Session 3 - Tamil Nadu Agricultural University
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GREEN SUPER RICE (GSR)<br />

BREEDING TECHNOLOGY:<br />

ACHIEVEMENTS & ADVANCES<br />

Drought tolerance Screening<br />

Jauhar Ali<br />

Plant Breeder, Senior Scientist I<br />

GSR Project Leader &<br />

Regional Project Coordinator (Asia) GSR<br />

PBGB, IRRI

Why GSR?<br />

• Food Security –threat-2008-global<br />

village concept<br />

• Stable sustainable yields using lesser<br />

inputs-farmer practice-rainfed &<br />

irrigated<br />

• Diseases & Insect pest threats-high<br />

input environments<br />

• Caring for environment-pollution of<br />

water systems-chemical residues

What is “GSR” ?<br />

Rice cultivars that produce higher and more stable yields<br />

with lesser inputs (water, fertilizers and pesticides)<br />

High yielding GSR cultivars with “Green” traits:<br />

Resistances/ tolerances to:<br />

Abiotic stresses: Drought, salinity, alkalinity, iron toxicity, etc.<br />

Diseases:<br />

Blast, bacterial blight, sheath blight, viruses,<br />

and false smut etc<br />

Insects:<br />

Brown plant hopper, Green leaf hopper, etc<br />

Grain quality<br />

Mostly in elite RP background- later in RP-NARES<br />

High resource-use efficiency: Water and nutrients (N P K)<br />

• TEST SITES: AFRICA & ASIA=15countries<br />

Asia: Cambodia,Indonesia,Laos,Vietnam,Bangladesh,Pakistan,Sri Lanka<br />

Africa: Liberia, Mali, Mozambique, Nigeria, Rwanda, Senegal, Tanzania, Uganda<br />

China: Guangxi,Guizhou,Suchuan,Yunnan<br />

GSR Materials given to NARES=Hybrids(193) + Inbreds (152)<br />

Less inputs, more production & environment sustainability

Li, Z.K. and Xu, J.L. (2007) “Advances in Molecular Breeding Toward<br />

Drought and Salt Tolerant Crops” Springer pp. 531-565.<br />

Ali et al (2006) FCR 97:66-76<br />

Development of GSR materials by designed QTL pyramiding (DQP)<br />

strategy for select target component traits for a given ecosystem<br />

RP (3) x donors(205) F 1 s x RP BC 1 F 1 s x RP<br />

Self and bulk<br />

harvest<br />

~25 BC 2 F 1 s/donor x RP<br />

x<br />

Bulk BC 2 F 2 populations<br />

1, 2, 3, 4, 5, 6, ……<br />

BC 3 F 1 s x RP<br />

Self and bulk<br />

x<br />

harvest<br />

BC 3 F 2 populations<br />

1, 2, 3, 4, 5, 6, ……<br />

Screening for target traits such as tolerances to<br />

drought, salinity, submergence, anaerobic<br />

germ., P & Zn def., BPH, etc.<br />

Selection for target traits<br />

and backcrossing<br />

BC 4 F 1 s<br />

x<br />

BC 4 F 2 s<br />

Confirmation of the selected traits by replicated phenotyping<br />

and genotyping of ILs for gene/QTL identification<br />

Crosses made between sister ILs<br />

having unlinked desirable genes/<br />

QTLs for target ecosystem<br />

DQP &MAS for pyramiding desirable<br />

genes/QTLs and against undesirable donor<br />

segments for target ecosystem<br />

Development of GSR materials with improved target traits for wide scale<br />

testing in different ecosystems and its release.<br />

NILs for individual genes/QTLs for functional genomic studies

Development of ILS for different abiotic and<br />

biotic stress tolerances at IRRI<br />

Z.K. Li et al (2005) PMB 59:33-52; Ali et al (2006) FCR 97:66-76

Hidden diversity for abiotic and biotic<br />

tolerance in the primary gene pool of rice<br />

• Tremendous amounts of hidden diversity-BC progenytransgressive<br />

-target traits-regardless of donor performance-severe<br />

stress screening<br />

• Common to identify in BC progeny-extreme phenotypes (tolerances)<br />

• Selection efficiency –highly dependent upon background<br />

• Selection efficiency-affected by level of stress applied<br />

• Selection efficiency for different target traits vary in BC generations.<br />

• More distantly related donors, particularly landraces, tend to give more<br />

transgressive segregations for complex phenotypes in the BC<br />

progenies.<br />

• Wide presence and random distribution of stress tolerance genes in<br />

primary gene pool of rice –good news for rice breeders<br />

Yu et al (2003) TAG 108:131-140; Ali et al (2006) FCR 97:66-76

Donors that gave better results with varying<br />

recurrent parental backgrounds<br />

S.No.<br />

ST<br />

ZDT<br />

AG<br />

SUBT<br />

LTG<br />

BPH<br />

MULTI-<br />

TRAITS<br />

FAVOURABLE DONORS (VARY ACCORDING TO RP)<br />

OM1706,OM1723,FR13A,NAN29-2,BABOAMI, KHAZAR<br />

TKM9,HEI-HE-AI-HUI(HHAH),JIANGXI-SI-MIAO(JSM), KHAZAR, MADHUKAR,<br />

SHWE-THWE-YIN-HYE (STYH), BASMATI385, IKSAN438, YU-QIU-GU, TETEP,<br />

NIPPONBARE, CO43, RASI, YUNHUI, BG304,BR24, FR13A GAYABYEO<br />

Y134,TKM9,KHAZAR,GAYABYEO,STYH,NAN29-2,<br />

BABOAMI,JSM,FR13A,OM1706<br />

CISEDANE,FR13A,IR50,NAN29-2,OM1706,STYH,TAROM MOLAEI,TKM9,Y134<br />

NAN29-2,GAYABYEO<br />

JSM,BABOAMI,TKM9,BG300,C418,LEMONT,MADHUKAR,MR167,OM1706,STYH,<br />

Y134<br />

BABOAMI, GAYABYEO, SHWE-THWE-YIN-HYE (STYH), NAN29-2, FR13A,<br />

OM1706, KHAZAR, JIANGXI-SI-MIAO<br />

Ali et al (2006) FCR 97:66-76

Experiment set I<br />

Experiment set II<br />

Experiment set III<br />

Designed QTL pyramiding experiments<br />

IR64 x BR24<br />

F 1<br />

x IR64<br />

BC 2 F 2<br />

IR64 x Binam<br />

F 1 x IR64<br />

BC 2 F 2<br />

IR64 x STYH<br />

F 1 x IR64<br />

BC 2 F 2<br />

IR64 x OM1723<br />

F 1<br />

x IR64<br />

BC 2 F 2<br />

IR64 x Type3<br />

13 BC 2 F 2 populations screened under two types of severe drought, resulting in 221 survived<br />

DT BC 2 F 3 introgression lines (ILs), which were genotyped with SSR markers<br />

F 1<br />

x IR64<br />

BC 2 F 2<br />

IR64 x HAN<br />

F 1<br />

x IR64<br />

BC 2 F 2<br />

IR64 x Zihui100<br />

F 1<br />

x IR64<br />

BC 2 F 2<br />

IL 1 x IL 2<br />

F 1<br />

X<br />

IL 3 x IL 4<br />

F 1<br />

X<br />

IL 7 x IL 15<br />

F 1<br />

X<br />

9 1 st round pyramiding<br />

F 2 populations from<br />

crosses between 15 ILs<br />

F 2<br />

F 2<br />

F 2<br />

Screened under severe drought at the reproductive stage, resulting in 455 survived<br />

DT F 2 plants, which were progeny tested and genotyped with SSR markers<br />

(PL 1 , PL 2 , PL 3 ) x (PL 4 , PL 5 , PL 6 , PL 7 , PL 8 )<br />

F 1 s<br />

X<br />

F 2 s<br />

14 2 nd round pyramiding F 2<br />

populations from crosses<br />

between 8 1 st round PLs<br />

Screened under severe drought at the reproductive stage and 667 survived<br />

DT F 3 lines were progeny tested and genotyped with SSR markers

Putative genetic networks identified in 455 DT PLs derived<br />

from 9 crosses between DT IR64 ILs<br />

A:<br />

Drought<br />

B:<br />

Drought<br />

I: Drought<br />

AG 1-1 (7)<br />

1.00<br />

AG 2-1 (5)<br />

0.994<br />

AL 9-1 (3)<br />

1.000<br />

AG 1-2 (7)<br />

0.979<br />

RM347<br />

(3.8)<br />

0.691<br />

RM561<br />

(2.6)<br />

0.618<br />

RM342<br />

(8.5)<br />

0.673<br />

AG 2-2 (6)<br />

0.891<br />

RM309<br />

(12.5)<br />

0.927<br />

RM469<br />

(6.1)<br />

0.818<br />

RM575<br />

(1.4)<br />

0.745<br />

RM179<br />

(12.3)<br />

0.727<br />

RM211<br />

(2.2)<br />

0.800<br />

RM350<br />

(8.4)<br />

0.800<br />

AG 9-5 (3)<br />

0.553<br />

AG 9-2 (2)<br />

0.915<br />

AG 9-4 (5)<br />

0.500<br />

RM152<br />

(8.1)<br />

0.930<br />

RM215<br />

(9.7)<br />

0.870<br />

RM554<br />

(3.7)<br />

0.700<br />

RM109<br />

(2.1)<br />

0.617<br />

AG 1-3<br />

(13)<br />

0.748<br />

AG 1-4<br />

(4)<br />

0.688<br />

AG 1-5<br />

(5)<br />

0.726<br />

RM418<br />

(7.3)<br />

0.717<br />

RM179<br />

(12.3)<br />

0.607<br />

RM202<br />

(11.3)<br />

0.745<br />

RM463<br />

(12.5)<br />

0.745<br />

RM544<br />

(8.2)<br />

0.727<br />

RM215<br />

(9.8)<br />

0.527<br />

AG 9-3(24)<br />

0.870<br />

RM446<br />

(1.6)<br />

0.830<br />

D:<br />

Drought<br />

E:<br />

Drought<br />

G:<br />

Drought<br />

AG 4-1<br />

(6)<br />

RM543<br />

(1.1)<br />

1.000<br />

AG 7-1 (18)<br />

1.00<br />

RM271<br />

(10.4)<br />

AG 4-2<br />

(4)<br />

RM23<br />

(1.5)<br />

AG 4-3<br />

(4)<br />

RM433<br />

(8.7)<br />

0.867<br />

AG 5-1 (12)<br />

0.711<br />

RM401<br />

(4.1)<br />

0.733<br />

AG 5-4 (2)<br />

0.767<br />

RM298<br />

(7.1)<br />

0.767<br />

AG 5-2 (9)<br />

0.809<br />

RM53<br />

(2.3)<br />

0.833<br />

RM85<br />

(3.12)<br />

AG7-5<br />

(2)<br />

RM286<br />

(11.1)<br />

AG7-3<br />

(16)<br />

RM44<br />

(8.3)<br />

AG 7-2<br />

(2)<br />

RM469<br />

(6.1)<br />

RM289<br />

(5.3)<br />

RM516<br />

(5.3)<br />

AG7-7<br />

(2)<br />

RM245<br />

(9.8)<br />

RM36<br />

(3.3)<br />

RM18<br />

(7.6)<br />

RM215<br />

(9.8)<br />

RM544<br />

(8.3)<br />

RM272<br />

(1.3)<br />

RM179<br />

(12.3)<br />

RM441<br />

(11.2)<br />

AG4-4<br />

(3)<br />

RM220<br />

(1.2)<br />

RM222<br />

(10.1)<br />

0.567<br />

RM270<br />

(12.6)<br />

0.567<br />

RM17<br />

(12.7)<br />

0.500<br />

RM424<br />

(2.5)<br />

0.667<br />

RM244<br />

(10.1)<br />

0.583<br />

RM101<br />

(12.4)<br />

0.766<br />

AG 5-3(2)<br />

0.525<br />

RM248<br />

(7.7)<br />

0.500<br />

RM275 RM294B RM224 RM110 RM435<br />

(6.6) (1.6) (11.7) (2.1) (6.1)<br />

AG7-4<br />

(7)<br />

RM13<br />

(5.2)<br />

RM5<br />

(1.7)<br />

RM30 RM465A<br />

(6.8) (2.5)<br />

F:<br />

Drought<br />

H:<br />

Drought<br />

AG 6-1 (8)<br />

1.000<br />

AG 6-2 (5)<br />

0.967<br />

RM307<br />

(2.1)<br />

RM446<br />

(1.6)<br />

RM5<br />

(1.7)<br />

RM535<br />

(2.12)<br />

RM331<br />

(8.4)<br />

AG 8-1 (26)<br />

1.00<br />

RM197<br />

(6.1)<br />

RM449<br />

(1.6)<br />

RM481<br />

(7.1)<br />

RM32<br />

(8.3)<br />

RM448<br />

(3.10)<br />

C:<br />

Drought<br />

AG 3-1 (4)<br />

1.00<br />

RM51<br />

(7.1)<br />

0.833<br />

AG 6-3<br />

(12)<br />

0.894<br />

AG 6-4<br />

(2)<br />

0.772<br />

RM44<br />

(8.3)<br />

0.633<br />

RM235<br />

(12.6)<br />

0.667<br />

RM14<br />

(1.13)<br />

RM211<br />

(2.2)<br />

RM154 RM317 RM562<br />

(2.1) (4.6) (1.6)<br />

AG8-3<br />

(3)<br />

AG8-4<br />

(3)<br />

RM589<br />

(6.1)<br />

AG8-5<br />

(2)<br />

RM30<br />

(6.7)<br />

RM547<br />

(8.3)<br />

AG8-2<br />

(2)<br />

RM275 RM335<br />

(6.5) (3.12)<br />

RM143<br />

(3.12)<br />

RG8-6<br />

(2)<br />

AG 3-3<br />

(3)<br />

0.736<br />

AG 3-2<br />

(4)<br />

0.855<br />

RM302<br />

(1.10)<br />

0.782<br />

RM172<br />

(7.7)<br />

0.727<br />

RM20<br />

12.1<br />

0.567<br />

RM258<br />

(10.4)<br />

RM246<br />

(1.8)<br />

RM169<br />

(5.3)<br />

RM245<br />

(9.8)<br />

Li et al 2012 unpubl.

