FOODCROPS.VN 2018. Jauhar Ali. Green Super Rice breeding technology Achievements and Advances

1. Jauhar Ali
Plant Breeder, Senior Scientist I
GSR Project Leader &
Regional Project Coordinator (Asia) GSR
PBGB, IRRI
GREEN SUPER RICE (GSR)
BREEDING TECHNOLOGY:
ACHIEVEMENTS & ADVANCES
Drought tolerance Screening

2. Why GSR?
 Food Security –threat-2008-global
village concept
 Stable sustainable yields using lesser
inputs-farmer practice-rainfed &
irrigated
 Diseases & Insect pest threats-high
input environments
 Caring for environment-pollution of
water systems-chemical residues

3. What is “GSR” ?
Rice cultivars that produce higher and more stable yields
with lesser inputs (water, fertilizers and pesticides)
High yielding GSR cultivars with “Green” traits:
Resistances/ tolerances to:
Abiotic stresses: Drought, salinity, alkalinity, iron toxicity, etc.
Diseases: Blast, bacterial blight, sheath blight, viruses,
and false smut etc
Insects: Brown plant hopper, Green leaf hopper, etc
Grain quality Mostly in elite RP background- later in RP-NARES
High resource-use efficiency: Water and nutrients (N P K)
 TEST SITES: AFRICA & ASIA=15countries
Asia: Cambodia,Indonesia,Laos,Vietnam,Bangladesh,Pakistan,Sri Lanka
Africa: Liberia, Mali, Mozambique, Nigeria, Rwanda, Senegal, Tanzania, Uganda
China: Guangxi,Guizhou,Suchuan,Yunnan
GSR Materials given to NARES=Hybrids(193) + Inbreds (152)
Less inputs, more production & environment sustainability

4. RP (3) x donors(205) F1s x RP BC1F1s x RP
~25 BC2F1s/donor x RP
x
Bulk BC2F2 populations
BC3F1s x RP
1, 2, 3, 4, 5, 6, ……
BC3F2 populations
Self and bulk
harvest
Selection for target traits
and backcrossing
BC4F1s
BC4F2s
Confirmation of the selected traits by replicated phenotyping
and genotyping of ILs for gene/QTL identification
Crosses made between sister ILs
having unlinked desirable genes/
QTLs for target ecosystem
DQP &MAS for pyramiding desirable
genes/QTLs and against undesirable donor
segments for target ecosystem
Development of GSR materials with improved target traits for wide scale
testing in different ecosystems and its release.
NILs for individual genes/QTLs for functional genomic studies
x
x
Self and bulk
harvest
1, 2, 3, 4, 5, 6, ……
Screening for target traits such as tolerances to
drought, salinity, submergence, anaerobic
germ., P & Zn def., BPH, etc.
Development of GSR materials by designed QTL pyramiding (DQP)
strategy for select target component traits for a given ecosystem
Alietal(2006)FCR97:66-76
Li,Z.K.andXu,J.L.(2007)“AdvancesinMolecularBreedingToward
DroughtandSaltTolerantCrops”Springerpp.531-565.

5. Development of ILS for different abiotic and
biotic stress tolerances at IRRI
Z.K. Li et al (2005) PMB 59:33-52; Ali et al (2006) FCR 97:66-76

6. Hidden diversity for abiotic and biotic
tolerance in the primary gene pool of rice
 Tremendous amounts of hidden diversity-BC progeny-
transgressive -target traits-regardless of donor performance-severe
stress screening
 Common to identify in BC progeny-extreme phenotypes (tolerances)
 Selection efficiency –highly dependent upon background
 Selection efficiency-affected by level of stress applied
 Selection efficiency for different target traits vary in BC generations.
 More distantly related donors, particularly landraces, tend to give more
transgressive segregations for complex phenotypes in the BC
progenies.
 Wide presence and random distribution of stress tolerance genes in
primary gene pool of rice –good news for rice breeders
Yu et al (2003) TAG 108:131-140; Ali et al (2006) FCR 97:66-76

7. FAVOURABLE DONORS (VARY ACCORDING TO RP)S.No.
OM1706,OM1723,FR13A,NAN29-2,BABOAMI, KHAZARST
TKM9,HEI-HE-AI-HUI(HHAH),JIANGXI-SI-MIAO(JSM), KHAZAR, MADHUKAR,
SHWE-THWE-YIN-HYE (STYH), BASMATI385, IKSAN438, YU-QIU-GU, TETEP,
NIPPONBARE, CO43, RASI, YUNHUI, BG304,BR24, FR13A GAYABYEO
ZDT
Y134,TKM9,KHAZAR,GAYABYEO,STYH,NAN29-2,
BABOAMI,JSM,FR13A,OM1706AG
CISEDANE,FR13A,IR50,NAN29-2,OM1706,STYH,TAROM MOLAEI,TKM9,Y134SUBT
NAN29-2,GAYABYEOLTG
JSM,BABOAMI,TKM9,BG300,C418,LEMONT,MADHUKAR,MR167,OM1706,STYH,
Y134BPH
BABOAMI, GAYABYEO, SHWE-THWE-YIN-HYE (STYH), NAN29-2, FR13A,
OM1706, KHAZAR, JIANGXI-SI-MIAO
MULTI-
TRAITS
Donors that gave better results with varying
recurrent parental backgrounds
Ali et al (2006) FCR 97:66-76

8. ExperimentsetI
IR64 x BR24
F1 x IR64
BC2F2
IR64 x Binam IR64 x STYH
F1 x IR64
BC2F2
IR64 x OM1723
F1 x IR64
BC2F2
F1 x IR64
BC2F2
13 BC2F2 populations screened under two types of severe drought, resulting in 221 survived
DT BC2F3 introgression lines (ILs), which were genotyped with SSR markers
IR64 x Type3
F1 x IR64
BC2F2
IR64 x HAN
F1 x IR64
BC2F2
IR64 x Zihui100
F1 x IR64
BC2F2
ExperimentsetII
Screened under severe drought at the reproductive stage, resulting in 455 survived
DT F2 plants, which were progeny tested and genotyped with SSR markers
IL1 x IL2
F1
F2
X
IL3 x IL4
F1
F2
X
IL7 x IL15
F1
F2
X
9 1st round pyramiding
F2 populations from
crosses between 15 ILs
ExperimentsetIII
Screened under severe drought at the reproductive stage and 667 survived
DT F3 lines were progeny tested and genotyped with SSR markers
(PL1 , PL2, PL3) x (PL4, PL5, PL6, PL7, PL8)
F1s
F2s
X
14 2nd round pyramiding F2
populations from crosses
between 8 1st round PLs
Designed QTL pyramiding experiments

9. Putative genetic networks identified in 455 DT PLs derived
from 9 crosses between DT IR64 ILs
Drought
AG2-1 (5)
0.994
RM575
(1.4)
0.745
RM342
(8.5)
0.673
AG2-2 (6)
0.891
RM347
(3.8)
0.691
RM469
(6.1)
0.818
RM215
(9.8)
0.527
RM561
(2.6)
0.618
RM544
(8.2)
0.727
RM309
(12.5)
0.927
RM202
(11.3)
0.745
RM463
(12.5)
0.745
RM179
(12.3)
0.727
B:
Drought
AG3-1 (4)
1.00
AG3-2
(4)
0.855
RM302
(1.10)
0.782
AG3-3
(3)
0.736
RM172
(7.7)
0.727
C:
Drought
AG1-1 (7)
1.00
AG1-3
(13)
0.748
AG1-2 (7)
0.979
AG1-5
(5)
0.726
RM418
(7.3)
0.717
AG1-4
(4)
0.688
RM109
(2.1)
0.617
RM179
(12.3)
0.607
A:
DroughtD:
AG4-1
(6)
RM271
(10.4)
RM23
(1.5)
AG4-2
(4)
AG4-3
(4)
AG4-4
(3)
RM544
(8.3)
RM179
(12.3)
RM215
(9.8)
RM220
(1.2)
RM272
(1.3)
RM441
(11.2)
AG7-1 (18)
1.00
RM36
(3.3)
AG7-5
(2)
AG7-2
(2)
AG7-3
(16)
Drought
G:
AG7-7
(2)
AG7-4
(7)
RM275
(6.6)
RM110
(2.1)
RM224
(11.7)
RM294B
(1.6)
RM435
(6.1)
RM13
(5.2)
RM5
(1.7)
RM245
(9.8)
RM30
(6.8)
RM18
(7.6)
RM465A
(2.5)
RM469
(6.1)
RM286
(11.1)
RM289
(5.3)
RM44
(8.3)
RM516
(5.3)
RM85
(3.12)
AG8-1 (26)
1.00
RM448
(3.10)
RM331
(8.4)
RM481
(7.1)
RM535
(2.12)
DroughtH:
RM32
(8.3)
AG8-2
(2)
RM30
(6.7)
AG8-3
(3)
RM562
(1.6)
RM547
(8.3)
RM275
(6.5)
RM143
(3.12)
RG8-6
(2)
RM197
(6.1)
RM5
(1.7)
RM307
(2.1)
RM449
(1.6)
RM14
(1.13)
RM169
(5.3)
AG8-4
(3)
RM246
(1.8)
AG8-5
(2)
RM589
(6.1)
RM317
(4.6)
RM258
(10.4)
RM154
(2.1)
RM245
(9.8)
RM335
(3.12)
RM446
(1.6)
RM211
(2.2)
Drought
AG6-1 (8)
1.000
AG6-3
(12)
0.894
AG6-2 (5)
0.967
RM44
(8.3)
0.633
RM235
(12.6)
0.667
AG6-4
(2)
0.772
RM51
(7.1)
0.833
RM20
12.1
0.567
F:
I: Drought
AL9-1 (3)
1.000
RM152
(8.1)
0.930
AG9-5(3)
0.553
AG9-2(2)
0.915
RM211
(2.2)
0.800
RM446
(1.6)
0.830
RM350
(8.4)
0.800
AG9-4 (5)
0.500
AG9-3(24)
0.870
RM215
(9.7)
0.870
RM554
(3.7)
0.700
Drought
RM543
(1.1)
1.000
AG5-4 (2)
0.767
RM53
(2.3)
0.833
AG5-1 (12)
0.711
AG5-2 (9)
0.809
RM401
(4.1)
0.733
RM433
(8.7)
0.867
RM298
(7.1)
0.767
RM17
(12.7)
0.500
RM270
(12.6)
0.567
RM222
(10.1)
0.567
RM424
(2.5)
0.667
AG5-3(2)
0.525
RM244
(10.1)
0.583
RM248
(7.7)
0.500
RM101
(12.4)
0.766
E:
Li et al 2012 unpubl.

