistatic effects </li></ul><ul><li>Can build in XLS or import from: </li></ul><ul><ul><li>GGT </li></ul></ul><ul><ul><li>iMAS </li></ul></ul><ul><ul><li>FlapJack </li></ul></ul><ul><ul><li>Marker CSV </li></ul></ul>
  7. 7. Example 1: Cross-prediction and Gene pyramiding Starting population <ul><li>Alleleic values set up for all lines in starting population </li></ul><ul><li>Build simulations from this </li></ul>
  8. 8. Example 1: Cross-prediction and Gene pyramiding Breeding program definition <ul><li>Describe every step and selection options </li></ul>
  9. 9. Example 1: Cross-prediction and Gene pyramiding Run selection strategies
  10. 10. Example 1: Cross-prediction and Gene pyramiding Compare strategies for different haplotypes
  11. 11. Two examples <ul><li>Cross prediction and gene pyramiding </li></ul><ul><ul><li>Simulating large numbers of crosses and strategies to find the ‘best’ way to create a specific haplotype </li></ul></ul><ul><li>Simulating QTL experiments, MAS and MARS </li></ul><ul><ul><li>Input data from GenStat analysis </li></ul></ul><ul><ul><li>Crossing strategies to pyramid QTL </li></ul></ul><ul><ul><li>MARs strategies to pyramid QTL </li></ul></ul>
  12. 12. Example 2 Combining QTL GenStat QTL analysis: Six yield QTL in eight Env
  13. 13. Getting data into simulation <ul><li>Uses R code from GenStat QTL batch program </li></ul><ul><li>Builds FlapJack text files </li></ul><ul><ul><li>*.map (marker, chromosome, position) </li></ul></ul><ul><ul><li>*.dat (loci file) </li></ul></ul><ul><ul><li>*.qtl (QTL locations and effects) </li></ul></ul><ul><ul><li>*.phe (phen values and fitted phen values) </li></ul></ul><ul><li>Run FlapJack and import the text files to create FlapJack project file </li></ul><ul><li>Run QuGeneUI to import FlapJack project and automatically build a Gene-Environment System </li></ul>
  14. 14. Example 2: Combining QTL FlapJack input files <ul><li>*.map </li></ul><ul><li>C1P0 1 0 </li></ul><ul><li>C1P7 1 7.4 </li></ul><ul><li>C1P16 1 15.88 </li></ul><ul><li>C1P24 1 24.35 </li></ul><ul><li>*.dat </li></ul><ul><li>C1P0 C1P7 C1P16 C1P24 </li></ul><ul><li>1 1/2 1/2 1/2 1/2 </li></ul><ul><li>2 1/2 1/2 1/2 1/2 </li></ul><ul><li>1/2 1/2 1/2 2 </li></ul><ul><li>*.phe </li></ul><ul><li>yld.IS94a yld.SS94a yld.HN96b yld.LN96b yld.LN96a yld.IS92a yld.NS92a yld.SS92a </li></ul><ul><li>1 337.3 447.6004 656.9997 71.0001 145.0003 671.9998 1260.005 493.4 </li></ul><ul><li>2 603.1 331.5 406.9998 140 88 731.5999 1142.995 438.40045 </li></ul><ul><li>3 342.1 363.2 573.9998 108.0003 170.99974 679.7999 1151.996 409.3005 </li></ul>
  15. 15. Example 2: Combining QTL FlapJack input files <ul><li>*.qtl </li></ul><ul><li>Name Chromosome Position Pos.Min Pos.Max Trait Experiment PLFM PRFM Effect seEffect waldstat waldprob… </li></ul><ul><li>C1P134 1 133.5 123.5 143.5 yld IS94a 0 0 50.7771 14.6588 10.9743 4.96251e-14… </li></ul><ul><li>C2P36 2 35.9 25.9 45.9 yld IS94a 0 0 -6.95176 17.1055 4.56213 2.47046e-05… </li></ul><ul><li>C3P47 3 47.35 37.35 57.35 yld IS94a 0 0 40.7695 15.6544 4.61789 2.078e-05… </li></ul><ul><li>C4P137 4 136.6 126.6 146.6 yld IS94a 0 0 -16.1657 4.47519 13.0487 0.000356558… </li></ul><ul><li>C6P125 6 125 115 135 yld IS94a 0 0 8.63913 13.7472 3.67889 0.000370934… </li></ul><ul><li>C10P60 10 60.15 50.15 70.15 yld IS94a 0 0 45.073 14.786 6.68167 3.20496e-08… </li></ul><ul><li>C1P134 1 133.5 123.5 143.5 yld SS94a 0 0 25.6525 15.1615 10.9743 4.96251e-14… </li></ul><ul><li>C2P36 2 35.9 25.9 45.9 yld SS94a 0 0 -14.5456 17.692 4.56213 2.47046e-05… </li></ul>
  16. 16. Example 2: Combining QTL Import data into FlapJack
  17. 17. Example 2: Combining QTL Flapjack - Chromosome + QTL view
