Major and consistent drought grain

yield QTLs for marker assisted

breeding in rice

Prashant Vikram

Visiting Research Fellow

PBGB Division, IRRI, Los Baños, Laguna

Introduction

qDTY1.1 : A QTL effective in multiple genetic backgrounds

Phenotyping & Genotyping strategies

qDTY1.1 : QTL effects

qDTY1.1 : Elimination of linkage drag

qDTY1.1 : Allelic analysis

qDTY1.1 :Candidate gene analysis

qDTY3.2 : A loci with interaction effects

qDTY12.1 : QTL stability across ecosystems and environments

qDTY8.1 : Mapping QTLs with basmati variety

Marker Assisted QTL Pyramiding (MAQP) for grain yield under drought

stress

Conclusions

Outline

Global Water Resource & Rice

• Estimated water resource: 43750 km3/year

• 70% of fresh water resource is consumed in Agriculture

• Water resource per inhabitant is least in Asia where 90% world’s rice is grown

• Rice is a semi-aquatic plant and 1Kg rice consumes 3000-5000 Kg water

• 20% of global calorie intake; 35-60% calories source in Asia alone

Water resource Calorie intake Rice

Rice Cultivation and Water regimes

Rainfed Irrigated

(55%)

Rice

Rainfed

upland

(9%)

Rainfed

lowland

(34%)

Drought tolerant rice is a felt need

Sub-Saharan Africa: ~80% rice area is rainfed

South Asia: 50% harvested rice area is rainfed (Dawe, 2010)

Rice field affected by drought in India; June, 2010

(Source, Channel Asia, Times of India)

Bouman, 2007

Drought breeding approaches : Conventional & Molecular

Drought is a complex trait & improvement of drought tolerance

through indirect selection of secondary traits did not yield satisfactory

results

Direct selection for grain yield under drought is a well proven

criterion and several varieties have been released using this approach in

last 3 years:

Sahbhagi dhan (India);

Sukha dhan-1, Sukha dhan-2 & Sukha dhan-3 (Nepal);

BRRI Dhan-56 (Bangladesh);

Sahod ulan-3, 5, 6, 8 & Katihan-8 (Philippines)

Fast track improvement for drought tolerance : MAB

MAB: Enhancing efficiency of drought breeding

(for grain yield under drought)

India

Bangladesh

Nepal

Philippines

Marker products in pipeline (Swamy and Kumar, 2011)

MAB: Drought QTLs identified in rice

Vikram et al. 2012

Pub Med Search:

Drought: 7072

Drought + rice: 525

Drought +rice +QTLs: 48

Drought +rice +grain yield+ QTLs: 16

8 papers related to GY under drought

Gramene Search:

Drought + QTLs : 77

Drought + QTLs + rice: 42

Drought +rice + QTLs + grain yield: 0

QTLs identified in past: Mostly secondary traits

Specific to genetic backgrounds & Seldom used for MAS

Consistent drought grain yield QTLs worthy for MAS

1. 2.

3.

Literature Search:

Are drought grain yield QTLs real ?

qDTY1.1

(Vikram et al. 2011)

qDTY3.2

(Vikram et al.

2011)

qDTY3.1

(Venuprasad et al.

2009)

qDTY6.1

(Venuprasad

et al. 2012)

qDTY12.1

(Bernier et al.

2007)

Consistent drought grain yield QTLs :

Potential candidates for MAB

Drought grain yield QTLs are real !

Several genomic regions harbour consistent QTLs: Across backgrounds

qDTY1.1: A QTL effective in multiple genetic

backgrounds

QTL Mapping Strategy

Populations: common donor & multiple recipient parents

Phenotyping: GY under reproductive stage drought stress

Genotyping: WPG & BSA

Populations with common donor and multiple recipients

N22 × IR64

N22 × Swarna

N22 × MTU1010

F1 F2

Selected single seed of

each F2 plant

Single F3 plants were grown

and harvested individually.

