1. Major and consistent drought grain
yield QTLs for marker assisted
breeding in rice
Prashant Vikram
Visiting Research Fellow
PBGB Division, IRRI, Los Baños, Laguna

2. Outline
 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

3. 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 Rice Calorie intake

4. Rice Cultivation and Water regimes
Rice
Irrigated Rainfed
(55%)
Rainfed Rainfed
upland lowland
(9%) (34%)
Bouman, 2007
Sub-Saharan Africa: ~80% rice area is rainfed
South Asia: 50% harvested rice area is rainfed (Dawe, 2010)
Drought tolerant rice is a felt need
Rice field affected by drought in India; June, 2010
(Source, Channel Asia, Times of India)

5. 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
India
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);
Bangladesh
 Sahod ulan-3, 5, 6, 8 & Katihan-8 (Philippines)
Fast track improvement for drought tolerance : MAB
Marker products in pipeline (Swamy and Kumar, 2011) Nepal
MAB: Enhancing efficiency of drought breeding
(for grain yield under drought)
Philippines

6. MAB: Drought QTLs identified in rice
1. Literature Search:
2. 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
3. Gramene Search:
Drought + QTLs : 77
Drought + QTLs + rice: 42
Drought +rice + QTLs + grain yield: 0
Vikram et al. 2012
QTLs identified in past: Mostly secondary traits
Specific to genetic backgrounds & Seldom used for MAS
Consistent drought grain yield QTLs worthy for MAS

7. Are drought grain yield QTLs real ?
qDTY3.2 qDTY6.1
(Vikram et al. 2011) (Venuprasad et
al. 2012)
qDTY12.1
(Bernier et al.
2007)
qDTY1.1 qDTY3.1
(Venuprasad et al.
(Vikram et al. 2011)
2009)
Drought grain yield QTLs are real !
Several genomic regions harbour consistent QTLs: Across backgrounds
Consistent drought grain yield QTLs :
Potential candidates for MAB

8. 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

9. Development of mapping populations
N22 × IR64 Target varieties F1 F2
N22 × Swarna
N22 × MTU1010
Selected single seed of
each F2 plant
F3:4 plants were phenotyped for grain yield under
lowland reproductive stage drought stress. Single F3 plants were grown
and harvested individually.
Target varieties F1 F2
Dhagaddeshi × IR64
Dhagaddeshi × Swarna
Selected single seed of
each F2 plant
F3:4 lines of Dhagaddeshi derived populations were Single F3 plants were grown
grown for seed increase. and harvested individually.
F3:5 & F3:6 lines phenotyped & genotyped for grain yield under drought stress.
Populations with common donor and multiple recipients

10. Phenotyping: Larger populations required
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

11. 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)

12. Phenotyping: Screening for grain yield under drought
Drought stress experiment
• 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
Non Stress/ Irrigated experiment
•Same trial was repeated under non stress condition
•Under non stress a 5cm water maintained till maturity
Lines must be under stress
at least 2 weeks before flowering

13. Phenotyping: Characterization for grain yield under drought Stress
Depleting water level under drought stress
Water table goes below 80 KPa
Mild stress: ≤ 30% yield reduction
Moderate stress: 31-65 % yield reduction
Rainfall relative to meat trial flowering Severe stress: 65-85 % yield reduction
DS2010 RAINFALL mm
DS2011 RAINFALL mm Rainless days during flowering
Water table Rainfall
March 1-10
March 21-31
March 11-20
January 1-10
January 21-31
January 11-20
February 1-10
February 21-28
February 11-20
Flowering
DS2010
DS2011
FLOWERING RANGE

14. Genotyping: Whole population genotyping Vs BSA
BSA
Powerful and cost effective approach
Applicable to multiple populations simultaneously
Useful in identifying major and consistent QTLs

