The document discusses using rice science to develop rice varieties with better nutrition for humans. It outlines efforts to address micronutrient deficiencies through biofortification of rice with provitamin A, iron, and zinc. Biofortification aims to genetically increase the concentrations of these nutrients in rice grains in a sustainable and cost-effective way to help the billions who cannot afford supplements or an diversified diet. The document reviews variability in rice germplasm for these nutrients and modern breeding approaches using genomics tools to more efficiently develop biofortified rice varieties.

1. Using rice science to develop
rice varieties for better human
nutrition
B. P. Mallikarjuna Swamy
November 14, 2012

2. Outline
• Importance of rice in human nutrition
• Micronutrient deficiencies (MD)
• Resources, efforts and approaches to address MD
• Biofortification of Provitamin A
• Biofortification of Fe and Zn
• QTLs and genes for Fe and Zn enrichment in rice
• Environmental effect on Fe and Zn biofortification
• Proof of the concept and the status of biofortification
• Benefits of Fe and Zn biofortification in rice
• Plans and time line

3. Rice is Life Energy
• Rice is the major staple food crop for more than half of
the worlds population
• Rice supplies 30-50% to the daily caloric intake
• Rice is the major source of employment
• Rice plays an important role in food security
• Time has come to play role in nutritional security
Rice and Food security Nutrition
Compositon Brown rice White rice Units
800 8.0 Calories 111 123 Kcal
Production Population
700 7.0 Moisture 12.9 8.9 g Employment
600 6.0 Total fat 0.9 0.37 g
500 5.0
Protein 2.6 2.9 g
Total carbo 23 26 g
400 4.0
Dietary fibre 1.8 0.9 g
300 3.0 Sugars 0.35 0.11 g
200 2.0 Calcium 10 19 mg
100 1.0 Magnesium 43 9 mg
0 0.0
Iron 0.42 0.24 mg
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2009 Zinc 0.63 0.5 mg
Stuttgart, 1991

4. Nutritional improvement is a priority area of rice research
Vitamin A
Yield potential/Stability Biotic stresses eye sight problem
reduced immunity
night blindness
retarded growth, dry skin,
Iron (Fe)
Anemia
Reduced immunity,
Dizziness, headache,
Abiotic stresses chest pain, weakness, glossitis
Healthy and Quality rice
Frequent worm infestation
Zinc(Zn)
Reduced growth, immunity
Vitamin A, Iron and Zinc Loss of appetite , weight loss
diarrhea, skin dryness, rashes,
reduced fertility, allergy, hair loss, bowel
inflammation

5. Major causes of illness and disease in low income countries
Developed: 10%
Developing: 61%
Worldwide: 49%
> 3 billion people afflicted Zn deficiency

6. Approaches to relieve Vitamin A, Iron and Zinc deficiencies
1. Change of diet
2. Supplementation
Fe and Zn in modern rice varieties
3. Biofortification Fe (mg/100g Zn (mg/100g of
Varieties of brown rice) brown rice)
Swarna 0.78 2.28
- Increase in concentrations MTU1010 0.73 2.54
IR64 1.05 1.05
Suraksha 1.06 2.53
- Increase in promoter compounds BR 2655 1.05 2.37
Improved BPT 1.07 2.2
- decrease in anti-nutrients
Three billion live on less than 2 $ per
day, 1.5 billion on less than 1 $ per day
saving one healthy life year cost as little as
and cannot afford a diversified diet or
$US 0.73–7.31 if both wheat and rice biofortified
industrially produced supplements
Biofortification of rice varieties is the most sustainable, targeted
and cost-effective approach to alleviate the problems of Vit A and
micronutrient deficiency!!

