005 gene discovery and its application in rice, mathias lorieux


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005 gene discovery and its application in rice, mathias lorieux

  1. 1. Gene discovery and its applications in rice Mathias Lorieux (IRD/CIAT) Rice 2010 Conference September 2010
  2. 2. Plan 1. Using O. sativa related species to discover genes of g p g importance - Oryza sativa x O. glaberrima introgressions - Wild species 2. Sterility genes and interspecific bridges 3. Gene discovery resources: mutant libraries and NAM y populations; software 4. Applying gene discovery to selection: RHBV 5. Planned applications of new genomic tools
  3. 3. 1. Using O. sativa related species to discover genes of importance • Domestication allelic b bottleneck • Wild species still have the “lost” alleles • Many traits of agronomical interest • Several examples of successful introgression • Transgressive effects g
  4. 4. The A genome species of rice g p O. sativa japonica O. glaberrima O. sativa indica O. O barthii O. O rufipogon O. glumaepatula O. longistaminata O. O meridionalis
  5. 5. The triple domestication of rice p japonica j O. glaberrima indica i
  6. 6. Chromosome Segment Substitution Lines • CSSLs are specially useful for assessment of wild alleles p y – bypass sterility barriers – allow easier wild/cultivated phenotypic comparison • Q i k & easy l li i of genes/QTLs for traits Quick localization f /QTL f i of interest • Introduction in breeding programs gp g • Fixed lines • Derive NILs
  7. 7. Caiapo (japonica) x MG12 (IRGC103544) 312 lines scanned with 125 well distributed SSRs 59 BC3DH lines cover the O. glaberrima genome Residual background 3422 BC4F2 lines CSSLs C.P. Martinez
  8. 8. Mapping of a major resistance gene to Rice stripe necrosis virus Gutierrez et al, BMC Plant Biol. 2010
  9. 9. Yield components Gutierrez et al, BMC Plant Biol. 2010
  10. 10. Striga resistance Collab. J. Scholes, Sheffield Boisnard et al, 2010
  11. 11. A widely used population Trait Partner Name Striga resistance U. Sheffield J. Scholes Drought t l D ht tolerance AfricaRice, Embrapa, B M Af i Ri E b p B. Mannehh Fedearroz C. Guimaraes M. Diago Osmotic adjustment IVIC T. Ghneim Panicle architecture Cornell, CIAT S. McCouch Root development Cirad N. Ahmadi Agronomic traits A i i CIAT, ICAR, CIAT ICAR C.P. M i C P Martinez CIMMYT-India R. Gupta Bacterial blight R IRD - RPB V. Verdier Nematode resistance IRD - RPB G. G Reversat S. Bellafiore Breeding (recurrent) Cirad M. Châtel Bradyrhizobium y z LSTM-IRD B. Dreyfus y Iron toxicity U. Louvain P. Bertin Nitrogen UE CIAT J. Rane
  12. 12. IR64 (indica) x TOG5681 BC3F3 and BC2F4 population. Genotyping of 363 lines with 143 SSRs selected from the Core Map. 61 lines covering 95% O. glaberrima genome. Two gaps on Chr. 4 and 10
  13. 13. Performance of ILs under drought B. Manneh (AfricaRice)
  14. 14. Cultivated x wild CSSLs • Curinga x O. meridionalis acc. W2112/OR44 Laura Moreno – CIAT • Curinga x O. barthii acc. IRGC101937 Mamadou Cissoko – AfricaRice • Curinga x O. rufipogon acc. IRGC105491 J. David Arbelaez – Fedearroz • Curinga x O. glumaepatula acc. GEN1233 Priscila Rangel – CNPAF Capacity builing at Cornell Uty (S. McCouch) • Same genetic background: Acc. Curinga, tropical japonica elite line • Same SSR genetic map (Universal Core Genetic Map) • BC1F1s genetic map; selection of target chr. Segments Fedearroz • BC2F1s 600 plants / pop. produced; foreground check; background check • BC3F1 BC3F1s f foreground check d h k • BC3DH/F3 & BC4F1s • BC4F2/3 •BAC libraries & RefSeq (Rod Wing, AGI) Curinga x O. meridionalis BC3DH introgression lines
  15. 15. Tool: Universal Core Genetic Map O. meridionalis O. rufipogon O. barthii O. glumaepatula 85 - 91% polymorphism Orjuela et al, TAG 2010
  16. 16. 2. Genetic bases of the interspecific sterility RM190 0,9 09 RM19349 0,8 RM19350 0,8 RM19353 3,5 RM19357 0,9 0,0 RM19361 0,0 RM5199 0,8 RM19363 0,8 RM19367 0,0 RM19369 0,8 Os05260Int RM19377 5,1 • Maternal allelic transmission depends on 0,9 RM_S1_34 CG14 38E01 recombination around S1 2,0 RM19391 • Epistatic interaction between the three loci (BDM model) 0,9 0,0 RM19398 0,8 RM3805 0,8 RM19414 • Sequencing of the region 2 candidate genes RM19420 0,8 08 RM204 Garavito et al, Genetics 2010
  17. 17. Duplications at the S1A locus
  18. 18. Links Application: Opening the African rice diversity The O. sativa x O. glaberrima sterility barrier hampers full use of interspecific lines in breeding programs • Although O. sativa x O. glaberrima introgression lines (like CSSLs) can be fertile, they generally produce fertile +/- sterile hybrids with O. sativa • Sterility hampers full use of African rice for breeding interspecific bridges
