Ken McNally - ITQBsaibo/ADONIS/talks/Ken... · 3/4/2009 · Ken McNally Current position: •...

1ADONIS, March 4, 2009

Ken McNallyCurrent position: • Senior Scientist, Molecular Genetics/Molecular Taxonomy,

TT Chang Genetic Resources Center, IRRI, since 2003

Education:• PhD, Biochemistry, Oklahoma State University, 1990• BS, Mathematics, Biology, and Chemistry, Northwestern

Oklahoma State University, 1982

Professional work highlights:• Scientist, GRC, IRRI, 2001-2003• Affiliate Scientist, PBGB, IRRI, 1998-2001• Postdoctoral fellow, PBGB, IRRI, 1996-1998• Postdoctoral fellow, Cornell University, 1994-1996• Postdoctoral fellow, UC Santa Barbara, 1990-1993• Comp. Biology Consultant, University of Colorado, 1990

IRRI


EcoTILLINGEcoTILLING @ IRRI@ IRRI


Outline1) The CGIAR and IRRI2) Rice Genetic Resources3) Mining Alleles4) Population Structure

• GCP composite collection5) TILLING and EcoTILLING6) Targeted SNP discovery

• Candidate Genes• EcoTilling

7) Drought Phenotyping8) Drought Association9) Summary


CGIAR - Consultative Group for International Agricultural Research


IRRI - Int. Rice Research Institute• Founded in 1960 by Rockefeller and Ford Foundations• Flagship center of CGIAR• Green Revolution in Asia initiated through the release of

IR8 with sd1 for semi-dwarf stature

Mission• To reduce poverty and hunger, improve the health of rice

farmers and consumers, and ensure environmental sustainability through collaborative research, partnerships, and the strengthening of national agricultural research and extension systems.

QuickTime™ and a decompressor

are needed to see this picture.


0.0

1.0

2.0

3.0

4.0

5.0

6.0

1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997

Year

Irrigated(40%)

Rainfed(30%)

Trends in rice yield for irrigated and rainfed ecosystems in AsiaTrends in rice yield for irrigated and Trends in rice yield for irrigated and rainfedrainfed ecosystems in Asiaecosystems in Asia

Largely Irrigated(30%)

Source: Hossain et al 2000, IRRI

Sustain productivity in irrigated area ( pest tolerance, photosynthetic efficiency)

Raise yield in fragile ecosystems (problem soils, too much or too little water)


Organism Year Genome size, Mb

Number of predicted genes

Yeast 1996 13 6,000

Nematode 1998 97 19,000

Drosophila 2000 180 14,000

Arabidopsis 2000 150 26,000

Human 2001 3,000 35,000

Rice 2004-5 430-389 35,000-50,000

Decade of genomes

…

9ADONIS, March 4, 2009International Rice Genome Sequencing Project (IRGSP) 2005 NatureInternational Rice Genome Sequencing Project (IRGSP) 2005 Nature 436:793436:793--800 800


IRGC IRGC –– the International Rice the International Rice GenebankGenebank CollectionCollectionWorldWorld’’s largest collection of rice germplasm held in trust s largest collection of rice germplasm held in trust for the world community and source countriesfor the world community and source countries

•• Over 118,000 registered and Over 118,000 registered and incoming accessions from 117 incoming accessions from 117 source countriessource countries

•• Two cultivated speciesTwo cultivated speciesOryzaOryza sativasativaOryzaOryza glaberrimaglaberrima

•• 22 wild species22 wild species•• Relatively few accessions have Relatively few accessions have donated alleles to current, donated alleles to current, highhigh--yielding varietiesyielding varieties

•• http://http://www.irri.orgwww.irri.org/GRC/GRC

http://www.fao.org/rice2004/


Rice DiversityRice Diversity

O. sativaO. sativa PaniclesPanicles


Allelic series of induced and natural variants

Traditional GermplasmIRGC – 118,000 accessions

Elite Lines/VarietiesIR64 Mutant Collection

Breeding/Mapping populations

Array of Genetic Resources


Gene Discovery to Trait SynthesisGene Discovery to Trait Synthesis

Process

GeneticResources

Product

Genomic annotation,Forward and

Reverse Genetics,Gene arrays/gels

Candidate genes

NILs, RILsMapping pop.