Ch.1 Ch.2<br />

Ch.3 Ch.4 Ch.5 Ch.6<br />

RM499<br />

RM462<br />

RM428<br />

RM10287<br />

RM323<br />

RM84<br />

RM220<br />

RM86<br />

RM283<br />

RM522<br />

RM1<br />

RM272<br />

RM490<br />

RM575<br />

RM576<br />

RM259<br />

RM243<br />

RM583<br />

RM600<br />

RM572<br />

RM581<br />

RM580<br />

RM23<br />

RM129<br />

RM329<br />

RM446<br />

RM562<br />

RM594<br />

RM9<br />

RM5<br />

RM306<br />

RM488<br />

RM237<br />

RM246<br />

RM473A<br />

RM11570<br />

RM403<br />

RM128<br />

RM302<br />

RM212<br />

RM319<br />

RM265<br />

RM297<br />

RM315<br />

RM472<br />

RM431<br />

OSR23<br />

RM14<br />

RM436<br />

RM51<br />

RM481<br />

RM125<br />

RM180<br />

RM501<br />

OSR22<br />

RM214<br />

RM418<br />

RM432<br />

RM11<br />

RM346<br />

RM182<br />

RM336<br />

RM10<br />

RM351<br />

RM455<br />

RM505<br />

RM234<br />

RM18<br />

RM172<br />

RM248<br />

Ch.7<br />

Bin1.1<br />

Bin1.2<br />

Bin1.3<br />

Bin1.4<br />

Bin1.5<br />

Bin1.6<br />

Bin1.7<br />

Bin1.8<br />

Bin1.9<br />

Bin1.10<br />

Bin1.11<br />

Bin1.12<br />

Bin1.13<br />

Bin7.1<br />

Bin7.2<br />

Bin7.3<br />

Bin7.4<br />

Bin7.5<br />

Bin7.6<br />

Bin7.7<br />

RM109<br />

RM485<br />

RM154<br />

RM211<br />

RM236<br />

RM279<br />

RM423<br />

RM8<br />

RM53<br />

RM555<br />

RM233A<br />

RM174<br />

RM145<br />

RM71<br />

RM327<br />

RM521<br />

RM300<br />

RM324<br />

RM424<br />

RM262<br />

RM561<br />

RM341<br />

RM475<br />

RM106<br />

RM263<br />

RM526<br />

RM221<br />

RM525<br />

RM318<br />

RM450<br />

RM497<br />

RM6<br />

RM240<br />

RM530<br />

RM112<br />

RM250<br />

RM166<br />

RM197<br />

RM213<br />

RM48<br />

RM207<br />

RM266<br />

RM535<br />

RM138<br />

RM408<br />

RM506<br />

RM407<br />

OSR30<br />

RM547<br />

RM544<br />

RM25<br />

RM126<br />

RM407<br />

RM44<br />

RM72<br />

RM137<br />

RM331<br />

RM339<br />

RM342A<br />

RM515<br />

RM223<br />

RM284<br />

RM210<br />

RM556<br />

RM447<br />

RM256<br />

RM149<br />

RM60<br />

RM81B<br />

RM22<br />

RM523<br />

RM569<br />

RM231<br />

RM175<br />

RM545<br />

RM245<br />

RM517<br />

OSR13<br />

RM14963<br />

RM7<br />

RM232<br />

RM251<br />

RM282<br />

RM338<br />

RM156<br />

RM411<br />

RM487<br />

RM16<br />

RM347<br />

RM504<br />

RM203<br />

RM186<br />

RM55<br />

RM168<br />

RM416<br />

RM520<br />

RM293<br />

RM114<br />

RM130<br />

RM565<br />

RM514<br />

RM570<br />

RM227<br />

RM85<br />

RM307<br />

RM401<br />

RM537<br />

RM551<br />

RM335<br />

RM518<br />

RM261<br />

RM471<br />

RM142<br />

RM273<br />

RM252<br />

RM241<br />

RM470<br />

RM303<br />

RM317<br />

RM348<br />

RM349<br />

RM131<br />

RM280<br />

RM567<br />

RM559<br />

Ch.8 Ch.9 Ch.10 Ch.11 Ch.12<br />

RM230<br />

Bin2.1<br />

Bin2.2<br />

Bin2.3<br />

Bin2.4<br />

Bin2.5<br />

Bin2.6<br />

Bin2.7<br />

Bin2.8<br />

Bin2.9<br />

Bin2.10<br />

Bin2.11<br />

Bin2.12<br />

Bin8.1<br />

Bin8.2<br />

Bin8.3<br />

Bin8.4<br />

Bin8.5<br />

Bin8.6<br />

Bin8.7<br />

RM296<br />

RM285<br />

RM316<br />

RM23818<br />

RM444<br />

RM219<br />

RM524<br />

RM105<br />

RM321<br />

RM409<br />

RM460<br />

RM566<br />

RM434<br />

RM257<br />

RM108<br />

RM242<br />

RM278<br />

RM201<br />

RM107<br />

OSR28<br />

RM189<br />

RM215<br />

RM245<br />

RM205<br />

Bin9.1<br />

Bin9.2<br />

Bin9.3<br />

Bin9.4<br />

Bin9.5<br />

Bin9.6<br />

Bin9.7<br />

Bin9.8<br />

Bin3.1<br />

Bin3.2<br />

Bin3.3<br />

Bin3.4<br />

Bin3.5<br />

Bin3.6<br />

Bin3.7<br />

Bin3.8<br />

Bin3.9<br />

Bin3.10<br />

Bin3.11<br />

Bin3.12<br />

RM264<br />

RM281<br />

RM224<br />

Bin8.8<br />

RM144<br />

Bin11.7<br />

Genomic correspondences between FGUs identified in 150 ILs of 8 BC 2 populations,<br />

RM474<br />

RM25022<br />

RM25181<br />

RM222<br />

RM216<br />

RM239<br />

RM311<br />

RM467<br />

RM184<br />

RM271<br />

RM269<br />

RM258<br />

RM171<br />

RM304<br />

RM228<br />

RM147<br />

RM333<br />

RM496<br />

Bin4.1<br />

Bin4.2<br />

Bin4.3<br />

Bin4.4<br />

Bin4.5<br />

Bin4.6<br />

Bin4.7<br />

Bin4.8<br />

STYH segments<br />

BR24 segments<br />

OM1723 segments<br />

Binam segments<br />

200 PLs of 3 1 st round pyramiding crosses and 4 2 nd round pyramiding crosses.<br />

Li et al 2012 (unpubl)<br />

Bin10.1<br />

Bin10.2<br />

Bin10.3<br />

Bin10.4<br />

Bin10.5<br />

Bin10.6<br />

Bin10.7<br />

RM122<br />

RM153<br />

RM399<br />

RM413<br />

RM13<br />

RM267<br />

RM437<br />

RM289<br />

RM516<br />

RM169<br />

RM509<br />

RM598<br />

RM163<br />

RM164<br />

RM291<br />

RM161<br />

RM188<br />

RM19029<br />

RM233B<br />

RM421<br />

RM178<br />

RM26<br />

RM274<br />

RM87<br />

RM480<br />

RM538<br />

RM334<br />

RM181<br />

RM286<br />

RM4B<br />

RM26063<br />

RM332<br />

RM167<br />

RM120<br />

RM479<br />

RM202<br />

RM536<br />

RM260<br />

RM287<br />

RM209<br />

RM229<br />

RM457<br />

RM187<br />

RM21<br />

RM473E<br />

RM206<br />

RM254<br />

Bin5.1<br />

Bin5.2<br />

Bin5.3<br />

Bin5.4<br />

Bin5.5<br />

Bin5.6<br />

Bin5.7<br />

Bin11.1<br />

Bin11.2<br />

Bin11.3<br />

Bin11.4<br />

Bin11.5<br />

Bin11.6<br />

RM204<br />

RM540<br />

RM469<br />

RM587<br />

RM588<br />

RM190<br />

RM589<br />

RM510<br />

RM204<br />

RM585<br />

RM584<br />

RM557<br />

RM111<br />

RM225<br />

RM314<br />

RM253<br />

RM50<br />

RM549<br />

RM539<br />

RM136<br />

RM19778<br />

RM527<br />

RM3<br />

RM454<br />

RM162<br />

RM343<br />

RM528<br />

RM30<br />

RM340<br />

RM400<br />

RM439<br />

RM103<br />

RM141<br />

RM176<br />

RM494<br />

RM20A<br />

RM4A<br />

RM19<br />

RM247<br />

RM512<br />

RM179<br />

RM101<br />

RM277<br />

RM511<br />

RM519<br />

RM313<br />

RM309<br />

RM463<br />

RM235<br />

RM270<br />

RM17<br />

Bin6.1<br />

Bin6.2<br />

Bin6.3<br />

Bin6.4<br />

Bin6.5<br />

Bin6.6<br />

Bin6.7<br />

Bin6.8<br />

Bin6.9<br />

FGUs identified in cross II-1<br />

FGUs identified in cross II-2<br />

FGUs identified in cross II-3<br />

Cross III-1<br />

Cross III-2<br />

Cross III-3<br />

Cross III-4<br />

Bin12.1<br />

Bin12.2<br />

Bin12.3<br />

Bin12.4<br />

Bin12.5<br />

Bin12.6<br />

Bin12.7

Mean yield under the<br />

irrigated control (t/ha)<br />

6.5<br />

6.0<br />

5.5<br />

5.0<br />

IR64 (CK)<br />

C: 4.68±0.23<br />

VS: 1.49±0.14<br />

RS: 0.52±0.38<br />

Type II (N=5)<br />

C: 5.71±0.42<br />

VS: 1.36±0.38<br />

RS: 2.20±0.45<br />

Type IV (N=7)<br />

C: 4.66±0.48<br />

VS: 1.34±0.41<br />

RS: 1.86±0.51<br />

Type I (N=17)<br />

C: 5.76±0.53<br />

VS: 2.07±0.55<br />

RS: 1.79±0.47<br />

Type III (N=19)<br />

C: 5.06±0.47<br />

VS: 1.98±0.47<br />

RS: 1.94±0.52<br />

4.5<br />

4.0<br />

3.5<br />

3.0<br />

0.0<br />

0.5<br />

0.5<br />

1.0<br />

1.0<br />

1.5<br />

1.5<br />

2.0<br />

2.0<br />

2.5<br />

2.5<br />

3.0<br />

3.0<br />

The mean yield performances (t/ha) of 48 2 nd round PLs (4 types) as<br />

compared to IR64 (CK), under the irrigated control (C), drought stresses<br />

at the vegetative (VS) and reproductive stages (RS) in the 2007 and 2008<br />

dry-season. Guan et al. 2010 JXB

Highly<br />

salinity<br />

tolerant

GSR Drought tolerant pyramided lines in IR64 background<br />

Under zero input conditions at IRRI DS2010<br />

IRRI DT Check variety<br />

IR74371-70-1-1<br />

GSR-IR83142-B-19-B

PC 2<br />

-1.0 -0.5 0.0 0.5<br />

DT PDLs AMMI-Biplot: 6 Locations -2011DS<br />

BRAC-Gaz, VAAS-Gia, VAAS-Duo, ICRR-Jak, ICRR-Teg, & IRRI-Los Banos<br />

Entry<br />

No.<br />

GSR Lines<br />

Mean<br />

(t/ha)<br />

LSD<br />

Group<br />

15 IR 83142-B-57-B 5.46 a<br />

9 IR 83141-B-17-B 5.17 b<br />

19 IR 83142-B-7-B-B 5.13 bc<br />

18 IR 83142-B-79-B 5.12 bc<br />

11 IR 83142-B-19-B 5.06 bcd<br />

5 IR 83140-B-11-B 5.05 bcde<br />

10 IR 83141-B-18-B 5.02 bcdef<br />

6 IR 83140-B-28-B 4.94 bcdefg<br />

13 IR 83142-B-21-B 4.86 cdefg<br />

12 IR 83142-B-20-B 4.79 defg<br />

14 IR 83142-B-49-B 4.78 efg<br />

16 IR 83142-B-60-B 4.75 fg<br />

20 IR 83142-B-8-B-B 4.74 g<br />

7 IR 83140-B-32-B 4.74 g<br />

3 Best Check 4.67 g<br />

8 IR 83140-B-36-B 4.32 h<br />

1 2nd Best Check 4.29 h<br />

17 IR 83142-B-61-B 4.27 h<br />

4 IR 74371-70-1-1 3.57 i<br />

2 Apo 3.53 i<br />

10amBrGa PC %<br />

1<br />

2<br />

60.9<br />

24.7<br />

2 nd 1Best 17 Check<br />

8<br />

3Best<br />

Check<br />

10dsIRig<br />

Why such yield advantages?<br />

Mean LSD<br />

Environments<br />

(t/ha) Group<br />

Designed QTL Pyramiding<br />

IRRI-Los Banos 6.55 a<br />

Possible role of Epigenetics VAAS-Gia 6.53 a<br />

10dsIcTe<br />

Selection for grain yield, higher VAAS-Duo 6.06 b<br />

spikelet fertility, deeper and thicker BRAC-Gaz 4.29 c<br />

roots esp. under reproductive -0.5 0.0 0.5 1.0<br />

ICRR-Jak 3.18 d<br />

stage DT stress<br />

ICRR-Teg PC 1<br />

2.08 e<br />

2<br />

12<br />

13<br />

4<br />

10dsIcJa 16<br />

19<br />

6<br />

18<br />

IR 1183142-B-19-B<br />

7<br />

14 IR 1583142-B-57-B<br />

9<br />

IR 583140-B-11-B<br />

20<br />

10<br />

10suVaDu 10suVaGi

Promising GSR Drought + Salinity tolerant materials tested under Iloilo during WS2010<br />

