division seminar august 1,2012 prashant vikram
DESCRIPTION
TRANSCRIPT
Major and consistent drought grain yield QTLs for marker assisted
breeding in rice
Prashant VikramVisiting 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 intakeRice
Rice Cultivation and Water regimes
RainfedIrrigated
(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 traitsSpecific to genetic backgrounds & Seldom used for MASConsistent 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 × IR64N22 × SwarnaN22 × 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 × IR64Dhagaddeshi × 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
DS2010DS2011
Janu
ary
1-10
Janu
ary
11-2
0
Janu
ary
21-3
1
Feb
ruar
y 1-
10
Feb
ruar
y 11
-20
Feb
ruar
y 21
-28
Mar
ch 1
-10
Mar
ch 1
1-20
Mar
ch 2
1-31
Rainfall relative to meat trial flowering
Mild stress: ≤ 30% yield reductionModerate stress: 31-65 % yield reductionSevere 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
BSAPowerful and cost effective approachApplicable to multiple populations simultaneouslyUseful 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 H
IGH
BU
LK L
OW
DH
AG
AD
DE
SH
I
SW
AR
NA
BU
LK H
IGH
BU
LK L
OW
RM431
RM212
RM315
RM231
N22
SW
AR
NA
BU
LK H
IGH
BU
LK L
OW
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 effectsBSA 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
RM315RM3825
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/ IR64N22/ 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. 2
011
(BM
C G
enet
ics)
•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 MarkerMean grain yield of N22
homozygote (kg/ha)Mean grain yield of IR64, Swarna,
MTU1010 homozygote (kg/ha)p-value
N22/IR64RM431 1273 761 <0.001
RM11943 1239 878 <0.001
N22/SwarnaRM431 1517 926 <0.01
RM11943 1484 927 <0.01
N22/MTU1010RM431 1543 1149 <0.01
RM11943 1531 1199 <0.01
Single-marker analysis after covariance adjustment for DTF under drought stress
PopulationMean grain yield of N22
homozygoteMean grain yield of IR64/,
Swarna/, MTU1010 homozygotep-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
FGSelected 21 F1s
X~3000 BC3F2
217 dwarf plants
2 Plants segregating for qDTY1.1
Ratooned and split planted
X~180 BC3F3Full & partial QTL
linesScreened under ROS in WS2011
X
Single plant selected and
genotyped for foreground
FG
FG
Screened in DS2012X
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 backgroundNon 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
Sw arna
Apo
MT U1010
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 allelesConserved 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
N22Swarna
IR64
1 TBGI065107 40298480 C T T1 TBGI065108 40298598 T C C1 TBGI065127 40329203 A G G1 TBGI065129 40329319 C T T1 TBGI065130 40329422 G A A1 TBGI065133 40330056 G T T1 TBGI065139 40332364 T G G1 TBGI065142 40332797 G A A1 TBGI065146 40333650 A C C1 TBGI065154 40334497 C T T1 TBGI065155 40334597 C T T1 TBGI065156 40334719 T C C1 TBGI065158 40334855 G T T1 TBGI065161 40335346 T C C1 TBGI065169 40373741 G C C
1 TBGI065107 40298480 C T T1 TBGI065108 40298598 T C C1 TBGI065127 40329203 A G G1 TBGI065129 40329319 C T T1 TBGI065130 40329422 G A A1 TBGI065133 40330056 G T T1 TBGI065139 40332364 T G G1 TBGI065142 40332797 G A A1 TBGI065146 40333650 A C C1 TBGI065154 40334497 C T T1 TBGI065155 40334597 C T T1 TBGI065156 40334719 T C C1 TBGI065158 40334855 G T T1 TBGI065161 40335346 T C C1 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_Os01g656902. LOC_Os01g657803. LOC_Os01g660104. LOC_Os01g662905. 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
RM431Peak 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
qDTY1.1qDTY1.1
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 ta
ble
(cm
)
Duration
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 (m
m)
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 (m
m)
RAINFALL DATA OF DS2011
Rai
nfal
l (m
m)
Day
s to
50%
fl
ower
ing
(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
FLOWERING RANGE
Wat
er ta
ble
(cm
)R
ainf
all (
mm
)D
ays
to 5
0%
flow
erin
g (d
ays)
WATER TABLE DATA OF DS2011
Duration
RAINFALL DATA OF DS2011
WS2011, Nepal
Phenotyping at Nepal
RM
2808
9
(15.
41M
b)
RM
2819
9
(18.
15M
b)
Peak marker : RM28166Additive 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 ecosystemsUse of target variety in QTL study
WqDTY12.1
VqDTY2.3
qDTY3.2
+
Yield advantageUnder drought
Enhancedyield advantageUnder drought
(29-41%)
V/W
V/W
IqDTY12.1
SqDTY2.3
qDTY3.2
+
Yield advantageUnder drought
No additionalyield advantageUnder 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 2010F1 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….
BC1F5BC1F5
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……
AcknowledgementsTeam 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 JocelynTechnicians
• 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)