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Using Genomic Selection in Barley to Improve Disease Resistance
- 1. Using Genomic Selec.on in Barley to Improve Disease Resistance Kevin P. Smith, Vikas Vikram, Ahmad Sallam, Aaron Lorenz, Jean-‐Luc Jannink, Jeffrey Endleman, Richard Horsley, Shiaoman Chao, and Brian Steffenson
- 2. Genomic Selec.on Training populaGon Line 1 76 1 1 1 Line 2 56 1 1 1 Line 3 45 1 1 1 Line 4 67 0 1 0 Line n 22 1 1 1 Line Yield Mrk 1 Mrk 2 … Mrk p … Model training SelecGon candidates Line A 1 1 1 Line B 1 1 1 Line C 1 1 1 Line D 0 1 0 Line n 1 1 1 Line Yield Mrk 1 Mrk 2 … Mrk p … Parent selecGon Line A 80 1 1 1 Line B 67 1 1 1 Line C 56 1 1 1 Line D 89 0 1 0 Line n 23 1 1 1 Line GEBV Mrk 1 Mrk 2 … Mrk p … Basic framework GEBV = genomic es.mated breeding value PredicGon 1 ˆ p i j i j j GEBV b x = = ∑1 p i j i j j y b x = = ∑
- 3. R/T = i r ∂A Gain per Year Selection Intensity Accuracy Genetic Variance # Breeding Cycles Year Crossing 1 F1 F2 F3 2 F4 F5 Head Rows 3 1st Year Yield 4 2nd Year Yield 5 3rd Year Yield 6 Regional/Industry 7 Regional Industry 8 Variety Release Genomic Selec,on Improves Gain per Time
- 4. Barley Predic.on Data Sets USDA Regional Genotyping Centers Fargo Triticeae Toolbox http:// triticeaetoolbox.org/ SNP Map Ten U.S. Barley Breeding Programs Fargo, ND Raleigh, NC Manha[an, KS Pullman, WA
- 5. Assessing Predic.on Accuracy Training PredicGon Sub-‐sample Single Data Set Dis.nct Training and Predic.on Data Sets Training = Parents Predic.on = Progeny CROSS VALIDATION INTER-‐SET VALIDATION PROGENY VALIDATION RelaGve Accuracy = CorrelaGon (GEBV, Observed)/Sqrt(Heritability)
- 6. Fusarium Head Blight (FHB) Another challenging disease in Barley Major outbreak in Midwest U.S. in 1993 Mycotoxin deoxynivalenol (DON) Sources of resistance are unadapted Quan.ta.vely inherited resistance Many QTL with small effects Challenging to phenotype
- 7. Barley CAP FHB Six-‐row Midwest Data Set 896 six-‐row lines 3,072 SNPs Mean of 4 trials Evaluated over 4 years Busch Agriculture BA U. Minnesota UM North Dakota State ND CAP I CAP II CAP III CAP IV 96 96 96 96 32 32 32 32 96 96 96 96
- 8. Training Panel and Marker Set Size Lorenz et al., 2012 Training Pop = 200; 384 Markers
- 9. Cross and Inter-‐Set ValidaGon Training PopulaGon POP1 POP2 POP1 POP2 POP1 + POP2 POP1 + POP2 ValidaGon PopulaGon POP1 POP2 POP2 POP1 POP1 POP2 RelaGve Accuracy 0.78 0.56 0.38 0.24 0.65 0.68 UM BA ND Lorenz et al., 2012
- 10. UM – ND CollaboraGve Breeding UM ND 480 480 480 21 Parents Random Progeny 100 100 100 UM x UM UM x ND ND x ND
- 11. Progeny ValidaGon Progeny Panel UM x UM ND x ND UM x UM ND x ND UM x UM ND x ND UM x ND Training anel POP1 POP1 POP2 POP2 POP1 + POP2 POP1 + POP2 POP1 + POP2 RelaGve Accuracy 0.58 0.07 0.26 0.48 0.56 0.40 0.35 Cross ValidaGon Accuracy 0.78 0.38 0.24 0.56 0.65 0.68 Vikram et al., in prep.