F3:4 plants were phenotyped for grain yield under

lowland reproductive stage drought stress.

Target varieties

Development of mapping populations

F3:4 lines of Dhagaddeshi derived populations were

grown for seed increase.

F3:5 & F3:6 lines phenotyped & genotyped for grain yield under drought stress.

Dhagaddeshi × IR64

Dhagaddeshi × Swarna

F1 F2

Selected single seed of

each F2 plant

Single F3 plants were grown

and harvested individually.

Target varieties

1. Distribution of genotypes for grain yield under stress in N22 x IR64 population

2. Distribution of genotypes for grain yield under stress in N22 x Swarna population

3. Distribution of genotypes for grain yield under stress in N22 x MTU1010 population

Populations were large enough to show normal distribution

Phenotyping: Larger populations required

QTL identification for grain yield under drought: Population size

300-350 population size is good

enough for identification of

drought grain yield QTLs

Vikram et al. 2012 (FCR)

• All mapping populations planted in two replications 5m

single row plot in two consecutive Dry seasons

• Water stress was given 50 days after sowing

• Grain Yield and yield related trait data were recorded

• Days to 50% flowering

• Plant height

• Biomass

• Grain yield

• HI

Drought stress experiment

Non Stress/ Irrigated experiment

•Same trial was repeated under non stress condition

•Under non stress a 5cm water maintained till maturity

Phenotyping: Screening for grain yield under drought

Lines must be under stress

at least 2 weeks before flowering

FLOWERING RANGE

Depleting water level under drought stress

DS2010

DS2011

Januar

y 1

-10

Januar

y 1

1-2

0

Januar

y 2

1-3

1

Feb

ruar

y 1

-10

Feb

ruar

y 1

1-2

0

Feb

ruar

y 2

1-2

8

Mar

ch 1

-10

Mar

ch 1

1-2

0

Mar

ch 2

1-3

1

Rainfall relative to meat trial flowering

Mild stress: ≤ 30% yield reduction

Moderate stress: 31-65 % yield reduction

Severe stress: 65-85 % yield reduction

Phenotyping: Characterization for grain yield under drought Stress

Water table goes below 80 KPa

Rainless days during flowering

DS2010 RAINFALL mm

DS2011 RAINFALL mm

DS2010 RAINFALL mm

DS2011 RAINFALL mm

Water table Rainfall

Flowering

Genotyping: Whole population genotyping Vs BSA

BSA

Powerful and cost effective approach

Applicable to multiple populations simultaneously

Useful in identifying major and consistent QTLs

Bulk Segregant Strategy for high grain yield under drought

Vikram et al. 2012 (FCR)

BSA: genotype multiple populations simultaneously

RM11943 RM431

DH

AG

AD

DE

SH

I

SW

AR

NA

BU

LK

HIG

H

BU

LK

LO

W

DH

AG

AD

DE

SH

I

SW

AR

NA

BU

LK

HIG

H

BU

LK

LO

W

RM431

RM212

RM315

RM231

N22

SW

AR

NA

BU

LK

HIG

H

BU

LK

LO

W

BSA

Identify few markers

BSA with adjoining markers of

the identified one.

Validation of BSA results

BSA can be validated through

genotyping of phenotypic tails with

BSA markers (Kanagaraj et al. 2010)

BSA: QTL Effects

Selective genotyping lead to an upward estimation of QTL effects

BSA doesn’t lead to an upward estimation of QTL effects Vikram et al. 2012 (FCR)

Drought grain yield QTLs in N22 populations

*

* N22

N22

N22

qDTY1.1

N22 x Swarna

N22 x IR64

N22 x MTU1010

qDTY2.3

N22 x IR64

qDTY3.2

N22 x Swarna

RM431

RM315 RM3825

RM212

RM12023

RM11943

RM12091

RM12146

RM12233

QTL qDTY1.1 on tail end of chromosome 1

qDTY1.1 located at the distal

end of chromosome 1

Dhagaddeshi/ Swarna Dhagaddeshi/ IR64 N22/ MTU1010 N22/ IR64 N22/ Swarna

RM

31

5

RM

10

4

RM

43

1

RM

12

14

6

RM

31

5

RM

12

23

3

RM

31

5

RM

12

23

3

RM

21

2

RM

12

23

3

qDTY1.1 effect in different populations

Vikram et al. 2011 (BMC Genetics); Ghimire et al. 2012 (FCR)

