1. Gene discovery
and its applications in rice
Mathias Lorieux (IRD/CIAT)
Rice 2010 Conference
September 2010

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. 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. 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. The triple domestication of rice
p
japonica
j
O. glaberrima indica
i

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. 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. Mapping of a major resistance gene
to Rice stripe necrosis virus
Gutierrez et al, BMC Plant Biol. 2010

9. Yield components
Gutierrez et al, BMC Plant Biol. 2010

10. Striga resistance
Collab. J. Scholes, Sheffield Boisnard et al, 2010

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. 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. Performance of ILs under drought
B. Manneh
(AfricaRice)

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. Tool: Universal Core Genetic Map
O. meridionalis O. rufipogon O. barthii O. glumaepatula
85 - 91% polymorphism
Orjuela et al, TAG 2010

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. Duplications at the S1A locus

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. 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. 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. 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. 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. Gene discovery: T-DNA mutants

24. Gene2traits search

25. Gene2traits search

26. Gene2traits search

27. Gene discovery:
Nested Association Mapping (NAM)

28. Tools: Software for genetics
mapdisto.free.fr

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. RHBV resistance QTLs
Q MADR
Fedearroz 2000 x WC366
Chr 4
Chr 5

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. 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. 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. Diversity of LAC germplasm

35. Graphical genotypes versus Indica

36. Graphical genotypes versus Tropical Japonica

37. Expected Indica x Japonica genetic map
(
(IR64 x Azucena, NAM)
, )

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