GSR entry<br />

No of<br />

panicles<br />

Plant<br />

height<br />

(cm)<br />

Maturity<br />

(days)<br />

Yield<br />

(kg/ha)<br />

%<br />

increase<br />

over<br />

FL478<br />

SES<br />

score<br />

4WAT<br />

SES<br />

score<br />

Maturity<br />

IR83140-B-11-B 16 84 116 1140 103.6 4 5<br />

IR83140-B-28-B 13 86 114 876 56.4 4 5<br />

IR83140-B-32-B 15 85 114 657 17.3 4 5<br />

FL478 11 70 111 560 0.0 5 -<br />

NSIC 222 19 83 112 147 -73.8 4 -<br />

First two nominated for NCT Philippines WS2011

Grain yield t/ha<br />

IR83140-B-11-B<br />

Untung et al (2012) unpubl.<br />

PVS Purvakarta<br />

2.5ha trial area<br />

Indonesia 8.2011<br />

Site specific nutrient management (SSNM)

Summary of GSR data received from NARES in Asia<br />

HYBRIDS<br />

INBREDS<br />

Batch 1 Batch 2 Batch 2 Batch 3 Batch 4 Batch 1 Batch 2 Batch 3 IRRI-GSR<br />

Total<br />

No. of lines 24 80 42 37 9 22 31 9 47 301<br />

IRLL, HY IRLL, HY RFLL, DT IRLL, DT,<br />

RFLL, (I & RFLL, I, RFLL (I & DT, SubT, -<br />

HT, Nuse,<br />

J) DT, T-BL, J), DT, T- ST, HY<br />

T-BB, BL,<br />

GQ BL, BB,<br />

Line composition<br />

BPH, SB<br />

TBB, HT,<br />

WT, ST,<br />

GQ<br />

Total no. of experiment reported - 15 10 21 12 16 39 31 10 154<br />

No. of location - 14 8 17 11 14 21 18 8 111<br />

Year/Season - 5 4 5 3 5 7 6 4 39<br />

No. of data sets received from<br />

NARES<br />

- 12 10 10 12 13 23 27 9 116<br />

No. of replicated data - 5 5 10 10 4 23 19 76<br />

No. of data sets usable for GxE<br />

Analysis<br />

- 3 4 10 10 3 14 14 58<br />

5 Best Entries<br />

1 - IIyou3203 HanF1-40 CXY2 HuF1-9<br />

Zonghua<br />

1<br />

Luyin 46<br />

2 - CXY2 HanF1-41 QS2 HuF1-17 HHZ SAGC-4<br />

ZH1<br />

TME8051<br />

8<br />

3 - CXY727 HanF1-27 IIyou623 HuF1-8 BD007 926 FFZ<br />

4 - ZXY673 HanF1-36 Annong5 HuF1-4 SACG-4 SAGC-08 P35<br />

5 - XYR24 HanF1-39 3LYR24 HuF1-13 RC8 SAGC-02 HHZ<br />

Mean yield across location (t/ha) 7.13 5.83 5.49 6.17 4.21 5.09 5.26<br />

Average advantage over the best<br />

check<br />

8.3% 22.1% 6.2% 28.8% -1.6% 8.7% 12.5%<br />

Yield advantage of the best entry 13.3% 26.9% 13.1% 33.5% 7.9% 12.8% 19.6%<br />

ANOVA: Pr(>F)<br />

ENV 8.808E-09 2.334E-05 5.551E-16 1.143E-10 7.674E-06

List of the nominated GSR inbreds and<br />

hybrids for NCTs in the target SSA, SEA and<br />

SA countries<br />

Type of GSR<br />

lines in NCT<br />

trials<br />

Mali<br />

Senegal<br />

Rwanda<br />

Nigeria<br />

Mozambique<br />

Tanzania<br />

Uganda<br />

Bangladesh<br />

Vietnam<br />

Sri Lanka<br />

Pakistan<br />

Cambodia<br />

Lao PDR<br />

Indonesia<br />

Philippines<br />

Inbreds<br />

Hybrids<br />

Total<br />

2 4 1 3 3 2 5 7 4 1 6 2 4 15<br />

2 4 2 3 3 3 4 8 2<br />

4 8 3 6 3 3 5 9 13 4 1 6 2 6 15<br />

• A total of 20 GSR inbreds and 21 hybrids have been nominated to the NCTs<br />

of the 8 target SSA countries;<br />

• A total of 48 inbreds and 24 hybrids have been nominated in the NCTs of 8<br />

Asian countries.

List of the promising widely adaptable GSR inbreds<br />

identified from adaptation yield trials in SSA, SEA and SA<br />

Name<br />

Cote D’ivoir<br />

Mali<br />

Rwanda<br />

Nigeria<br />

Mozambique<br />

Tanzania<br />

Uganda<br />

Bangladesh<br />

Indonesia<br />

Lao PDR<br />

Pakistan<br />

Sri Lanka<br />

Vietnam<br />

Philippines<br />

All<br />

HHZ 2 1 1 2 1 2 1 1 11<br />

Zhongzu14 2 1 1 1 1 1 7<br />

ZH1 2 1 2 1 1 1 1 9<br />

KCD1 2 1 1 1 1 1 7<br />

RC8 1 2 1 1 1 6<br />

Weed Tolerant 1 1 2 1 2 1 7<br />

HUA-565 2 2 1 5<br />

FFZ 1 1 1 1 1 5<br />

SAGC-4 2 2 1 1 1 7<br />

WX763 2 1 1 1 5<br />

HHZ developed in GAAS is a mega-variety of high yield & superior quality grown in 8 provinces of<br />

South & Central China (Guangdong, Jiangxi, Fujian, Hunan, Hubei, Anhui, Yunan and Guangxi).

The complex pedigree of Huang-Hua-Zhan<br />

(HHZ) involving 14 parents

S. C. Zhou et al., unpublished Z.K. Li et al 2012 (unpubl.)<br />

P 1 (FHZ) P 2 (HXZ) HHZ P 1 (FHZ) P 2 (HXZ) HHZ

Ch. 1<br />

Ch. 2<br />

Ch. 3<br />

Ch. 4<br />

Ch. 5<br />

Ch. 6<br />

Ch. 7<br />

Ch. 8<br />

Ch. 9<br />

Ch. 10<br />

Ch.11<br />

Chr. 12<br />

Genomic composition of the HHZ genome based on the<br />

re-sequencing data (From S. C. Zhou et al., unpublished)<br />

Each colored vertical line corresponds to a window of 10 kb. Vertical lines distribute upper side on each<br />

chromosome represent AZ haplotype blocks (red for ≥200kb AZ blocks, light red for

IRRI-GSR breeding program & strategy

Two batches of 16 populations with the recurrent parent, Huang-<br />

Hua-Zhan (HHZ) and 16 donors from 9 different countries<br />

Batch Pop. Donor Country of origin Gen.(10 DS)<br />

1 HHZ5 OM1723 Vietnam (I) BC1F5<br />

1 HHZ8 Phalguna India (I) BC1F5<br />

1 HHZ9 IR50 IRRI (I) BC1F5<br />

1 HHZ11 IR64 IRRI (I) BC1F5<br />

1 HHZ12 Teqing China (I) BC1F5<br />

1 HHZ15 PSB Rc66 Philippines (I) BC1F5<br />

1 HHZ17 CDR22 India (I) BC1F5<br />

1 HHZ19 PSB Rc28 Philippines (I) BC1F5<br />

2 HHZ1 Yue-Xiang-Zhan China (I) BC1F4<br />

2 HHZ2 Khazar Iran (J) BC1F4<br />

2 HHZ3 OM1706 Vietnam (I) BC1F4<br />

2 HHZ6 IRAT352 CIAT (upland) BC1F4<br />

2 HHZ10 Zhong 413 China (I) BC1F4<br />

2 HHZ14 R644 China (I) BC1F4<br />

2 HHZ16 IR58025B IRRI (I) BC1F4<br />

2 HHZ18 Bg304 Sri Lanka (I) BC1F4

The Introgression Breeding Procedure<br />

06WS<br />

8 HHZ BC 1 F 2 populations (08WS)<br />

08WS<br />

Yield traits<br />

DT screen<br />

ST screen<br />

SUB screen<br />

Random plants<br />

Ist round<br />

selection<br />

82HY plants<br />

109DT plants<br />

120ST plants<br />

15SUBT plants<br />

09DS<br />

2nd round<br />

selection<br />

326 Genotyping/progeny testing for all target traits<br />

326Yield 326DT screen 326ST screen 311SUB screen<br />

73HY ILs<br />

47DT ILs 78ST ILs 171SUB ILs<br />

QTL/Allelic<br />

diversity<br />

discovery<br />

for target<br />

traits<br />

09WS<br />

3rd round<br />

selection<br />

10DS<br />

Selections can<br />

be continued if<br />

certain lines<br />

segregating<br />

10WS/11DS<br />

369Genotyping/progeny testing for all target traits<br />

108Preliminary yield trials under DT, low input, NC<br />

68Promising ILs<br />

68 Replicated<br />

yield trials<br />

3Demo<br />

2NCT &<br />

29 MET for 11WS<br />

~80 promising ILs as<br />

parents for designed<br />

QTL pyramiding<br />

Confirming genetic<br />

networks for target<br />

traits and their<br />

genetic relationships

06WS<br />

The Introgression Breeding Procedure<br />

8 HHZ BC 1 F 2 populations (09WS)<br />

09WS<br />

Yield traits<br />

DT screen<br />

ST screen<br />

SUB screen<br />

Random plants<br />

10DS<br />

119HY plants<br />

210DT plants<br />

287ST plants<br />

21SUBT plants<br />

637Genotyping/progeny testing for all target traits<br />

Yield under<br />

NC & LI<br />

DT screen<br />

ST screen<br />

SUB screen<br />

QTL/Allelic<br />

diversity<br />

discovery<br />

for target<br />

traits<br />

420HY&FUE ILs<br />

180DT ILs<br />

44ST ILs<br />

221SUB ILs<br />

10WS<br />

11DS<br />

865Genotyping/progeny testing for all target traits<br />

Yield under<br />

NC & LI<br />

DT screen<br />

ST screen<br />

SUB screen<br />

Confirming genetic<br />

networks for target<br />

traits and their<br />

genetic relationships<br />

HY&FUE ILs<br />

DT ILs<br />

ST ILs<br />

SUB ILs<br />

11WS<br />

12 DS<br />

136 PYT<br />

80 RYT<br />

2 Demo<br />

2 NCT & 11 MET<br />

12DS<br />

~80 promising ILs as parents<br />

for designed QTL pyramiding

2 nd Generation GSR materials<br />

Multiple abiotic stress tolerant ILs developed from 16 donors into Huanghuazhan<br />

background and nominated to NCT using GSR breeding scheme.<br />

Target traits<br />

Number of ILs<br />

Produced from Selected at PYT & Nominated to<br />

BN<br />

RYT MET & NCT<br />

Drought tolerance (DT) 613 79 21<br />

High yield under low-input (LI) 370 27 3<br />

Salinity tolerance (SAL) 502 73 18<br />

Submergence tolerance (SUB) 128 13 2<br />

High yield under irrigated (Y) 576 100 27<br />

DT+LI 246 15 2<br />

DT+SAL 326 19 5<br />

DT+SUB 82 6<br />

DT+Y 382 40 11<br />

LI+SAL 274 10 1<br />

LI+SUB 38 0<br />

LI+Y 178 1<br />

SAL+SUB 60 9<br />

SAL+Y 292 42 8<br />

SUB+Y 101 5 1<br />

DT+SAL+SUB 35 3 1<br />

DT+SAL+Y 154 9<br />

DT+SUB+Y 58 3<br />

LI+SAL+SUB 20 0<br />

LI+SAL+Y 117 0<br />

LI+SUB+Y 36 0<br />

SAL+SUB+Y 39 2<br />

total: 845 146 40<br />

IL=Introgression lines; BN=Backcross Nursery;PYT=Preliminary Yield Trial;RYT=Replicated Yield Trial; NCT=National Cooperative Testing<br />

(Philippines); Multi-environment testing (IRRI)