10. Ch.2Ch.1 Ch.3 Ch.4 Ch.5 Ch.6
RM109
RM485
RM154
RM211
RM236
RM279
RM423
RM8
RM53
RM233A
RM174
RM145
RM71
RM327
RM521
RM300
RM324
RM424
RM262
RM341
RM475
RM106
RM263
RM526
RM221
RM525
RM318
RM450
RM497
RM6
RM240
RM530
RM112
RM250
RM166
RM197
RM213
RM48
RM207
RM266
RM138
RM307
RM401
RM537
RM335
RM518
RM261
RM471
RM142
RM273
RM252
RM241
RM470
RM303
RM317
RM348
RM349
RM131
RM280
RM567
RM559
RM122
RM153
RM413
RM13
RM267
RM437
RM289
RM509
RM598
RM163
RM164
RM291
RM161
RM188
RM421
RM178
RM26
RM274
RM87
RM480
RM538
RM334
RM399
RM169
RM204
RM587
RM588
RM589
RM510
RM204
RM585
RM111
RM225
RM314
RM253
RM50
RM549
RM539
RM136
RM527
RM3
RM343
RM528
RM30
RM340
RM400
RM439
RM103
RM141
RM176
RM494
RM557
RM584
RM60
RM81B
RM22
RM523
RM569
RM231
RM175
RM545
RM517
OSR13
RM7
RM232
RM251
RM282
RM338
RM156
RM411
RM487
RM16
RM504
RM203
RM186
RM55
RM168
RM416
RM520
RM293
RM114
RM130
RM565
RM514
RM570
RM227
RM85
Ch.8 Ch.9 Ch.10 Ch.11 Ch.12Ch.7
RM474
RM222
RM216
RM239
RM311
RM467
RM184
RM271
RM269
RM258
RM171
RM304
RM228
RM147
RM333
RM496
RM436
RM51
RM481
RM125
RM180
RM501
OSR22
RM214
RM418
RM432
RM11
RM346
RM182
RM336
RM10
RM351
RM455
RM505
RM234
RM18
RM172
RM248
RM408
RM506
RM407
OSR30
RM544
RM25
RM407
RM44
RM72
RM137
RM331
RM339
RM342A
RM515
RM284
RM210
RM556
RM256
RM149
RM230
RM264
RM281
RM296
RM285
RM316
RM444
RM219
RM524
RM105
RM321
RM409
RM460
RM566
RM434
RM257
RM108
RM242
RM278
RM201
RM107
OSR28
RM189
RM215
RM205
RM286
RM332
RM167
RM120
RM479
RM181
RM202
RM536
RM260
RM287
RM209
RM229
RM457
RM187
RM21
RM206
RM254
RM224
RM144
RM20A
RM4A
RM19
RM247
RM512
RM179
RM101
RM277
RM511
RM519
RM313
RM309
RM463
RM235
RM270
RM17
RM4B
RM14
OSR23
RM431
RM472
RM297
RM265
RM319
RM315
RM128
RM212
RM403
RM473A
RM246
RM237
RM306
RM5
RM9
RM594
RM323
RM84
RM428
RM220
RM86
RM522
RM283
RM1
RM272
RM575
RM490
RM576
RM259
RM583
RM243
RM600
RM572
RM580
RM581
RM23
RM129
RM446
RM329
RM562
Bin1.1
Bin1.2
Bin1.3
Bin1.4
Bin1.5
Bin1.6
Bin1.7
Bin1.8
Bin1.9
Bin1.10
Bin1.11
Bin1.12
Bin1.13
Bin2.1
Bin2.2
Bin2.3
Bin2.4
Bin2.5
Bin2.6
Bin2.7
Bin2.8
Bin2.9
Bin2.10
Bin2.11
Bin2.12
Bin3.1
Bin3.2
Bin3.3
Bin3.4
Bin3.5
Bin3.6
Bin3.7
Bin3.8
Bin3.9
Bin3.10
Bin3.11
Bin3.12
Bin4.1
Bin4.2
Bin4.3
Bin4.4
Bin4.5
Bin4.6
Bin4.7
Bin4.8
Bin6.1
Bin6.2
Bin6.3
Bin6.4
Bin6.5
Bin6.6
Bin6.7
Bin6.8
Bin6.9
Bin5.1
Bin5.2
Bin5.3
Bin5.4
Bin5.5
Bin5.6
Bin5.7
Bin10.7
Bin10.1
Bin10.2
Bin10.3
Bin10.4
Bin10.5
Bin10.6
Bin7.1
Bin7.3
Bin7.5
Bin7.7
Bin7.2
Bin7.4
Bin7.6
Bin8.8
Bin8.1
Bin8.2
Bin8.3
Bin8.4
Bin8.5
Bin8.6
Bin8.7 Bin9.8
Bin9.1
Bin9.2
Bin9.3
Bin9.4
Bin9.5
Bin9.6
Bin9.7
Bin11.7
Bin11.1
Bin11.3
Bin11.4
Bin11.5
Bin11.6
Bin12.5
Bin12.1
Bin12.2
Bin12.3
Bin12.4
Bin12.6
Bin12.7
RM245
RM547
RM447
Bin11.2
Binam segments
BR24 segments
STYH segments
OM1723 segments
Genomic correspondences between FGUs identified in 150 ILs of 8 BC2 populations,
200 PLs of 3 1st round pyramiding crosses and 4 2nd round pyramiding crosses.
RM462
RM555
RM516
RM190
RM454
RM162
RM223
RM126
RM561
RM540
RM469
RM302
RM488
RM347
RM535
RM25022
RM23818
RM233B
RM19029
RM19778
RM245
RM25181
RM473E
RM499
RM10287
RM14963
RM26063
RM11570
RM551
Cross III-2
Cross III-1
Cross III-3
Cross III-4
FGUs identified in cross II-1
FGUs identified in cross II-2
FGUs identified in cross II-3
Li et al 2012 (unpubl)

11. The mean yield performances (t/ha) of 48 2nd round PLs (4 types) as
compared to IR64 (CK), under the irrigated control (C), drought stresses
at the vegetative (VS) and reproductive stages (RS) in the 2007 and 2008
dry-season. Guan et al. 2010 JXB
Meanyieldunderthe
irrigatedcontrol(t/ha)
3.0
0.5
1.0
1.5
2.0 2.5
0.5
1.0
1.5
2.0
2.5
3.0
Type III (N=19)
C: 5.06±0.47
VS: 1.98±0.47
RS: 1.94±0.52
Type I (N=17)
C: 5.76±0.53
VS: 2.07±0.55
RS: 1.79±0.47
Type II (N=5)
C: 5.71±0.42
VS: 1.36±0.38
RS: 2.20±0.45
Type IV (N=7)
C: 4.66±0.48
VS: 1.34±0.41
RS: 1.86±0.51
IR64 (CK)
C: 4.68±0.23
VS: 1.49±0.14
RS: 0.52±0.38
3.5
4.0
4.5
5.0
5.5
3.0
6.0
6.5
0.0

12. Highly
salinity
tolerant

13. IRRI DT Check variety
IR74371-70-1-1
GSR-IR83142-B-19-B
GSR Drought tolerant pyramided lines in IR64 background
Under zero input conditions at IRRI DS2010

14. Entry
No.
GSR Lines
Mean
(t/ha)
LSD
Group
15 IR 83142-B-57-B 5.46 a
9 IR 83141-B-17-B 5.17 b
19 IR 83142-B-7-B-B 5.13 bc
18 IR 83142-B-79-B 5.12 bc
11 IR 83142-B-19-B 5.06 bcd
5 IR 83140-B-11-B 5.05 bcde
10 IR 83141-B-18-B 5.02 bcdef
6 IR 83140-B-28-B 4.94 bcdefg
13 IR 83142-B-21-B 4.86 cdefg
12 IR 83142-B-20-B 4.79 defg
14 IR 83142-B-49-B 4.78 efg
16 IR 83142-B-60-B 4.75 fg
20 IR 83142-B-8-B-B 4.74 g
7 IR 83140-B-32-B 4.74 g
3 Best Check 4.67 g
8 IR 83140-B-36-B 4.32 h
1 2nd Best Check 4.29 h
17 IR 83142-B-61-B 4.27 h
4 IR 74371-70-1-1 3.57 i
2 Apo 3.53 i
-0.5 0.0 0.5 1.0
-1.0-0.50.00.5
PC 1
PC2
1
2
3
4
5
6
7
8
9
10
11
12
13
14 15
16
17
1819
20
10amBrGa
10dsIcJa
10dsIcTe
10dsIRig
10suVaDu10suVaGi
1
2
PC %
60.9
24.7
IR 83142-B-19-B
Best
Check
2nd Best Check
DT PDLs AMMI-Biplot: 6 Locations -2011DS
BRAC-Gaz, VAAS-Gia, VAAS-Duo, ICRR-Jak, ICRR-Teg, & IRRI-Los Banos
IR 83140-B-11-B
Environments
Mean
(t/ha)
LSD
Group
IRRI-Los Banos 6.55 a
VAAS-Gia 6.53 a
VAAS-Duo 6.06 b
BRAC-Gaz 4.29 c
ICRR-Jak 3.18 d
ICRR-Teg 2.08 e
IR 83142-B-57-B
Why such yield advantages?
Designed QTL Pyramiding
Possible role of Epigenetics
Selection for grain yield, higher
spikelet fertility, deeper and thicker
roots esp. under reproductive
stage DT stress

15. GSR entry No of
panicles
Plant
height
(cm)
Maturity
(days)
Yield
(kg/ha)
%
increase
over
FL478
SES
score
4WAT
SES
score
Maturity
IR83140-B-11-B 16 84 116 1140 103.6 4 5
IR83140-B-28-B 13 86 114 876 56.4 4 5
IR83140-B-32-B 15 85 114 657 17.3 4 5
FL478 11 70 111 560 0.0 5 -
NSIC 222 19 83 112 147 -73.8 4 -
Promising GSR Drought + Salinity tolerant materials tested under Iloilo during WS2010
First two nominated for NCT Philippines WS2011

16. IR83140-B-11-B
PVS Purvakarta
2.5ha trial area
Indonesia 8.2011
Grainyieldt/ha
Site specific nutrient management (SSNM)
Untungetal(2012)unpubl.