7. Target crops and alliance for Bio-fortification
Target crops in Africa Target crops in Asia
Rice alliance for enhancing micronutrients
Country/Region Institute
Australia Adelaide University School of Agriculture, Waite Campus
Bangladesh Bangladesh Rice Research Institute
Germany University of Freiburg
Indonesia Indonesian Institute for Rice Research, Plant Breeding Division
+ Japan
Agriculture University of Indonesia
University of Tokyo
Philippines International Rice Research Institute
Philippine Rice Research Institute Maligaya, Muñoz
Switzerland Syngenta
United States International Food Policy Research Institute (IFPRI)
Michigan State University
Children's Nutrition Research Center, Baylor College of Medicine

8. Frame of work for Biofortified food crops by Harvest Plus
Biofortfied crop must be:
• High yielding and profitable
• Effective and efficient in
reducing malnutrition
• Acceptable to both farmers
and consumers in the target
region

9. Variability for Pro Vitamin A, Iron and Zinc in rice germplasm
Provitamin A: No variability Rice germplasm at IRRI gene bank
O. sativa 111,123
Iron: Variability exists - upto 35ppm O. glaberrima 1,657
Wild Oryza spp. 4,465
Zinc: Variability exists - upto 45ppm Related genera 15
Total 117,263
Gene modification where necessary
Breeding where possible
Genetic engineering – Vitamin A
Breeding/GE - Fe and Zn
How to use this in a faster and efficient way?

10. “I challenge the next generation to use new
scientific tools and techniques to address
the problems that plague the world’s poor.”
Dr. Norman Borlaug
Combination of Breeding
and Biotechnology hastens
the process of development
of Commercial Products
Genomics based technologies have been successfully used
in plant breeding to address the complex problems

11. Advances in rice genomics and modern breeding programs
Nipponbare (Japonica)
93-11 (Indica)
SNP-detection assays for rice
Description # SNPs Platform Reference
O. sativa Diversity array 1M Affymetrix McCouch, per. Comm.
O. sativa Diversity Array 44,100 Affymetrix Tung et al. (2010)
Global Diversity Primer Set 4,357 Sanger sequencing Ebana et al. (2010)
O. sativa Diversity OPA 1,536 Illumina GoldenGate Zhao et al. (2010)
Japanese Core Set 768 Illumina GoldenGate Yamamoto et al. (2010)
O. sativa_indica × japonica 384 Illumina BeadXpress Thomson et al. (2011)
O. sativa_indica/aus 384 Illumina BeadXpress Thomson et al. (2011)
O. sativa_tropical japonica 384 Illumina BeadXpress Thomson et al. (2011)
O. sativa_indica × O. rufipogon 384 Illumina BeadXpress Thomson et al. (2011)
O. sativa_japonica × O. rufipogon 384 Illumina BeadXpress Thomson et al. (2011)
Mc Couch et al. 2011
1000 and 3000 rice genome sequencing projects
Next generation sequencing technologies (NGS) Further hasten the
Genotype by sequencing technology (GBS) breeding process!

12. Integration of genomics with breeding
Phenotyping
Transcriptomics
Tolerant and Susceptible parents
Proteomics
Metabalomics
Mapping
populations
Transcriptomics
Bioinformatics
Metabolomics
Phenotyping
Genotyping
Proteomics
Genetic
map e-QTL p-QTL m-QTL
Trait, Gene, QTL, marker
Marker aided selection Genetic engineering
MAB, MABB, MARS, GWS
Nutritionally Improved rice variety

13. Biofortification of Pro Vitamin A
Lycopene cyclases (LCYs), alpha and beta carotene
hydroxylases (HYD) are produced in the wild type endosperm
But phytoene synthase, and one or both carotene desaturases –
Phytoene desaturase and Z-Carotene desaturase are not
produced
- Schaub et al. 2005

14. Development of efficient construct for Pro Vitamin A
GR II – Best construct so far
•Endosperm specific promoter
Glutelin – Gt1p
• Nos terminator
•Carotene desaturase from E. uredovora
catalysing multiple steps in carotenoid
synthesis
• Maize phytoene synthase
• Maize ubiquitine promoter
• Phosphomannose –isomerase marker
system
• T-DNA right and left border sequence

15. Development of ß-Carotene rice in the background of
popular rice mega varieties by MABC
IRRI

16. Versions of Golden rice
Golden rice ß-Carotene
Prototype 1.6ug/g
Golden rice 1 (SGR1) - change of promoter 5- 7ug/g
Golden rice 2 – replacement of daffodil Pys with maize gene 31ug/g
GR2 GR1
Wild-Type
Paine et al. 2005