GSR Technology<br />

GSR<br />

Technology<br />

IL-Breeding,<br />

PDLs & DQP<br />

GSR<br />

500 donors<br />

56 RPs<br />

Ideal RP BG<br />

Ecosystem based approach<br />

Screening of<br />

released GSR<br />

materials under<br />

target ecosystems<br />

Screening of already<br />

developed PDLs for<br />

abiotic stresses DT,<br />

ST, SUB, LI in the<br />

target ecosystems<br />

DQP for a trait &<br />

ecosystem related<br />

traits<br />

ILs, PDLs, DQP<br />

with adaptable RP<br />

BG for different<br />

target ecosystem<br />

First Phase<br />

2009-2012<br />

Second Phase<br />

2012-2018<br />

Increase in success rate to develop highly<br />

adaptable genotypes for a given ecosystem

Blast evaluation of virulent strains Evaluation of BB resistance of >500<br />

lines (HHZ background) against 14<br />

strains of 10 Xoo races, 2010 WS<br />

Vera Cruz et al<br />

HHZ PSBRc66 BC1F5 # 329 BC1F5 #350

Viscosity, cP<br />

Temperature<br />

Rapid Visco Analyzer (RVA) Pasting properties of GSR lines in IR64 and HHZ RP<br />

backgrounds-suitable for varied consumers with different taste preferences<br />

6000<br />

5000<br />

120<br />

100<br />

4000<br />

80<br />

3000<br />

60<br />

2000<br />

40<br />

1000<br />

0<br />

20<br />

0 100 200 300 400 500 600 700 800<br />

-1000<br />

0<br />

Time, sec<br />

AC=14.5-31.6%;GT=H-I-L;Protein=7.8-11.2<br />

1 2<br />

3 4<br />

5 6<br />

7 8<br />

9 10<br />

11 12<br />

13 14<br />

15 16<br />

17 18<br />

19 20<br />

21 22<br />

23 24<br />

25 26<br />

27 28<br />

29 30<br />

31 32<br />

33 34<br />

35 36<br />

37 38<br />

39 40

HHZ12-DT10-SAL1-DT1- PVS trials (40 farmers) at<br />

Puypuy, Laguna –ranked best over farmer’s check<br />

NSiC214 during WS2011 with preference<br />

score=0.118 against -0.0063(NSiC214)<br />

High Yielding, suitable for Direct seeding & Irrigated conditions,<br />

Aromatic, Drought and Salinity tolerant

Plot size:<br />

30sqm<br />

SSNM<br />

Performance of IRRI bred GSR High Yield Potential<br />

Varieties under Irrigated Conditions<br />

Designation<br />

Grain Yield (t/ha)<br />

2010WS<br />

2011DS<br />

Mean<br />

over<br />

seasons<br />

% over<br />

IR72<br />

% over<br />

NSICRc<br />

158<br />

HHZ8-SAL6-SAL3-Y2 6.55ab 8.0ab 7.28 10.56 12.27<br />

Mestizo7 (Hybrid) 5.68 bcde 8.7a 7.19 9.27 10.96<br />

HHZ12-DT10-SAL1-DT1 6.75a 7.2 bcde 6.98 6.00 7.64<br />

IR83142-B-7-B-B 6.00 abcde 7.6 bc 6.80 3.34 4.94<br />

HHZ5-SAL10-DT1-DT1 6.14abcd 7.4 bcd 6.77 2.89 4.48<br />

IR72 5.96abcde 7.2 cde 6.58 0.00 1.54<br />

HHZ5-DT8-DT1-Y1 5.55 cde 7.6 bc 6.58 -0.08 1.47<br />

HHZ8-SAL12-Y2-DT1 6.43abc 6.7 def 6.57 -0.23 1.31<br />

NSICRc158 5.86 bcde 7.1 cdef 6.48 -1.52 0.00<br />

HHZ12-Y4-DT1-Y1 5.57cde 7.1 cdef 6.34 -3.72 -2.24<br />

IR83142-B-19-B 5.12 e 7.5 bcd 6.31 -4.10 -2.62<br />

IR83142-B-57-B 5.48 de 7.1 cdef 6.29 -4.41 -2.93<br />

IR83143-B-21-B 5.16 e 7.2 cde 6.18 -6.08 -4.63<br />

HHZ8-SAL9-DT2-Y1 5.78 bcde 6.4 defg 6.09 -7.45 -6.02<br />

HHZ5-SAL10-DT3-Y2 5.69 bcde 6.3 fg 6.00 -8.89 -7.48<br />

HHZ5-SAL10-DT2-DT1 5.47 de 6.0 g 5.74 -12.84 -11.50<br />

Reason:Higher HI, spikelets per panicle;panicles per sqm;total spikelets per sqm,CGR

Zhongzu14-ski-4-1

BPH and Virus Resistance Screening<br />

IRRI-ICRR joint project collaborators: Prof.Baehaki/Drs Muhsin,Untung<br />

BC2 F3 HHZ populations screened against<br />

virulent BPH strain that caused outbreak in<br />

Sukamandi in 2010<br />

Several populations showed ILs with comparable<br />

resistance with the checks in second round of<br />

screening.<br />

• 30 BC3F2 and BC2F3 population (CS 3)<br />

• 39 BC3F3 and BC2F4 population (CS 4;<br />

3 rd year)ongoing<br />

ICRR 8.2011

An additional tonne of rice in the<br />

rainfed and irrigated lowlands will<br />

change the livelihoods of millions of<br />

resource poor farmers from the<br />

clutches of poverty and sustained<br />

income source to prosper….<br />

THANKS

Acknowledements & Thanks:<br />

CAAS-IRRI-BMGF<br />

Dr Zhikang Li Director GSR project<br />

GSR National Coordinators (Asia & Africa)<br />

Drs Tuat, Untung, Rafiqul-Islam, Somphet, Nimal, Riaz and Arif, Makara,<br />

Two public/NGO sectors:<br />

Dr W.Xu (Boshima-SS,IDO) & Dr Sirajul Islam(BRAC)<br />

Dr C.X Mao GSR Training Consultant (CAAS-GAAS)<br />

GSR-CAAS team: Drs Z. Li, Gao, Xu, Judy, Fu, Yu & many unknown<br />

contributors to the GSR materials<br />

IRRI GSR: Drs Nollie, Glenn, Choi, Redonna, Pandey, Andy, Krishna,Wang,Tao<br />

GSR-GML group Gelo (Data & field); Corine(PVS), Lolit(field operations),<br />

Nina(Screenings)<br />

Visiting Research Fellows: Drs Ma, Dr Uzokwe PhD: Zilhas, Meng; OJT:Shahana,<br />

Dilruba<br />

GSR Project Adm.: Pauline; Secretarial Assistance: Badett<br />

“Cooperation & Collaboration makes the world a smaller place”

Dr. V. Ravindra Babu<br />

Principal Scientist, Plant Breeding<br />

Directorate of Rice Research,<br />

Rajendranagar, Hyderabad-30,<br />

rbvemuri1955@gmail.com

OUTLINE OF PRESENTATION<br />

INTRODUCTION<br />

METHODOLOGY<br />

FINDINGS<br />

CONCLUSIONS<br />

FUTURE COURSE OF ACTION

INTRODUCTION<br />

Rice is the dominant cereal crop in most Asian countries<br />

and is the staple food for more than half of the world’s<br />

population, even a small increase in its nutritive value<br />

would be highly beneficial for human health.<br />

Recently breeding rice with high nutrient content known as<br />

bio-fortification has evolved as a new strategy to address<br />

micronutrient mal-nutrition<br />

Bio-fortification provides a cost effective and sustainable<br />

solution to combat mal-nutrition<br />

At DRR, more than 200 varieties were tested for their iron<br />

and zinc content and also identified donors for them and<br />

breeding strategy was evolved to develop high iron and<br />

zinc content lines

Micronutrient fortification of plants<br />

through plant breeding:<br />

improve nutrition in man at low cost?<br />

Can it<br />

To be successful, the biofortification strategy must<br />

address four fundamental questions:<br />

1. Can commonly eaten food staple crops be developed that<br />

fortify their seeds with essential minerals and vitamins?<br />

2. Can farmers be induced to grow such varieties?<br />

3. If so, would this result in a significant Improvement in human<br />

nutrition at a lower cost than existing nutrition interventions?<br />

4. Bio-availability of these minerals?

GLOBAL SHARE OF DIETARY ENERGY SUPPLY<br />

FROM DIFFERENT PLANT SOURCES<br />

Others<br />

19%<br />

Other Veg. oils<br />

3%<br />

Wheat<br />

24%<br />

Soyabean oil<br />

3%<br />

Suger<br />

9%<br />

Sweet potatoes<br />

2%<br />

Millet & Sorghum<br />

4%<br />

Potatoes<br />

2%<br />

Maize<br />

7%<br />

Rice<br />

27%<br />

Source FAO. 1996

2.7 billion people globally are<br />

known to be affected by iron<br />

deficiency till to date (Hirschi,<br />

2009)<br />

Regulates enzyme activity<br />

and plays an important<br />

role in the immune system<br />

(Lynch, 2003)<br />

IRON<br />

REQUIREMENT PER<br />

DAY<br />

10-15 milligrams<br />

(mg)<br />

Health problems caused by iron deficiency<br />

Mental and psychomotor impairment in children, and<br />

Increased levels of morbidity and mortality rate of mother and<br />

child during childbirth (Frossard et al., 2000)

In Asia and Africa, it is<br />

estimated that 500-600 million<br />

people are at risk for low zinc<br />

intake (Harvest Plus, 2010)<br />

Regulates enzyme<br />

activity, essential for cell<br />

division and DNA<br />

replication<br />

ZINC<br />

REQUIREMENT PER<br />

DAY<br />

Males 12-15mg/day<br />

Females 68 mg/day<br />

Health problems caused by zinc deficiency<br />

Anorexia,<br />

Dwarfism,<br />

Weak immune system (Solomons, 2003)<br />

Skin lesions,<br />

Hypogonadism, and<br />

Diarrhoea (McClain et al., 1985).

In the last two decades, new research findings generated by the<br />

nutritionists have brought to light the importance of vitamins,<br />

minerals (micronutrients) and proteins in maintaining good health<br />

Nutritionist<br />

Breeder<br />

Biotechnologist<br />

RICE, WHEAT, MAIZE, PEARL<br />

MILLET, CANOLA<br />

A genetic approach called<br />

Biofortification (Bouis, 2002) has been<br />

developed, which aims at biological<br />

and genetic enrichment of food stuffs<br />

with vital nutrients

Breeders are now focusing on breeding for<br />

nutritional enhancement to overcome the problem of<br />

malnutrition.<br />

The range in brown rice<br />

Iron 6.3 - 24.4 ppm<br />

Zinc 13.5 - 28.4 ppm<br />

Suggesting some genetic potential to increase the<br />

concentration of these micronutrients in rice grains<br />

(Gregorio, 2002)

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

Collection of germplasm & screening for Fe and Zn contents<br />

Selection of parents for hybridization programme<br />

Crossing programme for developing high Iron and Zinc genotypes<br />

Selections in segregating generation<br />

G X E Interactions<br />

Study of losses due to polishing<br />

Impact of polishing on grain type<br />

Impact of parboling on Fe and Zn contents<br />

Correlation between Fe and Zn to yield<br />

Continued……………..

Conventional and molecular Breeding Approach<br />

Fe and Zn contents in red rices and popular varieties<br />

Genetic analysis of Fe and Zn contents<br />

Impact of agronomic management on Fe and Zn<br />

Developing High Iron and Zinc line with higher yields<br />

Variety testing in AICRIP programme and release<br />

Impact of Fe and Zn on grain quality<br />

Molecular Studies<br />

Bioavailability studies<br />

Studies on protein, bran oil, phytates & glycemic index

NUTRITIONAL STATUS OF STUDY MATERIAL<br />

Trait General Study material<br />

(max)<br />

Iron (ppm) 7.0 34.4<br />

Zinc (ppm) 14.0 28.3<br />

Protein (%) 6.8 12.48<br />

Fat (%) 0.5 3.77<br />

Fiber (%) 0.2 0.80<br />

Energy (kcal 100g -1 ) 345 376<br />

Thiamin (mg 100g -1 ) 0.06 1.91

Losses due to polishing rice (%)<br />

Protein 29<br />

Fat 79<br />

Lime 84<br />

Iron 67<br />

Losses due to washing of milled rice (%)<br />

Thiamine 40<br />

Riboflavin 25<br />

Niacin 23<br />

Losses from cooking & washing (%)<br />

Calories 15<br />

Proteins 10<br />

Iron 75<br />

Calcium 50<br />

Phosphorus 50

PERCENTAGE LOSSES AS COMPARED TO<br />

RAW MILLED RICE<br />

5% polishing 10% polishing<br />

Zinc 62.5 68.4<br />

Iron 61.0 69.3<br />

Thiamine 75.4 89.7<br />

Ash content 55.2 63.91<br />

Protein 7.08 12.70<br />

Fat 69.14 88.54<br />

Crude fibre 84.4 93.8<br />

Energy 3.2 3.5

Iron and Zinc contents in Brown, 5% & 10% polished rice of land races<br />

from Karnataka, Maharashtra and Manipur<br />

Fe (ppm)<br />

Zn (ppm)<br />

S.No.<br />

Name of Genotype<br />

Grain<br />

Type<br />

Brown<br />

Rice<br />

5%<br />

polished<br />

rice<br />

10%<br />

polished<br />

rice<br />

Brown<br />

Rice<br />

5%<br />

polishe<br />

d rice<br />

10%<br />

polished<br />

rice<br />

1 PANDY SB 20.8 12.9 8.6 22.3 19.3 15.9<br />

2 BHADAS SB 10.4 5.6 2.3 22.9 18.1 17.4<br />

3 MUNGA SB 22.5 4.0 2.1 31.1 18.4 17.3<br />

4 RALAK LS 14.3 5.2 8.0 19.9 16.3 14.8<br />

5 IMPHAL LS 10.1 5.4 4.1 23.1 18.5 17.9<br />

6 FOXTAIL SB 9.0 3.3 2.3 25.2 19.5 18.0<br />

7 DODDABYRA SB 3.2 2.6 0.9 29.0 21.4 17.1<br />

8 CHAGLEI SB 5.3 2.4 2.0 28.3 20.3 16.9<br />

9 THANU MS 4.6 3.0 2.5 24.1 18.6 14.7<br />

10 HEMAVATHI SB 5.1 3.3 2.4 27.7 24.7 20.1<br />

11 PANVEL-2 LS 5.2 4.2 3.8 29.6 28.3 25.2<br />

12 BYRANELU SB 9.7 8.0 6.3 28.4 19.5 17.2<br />

13 KANCHANA SB 10.7 3.5 2.3 25.7 18.7 20.2<br />

14 SHARAVATHI SB 8.7 4.3 2.5 30.4 23.0 17.1<br />

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<br />

from Karnataka, Maharashtra and Manipur<br />

Fe (ppm)<br />

Zn (ppm)<br />

S.No.<br />

Name of Genotype<br />

Grain<br />

Type<br />

Brown Rice<br />

5%<br />

polished<br />

rice<br />

10%<br />

polished<br />

rice<br />

Brown<br />

Rice<br />

5%<br />

polishe<br />

d rice<br />

10%<br />

polished<br />

rice<br />

16 MOIRANG PHOU SB 5.2 2.1 1.1 33.1 25.4 28.4<br />

17 ERIMA LB 10.5 4.7 4.2 23.5 19.6 18.8<br />

18 KOBRA MS 13.5 4.3 3.5 29.8 21.1 21.4<br />

19 SANNAMALLYA SB 9.8 5.0 4.5 26.0 19.1 17.3<br />

20 PHOUOBI LS 10.8 6.2 2.3 24.4 17.8 16.5<br />

21 GINTHOU LS 9.5 5.2 3.1 24.6 19.1 17.7<br />

22 AKUTPHOU LB 20.1 12.9 4.3 29.0 27.8 22.7<br />

23 KEIBITHOU SB 13.0 5.7 4.9 23.4 18.4 16.2<br />

24 SANATHOU LB 11.9 4.3 3.4 24.8 18.9 17.8<br />

25 JHOGARSI SB 8.0 4.7 2.8 21.0 16.5 14.9<br />

26 THUNGA LS 9.5 6.7 2.9 17.7 13.9 12.3<br />

27 PHOU DUM LS 5.3 5.6 0.9 33.1 26.9 21.1<br />

28 GANDHASALI SB 19.3 17.2 11.2 17.4 11.0 11.6<br />

29 MYSORE MALLIGE MS 8.8 6.4 5.1 19.7 14.9 13.9<br />

30 KMP-148 LS 9.1 6.8 3.3 25.3 19.9 18.9

Range of Fe & Zn in Brown Rice, 5% & 10% polished rice<br />

and loss(%) due to polishing<br />

Fe content<br />

(ppm)<br />

Zn content<br />

(ppm)<br />

Brown rice: 4.9 to 22.5 17.4 to 33.1<br />

5% polished rice:<br />

2.4 to 17.2<br />

11.0 to 28.3<br />

Loss: %<br />

10% polished:<br />

10.9 to 82.2<br />

1.1 to 11.2<br />

4.1 to 40.8<br />

11.6 to 28.4<br />

Loss: %<br />

26.9 to 90.7<br />

14.2 to 44.4

IRON (ppm)<br />

Mean 12.9 + 6.24<br />

Range 7.5 – 34.4<br />

Compared to general availability there are varieties<br />

with good content<br />

Top 5 entries: Kalanamak (34.4), Karjat 4 (30.6),<br />

Chittimuthyalu (24.9), MSE 9 (24.4), Kanchan (20.4)<br />

Top 5 entries with less loss on polishing: ADT 43,<br />

Manoharshali, Karjat 4, Swarna, Seshadri

IRON (mg 100g -1 )<br />

•Kalanamak (3.44),<br />

•Karjat 4 (3.06),<br />

•Chiti Muthyalu (2.49)<br />

•MSE 9 (2.44),<br />

•Kanchan (2.04)