Mutants

Beneficial allelesLinked to Traits

Genebank, Genetic Resources

GermplasmGenotyping &Phenotyping

Value-added varieties

Advanced breeding lines

as vehicles

Marker-aided Selection/

Transformation

Gene Identification

Allele Mining

CropImprovement

Comparative analysis


Goal of Allele MiningGoal of Allele Mining

100,000

11 50,000GenesGenes

Acc

essi

ons

Acc

essi

ons

MoroberekanAzucena

Binam

Pokkali

IR64

Without allele pyramid – stress sensitive

With allele pyramid – stress tolerant

Best allele for each gene

Useful genes


Mining AllelesMining Alleles

NRSH & KLM (2005) Unlocking the Genetic Vault, Rice Today, September 2005, pp. 32-33


Understanding the Diversity of RiceUnderstanding the Diversity of RicePopulation StructurePopulation Structure


Necessity for establishing population structure

•• Group accessions according to shared common Group accessions according to shared common historyhistory

•• PrePre--requisite for effective association geneticsrequisite for effective association genetics•• Preclude spurious associations based on shared Preclude spurious associations based on shared

ideotypesideotypes•• Traits having a common or similar phenotype may Traits having a common or similar phenotype may

not be a result of expression/regulation of the not be a result of expression/regulation of the same genessame genesoo e.g. grain shape of e.g. grain shape of indicasindicas and and javanicasjavanicas


indica

aus/boro

japonica

aromatic

FAC on 3,122 unique Isozyme patterns for 63 alleles at 15 loci

Summary of 24,157 accessions from IRGC (Glaszmann, Second, Juliano) and

PBGB (Brar et al., Virk, Courtois)


GCP Rice Composite Sethttp://www.generationcp.org


EcoEco--regional Partitioning of Rice regional Partitioning of Rice GenepoolsGenepools


Species CompositionSPECIES Frequency %

O. sativa* 2338 84.80O. glaberrima** 373 13.53O. rufipogon 14 0.51O. nivara 10 0.36O. glumaepatula 9 0.33O. barthii 7 0.25Oryza species*** 1 0.04O. meridionalis 5 0.18Total 2757* Lower than wanted due to lack of Chinese selections** Higher to compensate for lack of WARDA lines*** Recently reclassified, previously O. sativa, aus?


Marker System• SSR Panel defined at Cornell by McCouch et al• Subset of those previously used to establish

population structure by Garris et al 2005• 51 SSRs having repeat types:

o 31 dinucleotideo 13 trinucleotideo 3 tetranucleotideo 4 complex

• Distributed across the genome• Optimized for use on automated genotypers


Genotyping Platforms

IRRI Initially MJR BaseStation (gel-based, 4 dyes)Dropped due to technical issues

Switched to LiCor 4300s (gel-based, 2 dyes)

CIRAD LiCor Genotypers

CIAT ABI 377 (gel-based, 4 dyes)

EMBRAPA ABI 3730 (capillary, 5 dyes)

Cornell/WARDA ABI 3730 (capillary, 5 dyes)


Summary statistics by Summary statistics by Marker Major Allele

FrequencyGenotype

NoSample

SizeNo. of obs.

Allele No.

Availability

Gene Diversity

Hetero- zygosity

PIC f Loci to Remove

RM161 CIAT 0.337 150 2757 2382 43 0.864 0.834 0.135 0.821 0.839RM259 CIAT 0.109 164 2757 2456 53 0.891 0.950 0.071 0.948 0.925 allele callsRM307 CIAT 0.151 136 2757 2117 52 0.768 0.917 0.077 0.911 0.916 allele callsRM338 CIAT 0.413 49 2757 2443 19 0.886 0.731 0.150 0.693 0.794