17. Bioavailability tests and expected release of Golden rice
Bioconversion of ß –Carotene to Vit A in
Golden rice is better than conventional foods
• Excellent in both in Adults and children
• Effect of fat is minimal

18. Biofortification of Iron and Zinc in rice

19. Genotypic variation for Iron and Zinc content in rice grain
Fe and Zn content in land races and wild rice species
Fe content Zn content
Genotype ug/g ug/g
WR-1 (O. officinalis) 17.5 39.3
WR-2 (O. latifolia) 18.6 36.9
WR-3(O. nivara) 12.5 32.4
WR-8 (O. nivara) 21.2 41.7
WR-171
(O. longistaminata) 21.1 38.1
WR-175(O. officinalis) 22.6 39.1
Anandan et al.2011 Chandel et al.2011

20. Effect of polishing on Iron and Zinc content in rice grain
Longhva et al. 2011

21. Iron and Zinc in rice grains , antinutrients and promoters
Antinutrients
Phytic acid, tannins, lectins,
fibre, oxalic acid, phenols
heavy metals,
Promoters
Organic acids, fatty acids,
aminoacids, Vit A,
Low phytic acid rice mutant (lpa) Prebiotics
Furctans, Oligosaccharides
Chr 3 Phenotyping phytate in rice grains
Fine mapped region of lpa

22. Iron and Zinc phenotyping methods
More accurate methods - ICP-OES, AAS, XRF

23. Genetic basis of high Iron and Zinc content in rice grains
Heritability Genetic basis
• Moderate narrow sense heritability • Additive genetic effects
• Moderate to high broad sense heritability
• Dominance genetic effects
• Epistatic effects
• Maternal effects
• Significant genotype x
environment
Are there any major effect QTLs worthy of MAS? Yes!

24. Heterosis and Correlation for Iron and Zinc content in rice
Correlation Fe and Zn References
0.346* Anandan et al. 2011
0.691*** Stangoulis et al. 2007
0.215* Oliviera et al. ???
0.700*** Swamy et al. 2009

25. Molecular diverity analysis and mapping QTLs for Iron and
Zinc from wild progenitor species
Swamy et al. 2009

26. Development of mapping population
BC1
Parents F1
Swarna Trans segregant
BC2 BC2F2
Swamy et al. 2009

27. Phenotyping of mapping population
Fe concentrations in Swarna X O.nivara F2 population (S) Zn concentrations in Swarna X O.nivara F2 population (s)
140
60 55 54 123
120
50
100
No. of lines
No. of lines
40
80
26 28
30
60
20 40
24
10 16
4 20
5
0 0
1-3.9 4-6.9 7-9.0 10.0 - 13.9 14.0 - above 5.0 - 10.0 10.0 - 15.0 15.0 - 20.0 60.0 - 65.0
Iron in ppm Zn in ppm
….normal distribution

28. QTLs for Iron and Zinc from O. nivara x Swarna
IM CIM
Trait Marker Interval Allelic effect LOD R2 Additive LOD R2 Additive
Fe1.1 RM499-RM428 O.nivara 5.47 10 -6.55 4.97 8 -0.76
Fe3.1 RM520-RM514 O.nivara 4.19 16 -1.64 4.40 12 -2.86
Fe4.1 RM241- RM567 O.nivara 5.21 12 -5.57 9.18 15 -5.18
Zn3.1 RM517- RM16 O.nivara 3.25 23 -2.38 2.97 5 -1.34
Zn6.1 RM30- RM439 O.nivara 2.74 10 -0.77
Zn12.1 RM415- RM19 O.nivara 3.95 21 -2.28
1
3
RM499 fe1.1 4 6 12
RM428
RM22 RM551 RM190 RM415
RM84
zn12.1
RM517 RM518 RM204
RM1
RM7 zn3.1 RM261 RM584 RM19
RM490 RM314
RM156 RM185 RM217
RM243 RM3
RM23 RM16 RM241 RM453
RM454
RM255 RM30 RM247
RM579 RM85 zn6.1
RM582 RM439
RM293 RM303 RM228 RM341
RM580 fe4.1
RM282 RM348 RM519
RM81
RM24 RM6441 RM270
RM135 RM17
RM594 RM567
RM595 RM55
RM446 RM168
RM9
RM520
RM5 fe3.1
RM488 RM514
RM126
Chromosomal location of Fe and Zn QTLs
RM437
RM226 Swamy et al. 2009
RM431