ZINC (ppm)<br />

Mean 22.7 + 2.95<br />

Range 10.1 – 31.3<br />

Compared to general availability there are varities<br />

with good content<br />

Top 5 entries: Poornima(31.3), Ranbir Bas(30.9), ADT<br />

43(30.9), Chittimuthyalu (30.5), Type 3 (30.3)<br />

Top 5 entries with less loss on polishing: White<br />

Ponni, Bas 386, Kanishk, Giri, Karjat 4

Zinc (mg/100g)<br />

•Poornima(3.13)<br />

•Ranbir Bas(3.09)<br />

•ADT 43(3.09)<br />

•Chittimuthyalu (3.05)<br />

•Type 3 (3.03)

Nutritional Profiling of<br />

Parents & Segregating Lines<br />

Variety Fe (ppm) Zn (ppm)<br />

0% 5% 10% 0% 5% 10%<br />

PR 116 7.5 2.8 2.6 20.6 17.4 16.5<br />

BPT 5204 8.3 5.6 4.9 10.3 7.6 4.9<br />

Ranbir Basmati 13.0 9.5 7.1 30.9 28.3 27.4<br />

Chittimutyalu 24.9 14.0 9.8 30.5 25.7 24.4<br />

F 4 Generation<br />

PR 116 x Ranbir<br />

Basmati<br />

BPT 5204 x<br />

Chittimutyalu<br />

13.3 9.4 4.6 17.0 15.2 13.4<br />

10.5 7.6 7.0 22.1 19.9 16.6

Improvement of Fe & Zn (ppm) in<br />

Segregating Lines of BPT 5204 & PR 116<br />

Parents<br />

Crosses (F 4 ) PR 116 Ranbir Basmati<br />

Improvement in<br />

PR 116 x Ranbir<br />

Basmati<br />

Improvement in<br />

BPT 5204 x<br />

Chittimutyalu<br />

Iron Zinc Iron Zinc<br />

7.5 20.6 13.0 30.9<br />

13.3 (77%) ---<br />

BPT 5204<br />

Chittimuthyalu<br />

Iron Zinc Iron Zinc<br />

8.3 10.3 24.9 30.5<br />

10.5 (26.5%) 22.1 (114.5%)

IMPORTANT ACHIEVEMENT:<br />

Under biofortification programme at DRR, One line derived<br />

from a cross between BPT 5204 X Chittimuthyalu with short bold<br />

grains, semi dwarf with high yield potential (> 4.5t/ha) and<br />

medium duration with high Iron (31.2 ppm) and Zinc (40.0 ppm)<br />

in brown rice was identified. With good quality characters viz.<br />

good HRR% (67.5%), Intermediate ASV(5.01), AC(24.05%) with<br />

mild aroma.<br />

NIN :<br />

Brown rice- Fe-28.9 (ppm); Zn-37.5 (ppm )<br />

Polished rice-Fe-8.0(ppm); Zn-26.9(ppm)<br />

Some more fixed lines are also in the pipe line.

Fe and Zn contents in brown rice<br />

Fe 10.3 ppm & Zn 10.8 ppm<br />

Fe 24.9 ppm & Zn 30.5 ppm<br />

Fe 31.2 ppm & Zn 40.0 ppm

QUALITY PARAMETERS OF HIGH IRON & ZINC GENOTYPE<br />

Hull 76.8%<br />

Mill 68.8<br />

HRR 67.5<br />

KL 4.15<br />

KB 2.02<br />

L/B 2.05<br />

Grain Type<br />

Grain chalk<br />

Type<br />

SB<br />

A<br />

VER 4.8<br />

WU 155<br />

KLA 7.2<br />

ER 1.73<br />

ASV 5.0<br />

AC 24.03<br />

GC 22<br />

Aroma<br />

Iron (ppm)<br />

Zinc (ppm)<br />

MS<br />

31.2 (Brown Rice)<br />

40.0 (Brown Rice)

T1 Control (RFD 100%)<br />

TREATMENTS<br />

T2<br />

T3<br />

T4<br />

T5<br />

T6<br />

T7<br />

T8<br />

T9<br />

T10<br />

T11<br />

T12<br />

Control + Zn soil application<br />

Control + Zn foliar spray<br />

Control + Fe soil application<br />

Control + Fe foliar spray<br />

Control + Zn + Fe soil application<br />

Control + Zn + Fe foliar spray<br />

Control + micro mix soil application<br />

Control + micro mix foliar spray<br />

FYM (10 t/ha)<br />

FYM 50% + 50% RFD<br />

FYM 50% + 50% RFD + micro mix spray<br />

• Results showed that increase in iron and zinc contents through application of<br />

iron and zinc fertilizers either soil / foliar application.<br />

• Soil application of iron is better than foliar spray.<br />

• Foliar spray of Zn is better than soil application.

GENETIC STUDIES REVEALED THAT:<br />

The ratio of GCA to SCA variances showed that nonadditive<br />

gene action was predominant in inheritance of<br />

all characters studied.<br />

Chittimutyalu, Ranbir Basmati and Madhukar are found to<br />

be good general combiners for grain zinc content.<br />

PR116 X Chittimutyalu, Swarna X Ranbir Basmati,<br />

Mandya Vijaya X Type 3 were good specific combiners for<br />

grain zinc content.<br />

IR64 Chittimuthyalu and PR 116 Chittimuthyalu found<br />

to be good heterotic hybrids for grain iron and zinc<br />

content.<br />

Grain iron & zinc content had no correlation with grain<br />

yield.<br />

Grain iron had significant positive correlation with grain

MAPPING OF CHROMOSOMAL REGIONS ASSOCIATED<br />

WITH IRON AND ZINC CONTENT IN RICE GRAINS<br />

~ 200 germplasm lines were characterized for Fe and Zn content in the brown rice<br />

Genotype Iron (ppm) Zinc (ppm)<br />

Chittimutyalu : 24.9 30.5<br />

Ranbir Basmati : 13.0 30.9<br />

BPT 5204 : 8.3 10.3<br />

PR 116 : 7.5 20.6<br />

Based on that, two donors were selected<br />

1.Chittimuthyalu and Ranbir Basmati<br />

Iron - BPT5204/Chittimuthyalu<br />

154 F 2 plants – 0.6 to 238 ppm<br />

Zinc - BPT5204/Ranbir Basmati<br />

109 F 2 plants – 2.3 to 103 ppm

Putative genes involved in Fe and Zn as reported in rice<br />

genome database<br />

1. OsYs (Orzya sativa Yellow stripe like)<br />

2. NRAMP (Natural Resistance-Associated Macrophage<br />

Protein)<br />

3. Ferritin linked genes<br />

4. Zinc transport, Zinc Regulated Transporter<br />

5. ZIP genes for Zinc and Iron related Proteins<br />

Based on these candidate genes, 46 SSR markers were<br />

identified / designed

ZT<br />

SC 103<br />

SC 129<br />

ZIP<br />

Chr 3<br />

6.7<br />

15.6<br />

SC 435<br />

SC 123<br />

SC 120<br />

Chr 4<br />

6.2<br />

12.8<br />

13.6<br />

SC 126<br />

SC 448<br />

Chr 8<br />

cM<br />

cM cM<br />

}<br />

}<br />

YSL<br />

YSL<br />

}<br />

}<br />

}<br />

YSL<br />

}<br />

}<br />

12.7<br />

13.4<br />

ZT 13.5<br />

SC 116 }<br />

Tentative SSR based linkage maps for regions associated with enhanced<br />

iron accumulation in F 2 lines from Samba Mahsuri / Chittimuthyalu

ZT<br />

SC 103<br />

SC 129<br />

ZIP<br />

Chr 3<br />

}<br />

cM<br />

}<br />

26.2<br />

19.6<br />

SC 123<br />

YSL<br />

SC 435<br />

YSL<br />

SC 120<br />

Chr 4<br />

}<br />

cM<br />

8.7<br />

} 13.4<br />

} 21.5<br />

SC 448<br />

YSL<br />

SC 126<br />

SC 116<br />

Chr 8<br />

}<br />

}<br />

}<br />

cM<br />

11.6<br />

22.3<br />

15.3<br />

Tentative SSR based linkage maps for regions associated with enhanced zinc<br />

accumulation in F 2 lines from Samba Mahsuri / Chittimuthyalu

ZIP<br />

SC 129<br />

ZT<br />

SC 425<br />

Chr 3<br />

cM<br />

}<br />

}<br />

9.8<br />

9.8<br />

SC 434<br />

YSL<br />

Chr 4<br />

cM<br />

8.5<br />

Chr 5<br />

cM<br />

Chr 6<br />

cM<br />

Chr 12<br />

SC 430<br />

6.4<br />

SC 135<br />

SC 418<br />

} ZIP<br />

} }<br />

10.5<br />

14.5<br />

15.9<br />

ZIP<br />

} }<br />

SC 428<br />

NRAMP<br />

cM<br />

Tentative SSR based linkage maps for regions associated with enhanced zinc<br />

accumulation in F 2 lines from Samba Mahsuri / Ranbir Basmati

ZIP<br />

SC 129<br />

ZT<br />

SC 425<br />

Chr 3<br />

cM<br />

}<br />

}<br />

SC 434<br />

12.5<br />

YSL<br />

16.5<br />

Chr 4<br />

cM<br />

}<br />

13.9<br />

SC 135<br />

ZIP<br />

Chr 5<br />

cM<br />

}<br />

13.4<br />

SC 428<br />

SC 430<br />

Chr 6<br />

}<br />

}<br />

cM<br />

8.8<br />

10.5<br />

Chr 12<br />

cM<br />

SC 418<br />

}<br />

21.6<br />

NRAMP<br />

Tentative SSR based linkage maps for regions associated with enhanced<br />

iron accumulation in F 2 lines from Samba Mahsuri / Ranbir Basmati

The markers always co segregated for<br />

Fe and Zn together<br />

Three loci were identified common for two donors for both Fe & Zn<br />

1. Zinc transporter- Chr 3<br />

2. ZIP genes (Zinc and Iron related Proteins) – Chr 3<br />

3. OsYs (Orzya sativa Yellow stripe like) – Chr 4<br />

Two loci in Chittimuthyalu<br />

1. OsYs (Orzya sativa Yellow stripe like) – Chr 8<br />

2. Zinc transporter – Chr 8<br />

Three loci in Ranbir Basmati<br />

1. ZIP genes (Zinc and Iron related Proteins) – Chr5<br />

2. ZIP genes (Zinc and Iron related Proteins) – Chr6<br />

3. NRAMP (Natural Resistance-Associated Macrophage protein)- Chr12<br />

• Two loci from chromosome 3 and one locus from chromosome 4 found to be<br />

common between the two donors associated with iron and zinc metabolism.<br />

• A recombinant with sd1 gene and aroma gene was identified from BPT 5204 and<br />

Chittimuthyalu from F 4 families segregating population with maximum back ground<br />

genome of Chittimuthyalu.