17. HYBRIDS INBREDS
Total
Batch 1 Batch 2 Batch 2 Batch 3 Batch 4 Batch 1 Batch 2 Batch 3 IRRI-GSR
No. of lines 24 80 42 37 9 22 31 9 47 301
Line composition
IRLL, HY IRLL, HY RFLL, DT IRLL, DT,
HT, Nuse,
T-BB, BL,
BPH, SB
RFLL, (I &
J)
RFLL, I,
DT, T-BL,
GQ
RFLL (I &
J), DT, T-
BL, BB,
TBB, HT,
WT, ST,
GQ
DT, SubT,
ST, HY
-
Total no. of experiment reported - 15 10 21 12 16 39 31 10 154
No. of location - 14 8 17 11 14 21 18 8 111
Year/Season - 5 4 5 3 5 7 6 4 39
No. of data sets received from
NARES
- 12 10 10 12 13 23 27 9 116
No. of replicated data - 5 5 10 10 4 23 19 76
No. of data sets usable for GxE
Analysis
- 3 4 10 10 3 14 14 58
5 Best Entries
1 - IIyou3203 HanF1-40 CXY2 HuF1-9
Zonghua
1
Luyin 46 ZH1
2 - CXY2 HanF1-41 QS2 HuF1-17 HHZ SAGC-4
TME8051
8
3 - CXY727 HanF1-27 IIyou623 HuF1-8 BD007 926 FFZ
4 - ZXY673 HanF1-36 Annong5 HuF1-4 SACG-4 SAGC-08 P35
5 - XYR24 HanF1-39 3LYR24 HuF1-13 RC8 SAGC-02 HHZ
Mean yield across location (t/ha) 7.13 5.83 5.49 6.17 4.21 5.09 5.26
Average advantage over the best
check
8.3% 22.1% 6.2% 28.8% -1.6% 8.7% 12.5%
Yield advantage of the best entry 13.3% 26.9% 13.1% 33.5% 7.9% 12.8% 19.6%
ANOVA: Pr(>F)
ENV 8.808E-09 2.334E-05 5.551E-16 1.143E-10 7.674E-06 <2.2e-16 <2.2e-16
REP(ENV) 0.0008013 5.983E-08 <2.2e-16 <2.2e-16 0.723 7.58E-15 1.026E-07
GEN <2.2e-16 <2.2e-16 <2.2e-16 <2.2e-16 1.206E-07 <2.2e-16 <2.2e-16
ENV:GEN <2.2e-16 1.763E-08 <2.2e-16 <2.2e-16 0.2526 <2.2e-16 <2.2e-16
Summary of GSR data received from NARES in Asia

18. Type of GSR
lines in NCT
trials
Mali
Senegal
Rwanda
Nigeria
Mozambique
Tanzania
Uganda
Bangladesh
Indonesia
LaoPDR
Cambodia
Pakistan
SriLanka
Vietnam
Philippines
Inbreds 2 4 1 3 3 2 5 7 4 1 6 2 4 15
Hybrids 2 4 2 3 3 3 4 8 2
Total 4 8 3 6 3 3 5 9 13 4 1 6 2 6 15
List of the nominated GSR inbreds and
hybrids for NCTs in the target SSA, SEA and
SA countries
• A total of 20 GSR inbreds and 21 hybrids have been nominated to the NCTs
of the 8 target SSA countries;
• A total of 48 inbreds and 24 hybrids have been nominated in the NCTs of 8
Asian countries.

19. Name CoteD’ivoir
Mali
Rwanda
Nigeria
Mozambique
Tanzania
Uganda
Bangladesh
Indonesia
LaoPDR
Pakistan
SriLanka
Vietnam
Philippines
All
HHZ 2 1 1 2 1 2 1 1 11
Zhongzu14 2 1 1 1 1 1 7
ZH1 2 1 2 1 1 1 1 9
KCD1 2 1 1 1 1 1 7
RC8 1 2 1 1 1 6
Weed Tolerant 1 1 2 1 2 1 7
HUA-565 2 2 1 5
FFZ 1 1 1 1 1 5
SAGC-4 2 2 1 1 1 7
WX763 2 1 1 1 5
List of the promising widely adaptable GSR inbreds
identified from adaptation yield trials in SSA, SEA and SA
HHZ developed in GAAS is a mega-variety of high yield & superior quality grown in 8 provinces of
South & Central China (Guangdong, Jiangxi, Fujian, Hunan, Hubei, Anhui, Yunan and Guangxi).

20. The complex pedigree of Huang-Hua-Zhan
(HHZ) involving 14 parents

21. P1(FHZ) P2(HXZ) HHZ P1(FHZ) P2(HXZ) HHZ
S.C.Zhouetal.,unpublishedZ.K.Lietal2012(unpubl.)

22. Ch. 1
Ch. 2
Ch. 3
Ch. 4
Ch. 5
Ch. 6
Ch. 7
Ch. 8
Ch. 9
Ch. 10
Ch.11
Chr. 12
Genomic composition of the HHZ genome based on the
re-sequencing data (From S. C. Zhou et al., unpublished)
Each colored vertical line corresponds to a window of 10 kb. Vertical lines distribute upper side on each
chromosome represent AZ haplotype blocks (red for ≥200kb AZ blocks, light red for <200kb AZ blocks) and
QZ haplotype blocks (blue for ≥200kb QZ blocks and light blue for <200kb QZ blocks). Vertical lines
distribute lower side on each chromosome represent “Stress” related QTL region (light yellow), “Quality”
related QTL region (light green) and “Yield” related QTL region (light purple). Blue and red arrows indicate
QZ blocks overlapped with “Yield” related QTL regions and AZ blocks overlapped with “Quality” related QTL
regions, respectively.

23. IRRI-GSR breeding program & strategy

24. Two batches of 16 populations with the recurrent parent, Huang-
Hua-Zhan (HHZ) and 16 donors from 9 different countries
Batch Pop. Donor Country of origin Gen.(10 DS)
1 HHZ5 OM1723 Vietnam (I) BC1F5
1 HHZ8 Phalguna India (I) BC1F5
1 HHZ9 IR50 IRRI (I) BC1F5
1 HHZ11 IR64 IRRI (I) BC1F5
1 HHZ12 Teqing China (I) BC1F5
1 HHZ15 PSB Rc66 Philippines (I) BC1F5
1 HHZ17 CDR22 India (I) BC1F5
1 HHZ19 PSB Rc28 Philippines (I) BC1F5
2 HHZ1 Yue-Xiang-Zhan China (I) BC1F4
2 HHZ2 Khazar Iran (J) BC1F4
2 HHZ3 OM1706 Vietnam (I) BC1F4
2 HHZ6 IRAT352 CIAT (upland) BC1F4
2 HHZ10 Zhong 413 China (I) BC1F4
2 HHZ14 R644 China (I) BC1F4
2 HHZ16 IR58025B IRRI (I) BC1F4
2 HHZ18 Bg304 Sri Lanka (I) BC1F4

25. The Introgression Breeding Procedure
8 HHZ BC1F2 populations (08WS)
DT screen SUB screen
15SUBT plants
326 Genotyping/progeny testing for all target traits
108Preliminary yield trials under DT, low input, NC
Random plants
Confirming genetic
networks for target
traits and their
genetic relationships
109DT plants
Yield traits
QTL/Allelic
diversity
discovery
for target
traits
82HY plants
ST screen
120ST plants
68Promising ILs
326DT screen 311SUB screen326Yield 326ST screen
06WS
08WS
09DS
47DT ILs 171SUB ILs73HY ILs 78ST ILs
09WS 369Genotyping/progeny testing for all target traits
10DS
10WS/11DS 68 Replicated
yield trials
~80 promising ILs as
parents for designed
QTL pyramiding
2NCT &
29 MET for 11WS
3Demo
Ist round
selection
2nd round
selection
3rd round
selection
Selections can
be continued if
certain lines
segregating

26. The Introgression Breeding Procedure
8 HHZ BC1F2 populations (09WS)
DT screen SUB screen
21SUBT plants
637Genotyping/progeny testing for all target traits
Random plants
Confirming genetic
networks for target
traits and their
genetic relationships
210DT plants
Yield traits
QTL/Allelic
diversity
discovery
for target
traits
119HY plants
ST screen
287ST plants
DT screen SUB screenYield under
NC & LI
ST screen
06WS
09WS
10DS
180DT ILs 221SUB ILs420HY&FUE ILs 44ST ILs
10WS 865Genotyping/progeny testing for all target traits
11DS
~80 promising ILs as parents
for designed QTL pyramiding
DT screen SUB screenYield under
NC & LI
ST screen
DT ILs SUB ILsHY&FUE ILs ST ILs
136 PYT11WS
80 RYT12 DS
2 NCT & 11 MET
12DS
2 Demo

27. Target traits
Number of ILs
Produced from
BN
Selected at PYT &
RYT
Nominated to
MET & NCT
Drought tolerance (DT) 613 79 21
High yield under low-input (LI) 370 27 3
Salinity tolerance (SAL) 502 73 18
Submergence tolerance (SUB) 128 13 2
High yield under irrigated (Y) 576 100 27
DT+LI 246 15 2
DT+SAL 326 19 5
DT+SUB 82 6
DT+Y 382 40 11
LI+SAL 274 10 1
LI+SUB 38 0
LI+Y 178 1
SAL+SUB 60 9
SAL+Y 292 42 8
SUB+Y 101 5 1
DT+SAL+SUB 35 3 1
DT+SAL+Y 154 9
DT+SUB+Y 58 3
LI+SAL+SUB 20 0
LI+SAL+Y 117 0
LI+SUB+Y 36 0
SAL+SUB+Y 39 2
total: 845 146 40
IL=Introgression lines; BN=Backcross Nursery;PYT=Preliminary Yield Trial;RYT=Replicated Yield Trial; NCT=National Cooperative Testing
(Philippines); Multi-environment testing (IRRI)
Multiple abiotic stress tolerant ILs developed from 16 donors into Huanghuazhan
background and nominated to NCT using GSR breeding scheme.
2ndGenerationGSRmaterials

28. GSR Technology
GSR
Technology
IL-Breeding,
PDLs & DQP
Ideal RP BG
Screening of
released GSR
materials under
target ecosystems
Screening of already
developed PDLs for
abiotic stresses DT,
ST, SUB, LI in the
target ecosystems
DQP for a trait &
ecosystem related
traits
ILs, PDLs, DQP
with adaptable RP
BG for different
target ecosystem
Increase in success rate to develop highly
adaptable genotypes for a given ecosystem
First Phase
2009-2012
Second Phase
2012-2018
Ecosystem based approach
GSR
500 donors
56 RPs

29. HHZ PSBRc66 BC1F5 # 329 BC1F5 #350
Blast evaluation of virulent strains Evaluation of BB resistance of >500
lines (HHZ background) against 14
strains of 10 Xoo races, 2010 WS
Vera Cruz et al

30. Rapid Visco Analyzer (RVA) Pasting properties of GSR lines in IR64 and HHZ RP
backgrounds-suitable for varied consumers with different taste preferences
-1000
0
1000
2000
3000
4000
5000
6000
0 100 200 300 400 500 600 700 800
Time, sec
Viscosity,cP
0
20
40
60
80
100
120
Temperature
1 2
3 4
5 6
7 8
9 10
11 12
13 14
15 16
17 18
19 20
21 22
23 24
25 26
27 28
29 30
31 32
33 34
35 36
37 38
39 40
AC=14.5-31.6%;GT=H-I-L;Protein=7.8-11.2

37. HHZ12-DT10-SAL1-DT1- PVS trials (40 farmers) at
Puypuy, Laguna –ranked best over farmer’s check
NSiC214 during WS2011 with preference
score=0.118 against -0.0063(NSiC214)
High Yielding, suitable for Direct seeding & Irrigated conditions,
Aromatic, Drought and Salinity tolerant