29. QTLs for Iron and Zinc content in rice grains

30. QTLs conti……….

31. Co-location of Iron and Zinc QTLs with resistance genes
(H. Biradar et al. 2007)

32. Genes involved Iron and Zinc uptake and accumulation
Ferritin – Storage
Phytase – Antinutrient reduction
NAS – Uptake and transport

33. Location of genes involved Iron and Zinc homeostasis
Giraldo et al. at CIAT have developed and validated functional
SNP markers for 23 genes involved in Iron and Zinc Gross et al. 2003
accumulation in rice

34. Environmental influence on Fe and Zn content in rice grains
Effect of water management Effect of Nitrogen application
Effect of Nitrogen application Effect of various soil parameters on
Fe and Zn uptake

35. Iron and Zinc content of rice genotypes across the locations
soil properties influence phyto availability of micronutrients,
yet genetic makeup of a plant determines its response to varied soil conditions
Chandel et al.2010

36. Proof of the concept of Iron and Zinc Biofortified rice
MS13 – Maligaya Special – with high Fe and Zinc released in Philippines
Improved line IR68144-3B-2-2-3 – Fe (21ppm) and Zn (34 ppm) in Brown rice,
80% improvement over IR 72 after polishing
Three improved IR lines - with high Fe and Zn are in advanced stage of
evaluation in Bangaldesh
- IRRI
Nicaragua - CT15679-1-1-1-4 Fe (5-6 ppm) and zinc (21 ppm)
Panama - released four varieties
Colombia - FA336-1-1-V5-MA
Dominican Republic - CT18245-11-6-2-3-4-3-M Fe(4.62ppm) and Zn (14.17 ppm)
CT18247-11-5-2-3-1-1 Fe (5.60ppm) and Zn (15.93 ppm )
- Martinez et al.2010
Improved Nipponbare - OsNAS2 – Fe ( 14 -19ppm) and Zn( two fold increase)
- Jhonson et al.2011

37. Current status of Biofortification projects
Countries: Philippines, Bangladesh, Indonesia, and India
Higher beta carotene (Golden Rice)
• Current level: ~ 0 ppm Product Development and
• Target level: 6-8 ppm (+50 % EAR) Deployment; future improvements
• Level reached: 14 ppm
Higher Zinc Rice
Product Validation and Development;
• Current level: 16 ppm
research
• Target level : 24 ppm (+40 % EAR)
• Level reached: 25/51 ppm
• Higher Iron Rice
• Current level: 2 ppm Product Validation; research
• Target level: 12-14 ppm (+30 % EAR)
• Level reached: 8/12 ppm
EAR = estimated average requirement (USA)
G. Barry, IRRI

38. Biofortification of Vitamin A, high Iron and Zinc – Gist
• Golden rice 1 (6ppm) and GR2 (31ppm) developed, introgression to popular varieties by MAS in progress.
• GR- MAS products tested for bio availability, efforts are going on to release in partner countries.
• GR I expected to supply up to 40-50% of RDA and GR II expected to meet 100% of RDA.
• Variability exists within the rice gene pool for Fe and Zn and can be improved by breeding approaches.
• Fe and Zn accumulates in different compartments within rice, absorption and accumulation positively correlated,
moderate to high heritability, so simultaneous improvement is possible.
• Environmental factors influence the Fe and Zn content, genetic make up of the rice genotypes play very
important role in stable performance.
• Germplasm with high Fe and Zn identified , used in developing improved lines with high Fe and Zn.
• Lines with high Fe and Zn developed in the I phase of the project are being released.
• Bioavailability tests with high Fe rice showed 20% improvement.
• The improved lines expected to supply 40-50% of RDA of Fe and Zn.
• Major effect QTLs for Fe and Zn identified , needs to be validated and introgressed to develop high Fe and Zn
MAS products.