  19. 19. iBridges: specifications • Lines with significant content of donor (O. glaberrima) genome • iBridges x O. sativa F1 hybrids are fertile (sativa-homozygous for S1) direct use in breeding schemes (either MAS or classical; MARS) • From many donor accessions broad access to the diversity available in donor/wild species for plant breeding • A Generation Challenge Program competitive grant (starting July 2007) g g p g ( gJ y ) A. Ghesquière & M. Lorieux (IRD-LGDP/CIAT), D. Galbraith (AGI - Tucson), J. Tohme & C. P. Martinez (CIAT), M-N Ndjiondjop (AfricaRice) + selected NARs and Uties from Africa, Asia and South America
  20. 20. iBridges development scheme 25-30 accessions 3 O. sativa accessions X of O. glaberrima F1 Hybrids yb ds Backcross • SAM for S1s allele (5%) • Selection for fertility (50%) BILs (BC1F4) • SSR – SNP genotyping • Evaluation for traits of interest Selection for S1s leads to significant increase of plant fertility
  21. 21. What the iBridges will offer • 25 pools of fertile BC1F3-4 lines, compatible to O. sativa 40% of the lines are fertile vs < 5%! • A DNA microarray capable of revealing O. sativa x O. glaberrima polymorphisms (high throughput, high resolution genome scanning) • G ti markers around the S1 sterility gene Genetic k d th t ilit allow to screen quickly interspecific lines for the presence of O. sativa compatible allele of S1 • A well-described technology for developing additional iBridges between well described O. sativa and its other AA-genome (wild) relatives, to provide a broad access of the genetic diversity in the AA species complex • A physical map of the O sativa x O glaberrima sterility “genes” O. O. genes allow to develop even more efficient strategies for future selection of materials The approach could improve significantly the access to and the use of the genetic diversity available in African rice
  22. 22. 3. Genomic resources for gene discovery g y • T-DNA and Tos17 mutants • Nested Association Mapping Massive gene discovery platform New Generation Sequencing technologies will make these resources even more valuable
  23. 23. Gene discovery: T-DNA mutants
  24. 24. Gene2traits search
  25. 25. Gene2traits search
  26. 26. Gene2traits search
  27. 27. Gene discovery: Nested Association Mapping (NAM)
  28. 28. Tools: Software for genetics mapdisto.free.fr
  29. 29. 4. Applying gene discovery to selection • Marker-Assisted Selection can fasten (not always) the breeding process • Particulary valuable for traits that are difficult or expensive or time-consuming to evaluate • N used i routine by private companies Now d in ti b i t i • Example: Rice hoja blanca virus (MADR, Fedearroz) (2007-2011)
  30. 30. RHBV resistance QTLs Q MADR Fedearroz 2000 x WC366 Chr 4 Chr 5
  31. 31. RHBV incidence vs QTL presence 45 40 35 AA_Fedearroz 2000 EDIO VHBA 30 25 % PROME AB_Heterocigoto 20 15 10 BB_WC366 5 0 RM518 RM16368 RM16416 El efecto fenotípico significativo es una característica de importancia en el mejoramiento asistido por marcadores LOCUS
  32. 32. Introgression of resistance genes in elite lines Desarrollo d marcadores D ll de d Mapeo Fino: evaluación asociados con la resistencia en invernadero y genotipificación Introgresión de QTLs y uso Definición de un marcador de SNPs : evaluación en específico para el gen de campo y genotipificación ti ifi ió resistencia it i Comparación de la nueva Evaluación con el nuevo metodología con el método marcador sin evaluación clásico fenotípica Identificación y optimización de una metodología optimizada para la selección por RHBV
  33. 33. 5. New genomic tools: How we will use them g • SNP platform ( p (Constanza Quintero) Q ) – Genetic diversity – Genetic mapping (Genes, QTLs) – MAB MAS MARS MAB, MAS, • High throughput NGS-based SNP technologies – 1,000s of samples x 10,000s-100,000s of SNPs – Decipher genetic bases of interspecific sterility using advanced backcross lines – Fine mapping and cloning of Q pp g g QTLs – NAM • Bioinformatics (key) Platform for MAS
  34. 34. Diversity of LAC germplasm
  35. 35. Graphical genotypes versus Indica
  36. 36. Graphical genotypes versus Tropical Japonica
  37. 37. Expected Indica x Japonica genetic map ( (IR64 x Azucena, NAM) , )
  38. 38. Marker-Assisted Recurrent Selection and Genomic Selection Rice Association Analysis Initiative Upland Rice Breeding Cécile Grenier Association Panels Synthetic Populations Temperate Tropical p Japonica p Japonica PCT-4A PCT-4B PCT-4C PCT‐11 Nucleus (200) Oryza SNP (200) (24) Indicas (200) Agronomic traits Evaluation under Agronomic traits drought (leaf T°, WUE) Evaluation under Leaf T° Root traits drought (leaf T°, WUE) Agronomy Diseases (blast) Diseases (blast) Diseases Tropical Japonica SPn SPn+1 AP(200), low LD 600,000 (400), medium LD (400), medium LD 3000 3000 Association mapping SNP SNP SNP Pan genomic Pan-genomic approach Genomic Validation on Genomic Validation on estimated phenotypic estimated phenotypic breeding br din breeding breeding br din breeding values values values values MARS & GS MARS & GS