Population Additive effect Phenotypic variance

N22 × Swarna 29.30% 13.40%

N22 × IR64 24.30% 16.90%

N22 × MTU1010 16.10% 12.60%

Dhagaddeshi × Swarna 24.90% 32.00%

Dhagaddeshi × IR64 8.30% 9.30%

Swarna > IR64 > MTU1010

Drought tolerance

Additive effect

QTL identification: Contrasting parents Vs Target variety

Days to flowering

loci from MTU1010

qDTY2.3 effect under

severe stress only

qDTY3.2 effect under

moderate stress only

Vik

ram

et

al.

20

11

(B

MC

Gen

etic

s)

•QTL effect depends on contrast of the parents

•Large effect QTL in one background may not work in other

•Target variety should be used in QTL identification and MAB

Co-variate analysis for DTF and Plant height

Co-variate adjustment of DTF and plant height

qDTY1.1: Significant for grain yield under drought after the co-variate adjustment

Vikram et al. 2011 (BMC Genetics)

Population Marker Mean grain yield of N22

homozygote (kg/ha)

Mean grain yield of IR64, Swarna,

MTU1010 homozygote (kg/ha) p-value

N22/IR64 RM431 1273 761 <0.001

RM11943 1239 878 <0.001

N22/Swarna RM431 1517 926 <0.01

RM11943 1484 927 <0.01

N22/MTU1010 RM431 1543 1149 <0.01

RM11943 1531 1199 <0.01

Single-marker analysis after covariance adjustment for DTF under drought stress

Population Mean grain yield of N22

homozygote

Mean grain yield of IR64/,

Swarna/, MTU1010 homozygote p-value

N22/Swarna 1448 1267 <0.01

N22/IR64 1330 1073 <0.01

N22/MTU1010 1470 1381 NS

Single-marker interval analysis after covariance adjustment for PH under drought stress

N22 × Swarna

BC3F1

FG Selected 21 F1s

X ~3000 BC3F2

217 dwarf

plants

2 Plants segregating for

qDTY1.1

Ratooned and

split planted

X ~180 BC3F3 Full & partial QTL

lines

Screened under ROS in WS2011 X

Single plant selected and

genotyped for foreground

FG

FG

Screened in DS2012

X

Full QTL lines

Plants selected for Swarna plant

type and grain type

Plants with clear background

Phenotypically and genotypically

Being screened at IRRI

under ROS

Being screened at Hazaribagh,

India under ROS

Background genotyping Six introgressed

regions identified

Markers run on

N22/Swarna RIL

population

No effect on GY

under RS

Dwarf qDTY1.1 lines in Swarna background

Elimination of linkage drag: N22/Swarna

Selected recombinants

Background genotyping of dwarf qDTY1.1 lines (~ 90%

background clear; gaps need to be filled)

Dwarf qDTY1.1 lines in Swarna background

Non stress: they had similar flowering time as Swarna

115 Days after sowing

Swarna BIL

NON STRESS

Swarna BIL

STRESS

April, 16,2012

Elimination of linkage drag: N22/IR64 & N22/MTU1010

RILs with qDTY1.1 and height comparable

to IR64/ MTU1010 identified

N22/IR64 & N22/MTU1010 RILs

•segregating for qDTY1.1 ,

•<130 cm under non stress,

•Better yield under drought

stress

F5, F6 and

F7 planted

800 semi-dwarf (~400 from both

population) plants tagged and genotyped.