38. Designation
Grain Yield (t/ha) Mean
over
seasons
% over
IR72
% over
NSICRc
1582010WS 2011DS
HHZ8-SAL6-SAL3-Y2 6.55ab 8.0ab 7.28 10.56 12.27
Mestizo7 (Hybrid) 5.68 bcde 8.7a 7.19 9.27 10.96
HHZ12-DT10-SAL1-DT1 6.75a 7.2 bcde 6.98 6.00 7.64
IR83142-B-7-B-B 6.00 abcde 7.6 bc 6.80 3.34 4.94
HHZ5-SAL10-DT1-DT1 6.14abcd 7.4 bcd 6.77 2.89 4.48
IR72 5.96abcde 7.2 cde 6.58 0.00 1.54
HHZ5-DT8-DT1-Y1 5.55 cde 7.6 bc 6.58 -0.08 1.47
HHZ8-SAL12-Y2-DT1 6.43abc 6.7 def 6.57 -0.23 1.31
NSICRc158 5.86 bcde 7.1 cdef 6.48 -1.52 0.00
HHZ12-Y4-DT1-Y1 5.57cde 7.1 cdef 6.34 -3.72 -2.24
IR83142-B-19-B 5.12 e 7.5 bcd 6.31 -4.10 -2.62
IR83142-B-57-B 5.48 de 7.1 cdef 6.29 -4.41 -2.93
IR83143-B-21-B 5.16 e 7.2 cde 6.18 -6.08 -4.63
HHZ8-SAL9-DT2-Y1 5.78 bcde 6.4 defg 6.09 -7.45 -6.02
HHZ5-SAL10-DT3-Y2 5.69 bcde 6.3 fg 6.00 -8.89 -7.48
HHZ5-SAL10-DT2-DT1 5.47 de 6.0 g 5.74 -12.84 -11.50
Reason:Higher HI, spikelets per panicle;panicles per sqm;total spikelets per sqm,CGR
Performance of IRRI bred GSR High Yield Potential
Varieties under Irrigated Conditions
Plot size:
30sqm
SSNM

39. Zhongzu14-ski-4-1

40. BPH and Virus Resistance Screening
IRRI-ICRR joint project collaborators: Prof.Baehaki/Drs Muhsin,Untung
• 30 BC3F2 and BC2F3 population (CS 3)
• 39 BC3F3 and BC2F4 population (CS 4;
3rd year)ongoing
BC2 F3 HHZ populations screened against
virulent BPH strain that caused outbreak in
Sukamandi in 2010
Several populations showed ILs with comparable
resistance with the checks in second round of
screening.
ICRR 8.2011

41. An additional tonne of rice in the
rainfed and irrigated lowlands will
change the livelihoods of millions of
resource poor farmers from the
clutches of poverty and sustained
income source to prosper….
THANKS

42. Acknowledements & Thanks:
CAAS-IRRI-BMGF
Dr Zhikang Li Director GSR project
GSR National Coordinators (Asia & Africa)
Drs Tuat, Untung, Rafiqul-Islam, Somphet, Nimal, Riaz and Arif, Makara,
Two public/NGO sectors:
Dr W.Xu (Boshima-SS,IDO) & Dr Sirajul Islam(BRAC)
Dr C.X Mao GSR Training Consultant (CAAS-GAAS)
GSR-CAAS team: Drs Z. Li, Gao, Xu, Judy, Fu, Yu & many unknown
contributors to the GSR materials
IRRI GSR: Drs Nollie, Glenn, Choi, Redonna, Pandey, Andy, Krishna,Wang,Tao
GSR-GML group Gelo (Data & field); Corine(PVS), Lolit(field operations),
Nina(Screenings)
Visiting Research Fellows: Drs Ma, Dr Uzokwe PhD: Zilhas, Meng; OJT:Shahana,
Dilruba
GSR Project Adm.: Pauline; Secretarial Assistance: Badett
“Cooperation & Collaboration makes the world a smaller place”

43. Dr. V. Ravindra Babu
Principal Scientist, Plant Breeding
Directorate of Rice Research,
Rajendranagar, Hyderabad-30,
rbvemuri1955@gmail.com

44. OUTLINE OF PRESENTATION
INTRODUCTION
METHODOLOGY
FINDINGS
CONCLUSIONS
FUTURE COURSE OF ACTION

45. INTRODUCTION
Rice is the dominant cereal crop in most Asian countries
and is the staple food for more than half of the world’s
population, even a small increase in its nutritive value
would be highly beneficial for human health.
Recently breeding rice with high nutrient content known as
bio-fortification has evolved as a new strategy to address
micronutrient mal-nutrition
Bio-fortification provides a cost effective and sustainable
solution to combat mal-nutrition
At DRR, more than 200 varieties were tested for their iron
and zinc content and also identified donors for them and
breeding strategy was evolved to develop high iron and
zinc content lines

46. Micronutrient fortification of plants
through plant breeding: Can it
improve nutrition in man at low cost?
To be successful, the biofortification strategy must
address four fundamental questions:
1. Can commonly eaten food staple crops be developed that
fortify their seeds with essential minerals and vitamins?
2. Can farmers be induced to grow such varieties?
3. If so, would this result in a significant Improvement in human
nutrition at a lower cost than existing nutrition interventions?
4. Bio-availability of these minerals?

47. GLOBAL SHARE OF DIETARY ENERGY SUPPLY
FROM DIFFERENT PLANT SOURCES
Wheat
24%
Rice
27%
Maize
7%Potatoes
2%
Millet & Sorghum
4%
Sweet potatoes
2%
Soyabean oil
3%
Others
19%
Other Veg. oils
3%
Suger
9%
Source FAO. 1996

48. Regulates enzyme activity
and plays an important
role in the immune system
(Lynch, 2003)
IRON
REQUIREMENT PER
DAY
10-15 milligrams
(mg)
Health problems caused by iron deficiency
Mental and psychomotor impairment in children, and
Increased levels of morbidity and mortality rate of mother and
child during childbirth (Frossard et al., 2000)
2.7 billion people globally are
known to be affected by iron
deficiency till to date (Hirschi,
2009)

49. Regulates enzyme
activity, essential for cell
division and DNA
replication
ZINC
REQUIREMENT PER
DAY
Males 12-15mg/day
Females 68 mg/day
Health problems caused by zinc deficiency
Anorexia,
Dwarfism,
Weak immune system (Solomons, 2003)
Skin lesions,
Hypogonadism, and
Diarrhoea (McClain et al., 1985).
In Asia and Africa, it is
estimated that 500-600 million
people are at risk for low zinc
intake (Harvest Plus, 2010)

50. In the last two decades, new research findings generated by the
nutritionists have brought to light the importance of vitamins,
minerals (micronutrients) and proteins in maintaining good health
Nutritionist
Breeder
Biotechnologist
RICE, WHEAT, MAIZE, PEARL
MILLET, CANOLA
A genetic approach called
Biofortification (Bouis, 2002) has been
developed, which aims at biological
and genetic enrichment of food stuffs
with vital nutrients

51. Breeders are now focusing on breeding for
nutritional enhancement to overcome the problem of
malnutrition.
The range in brown rice
Iron 6.3 - 24.4 ppm
Zinc 13.5 - 28.4 ppm
Suggesting some genetic potential to increase the
concentration of these micronutrients in rice grains
(Gregorio, 2002)

52. 1/17/2012 9:59:16 PM 10

53. Selection of parents for hybridization programme
Crossing programme for developing high Iron and Zinc genotypes
Selections in segregating generation
G X E Interactions
Study of losses due to polishing
Impact of polishing on grain type
Impact of parboling on Fe and Zn contents
Correlation between Fe and Zn to yield
Continued……………..
Collection of germplasm & screening for Fe and Zn contents

54. Conventional and molecular Breeding Approach
Fe and Zn contents in red rices and popular varieties
Genetic analysis of Fe and Zn contents
Impact of agronomic management on Fe and Zn
Impact of Fe and Zn on grain quality
Molecular Studies
Bioavailability studies
Developing High Iron and Zinc line with higher yields
Variety testing in AICRIP programme and release
Studies on protein, bran oil, phytates & glycemic index

56. NUTRITIONAL STATUS OF STUDY MATERIAL
Trait General Study material
(max)
Iron (ppm) 7.0 34.4
Zinc (ppm) 14.0 28.3
Protein (%) 6.8 12.48
Fat (%) 0.5 3.77
Fiber (%) 0.2 0.80
Energy (kcal 100g-1) 345 376
Thiamin (mg 100g-1 ) 0.06 1.91

57. Losses due to polishing rice (%)
Protein 29
Fat 79
Lime 84
Iron 67
Losses due to washing of milled rice (%)
Thiamine 40
Riboflavin 25
Niacin 23
Losses from cooking & washing (%)
Calories 15
Proteins 10
Iron 75
Calcium 50
Phosphorus 50

58. 5% polishing 10% polishing
Zinc 62.5 68.4
Iron 61.0 69.3
Thiamine 75.4 89.7
Ash content 55.2 63.91
Protein 7.08 12.70
Fat 69.14 88.54
Crude fibre 84.4 93.8
Energy 3.2 3.5
PERCENTAGE LOSSES AS COMPARED TO
RAW MILLED RICE

59. S.No. Name of Genotype
Grain
Type
Fe (ppm) Zn (ppm)
Brown
Rice
5%
polished
rice
10%
polished
rice
Brown
Rice
5%
polishe
d rice
10%
polished
rice
1 PANDY SB 20.8 12.9 8.6 22.3 19.3 15.9
2 BHADAS SB 10.4 5.6 2.3 22.9 18.1 17.4
3 MUNGA SB 22.5 4.0 2.1 31.1 18.4 17.3
4 RALAK LS 14.3 5.2 8.0 19.9 16.3 14.8
5 IMPHAL LS 10.1 5.4 4.1 23.1 18.5 17.9
6 FOXTAIL SB 9.0 3.3 2.3 25.2 19.5 18.0
7 DODDABYRA SB 3.2 2.6 0.9 29.0 21.4 17.1
8 CHAGLEI SB 5.3 2.4 2.0 28.3 20.3 16.9
9 THANU MS 4.6 3.0 2.5 24.1 18.6 14.7
10 HEMAVATHI SB 5.1 3.3 2.4 27.7 24.7 20.1
11 PANVEL-2 LS 5.2 4.2 3.8 29.6 28.3 25.2
12 BYRANELU SB 9.7 8.0 6.3 28.4 19.5 17.2
13 KANCHANA SB 10.7 3.5 2.3 25.7 18.7 20.2
14 SHARAVATHI SB 8.7 4.3 2.5 30.4 23.0 17.1
15 NAHAZING SB 4.9 4.0 2.7 29.8 23.1 20.6
Iron and Zinc contents in Brown, 5% & 10% polished rice of land races
from Karnataka, Maharashtra and Manipur

