Download as PDF, PPTX
![SNPs - a snap shot across genome
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(211527)
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(236006)
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(162723)
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(201656)
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(188047)
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(197285)
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(151888)
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(201656)
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(188047)
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(197285)
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(151888)
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(176433)
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(224589)
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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](https://image.slidesharecdn.com/2018-181220083044/75/2018-jauhar-ali-green-super-rice-breeding-technology-achievements-and-advances-91-2048.jpg)
![InDels
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(20137)
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(17269)
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(15390)
Chr. 4
(13460)
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(11157)
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(13010)
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(11707)
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(12550)
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(9362)
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(10380)
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(13521)
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(12535)
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(20287)
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(17496)
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(15069)
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(14361)
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(11107)
Chr. 6
(13320)
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(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](https://image.slidesharecdn.com/2018-181220083044/75/2018-jauhar-ali-green-super-rice-breeding-technology-achievements-and-advances-93-2048.jpg)
More Related Content
Similar to 2018. jauhar ali. green super rice breeding technology achievements and advances
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? FoodSecurity –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) xdonors(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 ILSfor 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 forabiotic 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 (VARYACCORDING 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 F1x 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 networksidentified 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.4Ch.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 yieldperformances (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.
- 13. IRRI DT Checkvariety 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 IR83142-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 Noof 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 trialarea Indonesia 8.2011 Grainyieldt/ha Site specific nutrient management (SSNM) Untungetal(2012)unpubl.
- 17. HYBRIDS INBREDS Total Batch 1Batch 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 linesin 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 21 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 pedigreeof Huang-Hua-Zhan (HHZ) involving 14 parents
- 21. P1(FHZ) P2(HXZ) HHZP1(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 of16 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 BreedingProcedure 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 BreedingProcedure 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 ofILs 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.
- 40. BPH and VirusResistance 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 tonneof 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 DrZhikang 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. RavindraBabu Principal Scientist, Plant Breeding Directorate of Rice Research, Rajendranagar, Hyderabad-30, rbvemuri1955@gmail.com
- 44.
- 45. INTRODUCTION Rice is thedominant 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 ofplants 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 OFDIETARY 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 andplays 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, essentialfor 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 lasttwo 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 nowfocusing 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.
- 53. Selection of parentsfor 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 molecularBreeding 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 OFSTUDY 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 topolishing 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 ofGenotype 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 ofGenotype 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) •ADT43(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 programmeat 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 Zncontents 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 HRR67.5 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 (RFD100%) 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 REVEALEDTHAT: 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 involvedin 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 SC129 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 SC129 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 TentativeSSR 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 TentativeSSR 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 wereidentified 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 PM39 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 PM40 Thank you
- 83. Genome-wide variations between elitelines 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 isa 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... Enablingcrop improvement » Enhancing photosynthetic efficiency » Marker assisted selection, transgenics, etc. The way out » Improving productivity per ha
- 86. Heterosis refersto 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 resourcesin 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 222.62X 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 mappedreads 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.
- 91. SNPs - asnap 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.
- 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 3040 [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 SNPsand 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 - GenotypewiseCMSlinesRestorerlines 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.
- 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 mappingof 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 1assembly 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 SNPbetween 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 1assembly 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 trueSNP 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 1assembly 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 betweenInbred 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 Combinedmapping 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 biasin 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 wholegenome 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 involvingthe 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 ofScience 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.
- 117. GENETIC ENGINEERING FORSEMI DWARF RICE USING RNA INTERFERENCE (RNAi) G.Bindusree Research Scholar Guide Dr. M. Parani Prof. & Head Department Genetic Engineering SRM University
- 118. Why Semi DwarfRice ?? •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 SEMIDWARF ADT-44 ADT-37 CORH-2 ASD-20 ADTRH-1 DWARF Jyothi Annapoorna Annapurna-28 YIELD Q/ha YIELD
- 121. PARTICULARS White Ponni ParentageMayang,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.
- 123. GA20ox1 GA20ox2 (sd1) GA20ox3 GA20ox4 GA20 oxidase GA2oxidase 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 EXON2 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 1gene(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 RNAiconstructs 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 contiguousnucleotide identity from ClustalW 1. Identification of trigger sequence Work done so far 5’ UTR 3’ UTR
- 129. 2.Designing of theconstruct Act1-Promoter 1228bp Antisense 362bp Intron1 122bp Sense 362bp Kpn I Xba I hpRNA RNAi Pathway
- 130. 3. Amplification ofloop 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 ofloop 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 ofloop and sense ligation by sequencing CGCCAATGGGGTAATTAAAACGATGGTGGacGACATTGCATTTCAAATTCAAAACAAATTCAAAACACACCGAC CGAGATTATGcTGAATTCAAACGCGTTTGTGCGCGCAGGAGGGTGTACACGCGCTGGCTCGCGCCGCCGGCCGC CGACGCCGCCGCGACGGCGCAGGTCGAGGCAGCCAGCTGATCGCCGAACGGAACGAAACGGAACGAACAGAA GCCGATTTTTGGCGGGGCCCACGTGGGGGATTTGCCCACGTGAGGCCCCACGTGGACAGTGGGCCCGGGCGGA GGTGGCACCCACGTGGACCGCGGGCCCCGCGCCGCCTTCCAATTTTGGACCCTACCGCTGTACATATTCATATATT GCAAGAAGAAGCAAAACGTACGTGTGGGTTGGGTTGGGCTTCTCTCTATTACTAAAAAAAATATAATGGAACG ACGGATGAATGGATGCTTATTTATTTATCTAAATTGAATTCGAATTCGGcTCAA
- 133. 6. Amplification ofActin 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 forGA 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 functionalanalysis 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 Constitutivepromoters CaMv35S,maizeUbi, riceAct-1 Inducible promoters rd29A,PR1 Tissue specific promoters TA9,Gt1
- 139. Inducible promoter Inducedby 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 andcharacterization 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 analysisof (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 pglyIand 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 SixpglyI 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 GUSExpression GUS assay of shoots TS LS
- 149. GUS PCR Homozygousline identified in one of the above event atT2 generation PCR for uidA gene
- 150. 2. Function ofinduciblity 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 clonedpromoter 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.
- 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)