RM1_CDd CIRAD 0.171 150 2757 2155 32 0.782 0.914 0.078 0.908 0.915RM125 CIRAD 0.363 37 2757 2498 14 0.906 0.717 0.053 0.669 0.926RM154 CIRAD 0.125 105 2757 2313 36 0.839 0.925 0.052 0.920 0.944RM178 CIRAD 0.361 27 2757 1005 22 0.365 0.763 0.019 0.732 0.975 missing dataRM284 CIRAD 0.635 18 2757 2529 7 0.917 0.552 0.025 0.515 0.955RM454 CIRAD 0.352 44 2757 2219 21 0.805 0.794 0.034 0.771 0.957RM55 CIRAD 0.317 53 2757 2212 22 0.802 0.803 0.085 0.779 0.895RM11 EMBRAPA 0.160 160 2757 2615 33 0.948 0.916 0.223 0.911 0.756 heterozygosityRM144 EMBRAPA 0.167 225 2757 2293 57 0.832 0.940 0.211 0.937 0.776 allele callsRM484 EMBRAPA 0.361 26 2757 2632 19 0.955 0.742 0.009 0.705 0.988RM124 WARDA 0.523 11 2757 2581 6 0.936 0.607 0.043 0.537 0.930RM19 WARDA 0.288 60 2757 2344 26 0.850 0.841 0.053 0.824 0.937RM316 WARDA 0.451 57 2757 2648 22 0.960 0.743 0.070 0.718 0.906RM413 WARDA 0.339 72 2757 2643 26 0.959 0.781 0.073 0.751 0.906RM431 WARDA 0.371 48 2757 2629 15 0.954 0.745 0.056 0.708 0.925RM536 WARDA 0.332 33 2757 2642 12 0.958 0.795 0.051 0.768 0.936OSR13 IRRI 0.359 58 2757 2480 20 0.900 0.738 0.092 0.696 0.876RM105 IRRI 0.448 77 2757 2641 20 0.958 0.720 0.178 0.685 0.753RM118 IRRI 0.390 18 2757 2675 7 0.970 0.699 0.057 0.643 0.918RM1227 IRRI 0.233 88 2757 2656 25 0.963 0.877 0.120 0.866 0.864RM133 IRRI 0.419 16 2757 2696 8 0.978 0.706 0.031 0.657 0.956RM152 IRRI 0.403 28 2757 2746 11 0.996 0.745 0.098 0.710 0.869RM162 IRRI 0.246 93 2757 2712 24 0.984 0.855 0.109 0.841 0.872RM171 IRRI 0.339 52 2757 2601 14 0.943 0.805 0.126 0.782 0.844RM215 IRRI 0.214 61 2757 2740 17 0.994 0.860 0.044 0.845 0.949RM237 IRRI 0.393 61 2757 2718 21 0.986 0.767 0.036 0.740 0.953RM25 IRRI 0.196 76 2757 2680 19 0.972 0.884 0.111 0.874 0.875RM271 IRRI 0.182 88 2757 2669 23 0.968 0.879 0.077 0.868 0.913RM277 IRRI 0.417 20 2757 2747 8 0.996 0.648 0.076 0.582 0.883RM283 IRRI 0.294 52 2757 2700 15 0.979 0.823 0.040 0.802 0.951RM287 IRRI 0.208 84 2757 2689 17 0.975 0.881 0.104 0.870 0.882RM408 IRRI 0.381 39 2757 2709 12 0.983 0.763 0.142 0.731 0.814RM433 IRRI 0.454 44 2757 2608 20 0.946 0.721 0.028 0.689 0.961RM44 IRRI 0.461 75 2757 2676 19 0.971 0.748 0.169 0.731 0.774RM447 IRRI 0.177 60 2757 2711 20 0.983 0.867 0.056 0.852 0.935RM452 IRRI 0.520 19 2757 2683 7 0.973 0.621 0.098 0.560 0.842RM455 IRRI 0.469 33 2757 2691 14 0.976 0.675 0.032 0.627 0.953RM474 IRRI 0.250 89 2757 2559 33 0.928 0.888 0.054 0.880 0.939RM495 IRRI 0.399 25 2757 2720 9 0.987 0.668 0.070 0.607 0.895RM5 IRRI 0.249 77 2757 2481 23 0.900 0.846 0.142 0.829 0.832

RM507 IRRI 0.459 14 2757 2654 7 0.963 0.631 0.042 0.557 0.934RM510 IRRI 0.414 40 2757 2598 15 0.942 0.752 0.049 0.722 0.935RM514 IRRI 0.237 90 2757 2484 25 0.901 0.871 0.083 0.859 0.904RM538 IRRI 0.351 39 2757 2242 20 0.813 0.816 0.012 0.798 0.985RM312 IRRI 0.389 42 2757 2688 11 0.975 0.737 0.140 0.698 0.810RM552 IRRI 0.332 116 2757 2002 32 0.726 0.812 0.255 0.793 0.686 missing dataMean 0.332 66 2757 2520.2 21.1 0.914 0.786 0.085 0.7584 0.8923

26ADONIS, March 4, 20090 0.5

Population Structure of the GCP Rice Composite SetPopulation Structure of the GCP Rice Composite SetSPECIES Total No.O. sativa 2339O. glaberrima 373O. rufipogon 14O. nivara 10O. glumaepatula 9O. barthii 5O. meridionalis 5

2757

50 SSR Loci on 2757 Oryza accessions.

Indica

AusAromatic

Tropical

Trop/Temp

Japonica

Admixed

Admixed

O. glaberrima

O. rufipogon, O. nivaraO. meridionalis

O. glumaepatula

O. barthii

IRRI (60%)Cirad (14%)

Warda/ Cornell (12%)

CIAT (8%)Embrapa (6%)


Structure of Structure of SativaSativa48 Loci on 2339 lines.(DARwin5, unwtd NJ, SM coef.