Plant Breeding & BIOTECHNOLOGY –<br />

New ToolS for Fighting Micronutrient Malnutrition<br />

The final permanent solution to micronutrient<br />

malnutrition is breeding staple foods that are dense<br />

in minerals and vitamins provides a low-cost ,<br />

sustainable strategy for reducing levels of<br />

micronutrient malnutrition.<br />

Molecular marker technology expedites the<br />

development of rice varieties with improved iron and<br />

zinc content through identified genomic regions

SCIENTISTS INVOLVED IN THE PROJECT:<br />

• Dr. T. Longvah-Food Chemistry,NIN,HYD<br />

• Dr. C. N.Neeraja-Biotchnology,DRR<br />

• Dr. K. Surekha-Soil Science,DRR<br />

• Dr. B. Sreedevi-Agronomy,DRR<br />

• Dr. L. V. Subba Rao-Seed Technology,DRR<br />

• Dr. N. Shobha Rani-Seed Quality,DRR<br />

• Dr. B. C. Viraktamath-Hybrid Rice,DRR<br />

• M.Sc.(Ag.) & Ph.D. students from ANGRAU,HYD<br />

1/17/2012 9:59:16 PM 39

Thank you<br />

1/17/2012 9:59:16 PM<br />

40

International Symposium on “100 years of Rice Science and Looking Beyond”<br />

on 10 th January 2012 at TNAU, Coimbatore<br />

Genome-wide variations<br />

between elite lines of indica<br />

rice discovered through whole<br />

genome re-sequencing<br />

Gopala Krishnan S, Dan Waters and Robert Henry

Population (in billion)<br />

Rice<br />

‣ Rice is a staple food for over half of the world's population<br />

and accounts for over 20 percent of global calorie intake (FAO,<br />

2004)<br />

‣ Global rice production (2009) – 683 mt million tonnes (FAO,<br />

2011) and to feed projected population in 2050, rice yields to<br />

be increased by 50%<br />

10<br />

9<br />

8<br />

7<br />

6<br />

5<br />

4<br />

3<br />

2<br />

1<br />

0<br />

1900 1 2 3 4 5 1950 6 7 8 9 10 2000 11 12 13 14 15 2050 16<br />

8.91<br />

Year<br />

(Source: UN Population Division)

The options...<br />

‣ Enabling crop improvement<br />

» Enhancing photosynthetic efficiency<br />

» Marker assisted selection, transgenics, etc.<br />

‣ The way out<br />

» Improving productivity per ha

Heterosis<br />

‣ Heterosis refers to superior performance of F 1 hybrids in terms<br />

of increase in size, yield, vigor, etc. compared to their parental<br />

lines (Shull, 1914)<br />

‣ Hybrid rice yields 10-20% more than the elite inbred varieties<br />

‣ Primarily based on three line breeding system – CMS (A line), isonuclear<br />

maintainer (B line) and genetically diverse restorer (R<br />

line)<br />

‣ The challenge ?<br />

Ability to predict hybrid performance<br />

‣ Advances in genomic sequencing provide powerful tools to<br />

study allelic variations at whole genome level

Re-sequencing of elite rice inbreds<br />

‣ SNPs resources in rice based on only a few rice cultivars (Shen et<br />

al., 2004; Feltus et al., 2004, Yamamoto et al., 2010, Arai-Kichise et<br />

al., 2011)<br />

‣ Emphasis to sequence diverse set of additional rice genotypes to<br />

enlarge the pool of DNA polymorphisms<br />

‣ Three elite CMS and restorer indica rice inbreds each were<br />

sequenced using Illumina GAIIx<br />

‣ Whole genome re-sequencing yielded 3.38 billion 75-bp paired<br />

end reads (24.4 Gb of high quality raw data)

Assembly of reads<br />

Organelle<br />

Multi<br />

65.05 X 10 6<br />

Unmapped<br />

25.37 X 10 6<br />

24.96 X 10 6<br />

7.4 %<br />

7.5 %<br />

Total reads<br />

338.01 X 10 6<br />

Nuclear<br />

287.67 X 10 6<br />

(85.1 %)<br />

Unique<br />

222.62 X 10 6

Assembly of reads<br />

Chromosome<br />

Coverage<br />

(%)<br />

Uniquely mapped reads<br />

Sequencing<br />

depth (fold)<br />

Total number Mb<br />

Chromosome 1 87.99 27,012,387 # 1,960 45.17<br />

Chromosome 2 88.69 23,765,016 1,724 47.84<br />

Chromosome 3 91.01 # 24,086,909 1,747 48.17<br />

Chromosome 4 82.13 19,856,483 1,440 40.48<br />

Chromosome 5 86.53 18,783,399 1,362 45.76<br />

Chromosome 6 84.57 18,536,789 1,345 43.71<br />

Chromosome 7 83.37 16,996,652 1,233 41.56<br />

Chromosome 8 84.36 16,629,683 1,206 42.41<br />

Chromosome 9 84.24 13,328,131 9,67 42.54<br />

Chromosome 10 84.36 13,438,388 9,75 42.97<br />

Chromosome 11 81.92 15,116,942 1,096 38.62<br />

Chromosome 12 82.22 15,065,493 1,093 39.68<br />

Total 85.40 222,616,272 16,153 43.24

SNPs

o.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

2000<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs (No.)<br />

SNPs - a snap shot across<br />

0<br />

genome<br />

Chr. 5<br />

(162723)<br />

Chr. 6<br />

(201656)<br />

2000<br />

1000<br />

2000<br />

1000<br />

0<br />

10 20 [29.7 Mb]<br />

10 20 30 [30.7 Mb]<br />

Chr. 1<br />

(284078)<br />

2000<br />

1000<br />

0<br />

10 20 30 40 [43.2 Mb]<br />

Chr. 7<br />

(188047)<br />

2000<br />

1000<br />

0<br />

10 20 [29.6 Mb]<br />

Chr. 2<br />

(243923)<br />

2000<br />

1000<br />

0<br />

10 20 30 [36.0 Mb]<br />

Chr. 8<br />

(197285)<br />

2000<br />

1000<br />

0<br />

10 20 [28.4 Mb]<br />

Chr. 3<br />

(211527)<br />

2000<br />

1000<br />

0<br />

10 20 30 [36.2 Mb]<br />

Chr. 9<br />

(151888)<br />

2000<br />

1000<br />

0<br />

10 20 [22.7 Mb]<br />

Chr. 4<br />

(236006)<br />

2000<br />

1000<br />

0<br />

10 20 30 [35.5 Mb]<br />

Chr. 10<br />

(176433)<br />

2000<br />

1000<br />

0<br />

10 20 [22.7 Mb]<br />

Chr. 5<br />

(162723)<br />

2000<br />

1000<br />

0<br />

10 20 [29.7 Mb]<br />

Chr. 11<br />

(224589)<br />

2000<br />

1000<br />

0<br />

10 20 [28.4 Mb]<br />

Chr. 6<br />

(201656)<br />

2000<br />

1000<br />

0<br />

10 20 30 [30.7 Mb]<br />

Chr. 12<br />

(216598)<br />

2000<br />

1000<br />

0<br />

10 20 [27.6 Mb]<br />

2000<br />

Chr.<br />

‣<br />

7<br />

2,495,052 1000 SNPs were detected across the rice genome with an average density of<br />

(188047)<br />

0<br />

10 20 [29.6 Mb]<br />

6.78 SNPs/kb in the non repetitive region<br />

2000<br />

Chr. 8<br />

(197285) 1000<br />

‣ Average<br />

0<br />

polymorphism rate is significantly higher than the previous estimates of<br />

10 20 [28.4 Mb]<br />

4.31 SNPs/ kb (Nasu et al., 2002) and 1.70 SNPs/ kb (Feltus et al., 2004), offering<br />

2000<br />

Chr. 9<br />

(151888) high 1000 density coverage across the entire genome<br />

0<br />

10 20 [22.7 Mb]

Detection of InDels

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

Insertions (No.)<br />

Deletions (No.)<br />

InDels<br />

Chr. 1<br />

(20137)<br />

160<br />

80<br />

0<br />

Chr. 1<br />

(20287)<br />

150<br />

100<br />

50<br />

0<br />

10 20 30 40 [43.2 Mb]<br />

10 20 30 40 [43.2 Mb]<br />

Chr. 2<br />

(17269)<br />

160<br />

80<br />

0<br />

Chr. 2<br />

(17496)<br />

150<br />

100<br />

50<br />

0<br />

10 20 30 [36.0 Mb]<br />

10 20 30 [36.0 Mb]<br />

Chr. 3<br />

(15390)<br />

160<br />

80<br />

0<br />

Chr. 3<br />

(15069)<br />

150<br />

100<br />

50<br />

0<br />

10 20 30 [36.2 Mb]<br />

10 20 30 [36.2 Mb]<br />

Chr. 4<br />

(13460)<br />

160<br />

80<br />

0<br />

Chr. 4<br />

(14361)<br />

150<br />

100<br />

50<br />

0<br />

10 20 30 [35.5 Mb]<br />

10 20 30 [35.5 Mb]<br />

Chr. 5<br />

(11157)<br />

160<br />

80<br />

0<br />

Chr. 5<br />

(11107)<br />

150<br />

100<br />

50<br />

0<br />

10 20 [29.7 Mb]<br />

10 20 [29.7 Mb]<br />

Chr. 6<br />

(13010)<br />

160<br />

80<br />

0<br />

10 20 30 [30.7 Mb]<br />

Chr. 6<br />

(13320)<br />

150<br />

100<br />

50<br />

0<br />

10 20 30 [30.7 Mb]<br />

Chr. 7<br />

(11707)<br />

160<br />

80<br />

0<br />

10 20 [29.6 Mb]<br />

Chr. 7<br />

(12110)<br />

150<br />

100<br />

50<br />

0<br />

10 20 [29.6 Mb]<br />

Chr. 8<br />

(12550)<br />

Chr. 9<br />

(9362)<br />

Chr. 10<br />

(10380)<br />

Chr. 11<br />

(13521)<br />

Chr. 12<br />

(12535)<br />

160<br />

80<br />

0<br />

160<br />

80<br />

0<br />

160<br />

80<br />

0<br />

160<br />

80<br />

0<br />

160<br />

80<br />

0<br />

10 20 [28.4 Mb]<br />

10 20 [22.7 Mb]<br />

10 20 [22.7 Mb]<br />

10 20 [28.4 Mb]<br />

10 20 [27.6 Mb]<br />

Chr. 8<br />

(12788)<br />

Chr. 9<br />

(9576)<br />

Chr. 10<br />

(11063)<br />

Chr. 11<br />

(13483)<br />

Chr. 12<br />

(12896)<br />

150<br />

100<br />

50<br />

0<br />

150<br />

100<br />

50<br />

0<br />

150<br />

100<br />

50<br />

0<br />

150<br />

100<br />

50<br />

0<br />

150<br />

100<br />

50<br />

0<br />

10 20 [28.4 Mb]<br />

10 20 [22.7 Mb]<br />

10 20 [22.7 Mb]<br />

10 20 [28.4 Mb]<br />

10 20 [27.6 Mb]<br />

‣ 224,034 InDels were across the rice genome with an average density of 4.32<br />

insertions/kb and 4.41 deletions/kb

Annotation of SNPs and InDels<br />

Synonymous<br />

Non-synonymous<br />

63342 83262<br />

CDS<br />

UTRs 146604<br />

73051<br />

Genic<br />

497250<br />

Introns & Reg.<br />

Sequences<br />

277595<br />

Genic<br />

35871<br />

UTRs<br />

6814<br />

CDS<br />

1733<br />

Introns & Reg.<br />

Sequences<br />

27324<br />

Genic<br />

36731<br />

UTRs<br />

6663<br />

CDS<br />

1887<br />

Introns & Reg.<br />

Sequences<br />

27821<br />

Intergenic<br />

1987802<br />

Intergenic<br />

124607<br />

Intergenic<br />

127185<br />

Non repeat<br />

regions<br />

2495052<br />

Non repeat<br />

regions<br />

160478<br />

Non repeat<br />

regions<br />

163556<br />

Repeat<br />

regions<br />

2151486<br />

Repeat<br />

regions<br />

36147<br />

Repeat<br />

regions<br />

42589<br />

‣ About 1/3 rd of the SNPs occur in the non-repeat regions while 10.7 % of the total<br />

SNPs have been found in 25,591 genes<br />

‣ Overall, 83,262 non-synonymous SNPs spanning 16,379 genes and 3,620 InDels<br />

in the coding sequences 2,625 genes have been identified

CMS lines<br />

Restorer lines<br />

Polymorphisms - Genotype wise<br />

SNPs<br />

InDels<br />

A<br />

(988986)<br />

B<br />

(912695)<br />

C<br />

(1061109)<br />

D<br />

(879916)<br />

E<br />

(1093043)<br />

F<br />

(2445994)<br />

100000<br />

50000<br />

0<br />

100000<br />

50000<br />

0<br />

100000<br />

50000<br />

0<br />

100000<br />

50000<br />

0<br />

100000<br />

50000<br />

0<br />

100000<br />

50000<br />

0<br />

1<br />

2 3 4 5 6 7 8 9 10 11 12<br />

Chromosomes<br />

15000<br />

10000<br />

5000<br />

0<br />

15000<br />

10000<br />

5000<br />

0<br />

15000<br />

10000<br />

5000<br />

0<br />

15000<br />

10000<br />

5000<br />

0<br />

15000<br />

10000<br />

5000<br />

0<br />

15000<br />

10000<br />

5000<br />

0<br />

1 2 3 4 5 6 7 8 9 10 11 12<br />

Chromosomes

Detecting polymorphism<br />

between inbreds

Pairwise Polymorphism<br />

‣ At present, all bioinformatic tools helps in detecting SNPs in comparison<br />

to a reference genome<br />

‣ The challenge?<br />

‣ To identify SNPs between two inbreds<br />

‣ Try obtaining consensus sequence by mapping an inbred to reference<br />

genome, and then use the consensus as reference for further mapping?<br />

‣ Did not work as consensus is not absolute genotype<br />

‣ Cumbersome process<br />

‣ Loss of the annotations and need to reannotate the consensus

Potential problems<br />

‣ Combined mapping of inbreds to reference may help<br />

‣ Potential problems while using combined mapping approach for<br />

identifying polymorphisms between inbreds<br />

‣ It will still detect SNPs based on polymorphism in comparison to<br />

reference genome, then how to identify a SNP between inbreds?<br />

‣ Expect 50:50 alleles at a given SNP loci of inbreds?<br />

‣ Bias in number of reads from an inbred being mapped to each<br />

position?<br />

‣ False positives between inbreds?

