Approaches,resources and toolsfor gene discoveryand breeding in riceMathias Lorieux (IRD/CIAT)GCP GRM, Hyderabad, Septembe...
Plan1. CSSLs of AA genome species to discover genes ofimportance- Oryza sativa x O. glaberrima introgressions- Wild specie...
Using AA genome species of riceto discover genes of importance• Domestication  allelic bottleneck• Wild species still hav...
CSSLs: Mapping of a major resistance geneto Rice stripe necrosis virus from O. glaberrimaGutierrez et al, BMC Plant Biol. ...
Yield components QTLsGutierrez et al, BMC Plant Biol. 2010
Striga resistanceCollab. J. Scholes, Sheffield Boisnard et al, 2010
Performance of CSSLs under droughtB. Manneh(AfricaRice)
A widely used populationTrait Partner NameStriga resistance U. Sheffield J. ScholesDrought tolerance AfricaRice, Embrapa,F...
• Curinga x O. meridionalis CIAT• Curinga x O. barthii AfricaRice• Curinga x O. rufipogon Fedearroz• Curinga x O. glumaepa...
• O. rufipogon, O. meridionalis– Currently evaluated at Fedearroz for agronomical traits (late 2011)– Genotype at higher r...
Universal Core Genetic Map85 - 91% polymorphismO. meridionalis O. rufipogon O. barthii O. glumaepatulaOrjuela et al, TAG 2...
iBridges: Opening the Africanrice diversity to breedersThe O. sativa x O. glaberrima sterility barrier hampers full use of...
iBridges outcomes• 25 pools of BC1F3-4 lines, compatible to O. sativa 20-25% of O. glaberrima genesiBridges will facilit...
First iBridges are coming out!• CIAT• AfricaRice• PhilRice 40% of the lines are fertile vs < 5%Next-generation iBridges• ...
RM190RM19349RM19350RM19353RM19357RM19361RM5199RM19363RM19367RM19369Os05260IntRM_S1_34CG14 38E01RM19391RM19398RM3805RM19414...
Identification of S1 & S1A genesGuyot et al, PLoS One 2011
Other sterility genes Interactions with S1 A complete genetic model for the reproductive barrier
Nested Association Mapping (NAM):Background/Aim• Ed Buckler’s lab: NAM population of maize• Initiatives for sorghum, wheat...
Linkage analysis vs. Association mappingMMMMMMMMMt generationsMMMMMM500 genes Single geneFrom: Yu et al - Power analysis o...
Linkage analysis Genome wide Robust to genetic heterogeneity High resolution Large reference population &allele number...
NAM12200B73× F1sSSD     25 DLB97CML103CML228CML247CML277CML322CML333CML52CML69Hp301Il14HKi11Ki3Ky21M162W...
Average power of NAM (h2 = 0.7)q = 20 QTLComplete markerCPS marker onlyq = 50 QTLPowerFrom: Yu et al - Power analysis of a...
Heat map for Days to Silking QTL effectsBuckler et al 2009
Rice NAMx IR64Founders : O. sativacultivars popular inAfrica & LAAll crossed to IR64SSDF7123.....
Coefficient0.00 0.25 0.50 0.75 1.00Nerica5CG14Nerica4ITA150WAB56-104Nerica3WAB181-1WAB96-3IAC165FARO11MoroberekanLac23IRAT...
Current status & perspectives• indica (IR64) x tropical japonica combinations• CIAT: 2000 lines, 10 combinations, F7 harve...
Conclusions• Natural variation: a valuable source of genes for yieldpotential• Genetic populations are key to tackle impor...
Some questions for the near future• Wild/interspecific case– How many CSSL populations per trait? In which background?– Ho...
CIATM. LorieuxA. Garavito, A. Gutierrez, M. F. Alvarez, L.Moreno, J.D. Arbelaez, L. Melgarejo,J. Orjuela, J. Lozano,M. Bou...