39. Benefits of Iron and Zinc Biofortified rice
Higher yield
under Fe and
Zn deficiency
Seed enriched
with Fe and Zn

40. Iron and Zinc Biofortification – Time line
• New high Fe and Zn donors with low phytic acid identified
• Development of improved lines with high Fe and Zn
2013- 2015 • Mapping populations developed and large effect QTLs identified
• QTLs validated for consistency - genetic backgrounds and environment
Bio efficacy tests
• MAB –QTL pyramiding for Fe and Zn initiated
• Identification of novel QTLs from wild progenitor species of rice
• QTL pyramiding in new varieties, combined with Fe and Zn transgenic, with Vitamin A
2016 - 2018 • Fine mapping of selected large effect QTLs
• Candidate gene analysis and development of functional markers
Bioavailability ,
• MAS products successfully developed and tested in TPE and released
• Product development, testing, release, candidate gene identification continued
• Candidate genes validated by transgenic approaches in collaboration
2018 beyond • Genetic stocks developed for best combination of QTLs
• Understanding the mechanisms for high Fe and Zn accumulation in rice grains
• QTLs pyramided and tested in hybrid parental backgrounds
• MARS and GWS initiated for stable and better genetic gain

41. “ Food is the moral right of all who are born
into this world.” - Borlaug
“Nutritious food is the moral right of all who
are born into this world.”

42. QTLs for yield and grain quality from O. nivara
140
120
Number of plants
100 Yield Marker
80
per plant Chr Interval LOD R2 A LOD R2 A
60
40
yldp1.1 1 RM9-RM5 3.63 12 -3.69
20
0
1.0-10 10.1-20.0 20.1-30.0 30.1-40.0
yldp2.3 2 RM250-RM535 8.91 21 6.18 10.16 20 -7.2
% Introgression
yldp9.1 9 RM434-RM257 15.41 12 10.01 12.79 17 -16.56
Plants with minimal
introgression and yldp11.1 11 RM209-RM21 3.76 10 -4.14 6.02 2 0.94
having desirable QTL are
useful in MAS
Ch1 Ch2
RM110
RM499 Ch12
RM428
RM423 Ch9 Ch11
RM84
RM1 Ch6 RM415
RM174 mp12.1, wup12.1
RM105
ver12.1, klac12.1
RM490 RM243 RM332
lbr12.1, asv 12.1
RM438 RM190 ver12.1
RM243 RM566 RM287
RM23 RM324
RM209 RM19
RM579 RM341 RM434 RM204
RM582 Yldp9.1
RM262 Yldp11.1
RM580
RM457 RM584
RM81 RM257 mp12.2
RM21 RM314
RM24 RM475
RM242 RM453
RM594 RM263 RM217 mp6.1
mp6.2 RM247
RM595 RM3515 RM3 ac6.1
RM446
RM3874 RM454
RM9 Yldp1.1 RM107 RM254 RM341
RM5 RM106 RM30
RM488 RM6
RM215 RM439
RM128 RM250 RM224 RM519
RM228
RM166
RM437 Yldp2.3 RM270
RM189
RM226 RM208
RM17
RM431 RM535
Yield QTLs Grain Quality QTLs

44. How real are DTY?
Comparative genomics
DTY QTL validation
DTY QTLs % of lines
DTY1.1 64
DTY2.1 49
DTY3.1 77
DTY8.1 52
DTY12.1 85
DTY1.1 region in rice – Maize 3, wheat 4B, barley 6H
DTY3.1 region in rice – Maize 10
Meta analysis of DTY QTLs
MQTL Chr region Mean PV Initial CI (cM) MQTL (c M) QTL(Mb)
MQTL1.1 1 RG109–RM431 12 7.60 2.40 0.36
MQTL2.1 2 RM452–RM521 12 10.50 5.28 1.24
MQTL2.2 2 RM526–RM497 6 12.00 11.50 2.36
MQTL3.2 3 RM520– M16030 20 10.30 3.40 0.98
MQTL10.2 10 RM596–RM304 16 15.00 23.72 2.60
MQTL12.1 12 RM277–RM260 28 4.20 1.79 0.70
Swamy et al.2011. BMC genomics