15. Bulk Segregant Strategy for high grain yield under drought
Vikram et al. 2012 (FCR)

16. BSA: genotype multiple populations simultaneously
BSA
RM212 Identify few markers
BSA with adjoining markers
RM431 of the identified one.
Validation of BSA results
RM315
BSA can be validated through
genotyping of phenotypic tails with
DHAGADDESHI
DHAGADDESHI
BULK HIGH
BULK HIGH
BULK LOW
BULK LOW
BULK HIGH BSA markers (Kanagaraj et al. 2010)
SWARNA
BULK LOW
SWARNA
SWARNA
N22
RM11943 RM431 RM231

17. 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)

18. Drought grain yield QTLs in N22 populations
N22 *
N22
N22 *
qDTY2.3
qDTY1.1
N22 x IR64
N22 x Swarna
qDTY3.2
N22 x IR64
N22 x MTU1010 N22 x Swarna

19. QTL qDTY1.1 on tail end of chromosome 1
qDTY1.1 located at the distal
end of chromosome 1
RM212
RM3825
RM315
RM11943
RM431
RM12023
RM12091
RM12146
RM12233

20. qDTY1.1 effect in different populations
N22/ MTU1010 N22/ IR64 N22/ Swarna Dhagaddeshi/ Swarna Dhagaddeshi/ IR64
RM12233
RM12233
RM12233
RM12146
RM315
RM315
RM212
RM104
RM431
RM315
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%
Vikram et al. 2011 (BMC Genetics); Ghimire et al. 2012 (FCR)

21. QTL identification: Contrasting parents Vs Target variety
qDTY3.2 effect under
moderate stress only
Vikram et al. 2011 (BMC Genetics)
qDTY2.3 effect under
severe stress only
Days to flowering
loci from MTU1010
•QTL effect depends on contrast of the parents Additive effect
•Large effect QTL in one background may not work in other
Swarna > IR64 > MTU1010
•Target variety should be used in QTL identification and MAB
Drought tolerance

22. 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
Single-marker analysis after covariance adjustment for DTF under drought stress
Mean grain yield of N22 Mean grain yield of IR64, Swarna,
Population Marker p-value
homozygote (kg/ha) MTU1010 homozygote (kg/ha)
RM431 1273 761 <0.001
N22/IR64
RM11943 1239 878 <0.001
RM431 1517 926 <0.01
N22/Swarna
RM11943 1484 927 <0.01
RM431 1543 1149 <0.01
N22/MTU1010
RM11943 1531 1199 <0.01
Single-marker interval analysis after covariance adjustment for PH under drought stress
Mean grain yield of N22 Mean grain yield of IR64/,
Population p-value
homozygote Swarna/, MTU1010 homozygote
N22/Swarna 1448 1267 <0.01
N22/IR64 1330 1073 <0.01
N22/MTU1010 1470 1381 NS
Vikram et al. 2011 (BMC Genetics)

23. Elimination of linkage drag: N22/Swarna
N22 × Swarna
FG X FG
217 dwarf 2 Plants segregating for
BC3F1 Selected 21 F1s ~3000 BC3F2 qDTY1.1
plants
X FG X Ratooned and
Screened under ROS in WS2011 Full & partial QTL ~180 BC3F3
split planted
lines
Selected recombinants
X
Single plant selected and
Screened in DS2012 Full QTL lines
genotyped for foreground
Plants selected for Swarna plant
type and grain type
Background genotyping Six introgressed Markers run on
regions identified N22/Swarna RIL
population
Plants with clear background
Phenotypically and genotypically
No effect on GY
under RS
Being screened at IRRI Being screened at Hazaribagh,
under ROS India under ROS
Dwarf qDTY1.1 lines in Swarna background

24. Background genotyping of dwarf qDTY1.1 lines (~ 90%
background clear; gaps need to be filled)

25. 115 Days after sowing
STRESS
Swarna BIL
NON STRESS
Swarna BIL
April, 16,2012
Dwarf qDTY1.1 lines in Swarna background
Non stress: they had similar flowering time as Swarna