All these plants are grown

under rainfed situation

qDTY1.1: Allelic study •qDTY1.1 tolerant allele contributed by traditional donors: (1) N22 (2) Apo (3) Dhagaddeshi

RM431 in random varieties

N22

Dhagaddeshi

Samba Mahsuri

Swarna

Apo

MTU1010

IR 64

Basmati 334

0.06

N22 & Dhagaddeshi are

closer phylogenetically

RM431 in random varieties

qDTY1.1 was significant in more than 50% of drought QTL panel lines (Swamy et al. 2011)

landraces

Variety

Closeness of N22 & Dhagaddeshi

Marker loci where drought-tolerant varieties Dhagaddeshi and N22 have similar alleles,

different from the alleles in susceptible varieties Swarna and IR64

67 %

Basmati370NuadhusanuNuakalajeeraNaveenVasumatiDehulaSelumpikitIR55419SuskhasamratIR74371SadabaharApoSambamahsuriBhojBhuvanAshokaSwarnaMahamayaLalitagiriSatabdiVanprabhaDularJayaSaitaLalatKshitijKakroSaket4IR36KalingaIIIRatnaSahabhagiRajniPadminiDandiMehardhalaheeraHeeraUdayagirisattariMahsuriGanteswariSamantakhandagiriLalnakanda41SukhawanRajeshwariIR64IR83614Basmati334AbhishekShravaniChicken SoniSafri17TharaIR76569BrowngoraDurgabhogBirsagoraSathiKalakeriVandanaIR70844RaskadamLalsarN22AnjaliVirendraAnnadaASD17

Drought tolerant

cultivars

Drought tolerant

genotypes in one

cluster

Drought QTL alleles

Conserved in landraces

Candidate gene analysis for qDTY1.1

SNPs among N22, IR64 and Swarna, qDTY1.1 region were compared.

Based on available reports differentially expressed genes in qDTY1.1

region between N22 and IR64 were annotated.

SNPs in qDTY1.1 region: a region specific to N22

N22

Swarna IR64

1 TBGI065107 40298480 C T T

1 TBGI065108 40298598 T C C

1 TBGI065127 40329203 A G G

1 TBGI065129 40329319 C T T

1 TBGI065130 40329422 G A A

1 TBGI065133 40330056 G T T

1 TBGI065139 40332364 T G G

1 TBGI065142 40332797 G A A

1 TBGI065146 40333650 A C C

1 TBGI065154 40334497 C T T

1 TBGI065155 40334597 C T T

1 TBGI065156 40334719 T C C

1 TBGI065158 40334855 G T T

1 TBGI065161 40335346 T C C

1 TBGI065169 40373741 G C C

1 TBGI065107 40298480 C T T

1 TBGI065108 40298598 T C C

1 TBGI065127 40329203 A G G

1 TBGI065129 40329319 C T T

1 TBGI065130 40329422 G A A

1 TBGI065133 40330056 G T T

1 TBGI065139 40332364 T G G

1 TBGI065142 40332797 G A A

1 TBGI065146 40333650 A C C

1 TBGI065154 40334497 C T T

1 TBGI065155 40334597 C T T

1 TBGI065156 40334719 T C C

1 TBGI065158 40334855 G T T

1 TBGI065161 40335346 T C C

1 TBGI065169 40373741 G C C

SNP ID Position

A 90 Kb block specific to N22

in qDTY1.1 region

RM431

RM315

RM212

RM104

RM11943

RM529

RM2182

RM2227

RM2289

1. LOC_Os01g65690

2. LOC_Os01g65780

3. LOC_Os01g66010

4. LOC_Os01g66290

5. LOC_Os01g66860

(4,5-DOPA dioxygenase extradiol,

glycosyl transferase,

amino acid transporters,

MADS-box family gene,

serine/threonine protein kinase)

Differentially expressed genes between N22 & IR64 in

qDTY1.1 : Candidate genes

Vikram et al. 2011 (BMC Genetics)