45. IR64 QTLs lines under non-stress and stress
Line QTLs DF(NS) PH(NS) GY(NS) GY(S) GS (%)
DS11 DS11 DS10 DS11 DS10 DS11
IR 87729-69-B-B-B DTY9.1, DTY2.1, DTY10.1, DTY4.1 83 91 4312 6308 2011 1943 94.4
IR 87728-491-B-B DTY9.1, DTY2.1, DTY4.1 82 95 - 6232 1041 1879 92.6
IR 87707-186-B-B-B DTY2.1, DTY10.1, DTY4.1 78 99 4550 6103 2068 2632 96.9
IR 87707-446-B-B-B DTY2.1, DTY4.1 80 98 3752 4388 2556 3000 97.0
IR 87707-445-B-B-B DTY2.1, DTY4.1 77 96 5045 5844 2555 3023 96.9
IR 87728-162-B-B DTY9.1, DTY2.1 84 94 - 6115 1147 1636 92.4
IR 87705-83-12-B DTY2.1, DTY10.1 80 95 4796 5526 1916 2270 95.0
IR 87705-80-15-B DTY10.1, DTY4.1 81 89 3850 5516 2074 2151 94.6
IR64 80 96 2987 5435 636 1442
LSD0.05 3 7 1053 690
IR64
IR64 IR64 + QTL line
+ QTL line
IR 87707-445-B IR 87707-182-B IR64
Drought Stress2011DS, IRRI CRURRS, Hazaribag, India 2011 WS
PLOS One ( In review )

46. Pyramiding of major effect drought grain yield QTLs
DTY1.1, DTY2.1 and DTY3.1 in SwarnaSub1
Background recovery of Swarna ILs
BC4F3 Swarna lLs (Two QTLs + Sub1)
Submergence
screening
1 day after draining
BC4F3 Swarna lLs (Three QTLs + Sub1) 6 days after draining
Swarna

47. Introgression of major effect drought grain
yield QTLs DTY3.1 and DTY12.1 Anjali
IR81896-B-195 X Anjali (DS2010) Fore ground selection Major effect drought grain yield QTLs
( DTY3.1) Additive
QTLs Chr Interval R2 Donor
F1 X Anjali (WS2010) Fore ground selection effect
DTY3.1 3 RM520-RM16030 30 25 Apo
BC1 X Sub1Swarna (DS2011) Fore ground selection DTY 12.1 12 RM28048-RM28166 36 47 Way Rarem
BC2F1 (WS2011) Fore ground selection
Selection of
BC2F2 (DS2012)
homozygote for DTY3.1
IR 84984-83-15-18-B-B-93 X Anjali (DS2010) Fore ground selection
( DTY12.1)
F1 X Anjali (WS2010) Fore ground selection
BC1 X Sub1Swarna (DS2011) Fore ground selection Anjali lLs with DTY12.1 , 12DAS
BC2F1 (DTY3.1) X BC2F1 (DTY12.1) (WS2011)
Fore ground selection
Fore ground selection
Selection of
BC3F1 BC2F2 (DS2012) homozygote for DTY
12.1
Selection of homozygote
BC3F2 (WS2012) for DTY3.1 and DTY12.1
Selected homozygotes with DTY3.1, DTY12.1 and their
combinations will be tested under drought DS2013 Anjali lLs with DTY3.1 12DAS