26. Elimination of linkage drag: N22/IR64 & N22/MTU1010
N22/IR64 & N22/MTU1010 RILs
•segregating for qDTY1.1 ,
F5, F6 and
•<130 cm under non stress,
F7 planted
•Better yield under drought
stress
All these plants are grown
under rainfed situation
800 semi-dwarf (~400 from both
population) plants tagged and
genotyped.
RILs with qDTY1.1 and height comparable
to IR64/ MTU1010 identified

27. qDTY1.1: Allelic study
•qDTY1.1 tolerant allele contributed by traditional donors: (1) N22 (2) Apo (3)
Dhagaddeshi
RM431 in random varieties
qDTY1.1 was significant in more than 50% of drought QTL panel lines (Swamy et al. 2011)
RM431 in random varieties
0.06
N22
landraces
Dhagaddeshi
N22 & Dhagaddeshi are Samba Mahsuri
closer phylogenetically Sw arna
Apo Variety
MTU1010
IR 64
Basmat i 334

28. Closeness of N22 & Dhagaddeshi
67 %
Marker loci where drought-tolerant varieties Dhagaddeshi and N22 have similar alleles,
different from the alleles in susceptible varieties Swarna and IR64

29. Basmati370
Nuadhusanu
Nuakalajeera
Naveen
Vasumati
Dehula
Selumpikit
IR55419
Suskhasamrat Drought tolerant
IR74371
Sadabahar
Apo
genotypes in one
Sambamahsuri
Bhoj cluster
Bhuvan
Ashoka
Swarna
Mahamaya
Lalitagiri
Satabdi
Vanprabha
Dular
Jaya
Saita
Lalat
Kshitij
Kakro
Saket4
IR36
KalingaIII
Ratna
Sahabhagi
Rajni
Padmini
Dandi
Mehar
dhalaheera
Drought QTL alleles
Heera
Udayagiri Conserved in landraces
sattari
Mahsuri
Ganteswari
Samanta
khandagiri
Lalnakanda41
Sukhawan
Rajeshwari
IR64
IR83614
Basmati334
Abhishek
Shravani
Chicken Soni
Safri17
Thara
IR76569
Browngora
Durgabhog
Birsagora
Sathi
Kalakeri
Drought tolerant
Vandana
IR70844
cultivars
Raskadam
Lalsar
N22
Anjali
Virendra
Annada
ASD17

30. 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.

31. 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
A 90 Kb block specific to N22
1 TBGI065142 40332797 G A A in qDTY1.1 region
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

32. Differentially expressed genes between N22 & IR64 in
qDTY1.1 : Candidate genes
RM212
1. LOC_Os01g65690
2. LOC_Os01g65780
3. LOC_Os01g66010 RM315
4. LOC_Os01g66290 RM11943
5. LOC_Os01g66860
(4,5-DOPA dioxygenase extradiol,
RM431
glycosyl transferase,
amino acid transporters, RM104
MADS-box family gene, RM529
serine/threonine protein kinase)
RM2182
RM2227
(Lenka et al. 2011) RM2289
Vikram et al. 2011 (BMC Genetics)

33. qDTY1.1 peak marker RM431: A marker from Gene
containing zing finger
RM431
Peak marker in most studies
Meta-QTL analysis

34. 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

35. qDTY3.2 – qDTY1.1 interaction
qDTY3.2 qDTY3.2
qDTY1.1 qDTY1.1
N22/IR64 RIL population N22/Swarna RIL population

36. 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

37. Phenotyping at IRRI
Duration
WATER TABLE DATA OF DS2011
DS2011, IRRI
Water table (cm)
Rainfall (mm)
RAINFALL DATA OF DS2011
flowering (days)
Days to 50%
FLOWERING RANGE

38. Phenotyping at Nepal
Duration
WATER TABLE DATA OF DS2011
WS2011, Nepal
Water table (cm)
RAINFALL DATA OF DS2011
Rainfall (mm)
flowering (days)
Days to 50%
FLOWERING RANGE

39. qDTY12.1: QTL across ecosystems, environments & backgrounds
Peak marker : RM28166
Additive effect: 47.7%
Phenotypic variance: 24.6%
qDTY12.1
(18.15Mb)
(15.41Mb)
RM28199
RM28089
•Ecosystem: lowland and upland drought stress
•Environments: IRRI and Nepal
•Backgrounds –Vandana and Sabitri
Mishra et al. Unpublished