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GRM 2011: Approaches, resources and tools for rice gene discovery and breeding

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GRM 2011: Approaches, resources and tools for rice gene discovery and breeding

  1. 1. Approaches,resources and toolsfor gene discoveryand breeding in riceMathias Lorieux (IRD/CIAT)GCP GRM, Hyderabad, September 2011
  2. 2. Plan1. CSSLs of AA genome species to discover genes ofimportance- Oryza sativa x O. glaberrima introgressions- Wild species2. Breaking barriers: interspecific bridges & sterility genes3. New gene discovery resource: NAM population4. Software
  3. 3. Using AA genome species of riceto discover genes of importance• Domestication  allelic bottleneck• Wild species still have the “lost” alleles• Many traits of agronomical interest• Several examples of successful introgression• Transgressive effects
  4. 4. CSSLs: Mapping of a major resistance geneto Rice stripe necrosis virus from O. glaberrimaGutierrez et al, BMC Plant Biol. 2010Caiapo x MG12
  5. 5. Yield components QTLsGutierrez et al, BMC Plant Biol. 2010
  6. 6. Striga resistanceCollab. J. Scholes, Sheffield Boisnard et al, 2010
  7. 7. Performance of CSSLs under droughtB. Manneh(AfricaRice)
  8. 8. A widely used populationTrait Partner NameStriga resistance U. Sheffield J. ScholesDrought tolerance AfricaRice, Embrapa,FedearrozB. MannehC. GuimaraesM. DiagoOsmotic adjustment IVIC T. GhneimPanicle architecture Cornell, CIAT S. McCouchRoot development Cirad N. AhmadiAgronomic traits CIAT, ICAR,CIMMYT-IndiaC.P. MartinezR. GuptaBacterial blight R IRD - RPB V. VerdierNematode resistance IRD - RPB G. ReversatS. BellafioreBreeding (recurrent) Cirad M. ChâtelBradyrhizobium LSTM-IRD B. DreyfusIron toxicityNitrogen UEU. LouvainCIATP. BertinJ. Rane
  9. 9. • Curinga x O. meridionalis CIAT• Curinga x O. barthii AfricaRice• Curinga x O. rufipogon Fedearroz• Curinga x O. glumaepatula CNPAF• Same genetic background: Acc. Curinga, tropical japonica elite line• Same SSR genetic map (Universal Core Genetic Map)• BAC libraries & RefSeq (Rod Wing, AGI)Cultivated x wild CSSLsCuringa x O. meridionalisBC3DH introgression lines
  10. 10. • O. rufipogon, O. meridionalis– Currently evaluated at Fedearroz for agronomical traits (late 2011)– Genotype at higher resolution with 384 SNP (BeadXpress)• O. glumaepatula– CNPAF to send materials• O. barthii– New pop. from BC3 lines (Lemont x barthii) + SNP genotyping• O. glaberrima– Genotype at higher resolution with 384 SNP (BeadXpress)– Derive advanced materials for genetic studies (sterility)• O. rufipogon, O. meridionalis, O. glaberrima– Increase seed and send to IRRI for distribution (late 2011)On going & plans
  11. 11. Universal Core Genetic Map85 - 91% polymorphismO. meridionalis O. rufipogon O. barthii O. glumaepatulaOrjuela et al, TAG 2010
  12. 12. iBridges: Opening the Africanrice diversity to breedersThe O. sativa x O. glaberrima sterility barrier hampers full use ofinterspecific lines in breeding programs• Although O. sativa x O. glaberrima introgression lines (likeCSSLs) can be fertile, they generally produce+/- sterile hybrids with O. sativa• Sterility hampers full use of African rice for breeding interspecific bridgesLinks25 glaberrima x 3 sativa
  13. 13. iBridges outcomes• 25 pools of BC1F3-4 lines, compatible to O. sativa 20-25% of O. glaberrima genesiBridges will facilitate significantly the use of the geneticdiversity available in African rice• O. sativa x O. glaberrima reproductive barrier Genetic markers around the S1 sterility gene Allow quick screening for O. sativa compatible allele of S1 A map of the O. sativa x O. glaberrima sterility genes Allow development of even more efficient strategies for future selection ofmaterials• A technology for developing additional iBridges between O. sativa and itsother AA-genome (wild) relatives Provide a broad access of the genetic diversity in the AA species complex
  14. 14. First iBridges are coming out!• CIAT• AfricaRice• PhilRice 40% of the lines are fertile vs < 5%Next-generation iBridges• Selection on S1 + other sterility loci• Improved crossing scheme• Also O. glaberrima background compatible with O. sativa
  15. 15. RM190RM19349RM19350RM19353RM19357RM19361RM5199RM19363RM19367RM19369Os05260IntRM_S1_34CG14 38E01RM19391RM19398RM3805RM19414RM19420RM2040,90,80,83,50,90,00,00,80,80,00,8RM193775,10,92,00,90,00,80,80,8• Maternal allelic transmission depends onrecombination around S1• Epistatic interaction between the three loci(BDM model)• Sequencing of the region  2 candidate genesGenetic bases of interspecific sterilityGaravito et al, Genetics 2010