48. Genomic regions for MARS in IR55419-04 x Samba Mahsuri
Marker Aided Introgression
Additive
Chr Interval Trait Donor
Effect
1 RM212-RM486 700 Yield -NS IR55419-04
DTY11.1
11 Kid2746 – Kid 287 117 Yield -S IR55419-04
Kid3806 –RM520 Yield -S IR55419-04
3 and 6 350
Kid1613-Kid3434
1 RM212- RM486 10 Height -NS IR55419-04
2 RM525-RM221 -3 Height -NS IR55419-04
3 RM16-RM520 - Blast IR55419-04
Marker Aided Exclusion
Additive
Chr Interval Trait Donor
Effect
Kid8590 – Kid 9045
5&6 10 Height- NS IR55419-04
Kid8590 – Kid 9045 Line *RM212 *RM486 *RM525 *RM221 *RM16 *K_id3 *K_id3 *K_id3 *RM520 *K_id6 *K_6
10
IR55419-04 x Samba Mahsuri (MARS)
3 RM175- Kid6808 -149 Yield-S IR55419-04 1 1 2 2 2 1 1 1 1 1 1
30 1 1 3 3 1 1 1 1 2 3 .
31 1 3 1 1 1 1 1 1 2 1 1
80 2 2 2 2 2 1 3 3 3 1 1
82 2 3 3 2 2 1 1 1 1 1 1
84 1 2 1 2 2 1 1 1 1 1 1
FRS
102 1 1 3 3 1 3 1 1 2 2 1
SRS
109 1 1 2 2 2 1 1 3 3 1 1
123 3 3 1 1 1 3 3 1 2 2 1
199 1 3 1 1 1 2 2 3 3 . 3
201 1 1 1 1 1 . 2 1 2 . 1
Interactions for plant height under non-stress

49. Intogression of DTY QTLs in Korean parents (RDA)
Back ground Stage QTLs
Hanareumbyeo BC1 DTY1.1 and DTY2.2
Jinmybyeo BC1 DTY1.1 and DTY2.2
Gayabyeo BC1 DTY1.1 and DTY2.2
Sagnambatbyeo BC1 DTY1.1 and DTY3.1
BC2 – confirmed for foreground markers
and will be backcrossed
Introgression of QTLs in Smbha Mahsuri
• QTLs – DTY2.2 and DTY4.1
• Generation - BC2F2
• Foreground selection and selection of homozygote's
• Background selection

50. Wild species derived mapping population development
Rice varieties
SL No Female Parentage Male Parent
Diversity 1 MTU 1010/IRGC 81994 MTU1010
based on
SSR markers 2 MTU 1010/IRGC 105757 MTU1010
3 MTU 1010/IRGC 106109 MTU1010
4 MTU 1010/IRGC 106283 MTU1010
5 MTU 1010/IRGC 106285 MTU1010
6 Saro 5/IRGC 81994 Saro 5
7 Saro 5/IRGC 105757 Saro 5
Wild accessions
8 Saro 5/IRGC 106109 Saro 5
9 Saro 5/IRGC 106283 Saro 5
10 Saro 5/IRGC 106285 Saro 5
11 NericaL-14/IRGC 105757 NericaL-14
12 Nerica-L-14/IRGC 106277 NericaL-14
13 Nerica-L-14/IRGC 106285 NericaL-14
14 Nerica-L-31/IRGC 104639 Nerica-L-31
15 Nerica-L-31/IRGC 106277 Nerica-L-31
16 Nerica-L-5/IRGC 106109 Nerica-L-5
17 Nerica-L-7/IRGC 106283 Nerica-L-7
18 Nerica-L-8/IRGC 106285 Nerica-L-8
Can be used for mapping Fe and Zn content in rice grains!!

51. qDTY3.1 : Major effect and consistent QTL in Swarna and BR11
QTLs Chr Interval R2
Additive
effect
Donor Recipient • QTL validation
DTY3.1 3 RM520-RM16030 30 25 Apo Swarna
DTY3.1 3 RM15935-RM520 20-25 22 Apo BR11
• Fine mapping
• Physiogical
characterization
• Insilico candidate gene
identification
Apo x Swarna Apo x BR11
•Transciptome analysis
to identify differentially
expressed genes
• Validation of genes by
RT and QRT PCR