(Lenka et al. 2011)

qDTY1.1 peak marker RM431: A marker from Gene

containing zing finger

RM431

Peak marker in most studies

Meta-QTL analysis

qDTY3.2 : A loci with interaction effects

qDTY3.2: First identified in N22 x Swarna population for grain yield under drought

(Vikram et al. 2012-BMC Genetics)

Located on the proximal end (top) of the chromosome 3

This QTL showed significant interaction with qDTY1.1 in N22 × Swarna as well as N22

× IR64 populations

qDTY3.2 : interaction with qDTY12.1 (Dixit et al. 2012- Mol. breed)

qDTY3.2 : Significant effect for GY under drought in IR77298-5-6-18/Sabitri population.

Additive interaction of qDTY1.1 & qDTY3.2

: advantage for MAB

qDTY3.2

qDTY1.1 qDTY1.1

qDTY3.2

qDTY3.2 – qDTY1.1 interaction

N22/IR64 RIL population N22/Swarna RIL population

qDTY12.1: QTL stability across ecosystems and

environments

IR74371-46-1-1/ Sabitri BIL population

Sabitri is popular variety of Nepal

Screened under lowland drought stress at IRRI and Nepal

Genotyped through BSA

qDTY12.1 was found consistent at both locations

WATER TABLE DATA OF DS2011

Wat

er t

able

(cm

)

Duration

Rainfall data of DS2011

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Feb 1-10 Feb 11-20 Feb 21-28 March 1-10 March 11-20 March 21-31

Duration

Ra

infa

ll (

mm

)

Series1

Rainfall data of DS2011

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Feb 1-10 Feb 11-20 Feb 21-28 March 1-10 March 11-20 March 21-31

Duration

Ra

infa

ll (

mm

)

Series1

RAINFALL DATA OF DS2011

Rai

nfa

ll (

mm

) D

ays

to 5

0%

flow

erin

g (

day

s)

FLOWERING RANGE

DS2011, IRRI

Phenotyping at IRRI

0

50

100

150

200

250

300

Sep 11-20 Sep 21-30 Oct 1-10 Oct 11-20 Oct 21-31 Nov 1-10

Series1

FLOWERING RANGE

Wat

er t

able

(cm

) R

ainfa

ll (

mm

) D

ays

to 5

0%

flow

erin

g (

day

s)

WATER TABLE DATA OF DS2011

Duration

RAINFALL DATA OF DS2011

WS2011, Nepal

Phenotyping at Nepal

RM

28

08

9

(15

.41

Mb

)

RM

28

19

9

(18

.15

Mb

)

Peak marker : RM28166

Additive effect: 47.7%

Phenotypic variance: 24.6%

qDTY12.1

•Ecosystem: lowland and upland drought stress

•Environments: IRRI and Nepal

•Backgrounds –Vandana and Sabitri

Mishra et al. Unpublished

qDTY12.1: QTL across ecosystems, environments & backgrounds

qDTY12.1: Interaction effect analysis

•qDTY12.1 showed significant interaction with two other loci

(qDTY2.3 and qDTY3.2) Dixit et al. 2012 (Mol. Breed)

•No interaction was observed in lowland drought stress in

IR74371-46-1-1/ Sabitri population

Population\

QTL qDTY12.1 qDTY2.3 qDTY3.2 Interaction Ecosystem

V-W W V V √ Upland

I-S I I/S I/S × Lowland

•Under upland qDTY12.1 W allele interacts with qDTY2.3 &

qDTY3.2 allele of Vandana (V)

•Under lowland I/W allele of qDTY12.1 is effective alone

•Vandana is drought tolerant upland adapted variety

•Sabitri is drought susceptible lowland adapted variety

qDTY12.1 effect vary with backgrounds and ecosystems

Use of target variety in QTL study

W

qDTY12.1

V

qDTY2.3

qDTY3.2

+

Yield advantage

Under drought

Enhanced

yield advantage

Under drought

(29-41%)

V/W

V/W

I

qDTY12.1

S

qDTY2.3

qDTY3.2

+

Yield advantage

Under drought

No additional

yield advantage

Under drought

I/S

I/S

IR74371-46-1-1 (I) is

derivative of Wayrarem (W)

qDTY8.1: Mapping QTLs with basmati variety

• F3:5 Basmati334/ Swarna population

was screened for yield under drought

stress in Dry Season 2010.