Situation 1<br />

Combined assembly_Inbred 1 and 5<br />

Inbred 1 assembly<br />

Inbred 5 assembly<br />

SNP compared to<br />

reference<br />

(22 C/ 0T)<br />

SNP in Inbred 1<br />

compared to reference<br />

(9C/ 0T)<br />

SNP in Inbred 5<br />

compared to reference<br />

(13C/ 0T)

Not a SNP between Inbred 1 and 5<br />

Combined assembly_Inbred 1 and 5<br />

Inbred 1 assembly<br />

Inbred 5 assembly<br />

SNP compared to<br />

reference but not<br />

between the inbreds<br />

SNP in Inbred 1<br />

compared to reference<br />

assembly<br />

SNP in Inbred 5<br />

compared to reference<br />

assembly<br />

In order to be a SNP between Inbred 1 and Inbred 5, each inbred should have alternate<br />

allele (heterozygote like situation - 50:50) in combined assembly

Situation 2<br />

Combined assembly_Inbred 1 and 5<br />

Inbred 1 assembly<br />

Inbred 5 assembly<br />

Call it a SNP?<br />

Heterozygote<br />

compared to<br />

reference<br />

(12 G / 8A)<br />

Heterozygote in Inbred 1<br />

(4 G/ 4A)<br />

Heterozygote in Inbred 5<br />

(8G/ 4A)

Not a true SNP<br />

Combined assembly_Inbred 1 and 5 Inbred 1 assembly Inbred 5 assembly<br />

Heterozygote like<br />

situation (50:50)<br />

compared to<br />

reference (12 G / 8A)<br />

Heterozygote in Inbred 1<br />

(4 G/ 4A)<br />

Heterozygote in Inbred 5<br />

(8G/ 4A)<br />

Combined assembly results in heterozygote like situation (50:50) at a given position but<br />

not a true SNP between the Inbred 1 and Inbred 5

Situation 3<br />

Combined assembly_Inbred 1 and 5<br />

Inbred 1 assembly<br />

Inbred 5 assembly<br />

Call it a SNP?<br />

Heterozygote like<br />

situation (57:42)<br />

compared to<br />

reference<br />

(12C/ 9T)<br />

SNP in Inbred 1<br />

compared to reference<br />

(0C/ 9T)<br />

Not a SNP in Inbred 5<br />

compared to reference<br />

(12C/ 0T)

True SNP between Inbred 1 and 5<br />

Combined assembly_Inbred 1 and 5 Inbred 1 assembly Inbred 5 assembly<br />

Heterozygote like<br />

situation (57:43)<br />

compared to<br />

reference<br />

(12C/ 9T)<br />

SNP in Inbred 1<br />

compared to reference<br />

(0C/ 9T)<br />

Not a SNP in Inbred 5<br />

compared to reference<br />

(12C/ 0T)<br />

Combined assembly results in heterozygote like situation (57:43) at a given position and<br />

SNP between the Inbred 1 and Inbred 5

Pairwise comparison<br />

Step 1<br />

‣ Combined mapping of sequences from each pair of genotypes to the<br />

IRGSP Nipponbare reference genome<br />

Inbreds<br />

CMS lines<br />

Restorer lines<br />

Inbred 1 Inbred 2 Inbred 3 Inbred 4 Inbred 5 Inbred 6<br />

CMS lines<br />

Inbred 1 X A B D E F<br />

Inbred 2 X X C G H I<br />

Inbred 3 X X X J K L<br />

Restorer lines<br />

Inbred 4 X X X X M N<br />

Inbred 5 X X X X X O<br />

Inbred 6 X X X X X X<br />

Step 2<br />

‣ SNPs and InDels from pairwise assembly (Coverage > 9, count allele 1 ><br />

4, count allele 2 > 4)

Pairwise comparison<br />

Step 3<br />

‣ SNPs and InDels from each inbred using filters (Coverage > 4, count<br />

allele 1 > 0, count allele 2 > 0) in order to eliminate heterozygotes<br />

Step 4<br />

‣ Identify and eliminate the duplicates between each combination of<br />

assembly for a pair and eliminate<br />

‣ The SNPs remaining after eliminating the duplicates - SNPs between<br />

inbred 1 and inbred 2

Technique overcomes bias in reads<br />

SNPs_Combined assembly SNPS_Inbred 1 SNPs_Inbred 5<br />

15 reads<br />

10 reads 5 reads only

Polymorphisms - Pairwise (within group)<br />

SNPS<br />

InDels<br />

CMS line 2<br />

Restorer 2<br />

CMS line 2<br />

Restorer 2<br />

172,409<br />

(44,010)<br />

88,557<br />

(21,707)<br />

249,897<br />

(64,740)<br />

319,629<br />

(76,680)<br />

8,757<br />

(2,314)<br />

4,830<br />

(1,223)<br />

8,718<br />

(2,352)<br />

17,036<br />

(4,177)<br />

CMS line 1<br />

124,091<br />

(31,500)<br />

CMS line 3<br />

Restorer 1<br />

293,013<br />

(74,187)<br />

Restorer 3<br />

CMS line 1<br />

8,042<br />

(2,084)<br />

CMS line 3<br />

Restorer 1<br />

12,253<br />

(3,299)<br />

Restorer 3<br />

(a)<br />

(b)<br />

(c)<br />

(d)<br />

CMS line 1<br />

CMS line 2<br />

CMS line 3 Restorer 1<br />

229,124<br />

(61,337)<br />

260,081<br />

(55,033) 251,876<br />

(61,396)<br />

278,365<br />

(69,441)<br />

150,822<br />

(38,403)<br />

303,763<br />

(63,861)<br />

(a)<br />

164,685<br />

(41,669)<br />

263,476<br />

(66,207)<br />

185,849<br />

(36,946)<br />

Restorer 2<br />

Restorer 3<br />

CMS line 1<br />

CMS line 2<br />

CMS line 3 Restorer 1<br />

7,905<br />

(2,251)<br />

10,618<br />

(2,913) 10,945<br />

(3,035)<br />

14,338<br />

(3,756)<br />

6,444<br />

(1,689)<br />

19,010<br />

(4,668)<br />

(b)<br />

8,952<br />

(2,312)<br />

13,976<br />

(3,618)<br />

12,085<br />

(2,391)<br />

Restorer 2<br />

Restorer 3

Polymorphisms - Pairwise (within group)<br />

SNPS<br />

CMS lines Inbred 1 Inbred 2 Inbred 3<br />

Inbred 1 X 172,409 124,091<br />

Inbred 2 X X 88,557<br />

InDels<br />

CMS lines Inbred 1 Inbred 2 Inbred 3<br />

Inbred 1 X 8,757 8,042<br />

Inbred 2 X X 4,830<br />

Inbred 3 XDiverse among X CMS X lines - Inbred 31 and XInbred 2 X X<br />

Restorers Inbred 4 Inbred 5 Inbred 6<br />

Inbred 4 X 249,897 293,013<br />

Inbred 5 X X 319,629<br />

Restorers Inbred 4 Inbred 5 Inbred 6<br />

Inbred 4 X 8,718 12,253<br />

Inbred 5 X X 17,036<br />

Inbred 6 XDiverse among X Restorers X - Inbred 65 and Inbred X 6 X X

Polymorphisms in Genes- Pairwise<br />

(within group)<br />

SNPS<br />

InDels<br />

CMS lines Inbred 1 Inbred 2 Inbred 3<br />

CMS lines Inbred 1 Inbred 2 Inbred 3<br />

Inbred 1<br />

X<br />

44,010<br />

(5,097)<br />

31,500<br />

(3,764)<br />

Inbred 1<br />

X<br />

2,314<br />

(55)<br />

2,084<br />

(51)<br />

Inbred 2 X X<br />

21,707<br />

(2,515)<br />

Inbred 2 X X<br />

1,223<br />

(32)<br />

Inbred 3 X X X<br />

Diverse among CMS lines - Inbred 1 and Inbred 2<br />

Inbred 3 X X X<br />

Restorers Inbred 4 Inbred 5 Inbred 6<br />

Restorers Inbred 4 Inbred 5 Inbred 6<br />

Inbred 4<br />

X<br />

64,740<br />

(7,266)<br />

74,187<br />

(8,948)<br />

Inbred 4<br />

X<br />

2,352<br />

(50)<br />

3,299<br />

(79)<br />

Inbred 5 X X<br />

76,680<br />

(9,388)<br />

Inbred 5 X X<br />

4,177<br />

(188)<br />

Inbred 6 X X X<br />

Diverse among Restorers - Inbred 5 and Inbred 6<br />

Inbred 6 X X X

Polymorphism - Pairwise (between group)<br />

SNPs<br />

Restorer lines<br />

Inbred 4 Inbred 5 Inbred 6<br />

Most diverse - Inbred 1 and Inbred 6<br />

CMS lines<br />

Inbred 1 260,081 278,365 303,763<br />

Inbred 2 229,124 251,876 263,476<br />

Inbred 3 150,822 164,685 185,849<br />

Least diverse - Inbred 3 and inbred 4<br />

Most diverse - Inbred 1 and Inbred 6<br />

Least diverse - Inbred 3 and inbred 4<br />

CMS lines<br />

InDels<br />

Restorer lines<br />

Inbred 4 Inbred 5 Inbred 6<br />

Inbred 1 10,618 14,338 19,010<br />

Inbred 2 7,905 10,945 13,976<br />

Inbred 3 6,444 8,952 12,085

Polymorphism in Genes - Pairwise<br />

(between group)<br />

SNPs<br />

Restorer lines<br />

Inbred 4 Inbred 5 Inbred 6<br />

Most diverse - Inbred 1 and Inbred 5<br />

CMS lines<br />

Inbred 1<br />

55,033 69,441<br />

(6,108) (8,343)<br />

Inbred 2<br />

61,337 61,396<br />

(6,833) (7,084)<br />

Inbred 3<br />

38,403 41,669<br />

(4,317) (4,979)<br />

63,861<br />

(7,785)<br />

66,207<br />

(7,808)<br />

36,946<br />

(4,413)<br />

Most diverse - Inbred 1 and Inbred 6<br />

Least diverse - Inbred 3 and inbred 4<br />

Least diverse - Inbred 3 and inbred 6<br />

CMS lines<br />

InDels<br />

Restorer lines<br />

Inbred 4 Inbred 5 Inbred 6<br />

Inbred 1<br />

2,913 3,759<br />

(50) (81)<br />

Inbred 2<br />

2,251 3,035<br />

(43) (65)<br />

Inbred 3<br />

1,689 2,312<br />

(43) (69)<br />

4,668<br />

(269)<br />

3,618<br />

(96)<br />

2,391<br />

(105)

To summarise<br />

‣ Through whole genome re-sequencing 2,819,086 non-redundant<br />

DNA polymorphisms (2,495,052 SNPs, 160,478 insertions and<br />

163,556 deletions) were discovered<br />

‣ The non-synonymous SNPs spanning the genes across the<br />

genome rice will provide valuable insights into the molecular<br />

basis of heterosis<br />

‣ Enrich the SNP resources in rice - providing high density<br />

coverage which will help in molecular breeding applications

To proceed with…<br />

‣ Hybrids involving the elite rice inbred lines are being<br />

produced and will be evaluated for yield performance<br />

‣ Genome-wide association analysis with the phenotypic<br />

traits will help in determining key genes/ alleles for<br />

predicting hybrid performance

Acknowledgements<br />

‣ Department of Science and Technology, India<br />

(BOYSCAST Fellowship)<br />

‣ Indian Council of <strong>Agricultural</strong> Research, New Delhi<br />

‣ Indian <strong>Agricultural</strong> Research Institute, New Delhi<br />

‣ Southern Cross <strong>University</strong>, Lismore, NSW, Australia

Thank you

GENETIC ENGINEERING FOR SEMI DWARF RICE<br />

USING RNA INTERFERENCE (RNAi)<br />

G.Bindusree<br />

Research Scholar<br />

Guide<br />

Dr. M. Parani<br />

Prof. & Head Department<br />

Genetic Engineering<br />

SRM <strong>University</strong>

Why Semi Dwarf Rice ??<br />

•High yielding<br />

• Responsiveness to nitrogen fertilizers<br />

• Lodging resistance<br />

Classification<br />

Tall<br />

Medium Tall<br />

Semi Dwarf<br />

Dwarf<br />

Height<br />

More than 130cm<br />

110-130 cm<br />

80-110 cm<br />

Less than 80 cm

Semi dwarf gene (sd1)<br />

Dee-geo-woo-gen was used in breeding programs in eastern Asia to<br />

produce many of the high-yielding semi dwarf cultivars grown today<br />

(383-base-pair deletion)<br />

IR8 ‘Green Revolution’<br />

Parentage: Dee-geo-woo-gen x Peta,<br />

Dwarf (80-85 cm ) Yield: 50-55 Q/ha.<br />

Phenotypic<br />

Description<br />

Semi dwarf, resistant to lodging, high yielding. Elongation of<br />

lower internodes. Defective in biosynthetic enzyme<br />

GA20ox2 that catalyzed the conversion of GA53 to GA20<br />

Sd1 gene represents a loss-of-function deletion mutation in GA20ox2 gene<br />

that codes for GA20 oxidase.

