Approaches,

resources and tools

for gene discovery

and breeding in rice

Mathias Lorieux (IRD/CIAT)

GCP GRM, Hyderabad, September 2011

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

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

CSSLs: Mapping of a major resistance gene

to Rice stripe necrosis virus from O. glaberrima

Gutierrez et al, BMC Plant Biol. 2010 Caiapo x MG12

Yield components QTLs

Gutierrez et al, BMC Plant Biol. 2010

Striga resistance

Collab. J. Scholes, Sheffield Boisnard et al, 2010

Performance of CSSLs under drought

B. Manneh

(AfricaRice)

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

• 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

• 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

Universal Core Genetic Map

85 - 91% polymorphism

O. meridionalis O. rufipogon O. barthii O. glumaepatula

Orjuela et al, TAG 2010

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

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

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

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

Identification of S1 & S1A genes

Guyot 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…

• 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

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

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

NAM

1

2

200

B73 ×

F1s

SSD

25 DL

B9

7

CM

L1

03

CM

L2

28

CM

L2

47

CM

L2

77

CM

L3

22

CM

L3

33

CM

L5

2

CM

L6

9

Hp

30

1

Il1

4H

Ki1

1

Ki3

Ky2

1

M1

62

W

M3

7W

Mo

18

W

MS

71

NC

35

0

NC

35

8

Oh

43

Oh

7B

P3

9

Tx

30

3

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

Average power of NAM (h2 = 0.7)

q = 20 QTL

Complete marker

CPS marker only

q = 50 QTL

Po

wer

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

Heat map for Days to Silking QTL effects

Buckler et al 2009

Rice NAM

x IR64

Founders : O. sativa

cultivars popular in

Africa & LA

All crossed to IR64

SSD

F7

1

2

3

.

.

.

.

.

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

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

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

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)

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)