• qDTY8.1 was identified as significant

loci for yield under drought through

BSA.

Additive effect -160.53

Population mean 621.12

AE (%) -25.84 %

Marker interval RM210-RM447

Basmati334:traditional Basmati cultivar of Punjab (India and Pakistan)

Marker Assisted QTL Pyramiding (MAQP) for grain

yield under drought stress

Swarna

N22/ Swarna

Apo/ Swarna

Basmati334/ Swarna

Sabitri

IR74371-46-1-1/ Sabitri

IR77298-5-6-18/ Sabitri

Marker assisted Pyramiding: MABC followed by intercrossing

Vikram et al. 2012

F2

F3

F4

Basmati334-Swarna F4 X Apo-Swarna BC3F1

F1 X N22 x Swarna F4

F1

WS 2008

WS 2009

DS 2010 F1 plants with 3 QTLs

X Swarna

F1s with qDTY1.1+qDTY8.1

qDTY8.1 qDTY3.1

qDTY1.1

qDTY1.1 + qDTY3.1 +qDTY8.1

F1s with qDTY3.1 X

Four F1 plants selected with qDTY1.1+qDTY3.1+qDTY8.1

X

DS 2011

WS 2011

F1 plants individual QTLs X WS 2010

Four F2 families with qDTY1.1+qDTY3.1+qDTY8.1 planted DS 2012

THREE QTL LINES READY FOR PHENOTYPIC SCREENING

Marker assisted Pyramiding in Swarna background

Swarna

IR74371-46-1-1/Sabitri X IR77298-5-6-18/Sabitri

F1

WS 2011

DS 2012

qDTY12.1 qDTY3.2

qDTY12.1 + qDTY3.2

1000F2

Genotyping of F2 contd….

BC1F5 BC1F5

F2

Marker assisted Pyramiding in Sabitri background

• A large effect QTL on chromosome 1 was identified in multiple populations simultaneously through WPG/BSA.

• Bulked segregant Analysis is a powerful and cost-effective strategy in identifying drought grain yield QTLs

• QTL effects depend on ecosystems, environments and backgrounds. Target varieties should be used in QTL studies.

• DTY-QTLs showed interactions with other regions. Additive interactions are useful for MAB.

• qDTY1.1 linked with plant height. Linkage broken for product development.

• qDTY1.1 positive alleles are likely to be conserved in landraces

• qDTY1.1 harbors candidate genes –AA transporters, PK & ZFP.

• qDTY12.1 was consistent across-ecosystems, environments & backgrounds.

• Marker Assisted QTL Pyramiding (MAQP) is a preferred strategy for improving rice varieties for rainfed environments.

CONCLUSIONS……

Acknowledgements

Team Leader

• Dr. Arvind Kumar

Assistant Scientists

• Jennylyn Trinidad

• Paul C. Maturan

• MT Sta. Cruz

PDF /VRF

• Dr. BPM Swamy

• Dr. Shalabh Dixit

Researchers

• Ruth E Carpio

• Guevarra Jocelyn

Technicians

• Teody, Loui, Orly

Collaborating IRRI scientists

• Dr. Amelia Henry

• Dr. Ajay Kohli

Collaborators (NARS)

• Dr. N. K. Singh, NRCPB, IARI, India

• Dr.N.P.Mandal, Hazaribagh, India

• Dr.P.Swain, CRRI, Cuttack, India

• Dr.O.N.Singh, CRRI, Cuttack, India

• Krishna Kumar Mishra, Nepal

• Ram Baran Yadaw, Nepal

Funding Agencies

•Generation Challenge program (GCP)

•Bill and Melinda Gates Foundation (STRASA)