InStruct indicated that the best DIC value was for K =2 and the second best was for K = 9. The coloring reflects the group assignment for K= 9 with a minimum allele frequency of 0.65.

Indica

Aus

Aromatic

Tropical

Temperate

Japonica

Admixed

Admixed

Indica

Similar to Garris et al, 2005, Genetics 169:1631–1638


1:31:2

2:3

48 Loci on 2339 lines.(with DARwin 5, Simple matching coefficient, distance matrix, no transformation

Factorial Analysis on Sativa


Similar to Semon et al, 2005, Genetics 169:1639–1647

Structure of Structure of GlaberrimaGlaberrima

48 Loci on 12 sativa, 373 glaberrima, and 5 barthii

0 0.2

sativa

glaberrima

glaberrimabarthii

glaberrima

glaberrima

glaberrima

glaberrima


glaberrimabarthii

glumaepatula

meridionalis

sativa

Structure of the Wild RelativesStructure of the Wild Relatives48 Loci on 45 wild, 12 sativa, and 9 glaberrima

rufipogon

nivara

0 0.5

sat

sat

sat

sat

sat

sat

sat

satsat

satsat

sat

glabglab

glab glab

glab

glabglab

glab

glab

bar

bar

bar

bar

bar

barbar

rufi

rufi

rufi

rufirufi

rufi

rufi rufirufi

rufi

rufi

rufirufi

rufi

glum

glum

glumglum

glumglum

glum

glum

glum

mermermermermer

nivniv

niv

nivniv

nivniv

niv

niv

niv


TILLING and TILLING and EcoTILLINGEcoTILLING


TILLINGTargeted Local Lesion IN Genomes

• A reverse genetics method developed for Arabidopsis• Catalog allelic series of point mutations in chemically

induced mutant libraries• Applications to other organisms: rice, maize, hexaploid

wheat, lotus, soybean, poplar, Sorghum, zebrafish, Drosophila, Caenorhabitis, rat, mouse, …

• Detect natural sequence variation - EcoTILLING• SNP discovery and haplotyping


TILLING TILLING –– heteroduplexheteroduplex mismatch detection for reverse geneticsmismatch detection for reverse genetics

Colbert et. al. 2001. Plant Physiol. 126:480-484


CELI endonuclease

•described by Tony Yeung’s Laboratory, Fox Chase•isolated from celery stalks, ubiquitous•related to S1 endonuclease•secreted (glycosylated)•cuts preferentially 3’ of mismatches•Oleykowski, 1998, NAR 26:4597

Luca Comai


CelI detection of mutations

PCR, heat, anneal

fluorescent primers

CelI

denature

Luca Comai


A:T<>G:C

GAA simple repeat:5 <> 6

<> indel 9 nt

Luca Comai


Advantages of TILLING• Yields a spectrum of point alleles• EMS is reliable and irreversible• Efficient for small genes (<1 kb)• Can focus on region of interest• Applicable to essential genes• May be applied to any plant• High throughput potential


Uses of ecotilling

1. Discover variation at key genes2. Catalog and conserve landrace diversity3. Association mapping4. Molecular breeding

Well suited for surveying one locus in many individuals


Candidate genesCandidate genes


PositionPosition

FunctionFunction

SelectionSelection

ExpressionExpression

Candidate GenesCandidate Genes

Convergent evidence to identify candidate genes


1 protein kinases2 ubiquitin-related3 protective function4 signaling/defense5 cell cycle/turnover6 unknown