YIELD<br />

80<br />

TALL<br />

70<br />

Tikkana<br />

PMK-1<br />

60<br />

50<br />

40<br />

30<br />

20<br />

White Ponni<br />

Subramaniya Bharathi<br />

TKM-10<br />

SEMI DWARF<br />

ADT-44<br />

ADT-37<br />

CORH-2<br />

ASD-20<br />

ADTRH-1<br />

DWARF<br />

Jyothi<br />

10<br />

Annapoorna<br />

Annapurna-28<br />

0<br />

YIELD Q/ha

PARTICULARS White Ponni<br />

Parentage Mayang,Ebos-80;Taichung 65/2<br />

Duration (Days) 135-140<br />

Average Yield (kg/ha) 4500<br />

1000 grain wt (g) 16.4<br />

Grain L/B ratio 3.22<br />

Grain type<br />

Medium slender<br />

Habit<br />

Leaf sheath<br />

Septum<br />

Ligule<br />

Auricle<br />

Panicle<br />

Husk colour<br />

Rice colour<br />

Abdominal white<br />

Morphological Characters<br />

Length 8<br />

Breadth 3<br />

Thickness 2<br />

Medium tall (130-135 cm)<br />

Green<br />

Green<br />

White<br />

Colourless<br />

Long drooping<br />

Straw<br />

White<br />

Absent<br />

Grain size (mm)<br />

White Ponni

RNAi Pathway

GA Metabolic Enzymes<br />

Early steps in the pathway<br />

(CPS)-ent-copalyl diphosphate synthase<br />

(KS)-ent-Kaurene synthase<br />

(KO)-ent-Kaurene oxidase<br />

(KAO)-ent-Kaurenoic acid oxidase<br />

GA20 oxidase<br />

GA2 oxidase<br />

GA3 oxidase<br />

Later steps in the pathway<br />

GA20ox1<br />

GA20ox2 (sd1)<br />

GA20ox3<br />

GA20ox4<br />

GA2ox1<br />

GA2ox2<br />

GA2ox3<br />

GA2ox4<br />

GA3ox1<br />

GA3ox2

GA Biosynthesis in Plants<br />

trans-Geranylgeranly<br />

Diphosphate<br />

(GGDP)<br />

CDP<br />

synthase<br />

(CPS)<br />

ent-Copalyl<br />

Diphosphate<br />

(CDP)<br />

Kaurene<br />

Synthase<br />

(KS)<br />

ent-Kaurene<br />

Plastid<br />

Kaurene<br />

Oxidase (KO)<br />

GA53<br />

GA13ox<br />

ER Membrane<br />

GA12<br />

Kaurenoic acid<br />

Oxidase (KAO)<br />

ent-kaurenoic<br />

Acid (KA)<br />

GA20ox<br />

GA20ox<br />

GA20<br />

GA9<br />

GA2ox1,3<br />

GA29<br />

GA3ox<br />

GA1<br />

GA2ox1,3<br />

GA8<br />

GA2ox<br />

GA51<br />

Cytoplasm<br />

GA3ox<br />

GA4<br />

GA34<br />

GA2ox

648bp<br />

750bp<br />

1072bp<br />

2543bp<br />

3149bp<br />

GA20ox2 Gene<br />

Oryza sativa genomic DNA Acc No: AP003561, 183580bp.<br />

Gibberellin 20-oxidase gene (GA20ox2): 139292.<br />

GA20ox2 mRNA joins: EXON 1:139292 (291bp + 3’UTR 315bp = 606bp).<br />

Open Reading Frame GenBank: AP003561, 1770bp<br />

TOTAL GENOMIC CLONE WITH 2 INTRONS: 3149bp<br />

648bp 322bp 606bp<br />

EXON 1<br />

EXON 2 EXON 3

Rice Actin 1 gene(Act1)<br />

Act1-promoter Acc No: S44221, 1266bp.<br />

Efficient promoter for transgenic rice.<br />

It consists of the following-<br />

5’-flanking and 5’-transcribed sequence(Non coding exon1) and the 1Intron<br />

Long poly(dA) between -146 and -186<br />

Restriction sites-XhoI, BamHI, EcoRV

Objectives<br />

Designing RNAi constructs specific for GA20ox2<br />

Generation of transgenic rice plants by Agrobacteriummediated<br />

transformation.<br />

Molecular and Phenotypic analysis of the transgenic plants

100<br />

200<br />

300<br />

400<br />

500<br />

600<br />

700<br />

800<br />

900<br />

1000<br />

1100<br />

1200<br />

1300<br />

1400<br />

1500<br />

1576<br />

Work done so far<br />

1. Identification of trigger sequence<br />

5’ UTR<br />

OsGA20ox2<br />

3’ UTR<br />

OsGA20ox1<br />

OsGA20ox3<br />

OsGA20ox4<br />

OsGA2ox1<br />

211<br />

1195<br />

OsGA2ox2<br />

OsGA2ox3<br />

OsGA2ox4<br />

OsGA3ox1<br />

OsGA3ox2<br />

Minimum 10 contiguous nucleotide identity from ClustalW

2.Designing of the construct<br />

Kpn I<br />

Xba I<br />

Act1-Promoter 1228bp<br />

Antisense 362bp<br />

Intron1 122bp<br />

Sense 362bp<br />

hpRNA<br />

RNAi Pathway

3. Amplification of loop and Sense<br />

1 2 3<br />

Lane 1 – 100 bp marker<br />

600 bp<br />

500 bp<br />

100bp<br />

Lane 2 – amplified loop (122 bp)<br />

Lane 3 – Amplified sense (362 bp)<br />

Fig.1

4. Ligation of loop and Sense and PCR amplification of the ligated product<br />

1 2<br />

Lane 1 – 100 bp marker<br />

600 bp<br />

500 bp<br />

Lane 2 – PCR amplification of ligated<br />

product of loop+sense (484)<br />

100bp<br />

Fig.2

5. Confirmation of loop and sense ligation by sequencing<br />

CGCCAATGGGGTAATTAAAACGATGGTGGacGACATTGCATTTCAAATTCAAAACAAATTCAAAACACACCGAC<br />

CGAGATTATGcTGAATTCAAACGCGTTTGTGCGCGCAGGAGGGTGTACACGCGCTGGCTCGCGCCGCCGGCCGC<br />

CGACGCCGCCGCGACGGCGCAGGTCGAGGCAGCCAGCTGATCGCCGAACGGAACGAAACGGAACGAACAGAA<br />

GCCGATTTTTGGCGGGGCCCACGTGGGGGATTTGCCCACGTGAGGCCCCACGTGGACAGTGGGCCCGGGCGGA<br />

GGTGGCACCCACGTGGACCGCGGGCCCCGCGCCGCCTTCCAATTTTGGACCCTACCGCTGTACATATTCATATATT<br />

GCAAGAAGAAGCAAAACGTACGTGTGGGTTGGGTTGGGCTTCTCTCTATTACTAAAAAAAATATAATGGAACG<br />

ACGGATGAATGGATGCTTATTTATTTATCTAAATTGAATTCGAATTCGGcTCAA

6. Amplification of Actin promoter and cloning in to pUC18<br />

1 2<br />

1 2 3 4<br />

3 kb<br />

2 kb<br />

1 kb<br />

3 kb<br />

2 kb<br />

1 kb<br />

Fig.3<br />

Lane 1 – Amplified Actin promoter (1.2 kb)<br />

Lane 2 – 1 kb maker<br />

Lane 1,4 - 1 kb marker<br />

Lane 2<br />

Lane 3<br />

Fig.4<br />

– pUC 18 DD with XbaI and<br />

KpnI and eluted<br />

– Actin DD with XbaI and<br />

KpnI and eluted

Biochemical Pathway for GA Biosynthesis<br />

1 st stage (Proplastids)<br />

Geranylgeranyl<br />

diphosphate<br />

CPS<br />

ent- copalyl<br />

diphosphate<br />

KS<br />

Ent-kaurene<br />

2 nd stage (Endoplasmic Reticulum)<br />

GA13ox GA7ox<br />

GA 53 GA12 GA 12 -aldehyde<br />

KAO<br />

Ent-7α hydroxy<br />

Kaurenoic acid<br />

KAO<br />

KO<br />

Ent-Kaurenoic<br />

acid<br />

GA 51<br />

Non-13 hydroxylation pathway<br />

GA 12<br />

GA20ox<br />

GA 15<br />

GA 24<br />

GA 53 GA 44<br />

3 rd stage (Cytosol)<br />

GA20ox<br />

GA2ox<br />

GA20ox<br />

GA20ox GA20ox GA20ox<br />

GA 19<br />

GA 9<br />

GA3ox<br />

GA3ox<br />

GA2ox<br />

GA3ox<br />

GA 5<br />

Early-13 hydroxylation pathway<br />

GA 4<br />

GA 34<br />

GA 20<br />

GA3ox<br />

GA2ox<br />

GA 1 GA 8<br />

GA 3

Structural and functional analysis of<br />

glyoxalase I promoter from rice<br />

Arul L, Suresh Kumar*, Kushboo R, Sivaranjani S, LathaMageswari V, Kumar<br />

KKK, Kokiladevi E, Sudhakar D, Balasubramanian P<br />

Centre for Plant Molecular Biology & Biotechnology<br />

<strong>Tamil</strong> <strong>Nadu</strong> <strong>Agricultural</strong> <strong>University</strong>, Coimbatore -641 003 (TN)<br />

*Division of Crop Improvement, I.G.F.R.I., Jhansi -284 003 (UP)

About Promoters<br />

• cis-acting, regulatory element<br />

• Indispensible component for the expression of gene(s)<br />

+1<br />

5’ - ’ promoter Gene (CDS) Ter - 3’<br />

(mRNA)

Promoter types<br />

Constitutive promoters<br />

• CaMv35S, maize Ubi, rice Act-1<br />

Inducible promoters<br />

• rd29A, PR1<br />

Tissue specific promoters<br />

• TA9, Gt1

Inducible promoter<br />

• Induced by the presence of biotic or abiotic factors<br />

• Regulated expression<br />

need based, switching on/off of gene expression (only at<br />

times of stress)<br />

• Adds greater strength to the transgenic technology<br />

(Kasuga et al., 2004)<br />

• Recent research on ABA, salt and drought stress inducible<br />

promoters in rice<br />

OsABA2 (Rai et al., 2009)<br />

Wsi18 (N et al., 2011)

Current study<br />

Objectives:<br />

Cloning and characterization of the promoter of a known<br />

stress inducible gene, glyoxalase I (glyI) from rice<br />

Functional characterization of the isolated promoter for<br />

expression and inducibility under abiotic stress conditions<br />

in transgenic rice

About glyoxalase I (glyI)<br />

• Glyoxalase pathway is universal, off shoot of glyocolysis<br />

• GlyI catalyzes the first step towards detoxification of methylgloxal (MG)<br />

• Increased glyI activity in meristematic tissues and cells undergoing<br />

stress (abiotic)<br />

Plant cells under stress<br />

(Sethi et al., 1988; Deswal et al., 1993; Veena et al., 1999;<br />

Mustafiz et al, 2011)<br />

Adaptive measures<br />

Additional energy<br />

requirement(demand for ATP)<br />

Increased rate of glycolysis<br />

• Methylglyoxal is detoxified via S-D-Lactoylglutathione<br />

into lactate and glutathione<br />

Accumulation of methylglyoxal<br />

Upregulation of gly pathway<br />

Detoxification of methylglyoxal

Work done – promoter cloning<br />

1. The glyI sequence from cv. Nipponbare (Usui et al., 2001)<br />

2. A 3 kb sequence upstream of AUG of glyI was identified from<br />

the BAC clone (OSJNBa0056006) sequence<br />

3. PCR amplification of a 2120 bp region from the genomic DNA of<br />

Nipponbare<br />

4. Sequencing and in silico analysis<br />

rev<br />

for<br />

Pst I<br />

EcoR I

Work done - genetic transformation<br />

5. Cloning the putative pglyI promoter, Pst I - EcoR I restriction<br />

fragment of 1545 bp in front of a promoter less GUS vector<br />

(pCAMBIA 1391z)<br />

6. Generation of stable rice transformants (cv. Pusa Basmati1)<br />

using the putative pglyI -1391z

Results<br />

1. Structural analysis of (pglyI)<br />

• Transcription start site (TSS) predicted at 825 th base from the 5’-<br />

end of the sequence on the plus strand<br />

• TATA box “CTATAAATAC” was predicted between 791 and 801<br />

bases<br />

• Region between 826 base and 1545 base consisted of an initial<br />

UTR exon and first intron<br />

<br />

First intron fall between 1464 and 1545 bases<br />

• GenBank submission: EU605981.1

Structure of pglyI and maize pUbi<br />

Similar architecture, between pglyI and, maize ubiquitin promoter (Christensen et al., 1992)<br />

pUbi<br />

pglyI

Upstream (-825 to +1 bases) stress responsive<br />

motifs<br />

Motifs<br />

Conserved<br />

Sequence<br />

Location<br />

( 5’- end)<br />

Implicated function<br />

ABRE motif -A TACGTGTC 111 An Abscisic acid response element, ABA<br />

induced transcription in rice<br />

ABRE-like<br />

sequence<br />

ACGTG 267 Dehydration stress and dark-induced<br />

senescence<br />

Anaerobic box AAACAAA 421 Motifs found in anaerobically induced genes<br />

MYB core CNGTTR 556, 689 Binding site for MYB, responds to<br />

dehydration stress<br />

WRKY box TGAC 29, 43 WRKY proteins are involved in pathogen<br />

defense<br />

CE CGACG 544 Coupling element along with ABRE motif<br />

SAUR motif CATATG 490, 550 Auxin response modules<br />

G box TTTAA 752 bZIPs transcription binding site

Functional analysis<br />

• Six pglyI transgenic Pusa Basmati (T 0 ) events<br />

were confirmed by PCR<br />

• Stable GUS assay showed blue color<br />

development<br />

Transient GUS<br />

Expression<br />

Stable GUS<br />

Expression

Localization of GUS Expression<br />

TS<br />

GUS assay of shoots<br />

LS

GUS PCR<br />

• Homozygous line identified in one of the<br />

above event at T2 generation<br />

PCR for uidA gene

2. Function of induciblity<br />

• ABA stress (40 micro moles) @ 3 week seedlings in<br />

hydroponics<br />

• Semi-quantitative RT-PCR for GUS in two different<br />

transgenic lines (pglyI-GUS) & (pCaMV 35S-GUS)<br />

L1- pCaMV 35-GUS (0 hour)<br />

L2- pCaMV 35-GUS (4 hour)<br />

L3- pgly GUS (0 hour)<br />

L4- pgly GUS (4 hour)<br />

Rice Actin<br />

GUS<br />

L1 L2 L3 L4

Conclusion<br />

• The cloned promoter region (pglyI)<br />

successfully drive the expression of transgene<br />

(GUS)<br />

• Low/moderate level of constitutive GUS<br />

expression under normal conditions<br />

• Preliminary expression analysis suggest, the<br />

promoter is inturn inducible under ABA stress

• Thanks

+1 (825 base)<br />

5’-<br />

Promoter (825 bases)<br />

UTR exon (637 bases)<br />

Intron (81 bases)<br />

1 base 1545 base<br />

- 3’<br />

Initial UTR exon<br />

826 -1463<br />

First intron<br />

1464-1545

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