60. S.No. Name of Genotype
Grain
Type
Fe (ppm) Zn (ppm)
Brown Rice
5%
polished
rice
10%
polished
rice
Brown
Rice
5%
polishe
d rice
10%
polished
rice
16 MOIRANG PHOU SB 5.2 2.1 1.1 33.1 25.4 28.4
17 ERIMA LB 10.5 4.7 4.2 23.5 19.6 18.8
18 KOBRA MS 13.5 4.3 3.5 29.8 21.1 21.4
19 SANNAMALLYA SB 9.8 5.0 4.5 26.0 19.1 17.3
20 PHOUOBI LS 10.8 6.2 2.3 24.4 17.8 16.5
21 GINTHOU LS 9.5 5.2 3.1 24.6 19.1 17.7
22 AKUTPHOU LB 20.1 12.9 4.3 29.0 27.8 22.7
23 KEIBITHOU SB 13.0 5.7 4.9 23.4 18.4 16.2
24 SANATHOU LB 11.9 4.3 3.4 24.8 18.9 17.8
25 JHOGARSI SB 8.0 4.7 2.8 21.0 16.5 14.9
26 THUNGA LS 9.5 6.7 2.9 17.7 13.9 12.3
27 PHOU DUM LS 5.3 5.6 0.9 33.1 26.9 21.1
28 GANDHASALI SB 19.3 17.2 11.2 17.4 11.0 11.6
29 MYSORE MALLIGE MS 8.8 6.4 5.1 19.7 14.9 13.9
30 KMP-148 LS 9.1 6.8 3.3 25.3 19.9 18.9
Iron and Zinc contents in Brown, 5% & 10% polished rice of land races
from Karnataka, Maharashtra and Manipur

61. Range of Fe & Zn in Brown Rice, 5% & 10% polished rice
and loss(%) due to polishing
Fe content
(ppm)
Zn content
(ppm)
Brown rice: 4.9 to 22.5 17.4 to 33.1
5% polished rice:
Loss: %
2.4 to 17.2
10.9 to 82.2
11.0 to 28.3
4.1 to 40.8
10% polished:
Loss: %
1.1 to 11.2
26.9 to 90.7
11.6 to 28.4
14.2 to 44.4

62. IRON (ppm)
Mean 12.9 + 6.24
Range 7.5 – 34.4
Compared to general availability there are varieties
with good content
Top 5 entries: Kalanamak (34.4), Karjat 4 (30.6),
Chittimuthyalu (24.9), MSE 9 (24.4), Kanchan (20.4)
Top 5 entries with less loss on polishing: ADT 43,
Manoharshali, Karjat 4, Swarna, Seshadri

63. IRON (mg 100g-1)
•Kalanamak (3.44),
•Karjat 4 (3.06),
•Chiti Muthyalu (2.49)
•MSE 9 (2.44),
•Kanchan (2.04)

64. ZINC (ppm)
Mean 22.7 + 2.95
Range 10.1 – 31.3
Compared to general availability there are varities
with good content
Top 5 entries: Poornima(31.3), Ranbir Bas(30.9), ADT
43(30.9), Chittimuthyalu (30.5), Type 3 (30.3)
Top 5 entries with less loss on polishing: White
Ponni, Bas 386, Kanishk, Giri, Karjat 4

65. Zinc (mg/100g)
•Poornima(3.13)
•Ranbir Bas(3.09)
•ADT 43(3.09)
•Chittimuthyalu (3.05)
•Type 3 (3.03)

66. Nutritional Profiling of
Parents & Segregating Lines
Variety Fe (ppm) Zn (ppm)
0% 5% 10% 0% 5% 10%
PR 116 7.5 2.8 2.6 20.6 17.4 16.5
BPT 5204 8.3 5.6 4.9 10.3 7.6 4.9
Ranbir Basmati 13.0 9.5 7.1 30.9 28.3 27.4
Chittimutyalu 24.9 14.0 9.8 30.5 25.7 24.4
F4 Generation
PR 116 x Ranbir
Basmati
13.3 9.4 4.6 17.0 15.2 13.4
BPT 5204 x
Chittimutyalu
10.5 7.6 7.0 22.1 19.9 16.6

67. Improvement of Fe & Zn (ppm) in
Segregating Lines of BPT 5204 & PR 116
Parents
Crosses (F4) PR 116 Ranbir Basmati
Iron Zinc Iron Zinc
7.5 20.6 13.0 30.9
Improvement in
PR 116 x Ranbir
Basmati
13.3 (77%) ---
BPT 5204 Chittimuthyalu
Iron Zinc Iron Zinc
8.3 10.3 24.9 30.5
Improvement in
BPT 5204 x
Chittimutyalu
10.5 (26.5%) 22.1 (114.5%)

68. Under biofortification programme at DRR, One line derived
from a cross between BPT 5204 X Chittimuthyalu with short bold
grains, semi dwarf with high yield potential (> 4.5t/ha) and
medium duration with high Iron (31.2 ppm) and Zinc (40.0 ppm)
in brown rice was identified. With good quality characters viz.
good HRR% (67.5%), Intermediate ASV(5.01), AC(24.05%) with
mild aroma.
NIN :
Brown rice- Fe-28.9 (ppm); Zn-37.5 (ppm )
Polished rice-Fe-8.0(ppm); Zn-26.9(ppm)
Some more fixed lines are also in the pipe line.
IMPORTANT ACHIEVEMENT:

69. Fe and Zn contents in brown rice
Fe 10.3 ppm & Zn 10.8 ppm Fe 24.9 ppm & Zn 30.5 ppm
Fe 31.2 ppm & Zn 40.0 ppm

70. Hull 76.8%
Mill 68.8
HRR 67.5
KL 4.15
KB 2.02
L/B 2.05
Grain Type SB
Grain chalk
Type A
VER 4.8
WU 155
KLA 7.2
ER 1.73
ASV 5.0
AC 24.03
GC 22
Aroma MS
Iron (ppm) 31.2 (Brown Rice)
Zinc (ppm) 40.0 (Brown Rice)
QUALITY PARAMETERS OF HIGH IRON & ZINC GENOTYPE

71. TREATMENTS
T1 Control (RFD 100%)
T2 Control + Zn soil application
T3 Control + Zn foliar spray
T4 Control + Fe soil application
T5 Control + Fe foliar spray
T6 Control + Zn + Fe soil application
T7 Control + Zn + Fe foliar spray
T8 Control + micro mix soil application
T9 Control + micro mix foliar spray
T10 FYM (10 t/ha)
T11 FYM 50% + 50% RFD
T12 FYM 50% + 50% RFD + micro mix spray
• Results showed that increase in iron and zinc contents through application of
iron and zinc fertilizers either soil / foliar application.
• Soil application of iron is better than foliar spray.
• Foliar spray of Zn is better than soil application.

72. GENETIC STUDIES REVEALED THAT:
The ratio of GCA to SCA variances showed that non-
additive gene action was predominant in inheritance of
all characters studied.
Chittimutyalu, Ranbir Basmati and Madhukar are found to
be good general combiners for grain zinc content.
PR116 X Chittimutyalu, Swarna X Ranbir Basmati,
Mandya Vijaya X Type 3 were good specific combiners for
grain zinc content.
IR64 Chittimuthyalu and PR 116 Chittimuthyalu found
to be good heterotic hybrids for grain iron and zinc
content.
Grain iron & zinc content had no correlation with grain
yield.
Grain iron had significant positive correlation with grain

73. Genotype Iron (ppm) Zinc (ppm)
Chittimutyalu : 24.9 30.5
Ranbir Basmati : 13.0 30.9
BPT 5204 : 8.3 10.3
PR 116 : 7.5 20.6
MAPPING OF CHROMOSOMAL REGIONS ASSOCIATED
WITH IRON AND ZINC CONTENT IN RICE GRAINS
~ 200 germplasm lines were characterized for Fe and Zn content in the brown rice
Based on that, two donors were selected
1.Chittimuthyalu and Ranbir Basmati
Iron - BPT5204/Chittimuthyalu
154 F2 plants – 0.6 to 238 ppm
Zinc - BPT5204/Ranbir Basmati
109 F2 plants – 2.3 to 103 ppm

74. Putative genes involved in Fe and Zn as reported in rice
genome database
1. OsYs (Orzya sativa Yellow stripe like)
2. NRAMP (Natural Resistance-Associated Macrophage
Protein)
3. Ferritin linked genes
4. Zinc transport, Zinc Regulated Transporter
5. ZIP genes for Zinc and Iron related Proteins
Based on these candidate genes, 46 SSR markers were
identified / designed

75. Chr 3
6.7
15.6
SC 103
SC 129
Chr 4
6.2
12.8
13.6
SC 435
SC 123
SC 120
Chr 8
12.7
13.4
13.5
SC 126
SC 448
SC 116
Tentative SSR based linkage maps for regions associated with enhanced
iron accumulation in F2 lines from Samba Mahsuri / Chittimuthyalu
cM cM cM
ZT
}
ZIP
}
}
}
}
YSL
YSL
}
}YSL
ZT
}
cM

76. Chr 3
19.6
26.2
SC 103
SC 129
Chr 4
8.7
13.4
21.5
SC 435
SC 123
SC 120
Chr 8
11.6
15.3
22.3
SC 448
SC 116
SC 126
Tentative SSR based linkage maps for regions associated with enhanced zinc
accumulation in F2 lines from Samba Mahsuri / Chittimuthyalu
cMcM cM
ZT
}
}
}
}
}
ZIP
YSL
YSL
YSL
}
}
}

77. Chr 4
8.5
SC 434
Tentative SSR based linkage maps for regions associated with enhanced zinc
accumulation in F2 lines from Samba Mahsuri / Ranbir Basmati
Chr 3
9.8
SC 129
SC 425
Chr 5
10.5
SC 135
Chr 12
14.5
SC 418
Chr 6
6.4SC 430
15.9
SC 428
cMcMcMcMcM
9.8
ZT
}
}
}} }
YSL
ZIP
ZIP } }
NRAMP
ZIP

78. Chr 4
13.9
SC 434
Tentative SSR based linkage maps for regions associated with enhanced
iron accumulation in F2 lines from Samba Mahsuri / Ranbir Basmati
SC 129
Chr 5
13.4
SC 135
Chr 12
21.6
SC 418
Chr 6
8.8
SC 428
10.5
SC 430
SC 425
Chr 3
12.5
16.5
cM
cM cM cMcM
YSL
ZIP
ZT
}
}
}
ZIP
}
}
}
NRAMP
}

79. Three loci were identified common for two donors for both Fe & Zn
1. Zinc transporter- Chr 3
2. ZIP genes (Zinc and Iron related Proteins) – Chr 3
3. OsYs (Orzya sativa Yellow stripe like) – Chr 4
Two loci in Chittimuthyalu
1. OsYs (Orzya sativa Yellow stripe like) – Chr 8
2. Zinc transporter – Chr 8
Three loci in Ranbir Basmati
1. ZIP genes (Zinc and Iron related Proteins) – Chr5
2. ZIP genes (Zinc and Iron related Proteins) – Chr6
3. NRAMP (Natural Resistance-Associated Macrophage protein)- Chr12
• Two loci from chromosome 3 and one locus from chromosome 4 found to be
common between the two donors associated with iron and zinc metabolism.
• A recombinant with sd1 gene and aroma gene was identified from BPT 5204 and
Chittimuthyalu from F4 families segregating population with maximum back ground
genome of Chittimuthyalu.
The markers always co segregated for
Fe and Zn together

80. Plant Breeding & BIOTECHNOLOGY –
New ToolS for Fighting Micronutrient Malnutrition
The final permanent solution to micronutrient
malnutrition is breeding staple foods that are dense
in minerals and vitamins provides a low-cost ,
sustainable strategy for reducing levels of
micronutrient malnutrition.
Molecular marker technology expedites the
development of rice varieties with improved iron and
zinc content through identified genomic regions