QTL Azucena/Bala

QTL Teqing/Lemont

Allele Shifts

Other Populations

RM3387 0.00

RM157a 1.00

RM3413 1.00RM7117 1.00

RM7332 1.00

RM132 3.90

RM6013 7.60RM4266 7.90

RM569 9.30

RM231 11.50RM6849 15.20

RM2421 16.80

RM175 17.90RM3392 17.90

RM2420 24.70

RM218 40.30RM5686 40.30

RM5896 40.30

RM7197 44.40

RM563 47.50RM554 54.70RM282 55.80

RM5928 57.20RM156 85.20

RM6146 86.00RM7370 87.20

RM7395 87.90

RM6832 88.95RM8208 88.95

RM503 119.70RM186 127.40

RM6329 127.40

RM1352 145.60

RM5009 146.10

RM6970 146.10RM468 146.40

RM1230 146.40RM6876 153.70

RM514 158.20

CH03

RM3740 12.50RM1880 13.20RM8105 19.90RM5552 20.20RM6521 20.20

RM522 26.30RM3360 27.30

RM151 28.40RM1118 28.40RM6277 28.90RM7466 28.90RM8081 28.90RM1220 37.40RM6167 37.40RM6651 37.40RM8077 37.40RM8083 46.30RM8222 46.30

RM581 49.00RM582 49.00

RM6466 49.00RM8268 49.00

RM600 49.60RM8132 49.60

RM292 50.80RM580 50.80

RM6039 50.80RM6190 50.80RM8063 50.80

RM572 52.40RM8045 52.40RM8046 52.70RM3627 54.30

RM140 65.40RM449 73.10RM594 73.10RM595 73.10RM113 75.70

RM24 76.10RM6334 96.10

RM5 98.50RM5461 112.70RM3143 113.00RM8128 113.30

RM246 115.00RM403 123.20RM128 126.50

RM1183 127.30RM7643 127.30RM6648 132.80RM6387 134.70

RM543 135.00RM8002 136.10RM8124 136.10

RM226 136.90RM7180 137.20RM8061 137.20RM6950 138.00RM6703 139.10RM5811 139.40RM8084 139.90RM3403 140.50

RM486 142.50RM3917 145.30RM6407 176.30RM6840 181.80

CH01

5.00

31.2032.8032.8036.3036.3036.80

52.0052.00

92.50

107.40109.30109.30109.30110.60110.90118.00118.00121.00121.00121.00121.00126.40126.40126.40135.50137.70138.00138.00141.70141.70156.30156.30156.30

RM154

RM7006RM5897RM6233

RM322RM2939

RM145

RM1190RM5439

RM475

RM7245RM526

RM5706RM7511RM6366RM1367RM1342RM5305RM1168RM3212RM5833RM8248RM3421RM5378RM7598

RM240RM2578

RM425RM4883RM6733RM7009RM7009

RM498RM6312

RM438 167.00

CH02

DREB2DREB2

ERF4ERF4

2 2 CDPK,CDPK,1414ADFADFCINCIN

TPPTPP,,

--33--33,,,,

AqpAqp

pp2a4pp2a4

Cross reference EST and other genes with QTL postions for yield components under stress, cross-reference to gene annotations, identify homologs/paralogs in gene families. Up or down regulated ESTs and other candidate genes cluster in QTL bins – evidence for regulons in rice?


Targeted SNP detection by Targeted SNP detection by EcoTILLINGEcoTILLING


EcoTILLING technique (Targeting Induced Local Lesions IN Genomes)

Adapted from Colbert et. al 2001. Plant Physiol. 126:480-484


Code GERMPLASM IRGC Accno VG Code GERMPLASM IRGC Accno VG1 CT9993-5-10-1-M jap/ind 48 TE-QING 81093 ind4 IR62266-42-6-2 ind/jap 3 VANDANA aus8 CAROLINA GOLD 1723 11 N 22 6264 aus

13 KALUKANTHA 7755 20 ARC 10177 12386 aus17 VARY VATO 462 10964 25 JAGLI BORO 27516 aus18 FANDRAPOTSY 104 10984 31 DULAR 32561 aus33 MILYANG 23 34393 jap/ind 44 AUS JOTA 66767 aus36 BLACK GORA 40275 24 ASWINA 26289 dp337 PACHOLINHA 50531 34 MATIA AMAN 53-13 37764 dp42 APO (IR55423-01) ind 47 RAYADA 77210 dp46 DEE-GEO-WOO-GEN 123 ind 12 BASMATI 370 6426 aro

10 SINTANE DIOFOR 5418 ind 42 CHHOTE DHAN 58930 aro14 KUN MIN TSIEH HUNAN 8195 ind 30 TCHAMPA 32368 aro15 RTS 12 8234 ind 5 IR60080-46A trop jap16 POKKALI 8948 ind 7 AZUCENA 328 trop jap23 KHAO KAP XANG 23423 ind 9 IGUAPE CATETO 4122 jap26 KHAO DAWK MALI 105 27748 ind 19 MOROBEREKAN 12048 trop jap27 LEUANG PRATEW 27762 ind 21 NIPPON-BARE 12731 temp jap32 PETA 32571 ind 22 YANCAOUSSA 16071 trop jap35 IR36 39292 ind 28 RATHAL 31524 jap38 BALA 50927 ind 29 GERDEH 32301 temp jap39 CO 39 51231 ind 40 CHODONGJI 55471 temp jap45 IR64 66970 ind 41 PATBYEO 55607 temp jap46 JING XI 17 67676 ind 43 LEMONT 66756 temp jap