  18. 18. Loading data from Flapjack formats
  19. 19. Loading data from Flapjack formats
  20. 20. Summary of Gene-Environment System - environments
  21. 21. Summary of Gene-Environment System - Trait heritability
  22. 22. Summary of Gene-Environment System - genome view
  23. 23. Summary of Gene-Environment System - flanking markers
  24. 24. Run Qu-Gene engine to build files for simulation
  25. 25. Program view of Breeding module Load or create new breeding program <ul><li>Load and edit </li></ul><ul><ul><li>existing simulation files </li></ul></ul><ul><ul><li>Selection steps </li></ul></ul><ul><ul><li>Data tables of structured parents for crosses </li></ul></ul><ul><li>Links </li></ul><ul><ul><li>trait and environment names to breeding strategies </li></ul></ul>automated description of breeding program toolbox to load or create new files
  26. 26. Build simulation
  27. 27. <ul><li>Build simulations for selection for yield or F2 selection for QTL </li></ul><ul><li>Selection for yield </li></ul><ul><ul><li>Produce 1000 F2s </li></ul></ul><ul><ul><li>Self to F5 </li></ul></ul><ul><ul><li>Select best 5% of lines for yield in two env(2 reps x 20 plants per plot) </li></ul></ul><ul><ul><li>Self to F8 </li></ul></ul><ul><li>Selection for yield following F2 screening </li></ul><ul><ul><li>Produce 1000 F2s, self to F5 </li></ul></ul><ul><ul><li>Select 200 lines based on the two largest of 6 QTL </li></ul></ul><ul><ul><li>Self to F5 </li></ul></ul><ul><ul><li>Select best 50 of lines for yield in two environments (2 reps x 20 plants per plot) </li></ul></ul><ul><ul><li>Self to F8 </li></ul></ul>
  28. 28. Yield of 50 F8 lines derived from 1000 F2s Selection for yield vs F2 selection for 2 QTL
  29. 29. Yield of 50 F8 lines derived from 1000 F2s Selection for yield vs F2 selection for 2 QTL
  30. 30. Yield of 50 F8 lines derived from 1000 F2s Linkage drag in Chromosome 1
  31. 31. Opportunities <ul><li>Substantial number of options to investigate </li></ul><ul><li>On-going applications in the further development of statistical methods based on the input of models where we control: </li></ul><ul><ul><li>Environments </li></ul></ul><ul><ul><li>Genes </li></ul></ul><ul><ul><li>Physiological relationships </li></ul></ul><ul><ul><li>APSIM </li></ul></ul><ul><li>Simulation of specific examples from the GCP programs </li></ul><ul><ul><li>Gene/QTL pyramiding </li></ul></ul><ul><ul><ul><li>Wheat, cowpea? </li></ul></ul></ul><ul><ul><li>MARS – integrating methods from Charcosset to choose parents </li></ul></ul><ul><ul><ul><li>Maize, which else? </li></ul></ul></ul>
  32. 32. Simulation applications for QTL <ul><li>Steps in a tactical simulation: </li></ul><ul><ul><li>Import the data from GenStat (Flapjack format) </li></ul></ul><ul><ul><li>Generate flanking markers etc </li></ul></ul><ul><ul><li>Build Gene-Environment Systems and create populations (.XLS) </li></ul></ul><ul><ul><li>Design breeding schemes </li></ul></ul><ul><ul><li>Build simulations </li></ul></ul><ul><ul><li>Interpret </li></ul></ul>
  33. 33. Future <ul><li>Improved functionality </li></ul><ul><ul><li>Polypoids </li></ul></ul><ul><ul><li>Sex differentiation </li></ul></ul><ul><ul><li>Mutation </li></ul></ul><ul><ul><li>Epistatic effects </li></ul></ul><ul><ul><li>Cross generator </li></ul></ul><ul><ul><li>Module development – QuHybrid </li></ul></ul><ul><li>Improved useability </li></ul><ul><ul><li>Ongoing enhancements to interface </li></ul></ul><ul><ul><li>New module development – QuMARS </li></ul></ul><ul><ul><li>Enhanced computational efficiency </li></ul></ul>
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