81. 1/17/2012 9:59:16 PM 39
SCIENTISTS INVOLVED IN THE PROJECT:
• Dr. T. Longvah-Food Chemistry,NIN,HYD
• Dr. C. N.Neeraja-Biotchnology,DRR
• Dr. K. Surekha-Soil Science,DRR
• Dr. B. Sreedevi-Agronomy,DRR
• Dr. L. V. Subba Rao-Seed Technology,DRR
• Dr. N. Shobha Rani-Seed Quality,DRR
• Dr. B. C. Viraktamath-Hybrid Rice,DRR
• M.Sc.(Ag.) & Ph.D. students from ANGRAU,HYD

82. 1/17/2012 9:59:16 PM 40
Thank you

83. Genome-wide variations
between elite lines of indica
rice discovered through whole
genome re-sequencing
Gopala Krishnan S, Dan Waters and Robert Henry
International Symposium on “100 years of Rice Science and Looking Beyond”
on 10th January 2012 at TNAU, Coimbatore

84. Rice
 Rice is a staple food for over half of the world's population
and accounts for over 20 percent of global calorie intake (FAO,
2004)
 Global rice production (2009) – 683 mt million tonnes (FAO,
2011) and to feed projected population in 2050, rice yields to
be increased by 50%
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161900 1950 2000 2050
Year
Population(inbillion)
(Source: UN Population Division)
8.91

85. The options...
 Enabling crop improvement
» Enhancing photosynthetic efficiency
» Marker assisted selection, transgenics, etc.
 The way out
» Improving productivity per ha

86.  Heterosis refers to superior performance of F1 hybrids in terms
of increase in size, yield, vigor, etc. compared to their parental
lines (Shull, 1914)
 Hybrid rice yields 10-20% more than the elite inbred varieties
 Primarily based on three line breeding system – CMS (A line), iso-
nuclear maintainer (B line) and genetically diverse restorer (R
line)
 The challenge ?
Ability to predict hybrid performance
 Advances in genomic sequencing provide powerful tools to
study allelic variations at whole genome level
Heterosis

87.  SNPs resources in rice based on only a few rice cultivars (Shen et
al., 2004; Feltus et al., 2004, Yamamoto et al., 2010, Arai-Kichise et
al., 2011)
 Emphasis to sequence diverse set of additional rice genotypes to
enlarge the pool of DNA polymorphisms
 Three elite CMS and restorer indica rice inbreds each were
sequenced using Illumina GAIIx
 Whole genome re-sequencing yielded 3.38 billion 75-bp paired
end reads (24.4 Gb of high quality raw data)
Re-sequencing of elite rice inbreds

88. Assembly of reads
Unique
222.62 X 106
Multi
65.05 X 106
Organelle
24.96 X 106
Unmapped
25.37 X 106
Nuclear
287.67 X 106
Total reads
338.01 X 106
7.5 %
(85.1 %)
7.4 %

89. Chromosome Coverage
(%)
Uniquely mapped reads Sequencing
depth (fold)
Total number Mb
Chromosome 1 87.99 27,012,387# 1,960 45.17
Chromosome 2 88.69 23,765,016 1,724 47.84
Chromosome 3 91.01# 24,086,909 1,747 48.17
Chromosome 4 82.13 19,856,483 1,440 40.48
Chromosome 5 86.53 18,783,399 1,362 45.76
Chromosome 6 84.57 18,536,789 1,345 43.71
Chromosome 7 83.37 16,996,652 1,233 41.56
Chromosome 8 84.36 16,629,683 1,206 42.41
Chromosome 9 84.24 13,328,131 9,67 42.54
Chromosome 10 84.36 13,438,388 9,75 42.97
Chromosome 11 81.92 15,116,942 1,096 38.62
Chromosome 12 82.22 15,065,493 1,093 39.68
Total 85.40 222,616,272 16,153 43.24
Assembly of reads

90. SNPs

91. SNPs - a snap shot across genome
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
2000
Chr. 1
(284078)
Chr. 2
(243923)
Chr. 3
(211527)
Chr. 4
(236006)
Chr. 5
(162723)
Chr. 6
(201656)
Chr. 7
(188047)
Chr. 8
(197285)
Chr. 9
(151888)
10 20 30 40 [43.2 Mb]
SNPs(No.)
10 20 30 [36.0 Mb]
SNPs(No.)
10 20 30 [36.2 Mb]
SNPs(No.)
10 20 30 [35.5 Mb]
SNPs(No.)
10 20 [29.7 Mb]
SNPs(No.)
10 20 30 [30.7 Mb]
SNPs(No.)
10 20 [29.6 Mb]
SNPs(No.)
10 20 [28.4 Mb]
SNPs(No.)
10 20 [22.7 Mb]
SNPs(No.)o.)
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
0
1000
2000
Chr. 5
(162723)
Chr. 6
(201656)
Chr. 7
(188047)
Chr. 8
(197285)
Chr. 9
(151888)
Chr. 10
(176433)
Chr. 11
(224589)
Chr. 12
(216598)
10 20 [29.7 Mb]
SNPs(No.)
10 20 30 [30.7 Mb]
SNPs(No.)
10 20 [29.6 Mb]
SNPs(No.)
10 20 [28.4 Mb]
SNPs(No.)
10 20 [22.7 Mb]
SNPs(No.)
10 20 [22.7 Mb]
SNPs(No.)
10 20 [28.4 Mb]
SNPs(No.)
10 20 [27.6 Mb]
SNPs(No.)
 2,495,052 SNPs were detected across the rice genome with an average density of
6.78 SNPs/kb in the non repetitive region
 Average polymorphism rate is significantly higher than the previous estimates of
4.31 SNPs/ kb (Nasu et al., 2002) and 1.70 SNPs/ kb (Feltus et al., 2004), offering
high density coverage across the entire genome

92. Detection of InDels

93. InDels
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
0
80
160
10 20 30 40 [43.2 Mb]
Insertions(No.)
10 20 30 [36.0 Mb]
Insertions(No.)
10 20 30 [36.2 Mb]
Insertions(No.)
10 20 30 [35.5 Mb]
Insertions(No.)
10 20 [29.7 Mb]
Insertions(No.)
10 20 30 [30.7 Mb]
Insertions(No.)
10 20 [29.6 Mb]
Insertions(No.)
10 20 [28.4 Mb]
Insertions(No.)
10 20 [22.7 Mb]
Insertions(No.)
10 20 [22.7 Mb]
Insertions(No.)
10 20 [28.4 Mb]
Insertions(No.)
10 20 [27.6 Mb]
Insertions(No.)
Chr. 1
(20137)
Chr. 2
(17269)
Chr. 3
(15390)
Chr. 4
(13460)
Chr. 5
(11157)
Chr. 6
(13010)
Chr. 7
(11707)
Chr. 8
(12550)
Chr. 9
(9362)
Chr. 10
(10380)
Chr. 11
(13521)
Chr. 12
(12535)
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
10 20 30 40 [43.2 Mb]
Deletions(No.)
10 20 30 [36.0 Mb]
Deletions(No.)
10 20 30 [36.2 Mb]
Deletions(No.)
10 20 30 [35.5 Mb]
Deletions(No.)
10 20 [29.7 Mb]
Deletions(No.)
10 20 30 [30.7 Mb]
Deletions(No.)
10 20 [29.6 Mb]
Deletions(No.)
10 20 [28.4 Mb]
Deletions(No.)
10 20 [22.7 Mb]
Deletions(No.)
10 20 [22.7 Mb]
Deletions(No.)
10 20 [28.4 Mb]
Deletions(No.)
10 20 [27.6 Mb]
Deletions(No.)
Chr. 1
(20287)
Chr. 2
(17496)
Chr. 3
(15069)
Chr. 4
(14361)
Chr. 5
(11107)
Chr. 6
(13320)
Chr. 7
(12110)
Chr. 8
(12788)
Chr. 9
(9576)
Chr. 10
(11063)
Chr. 11
(13483)
Chr. 12
(12896)
 224,034 InDels were across the rice genome with an average density of 4.32
insertions/kb and 4.41 deletions/kb

94. Annotation of SNPs and InDels
UTRs
6814
Intergenic
124607
Genic
35871
Introns & Reg.
Sequences
27324
Repeat
regions
36147
Non repeat
regions
160478
CDS
1733
UTRs
6663
Introns & Reg.
Sequences
27821
Repeat
regions
42589
Non repeat
regions
163556
Intergenic
127185
Genic
36731
CDS
1887
Repeat
regions
2151486
Non repeat
regions
2495052
Intergenic
1987802
Genic
497250
63342 83262
UTRs
73051
CDS
146604
Introns & Reg.
Sequences
277595
Non-synonymousSynonymous
 About 1/3rd of the SNPs occur in the non-repeat regions while 10.7 % of the total
SNPs have been found in 25,591 genes
 Overall, 83,262 non-synonymous SNPs spanning 16,379 genes and 3,620 InDels
in the coding sequences 2,625 genes have been identified

95. Polymorphisms - Genotype wiseCMSlinesRestorerlines
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
0
5000
10000
15000
2 3 4 5 6 7 8 9 10 11
Chromosomes
1 12
SNPs InDels
0
50000
100000
0
50000
100000
0
50000
100000
0
50000
100000
0
50000
100000
0
50000
100000
2 3 4 5 6 7 8 9 10 11 12
Chromosomes
1
A
(988986)
B
(912695)
C
(1061109)
D
(879916)
E
(1093043)
F
(2445994)

96. Detecting polymorphism
between inbreds

97.  At present, all bioinformatic tools helps in detecting SNPs in comparison
to a reference genome
 The challenge?
 To identify SNPs between two inbreds
 Try obtaining consensus sequence by mapping an inbred to reference
genome, and then use the consensus as reference for further mapping?
 Did not work as consensus is not absolute genotype
 Cumbersome process
 Loss of the annotations and need to reannotate the consensus
Pairwise Polymorphism

98.  Combined mapping of inbreds to reference may help
 Potential problems while using combined mapping approach for
identifying polymorphisms between inbreds
 It will still detect SNPs based on polymorphism in comparison to
reference genome, then how to identify a SNP between inbreds?
 Expect 50:50 alleles at a given SNP loci of inbreds?
 Bias in number of reads from an inbred being mapped to each
position?
 False positives between inbreds?
Potential problems

99. Situation 1
Inbred 1 assembly
SNP in Inbred 1
compared to reference
(9C/ 0T)
Inbred 5 assembly
SNP in Inbred 5
compared to reference
(13C/ 0T)
Combined assembly_Inbred 1 and 5
SNP compared to
reference
(22 C/ 0T)

100. Not a SNP between Inbred 1 and 5
Inbred 1 assembly
SNP in Inbred 1
compared to reference
assembly
Inbred 5 assembly
SNP in Inbred 5
compared to reference
assembly
Combined assembly_Inbred 1 and 5
SNP compared to
reference but not
between the inbreds
In order to be a SNP between Inbred 1 and Inbred 5, each inbred should have alternate
allele (heterozygote like situation - 50:50) in combined assembly

101. Situation 2
Inbred 1 assembly
Heterozygote in Inbred 1
(4 G/ 4A)
Inbred 5 assembly
Heterozygote in Inbred 5
(8G/ 4A)
Combined assembly_Inbred 1 and 5
Heterozygote
compared to
reference
(12 G / 8A)
Call it a SNP?