  16. 16. Identification of S1 & S1A genesGuyot et al, PLoS One 2011
  17. 17. Other sterility genes Interactions with S1 A complete genetic model for the reproductive barrier
  18. 18. Nested Association Mapping (NAM):Background/Aim• Ed Buckler’s lab: NAM population of maize• Initiatives for sorghum, wheat…• Combine power of Association Mapping and Fine MappingHigh resolution QTL mapping (gene level)• To develop a similar tool for rice, with special focus ondrought tolerance• IRD-CIAT & WARDA as PIs
  19. 19. Linkage analysis vs. Association mappingMMMMMMMMMt generationsMMMMMM500 genes Single geneFrom: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th MaizeGenetics Conference, Pacific Grove, CA, Mar 9-12, 2006
  20. 20. Linkage analysis Genome wide Robust to genetic heterogeneity High resolution Large reference population &allele numbers Low resolution Small reference population& allele numbers Require large amounts of SNPs /individuals Sensitive to genetic heterogeneityNested Association MappingFrom: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th MaizeGenetics Conference, Pacific Grove, CA, Mar 9-12, 2006Association mapping
  21. 21. NAM12200B73× F1sSSD     25 DLB97CML103CML228CML247CML277CML322CML333CML52CML69Hp301Il14HKi11Ki3Ky21M162WM37WMo18WMS71NC350NC358Oh43Oh7BP39Tx303Tzi8From: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th MaizeGenetics Conference, Pacific Grove, CA, Mar 9-12, 2006
  22. 22. Average power of NAM (h2 = 0.7)q = 20 QTLComplete markerCPS marker onlyq = 50 QTLPowerFrom: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th MaizeGenetics Conference, Pacific Grove, CA, Mar 9-12, 2006
  23. 23. Heat map for Days to Silking QTL effectsBuckler et al 2009
  24. 24. Rice NAMx IR64Founders : O. sativacultivars popular inAfrica & LAAll crossed to IR64SSDF7123.....
  25. 25. Coefficient0.00 0.25 0.50 0.75 1.00Nerica5CG14Nerica4ITA150WAB56-104Nerica3WAB181-1WAB96-3IAC165FARO11MoroberekanLac23IRAT104ChocotoCo39Rock5GambiakaIET3137ITA306BG90-2CisadaneNerica19IR13240Tox3100SuakokoKogoni91-1IR64Foniap2000BW348-1Bouake189DjoukemeWAB638-1ITA212B6144FARO31WAB96-1-1indicajaponicaNAM Parental lines Diversity (SSRs)Variety of traitsAgronomicyield, aroma, N responsivity,grain quality, etcAbiotic stresses toleranceDrought, iron toxicity, etcBiotic stresses:weed competitiveness, AfRGM,Blast resistance, etc• 20 NAMparental lines• 15 controlvarieties• 36 SSRs
  26. 26. Current status & perspectives• indica (IR64) x tropical japonica combinations• CIAT: 2000 lines, 10 combinations, F7 harvestexpected march 2012• AfricaRice: 2000 lines, 10+10 combinations, currentlyF3-F5• Strategy still to be defined for genotyping andphenotyping – and fundingGenotyping by sequencing?GRiSP Phenotyping Network• Tools for data analysisIR64 xWAB638-1F4 Plants
  27. 27. Conclusions• Natural variation: a valuable source of genes for yieldpotential• Genetic populations are key to tackle important genes• Intra-specific populations are easy to produce• Interspecific populations can be more difficult to create• Understanding and overcoming reproductive barriers is keyto develop interspecific populations• Analysis tools
  28. 28. Some questions for the near future• Wild/interspecific case– How many CSSL populations per trait? In which background?– How to optimize the use of iBridges use in MAS/MARS/MAB?– When to use GWA in the case of wild species? (Could help toovercome reproductive barriers)– Can transgressive behavior of interspecific progenies be helpful forhybrid development? Transgression  Heterosis?• Intraspecific case– NAM & MAGIC: which traits to measure? (Large population size)
  29. 29. CIATM. LorieuxA. Garavito, A. Gutierrez, M. F. Alvarez, L.Moreno, J.D. Arbelaez, L. Melgarejo,J. Orjuela, J. Lozano,M. Bouniol,V.H. LozanoC. P. MartinezS. Carabali, O. X. GiraldoJ. TohmeF. Rodriguez, C. Quintero, G. PlataM.C. Duque, J. SilvaEMBRAPA-CNPAFC. Brondani, P. RangelC. GuimaraesWARDAM. N. Ndjiondjop, B. Manneh,G. Djedatin, H. GridleyFedearrozM. Diago, J. D. Arbelaez, J. SierraCornell UtyS. McCouch, J. KimballThanks to:R. Guyot (LGDP-IRD)O. Panaud (LGDP-Perpignan Uty)A. Ghesquière (LGDP-IRD)C. Tranchant (IRD)A. Famoso (Cornell Uty)G. Wilson (Cornell Uty)J. Luis (AGI, Uty of Arizona)R. Wing (AGI, Uty of Arizona)J. Edwards (Uty of Arizona)D. Galbraith (Uty of Arizona)

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