40. qDTY12.1: Interaction effect analysis
V/W
•qDTY12.1 showed significant interaction with two other loci W Yield advantage
qDTY12.1 Under drought
(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 V V/W Enhanced
qDTY2.3 yield advantage
Population qDTY12.1 qDTY2.3 qDTY3.2 qDTY3.2 Under drought
Interaction Ecosystem
QTL (29-41%)
V-W W V V √ Upland
I-S I I/S I/S × Lowland IR74371-46-1-1 (I) is
derivative of Wayrarem (W)
•Under upland qDTY12.1 W allele interacts with qDTY2.3 & I/S
I Yield advantage
qDTY3.2 allele of Vandana (V) qDTY12.1 Under drought
•Under lowland I/W allele of qDTY12.1 is effective alone
+
•Vandana is drought tolerant upland adapted variety
S I/S No additional
•Sabitri is drought susceptible lowland adapted variety qDTY2.3 yield advantage
qDTY3.2 Under drought
qDTY12.1 effect vary with backgrounds and ecosystems
Use of target variety in QTL study

41. qDTY8.1: Mapping QTLs with basmati variety
Basmati334:traditional Basmati cultivar of Punjab (India and Pakistan)
• 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

42. Marker Assisted QTL Pyramiding (MAQP) for grain
yield under drought stress
N22/ Swarna
Swarna Apo/ Swarna
Basmati334/ Swarna
IR74371-46-1-1/ Sabitri
Sabitri
IR77298-5-6-18/ Sabitri

43. Marker assisted Pyramiding: MABC followed by intercrossing
F2
F3
F4
Vikram et al. 2012

44. Marker assisted Pyramiding in Swarna background
Basmati334-Swarna F4 X Apo-Swarna BC3F1 WS 2008
qDTY8.1 qDTY3.1
F1 X N22 x Swarna F4 WS 2009
qDTY1.1
qDTY1.1 + qDTY3.1 +qDTY8.1
F1
DS 2010
F1 plants with 3 QTLs X Swarna
WS 2010
F1 plants individual QTLs X Swarna
F1s with qDTY3.1 X F1s with qDTY1.1+qDTY8.1 DS 2011
Four F1 plants selected with qDTY1.1+qDTY3.1+qDTY8.1
WS 2011
X
Four F2 families with qDTY1.1+qDTY3.1+qDTY8.1 planted DS 2012
T RE QT L S RE
H E L INE ADY FOR PH NOT IC SCRE NING
E YP E

45. Marker assisted Pyramiding in Sabitri background
IR74371-46-1-1/Sabitri X IR77298-5-6-18/Sabitri WS 2011
BC1F5 BC1F5
qDTY12.1 qDTY3.2
F1
F2 qDTY12.1 + qDTY3.2
DS 2012
1000F2
Genotyping of F2 contd….

46. CONCLUSIONS……
• 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.

47. Acknowledgements
Funding Agencies Team Leader
•Generation Challenge program (GCP) • Dr. Arvind Kumar
•Bill and Melinda Gates Foundation (STRASA)
PDF /VRF Collaborating IRRI scientists
• Dr. BPM Swamy • Dr. Amelia Henry
• Dr. Ajay Kohli
• Dr. Shalabh Dixit
Collaborators (NARS)
Assistant Scientists • Dr. N. K. Singh, NRCPB, IARI, India
• Jennylyn Trinidad • Dr.N.P.Mandal, Hazaribagh, India
• Paul C. Maturan • Dr.P.Swain, CRRI, Cuttack, India
• MT Sta. Cruz • Dr.O.N.Singh, CRRI, Cuttack, India
Researchers • Krishna Kumar Mishra, Nepal
• Ruth E Carpio • Ram Baran Yadaw, Nepal
• Guevarra Jocelyn
Technicians
• Teody, Loui, Orly