102. Not a true SNP
Combined assembly_Inbred 1 and 5 Inbred 1 assembly Inbred 5 assembly
Heterozygote in Inbred 1
(4 G/ 4A)
Heterozygote in Inbred 5
(8G/ 4A)
Heterozygote like
situation (50:50)
compared to
reference (12 G / 8A)
Combined assembly results in heterozygote like situation (50:50) at a given position but
not a true SNP between the Inbred 1 and Inbred 5

103. Situation 3
Inbred 1 assembly
SNP in Inbred 1
compared to reference
(0C/ 9T)
Inbred 5 assembly
Not a SNP in Inbred 5
compared to reference
(12C/ 0T)
Combined assembly_Inbred 1 and 5
Heterozygote like
situation (57:42)
compared to
reference
(12C/ 9T)
Call it a SNP?

104. True SNP between Inbred 1 and 5
Combined assembly_Inbred 1 and 5 Inbred 1 assembly Inbred 5 assembly
SNP in Inbred 1
compared to reference
(0C/ 9T)
Not a SNP in Inbred 5
compared to reference
(12C/ 0T)
Heterozygote like
situation (57:43)
compared to
reference
(12C/ 9T)
Combined assembly results in heterozygote like situation (57:43) at a given position and
SNP between the Inbred 1 and Inbred 5

105. Step 1
 Combined mapping of sequences from each pair of genotypes to the
IRGSP Nipponbare reference genome
Pairwise comparison
Inbreds
CMS lines Restorer lines
Inbred 1 Inbred 2 Inbred 3 Inbred 4 Inbred 5 Inbred 6
CMSlines
Inbred 1 X A B D E F
Inbred 2 X X C G H I
Inbred 3 X X X J K L
Restorerlines
Inbred 4 X X X X M N
Inbred 5 X X X X X O
Inbred 6 X X X X X X
Step 2
 SNPs and InDels from pairwise assembly (Coverage > 9, count allele 1 >
4, count allele 2 > 4)

106. Pairwise comparison
Step 3
 SNPs and InDels from each inbred using filters (Coverage > 4, count
allele 1 > 0, count allele 2 > 0) in order to eliminate heterozygotes
Step 4
 Identify and eliminate the duplicates between each combination of
assembly for a pair and eliminate
 The SNPs remaining after eliminating the duplicates - SNPs between
inbred 1 and inbred 2

107. Technique overcomes bias in reads
SNPs_Combined assembly SNPS_Inbred 1 SNPs_Inbred 5
10 reads 5 reads only
15 reads

108. Polymorphisms - Pairwise (within group)
SNPS InDels
CMS line 1
CMS line 2
CMS line 3
88,557
(21,707)
172,409
(44,010)
124,091
(31,500)
Restorer 1
Restorer 2
Restorer 3
319,629
(76,680)
249,897
(64,740)
293,013
(74,187)
CMS line 1
CMS line 2
CMS line 3
4,830
(1,223)
8,757
(2,314)
8,042
(2,084)
Restorer 1
Restorer 2
Restorer 3
17,036
(4,177)
8,718
(2,352)
12,253
(3,299)
(b) (c)(a) (d)
CMS line 1
CMS line 2
CMS line 3 Restorer 1
Restorer 2
Restorer 3
260,081
(55,033) 251,876
(61,396)
278,365
(69,441)
303,763
(63,861)
229,124
(61,337)
263,476
(66,207)
164,685
(41,669)
150,822
(38,403)
185,849
(36,946)
CMS line 1
CMS line 2
CMS line 3 Restorer 1
Restorer 2
Restorer 3
10,618
(2,913) 10,945
(3,035)
14,338
(3,756)
19,010
(4,668)
7,905
(2,251)
13,976
(3,618)
8,952
(2,312)
6,444
(1,689)
12,085
(2,391)
(a) (b)

109. Polymorphisms - Pairwise (within group)
CMS lines Inbred 1 Inbred 2 Inbred 3
Inbred 1 X 172,409 124,091
Inbred 2 X X 88,557
Inbred 3 X X X
Restorers Inbred 4 Inbred 5 Inbred 6
Inbred 4 X 249,897 293,013
Inbred 5 X X 319,629
Inbred 6 X X X
SNPS
CMS lines Inbred 1 Inbred 2 Inbred 3
Inbred 1 X 8,757 8,042
Inbred 2 X X 4,830
Inbred 3 X X X
Restorers Inbred 4 Inbred 5 Inbred 6
Inbred 4 X 8,718 12,253
Inbred 5 X X 17,036
Inbred 6 X X X
InDels
Diverse among CMS lines - Inbred 1 and Inbred 2
Diverse among Restorers - Inbred 5 and Inbred 6

110. Polymorphisms in Genes- Pairwise
(within group)
CMS lines Inbred 1 Inbred 2 Inbred 3
Inbred 1 X
44,010
(5,097)
31,500
(3,764)
Inbred 2 X X
21,707
(2,515)
Inbred 3 X X X
Restorers Inbred 4 Inbred 5 Inbred 6
Inbred 4 X
64,740
(7,266)
74,187
(8,948)
Inbred 5 X X
76,680
(9,388)
Inbred 6 X X X
SNPS
CMS lines Inbred 1 Inbred 2 Inbred 3
Inbred 1 X
2,314
(55)
2,084
(51)
Inbred 2 X X
1,223
(32)
Inbred 3 X X X
Restorers Inbred 4 Inbred 5 Inbred 6
Inbred 4 X
2,352
(50)
3,299
(79)
Inbred 5 X X
4,177
(188)
Inbred 6 X X X
InDels
Diverse among CMS lines - Inbred 1 and Inbred 2
Diverse among Restorers - Inbred 5 and Inbred 6

111. Polymorphism - Pairwise (between group)
SNPs
Restorer lines
Inbred 4 Inbred 5 Inbred 6
CMSlines
Inbred 1 260,081 278,365 303,763
Inbred 2 229,124 251,876 263,476
Inbred 3 150,822 164,685 185,849
InDels
Restorer lines
Inbred 4 Inbred 5 Inbred 6CMSlines
Inbred 1 10,618 14,338 19,010
Inbred 2 7,905 10,945 13,976
Inbred 3 6,444 8,952 12,085
Most diverse - Inbred 1 and Inbred 6
Least diverse - Inbred 3 and inbred 4
Most diverse - Inbred 1 and Inbred 6
Least diverse - Inbred 3 and inbred 4

112. Polymorphism in Genes - Pairwise
(between group)
SNPs
Restorer lines
Inbred 4 Inbred 5 Inbred 6
CMSlines
Inbred 1
55,033
(6,108)
69,441
(8,343)
63,861
(7,785)
Inbred 2
61,337
(6,833)
61,396
(7,084)
66,207
(7,808)
Inbred 3
38,403
(4,317)
41,669
(4,979)
36,946
(4,413)
InDels
Restorer lines
Inbred 4 Inbred 5 Inbred 6CMSlines
Inbred 1
2,913
(50)
3,759
(81)
4,668
(269)
Inbred 2
2,251
(43)
3,035
(65)
3,618
(96)
Inbred 3
1,689
(43)
2,312
(69)
2,391
(105)
Most diverse - Inbred 1 and Inbred 5
Least diverse - Inbred 3 and inbred 6
Most diverse - Inbred 1 and Inbred 6
Least diverse - Inbred 3 and inbred 4

113.  Through whole genome re-sequencing 2,819,086 non-redundant
DNA polymorphisms (2,495,052 SNPs, 160,478 insertions and
163,556 deletions) were discovered
 The non-synonymous SNPs spanning the genes across the
genome rice will provide valuable insights into the molecular
basis of heterosis
 Enrich the SNP resources in rice - providing high density
coverage which will help in molecular breeding applications
To summarise

114.  Hybrids involving the elite rice inbred lines are being
produced and will be evaluated for yield performance
 Genome-wide association analysis with the phenotypic
traits will help in determining key genes/ alleles for
predicting hybrid performance
To proceed with…

115. Acknowledgements
 Department of Science and Technology, India
(BOYSCAST Fellowship)
 Indian Council of Agricultural Research, New Delhi
 Indian Agricultural Research Institute, New Delhi
 Southern Cross University, Lismore, NSW, Australia

116. Thank you

117. GENETIC ENGINEERING FOR SEMI DWARF RICE
USING RNA INTERFERENCE (RNAi)
G.Bindusree
Research Scholar
Guide
Dr. M. Parani
Prof. & Head Department
Genetic Engineering
SRM University

118. Why Semi Dwarf Rice ??
•High yielding
• Responsiveness to nitrogen fertilizers
• Lodging resistance
Classification Height
Tall More than 130cm
Medium Tall 110-130 cm
Semi Dwarf 80-110 cm
Dwarf Less than 80 cm

119. IR8 ‘Green Revolution’
Parentage: Dee-geo-woo-gen x Peta,
Dwarf (80-85 cm ) Yield: 50-55 Q/ha.
Semi dwarf gene (sd1)
Dee-geo-woo-gen was used in breeding programs in eastern Asia to
produce many of the high-yielding semi dwarf cultivars grown today
(383-base-pair deletion)
Phenotypic
Description
Semi dwarf, resistant to lodging, high yielding. Elongation of
lower internodes. Defective in biosynthetic enzyme
GA20ox2 that catalyzed the conversion of GA53 to GA20
Sd1 gene represents a loss-of-function deletion mutation in GA20ox2 gene
that codes for GA20 oxidase.