- 12. UM – ND Breeding Lines UM ND 480 480 480 21 Parents Random Progeny 100 100 100 89 UM Phenotypic SelecGon 89 CAP Training Panel 384 SNP markers DON and Yield 2 Loca.on / 2 Rep FHB and DON
- 13. Gain from SelecGon for DON (Cycle 1) 0 50 100 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 0 20 40 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 Genomic Selec.on Random Selec.on 0 20 40 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 Phenotypic Selec.on
- 14. Vulnerability of Barley to Race TTKSK • Over 2,800 Hordeum accessions evaluated as seedlings & adults • More than 97% were suscep.ble including those carrying Rpg1
- 15. Genetics of Resistance to Race TTKSK • Six diverse resistant Hordeum accessions were subject to genetic analysis • All were found to carry rpg4/ Rpg5 complex, the only major genes known to confer resistance to TTKSK • Further highlights the extreme vulnerability of barley
- 16. Univ. Minnesota North Dakota State (2-‐row) North Dakota State (6-‐row) Washington State Montana State USDA – Idaho Utah State Busch Agriculture 8 Spring Barley Breeding Programs Screened in Kenya for Ug99 CAP I CAP II CAP III CAP IV Screened in 2010 Screened in 2011 Barley CAP Spring Barley Adult Stem Rust (TTKSK) Data Set
- 17. Barley CAP Mapping and Breeding Infrastructure University of Minnesota Breeding Program U. Minnesota UM CAP I CAP II CAP III CAP IV 192 lines 192 lines
- 18. Kenya Adult Plant Screening for UM Breeding Lines 0 20 40 60 80 100 0 10 20 30 40 50 60 70 0 50 100 150 200 S MS MR R InfecGon Type Disease Severity
- 19. Kenya Adult Plant Screening for UM Breeding Lines Inter-‐Set Valida.on Rela.ve Training Predic.on Accuracy I & II III & IV 0.28 III & IV I & II 0.29
- 20. Expand Training PopulaGon and Parents 0 20 40 60 80 100 0 10 20 30 40 50 60 70 CAP III and IV All Programs CAP MN Only 0 200 400 600 0 10 20 30 40 50 60 70 80
- 21. Summary Reasonable rela.ve accuracies (>0.50) possible with: Training panels of 200 individuals 384 SNP markers “Relevant” training popula.ons Good predic.on accuracy seems to translate into gain from selec.on GS takes into account mul.ple traits in addi.on to disease resistance. GS for adult plant stem rust resistance in elite germplasm could complement deployment of major genes.
- 22. Minnesota Agricultural Experiment Sta.on SMALL GRAINS INITIATIVEU.S. Wheat & Barley Scab IniGaGve Project Members / Collaborators / Support American Mal.ng Barley Associa.on University of Minnesota Brian Steffenson, Ruth Dill-Macky Yanhong Dong, Smith Lab Ed Schiefelbein Guillermo Velasquez Karen Beaubien Ahmad Sallam Stephanie Navarra Vikas Vikram Danelle Dykema Chris Kucek Mathilde Chapuis Other Institutions Kay Simmons, USDA Dave Marshall, USDA Shiaoman Chao, USDA; Richard Horsley, NDSU; Jean-Luc Jannink, USDA Jeff Endelman, Univeristy of Wisconsin Aaron Lorenz, University of Nebraska
- 23. Ques.ons
- 24. Genomic SelecGon 2006 2007 2008 2009 Training Popula.on 2009 2010 2011 2012 Fall Crossing 21 parents Winter F1 Summer F2 Fall F3 Winter F4 Summer C1 Ran C1 Sel F1 Fall F2 Winter F3 Crossing Parents Summer C2 Ran C2 Sel F1 Fall F2 Winter F3 Summer C2 Ran C2 Sel 2013 Crossing Parents