1. Approaches,
resources and tools
for gene discovery
and breeding in rice
Mathias Lorieux (IRD/CIAT)
GCP GRM, Hyderabad, September 2011

2. Plan
1. CSSLs of AA genome species to discover genes of
importance
- Oryza sativa x O. glaberrima introgressions
- Wild species
2. Breaking barriers: interspecific bridges & sterility genes
3. New gene discovery resource: NAM population
4. Software

3. Using AA genome species of rice
to 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. CSSLs: Mapping of a major resistance gene
to Rice stripe necrosis virus from O. glaberrima
Gutierrez et al, BMC Plant Biol. 2010Caiapo x MG12

5. Yield components QTLs
Gutierrez et al, BMC Plant Biol. 2010

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

7. Performance of CSSLs under drought
B. Manneh
(AfricaRice)

8. A widely used population
Trait Partner Name
Striga resistance U. Sheffield J. Scholes
Drought tolerance AfricaRice, Embrapa,
Fedearroz
B. Manneh
C. Guimaraes
M. Diago
Osmotic adjustment IVIC T. Ghneim
Panicle architecture Cornell, CIAT S. McCouch
Root development Cirad N. Ahmadi
Agronomic traits CIAT, ICAR,
CIMMYT-India
C.P. Martinez
R. Gupta
Bacterial blight R IRD - RPB V. Verdier
Nematode resistance IRD - RPB G. Reversat
S. Bellafiore
Breeding (recurrent) Cirad M. Châtel
Bradyrhizobium LSTM-IRD B. Dreyfus
Iron toxicity
Nitrogen UE
U. Louvain
CIAT
P. Bertin
J. Rane

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 CSSLs
Curinga x O. meridionalis
BC3DH introgression lines

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

12. iBridges: Opening the African
rice diversity to breeders
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
+/- sterile hybrids with O. sativa
• Sterility hampers full use of African rice for breeding
 interspecific bridges
Links
25 glaberrima x 3 sativa

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 genetic
diversity 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 of
materials
• A technology for developing additional iBridges between O. sativa and its
other AA-genome (wild) relatives
 Provide a broad access of the genetic diversity in the AA species complex

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. RM190
RM19349
RM19350
RM19353
RM19357
RM19361
RM5199
RM19363
RM19367
RM19369
Os05260Int
RM_S1_34
CG14 38E01
RM19391
RM19398
RM3805
RM19414
RM19420
RM204
0,9
0,8
0,8
3,5
0,9
0,0
0,0
0,8
0,8
0,0
0,8
RM19377
5,1
0,9
2,0
0,9
0,0
0,8
0,8
0,8
• Maternal allelic transmission depends on
recombination around S1
• Epistatic interaction between the three loci
(BDM model)
• Sequencing of the region  2 candidate genes
Genetic bases of interspecific sterility
Garavito et al, Genetics 2010

16. Identification of S1 & S1A genes
Guyot et al, PLoS One 2011

17. Other sterility genes
 Interactions with S1
 A complete genetic model for the reproductive barrier

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 on
drought tolerance
• IRD-CIAT & WARDA as PIs

19. Linkage analysis vs. Association mapping
M
M
M
M
M
M
M
M
M
t generations
M
M
M
M
M
M
500 genes Single gene
From: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th Maize
Genetics Conference, Pacific Grove, CA, Mar 9-12, 2006

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 heterogeneity
Nested Association Mapping
From: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th Maize
Genetics Conference, Pacific Grove, CA, Mar 9-12, 2006
Association mapping

21. NAM
1
2
200



B73×


 




F1s
SSD
     
25 DL
B97
CML103
CML228
CML247
CML277
CML322
CML333
CML52
CML69
Hp301
Il14H
Ki11
Ki3
Ky21
M162W
M37W
Mo18W
MS71
NC350
NC358
Oh43
Oh7B
P39
Tx303
Tzi8
From: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th Maize
Genetics Conference, Pacific Grove, CA, Mar 9-12, 2006

22. Average power of NAM (h2 = 0.7)
q = 20 QTL
Complete marker
CPS marker only
q = 50 QTL
Power
From: Yu et al - Power analysis of an integrated mapping strategy: Nested association mapping (NAM) - The 48th Maize
Genetics Conference, Pacific Grove, CA, Mar 9-12, 2006

23. Heat map for Days to Silking QTL effects
Buckler et al 2009

24. Rice NAM
x IR64
Founders : O. sativa
cultivars popular in
Africa & LA
All crossed to IR64
SSD
F7
1
2
3
.
.
.
.
.

25. Coefficient
0.00 0.25 0.50 0.75 1.00
Nerica5
CG14
Nerica4
ITA150
WAB56-104
Nerica3
WAB181-1
WAB96-3
IAC165
FARO11
Moroberekan
Lac23
IRAT104
Chocoto
Co39
Rock5
Gambiaka
IET3137
ITA306
BG90-2
Cisadane
Nerica19
IR13240
Tox3100
Suakoko
Kogoni91-1
IR64
Foniap2000
BW348-1
Bouake189
Djoukeme
WAB638-1
ITA212
B6144
FARO31
WAB96-1-1
indica
japonica
NAM Parental lines Diversity (SSRs)
Variety of traits
Agronomic
yield, aroma, N responsivity,
grain quality, etc
Abiotic stresses tolerance
Drought, iron toxicity, etc
Biotic stresses:
weed competitiveness, AfRGM,
Blast resistance, etc
• 20 NAM
parental lines
• 15 control
varieties
• 36 SSRs

26. Current status & perspectives
• indica (IR64) x tropical japonica combinations
• CIAT: 2000 lines, 10 combinations, F7 harvest
expected march 2012
• AfricaRice: 2000 lines, 10+10 combinations, currently
F3-F5
• Strategy still to be defined for genotyping and
phenotyping – and funding
Genotyping by sequencing?
GRiSP Phenotyping Network
• Tools for data analysis
IR64 x
WAB638-1
F4 Plants

27. Conclusions
• Natural variation: a valuable source of genes for yield
potential
• 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 key
to develop interspecific populations
• Analysis tools

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 to
overcome reproductive barriers)
– Can transgressive behavior of interspecific progenies be helpful for
hybrid development? Transgression  Heterosis?
• Intraspecific case
– NAM & MAGIC: which traits to measure? (Large population size)

29. CIAT
M. Lorieux
A. Garavito, A. Gutierrez, M. F. Alvarez, L.
Moreno, J.D. Arbelaez, L. Melgarejo,
J. Orjuela, J. Lozano,
M. Bouniol,V.H. Lozano
C. P. Martinez
S. Carabali, O. X. Giraldo
J. Tohme
F. Rodriguez, C. Quintero, G. Plata
M.C. Duque, J. Silva
EMBRAPA-CNPAF
C. Brondani, P. Rangel
C. Guimaraes
WARDA
M. N. Ndjiondjop, B. Manneh,
G. Djedatin, H. Gridley
Fedearroz
M. Diago, J. D. Arbelaez, J. Sierra
Cornell Uty
S. McCouch, J. Kimball
Thanks 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)