120. 0
10
20
30
40
50
60
70
80
TALL
Tikkana
PMK-1
White Ponni
Subramaniya Bharathi
TKM-10
SEMI DWARF
ADT-44
ADT-37
CORH-2
ASD-20
ADTRH-1
DWARF
Jyothi
Annapoorna
Annapurna-28
YIELD Q/ha
YIELD

121. PARTICULARS White Ponni
Parentage Mayang,Ebos-80;Taichung 65/2
Duration (Days) 135-140
Average Yield (kg/ha) 4500
1000 grain wt (g) 16.4
Grain L/B ratio 3.22
Grain type Medium slender
Morphological Characters
Habit Medium tall (130-135 cm)
Leaf sheath Green
Septum Green
Ligule White
Auricle Colourless
Panicle Long drooping
Husk colour Straw
Rice colour White
Abdominal white Absent
Grain size (mm)
Length 8
Breadth 3
Thickness 2
White Ponni

122. RNAi Pathway

123. GA20ox1
GA20ox2 (sd1)
GA20ox3
GA20ox4
GA20 oxidase
GA2 oxidase
GA3 oxidase
Early steps in the pathway Later steps in the pathway
(CPS)-ent-copalyl diphosphate synthase
(KS)-ent-Kaurene synthase
(KO)-ent-Kaurene oxidase
(KAO)-ent-Kaurenoic acid oxidase
GA Metabolic Enzymes
GA2ox1
GA2ox2
GA2ox3
GA2ox4
GA3ox1
GA3ox2

124. trans-Geranylgeranly
Diphosphate
(GGDP)
ent-Copalyl
Diphosphate
(CDP)
ent-Kaurene
ent-kaurenoic
Acid (KA)GA53
GA20
GA1 GA4GA51
CDP
synthase
(CPS)
Kaurene
Synthase
(KS)
Kaurene
Oxidase (KO)
Kaurenoic acid
Oxidase (KAO)
GA13ox
GA20ox GA20ox
GA29
GA2ox1,3 GA3ox
GA12
GA9
GA2ox GA3ox
GA8 GA34
GA2ox1,3 GA2ox
Plastid
ER Membrane
Cytoplasm
GA Biosynthesis in Plants

125. GA20ox2 Gene
EXON 1
648bp
750bp
1072bp
2543bp
3149bp
EXON 2 EXON 3
648bp 322bp 606bp
Oryza sativa genomic DNAAcc No: AP003561, 183580bp.
Gibberellin 20-oxidase gene (GA20ox2): <136550…..>139292.
GA20ox2 mRNA joins: EXON 1:<136550...137106 (557bp +5’UTR 91bp =648bp),
INTRON 1: 102bp
EXON 2: 137209...137530 (322bp),
INTRON 2: 1471bp
EXON 3: 139002...>139292 (291bp + 3’UTR 315bp = 606bp).
Open Reading Frame GenBank: AP003561, 1770bp
TOTAL GENOMIC CLONE WITH 2 INTRONS: 3149bp

126. Rice Actin 1 gene(Act1)
 Act1-promoter Acc No: S44221, 1266bp.
Efficient promoter for transgenic rice.
 It consists of the following-
5’-flanking and 5’-transcribed sequence(Non coding exon1) and the 1Intron
Long poly(dA) between -146 and -186
Restriction sites-XhoI, BamHI, EcoRV

127.  Designing RNAi constructs specific for GA20ox2
 Generation of transgenic rice plants by Agrobacterium-
mediated transformation.
Molecular and Phenotypic analysis of the transgenic plants
Objectives

128. OsGA20ox2
OsGA20ox1
OsGA20ox3
OsGA20ox4
OsGA2ox1
OsGA2ox2
OsGA2ox3
OsGA2ox4
OsGA3ox1
OsGA3ox2
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1576
1195211
Minimum 10 contiguous nucleotide identity from ClustalW
1. Identification of trigger sequence
Work done so far
5’ UTR
3’ UTR

129. 2.Designing of the construct
Act1-Promoter 1228bp
Antisense 362bp
Intron1 122bp
Sense 362bp
Kpn I Xba I
hpRNA
RNAi Pathway

130. 3. Amplification of loop and Sense
1 2 3
Lane 1 – 100 bp marker
Lane 2 – amplified loop (122 bp)
Lane 3 – Amplified sense (362 bp)
600 bp
500 bp
100bp
Fig.1

131. 4. Ligation of loop and Sense and PCR amplification of the ligated product
1 2
Lane 1 – 100 bp marker
Lane 2 – PCR amplification of ligated
product of loop+sense (484)
600 bp
500 bp
100bp
Fig.2

132. 5. Confirmation of loop and sense ligation by sequencing
CGCCAATGGGGTAATTAAAACGATGGTGGacGACATTGCATTTCAAATTCAAAACAAATTCAAAACACACCGAC
CGAGATTATGcTGAATTCAAACGCGTTTGTGCGCGCAGGAGGGTGTACACGCGCTGGCTCGCGCCGCCGGCCGC
CGACGCCGCCGCGACGGCGCAGGTCGAGGCAGCCAGCTGATCGCCGAACGGAACGAAACGGAACGAACAGAA
GCCGATTTTTGGCGGGGCCCACGTGGGGGATTTGCCCACGTGAGGCCCCACGTGGACAGTGGGCCCGGGCGGA
GGTGGCACCCACGTGGACCGCGGGCCCCGCGCCGCCTTCCAATTTTGGACCCTACCGCTGTACATATTCATATATT
GCAAGAAGAAGCAAAACGTACGTGTGGGTTGGGTTGGGCTTCTCTCTATTACTAAAAAAAATATAATGGAACG
ACGGATGAATGGATGCTTATTTATTTATCTAAATTGAATTCGAATTCGGcTCAA

133. 6. Amplification of Actin promoter and cloning in to pUC18
3 kb
2 kb
1 kb
1 2 3 41 2
3 kb
2 kb
1 kb
Lane 1 – Amplified Actin promoter (1.2 kb)
Lane 2 – 1 kb maker
Lane 1,4 - 1 kb marker
Lane 2 – pUC 18 DD with XbaI and
KpnI and eluted
Lane 3 – Actin DD with XbaI and
KpnI and eluted
Fig.3
Fig.4

135. Biochemical Pathway for GA Biosynthesis
1st stage (Proplastids)
2nd stage (Endoplasmic Reticulum)
3rd stage (Cytosol)
Geranylgeranyl
diphosphate
CPS ent- copalyl
diphosphate
KS Ent-kaurene
KO
Ent-Kaurenoic
acid
KAOEnt-7α hydroxy
Kaurenoic acid
KAO
GA12-aldehyde
GA7ox
GA12GA53
GA13ox
GA12
GA53
GA15
GA44
GA24
GA19
GA9
GA20
GA51
GA5
GA4
GA1
GA34
GA8
Non-13 hydroxylation pathway
Early-13 hydroxylation pathway
GA20ox
GA20ox GA20ox
GA20ox GA20ox GA20ox
GA3ox
GA2ox
GA2ox
GA2ox
GA3ox
GA3ox
GA3
GA3ox

136. Structural and functional analysis of
glyoxalase I promoter from rice
ArulL,SureshKumar*,Kushboo R,Sivaranjani S,LathaMageswari V,Kumar
KKK,Kokiladevi E,Sudhakar D,Balasubramanian P
CentreforPlantMolecularBiology&Biotechnology
TamilNaduAgriculturalUniversity,Coimbatore-641003(TN)
*DivisionofCropImprovement,I.G.F.R.I.,Jhansi-284003(UP)

137. About Promoters
 cis-acting, regulatory element
 Indispensible component for the expression of gene(s)
+1
(mRNA)
5’ - ’ - 3’promoter Gene (CDS) Ter

138. Promoter types
 Constitutive promoters
 CaMv35S,maizeUbi, riceAct-1
 Inducible promoters
 rd29A,PR1
 Tissue specific promoters
 TA9,Gt1

139. Inducible promoter
 Induced by the presence of biotic or abiotic factors
 Regulated expression
 need based, switching on/off of gene expression (only at
times of stress)
 Adds greater strength to the transgenic technology
(Kasuga et al., 2004)
 Recent research on ABA, salt and drought stress inducible
promoters in rice
 OsABA2 (Rai et al., 2009)
 Wsi18 (N et al., 2011)

140. Current study
Objectives:
Cloning and characterization of the promoter of a known
stress inducible gene, glyoxalase I (glyI) from rice
Functional characterization of the isolated promoter for
expression and inducibility under abiotic stress conditions
in transgenic rice

141. About glyoxalase I (glyI)
 Glyoxalase pathway is universal, off shoot of glyocolysis
 GlyI catalyzes the first step towards detoxification of methylgloxal (MG)
 Increased glyI activity in meristematic tissues and cells undergoing
stress (abiotic)
(Sethi et al., 1988; Deswal et al., 1993; Veena et al., 1999;
Mustafiz et al, 2011)
 Methylglyoxal is detoxified via S-D-Lactoylglutathione
into lactate and glutathione
Additional energy
requirement(demand for ATP)
Adaptive measures
Upregulation of gly pathway
Detoxification of methylglyoxal
Accumulation of methylglyoxal
Increased rate of glycolysis
Plant cells under stress

142. Work done – promoter cloning
1. The glyI sequence from cv. Nipponbare (Usui et al., 2001)
2. A 3 kb sequence upstream of AUG of glyI was identified from
the BAC clone (OSJNBa0056006) sequence
3. PCR amplification of a 2120 bp region from the genomic DNA of
Nipponbare
4. Sequencing and in silico analysis
Pst I EcoR Ifor
rev

143. Work done - genetic transformation
5. Cloning the putative pglyI promoter, Pst I - EcoR I restriction
fragment of 1545 bp in front of a promoter less GUS vector
(pCAMBIA 1391z)
6. Generation of stable rice transformants (cv. Pusa Basmati1)
using the putative pglyI -1391z

144. Results
1. Structural analysis of (pglyI)
 Transcription start site (TSS) predicted at 825th base from the 5’-
end of the sequence on the plus strand
 TATA box “CTATAAATAC” was predicted between 791 and 801
bases
 Region between 826 base and 1545 base consisted of an initial
UTR exon and first intron
 First intron fall between 1464 and 1545 bases
 GenBank submission: EU605981.1

145. Structure of pglyI and maize pUbi
Similar architecture, between pglyI and, maize ubiquitin promoter (Christensen et al., 1992)
pglyIpUbi

146. Upstream (-825 to +1 bases) stress responsive
motifs
Motifs Conserved
Sequence
Location
( 5’- end)
Implicated function
ABRE motif -A TACGTGTC 111 An Abscisic acid response element, ABA
induced transcription in rice
ABRE-like
sequence
ACGTG 267 Dehydration stress and dark-induced
senescence
Anaerobic box AAACAAA 421 Motifs found in anaerobically induced genes
MYB core CNGTTR 556, 689 Binding site for MYB, responds to
dehydration stress
WRKY box TGAC 29, 43 WRKY proteins are involved in pathogen
defense
CE CGACG 544 Coupling element along with ABRE motif
SAUR motif CATATG 490, 550 Auxin response modules
G box TTTAA 752 bZIPs transcription binding site

147. Functional analysis
 Six pglyI transgenic Pusa Basmati (T0) events
were confirmed by PCR
 Stable GUS assay showed blue color
development
Transient GUS
Expression
Stable GUS
Expression

148. Localization of GUS Expression
GUS assay of shoots
TS
LS

149. GUS PCR
 Homozygous line identified in one of the
above event atT2 generation
PCR for uidA gene

150. 2. Function of induciblity
 ABA stress (40 micro moles) @ 3 week seedlings in
hydroponics
 Semi-quantitative RT-PCR forGUS in two different
transgenic lines (pglyI-GUS) & (pCaMV 35S-GUS)
L1- pCaMV 35-GUS (0 hour)
L2- pCaMV 35-GUS (4 hour)
L3- pgly GUS (0 hour)
L4- pgly GUS (4 hour)
L1 L2 L3 L4
RiceActin
GUS

151. Conclusion
 The cloned promoter region (pglyI)
successfully drive the expression of transgene
(GUS)
 Low/moderate level of constitutive GUS
expression under normal conditions
 Preliminary expression analysis suggest, the
promoter is inturn inducible under ABA stress

152.  Thanks

153. 5’- UTR exon (637 bases)
+1 (825 base)
- 3’
1 base 1545 base
InitialUTR exon
826 -1463
First intron
1464-1545
Intron (81 bases)Promoter (825 bases)