Oryza sativa panel

Extended to 1536 O. sativa (mini-core) and 190 O. glaberrima accessions


We retrieve the gene model sequence, then design primers to tile across the gene structure

… so all putative functional SNPs can be found


Gene Bioprocess Functional Evidence Primer_Name Primer_sequence AmpliconDREB2upL ACgTCAAAACCCAACCCCAACCATDREB2upR gATTgAATCAgggCCATCgCTTTTC

DREB2-16cdL CCgTTgATTgCTgATAgCCTCCTTgADREB2-16cdR TgAAATATTCCTATTgACCCgCAgCA

EREBP-137cdL ACACCCAAACCCAACCTCCCAAAACEREBP-137cdR CATCgCCggCATgATCTgTTCTAAT

TPPL ggCACACTgTCgCCTATTgTggATg

TPPR gTTTACgAgCCgTgCgACCAgTTTC

TPP5082uxL CgAgCACACACACTCACTCCCTgTC

TPP5082uxR gTTggAgCTTggTCCCTTgATgTCCCG18upL CCATggCAgTTgCCCTATTgTTCACCG18upR CTCAgCAggCTgCgACATCTTCACT

CG18-1L ggCATgCTTgATTTgggACATgAgACG18-1R TCTCCTCATACCTCTCggCCTgCTC

CG18-cd2L TgAgCTTTCCCgTgAggAgAATgTgCG18-cd2R CAAgATTgCAAgCACggTCAggAgA

MAPk-1L CACCATCTCCTTCAgCCTCCgTTTCMAPk-1R CACACCTCCACCCCAATCAAATTCC

MAPk-2L ATgggCATgAAgATgAgCCTCTTggMAPk-2R gggTgCCggCTATggTACgCTAgAT

Suc-L CgCTCAgCgAgTgCgTgAgACTATTSuc-R ggCgATTgTACACTgCACATgATgg

Bzip-L ATggCATCggAgATgAgCAAgAACgBzip-R TTCggAgCAACATgTgTACCTgCTTTADFCH02aL CCTCCTTgCAgggCCTgTCTgTADFCH02aR TggCAgTgAggCATTCTCATAAAA

ADFCH02bL AAggAACAATgCTgCTTTTggTCAADFCH02bR ACCATATgACgCCACggCCTTT

repressor of ABA induced response

Transformation of homologue results ienhanced survival under stress

drought responsive element binding

protein 2DREB2

ERF4 AP2 domain TF 984

947

998

ADF

988

969

TPPTrehalose 6-phosphate

phosphatase

Up-regulated under drought on IR64 panicle arrays; 2 loci on CH2 are best

top hits to E.coli sequence that on transformation enhances survival

14-3-3Membrane

associated signal cascade

MAPKTranscriptional control of stress

response

838

975

1006

1009

1005

BZIP Transcriptional control

SucSase sucrose synthase

954up-regulated in proteomics of drought

QTL on CH7 is for leaf rollingActin depolymerizing

factor878

991

1004

EcoTILLING Primers for Stress Tolerance Candidate GenesEcoTILLING Primers for Stress Tolerance Candidate Genes

Extended to other loci


IR700 IR800

ERF4 upstream primers on 96 Oryza sativa contrasted to IR64

H. Wang


NipponbareNip control

IR64Azucena

Sintane DioforKalukantha

KDML 105Leung PratewGundil Kuning

Bat DoDudkhan

Tres MesesYebawyin

RopaSampatti

ARC 15530Dudh Kadar

MimidimGoo Meunag

NahCI 7387-1

Kawin 2ARC 14435

PawhtunBinuhangin

AedalArc 15589

BadoAvo 742

Sequence of Representative Sequence of Representative SNPsSNPs in ERF4 in ERF4 (Ch1 137cM) (Ch1 137cM) cdscds

H. Wang


NipponbareNip control

IR64Azucena

Sintane DioforKalukantha

KDML 105Leung PratewGundil Kuning

Bat DoDudkhan

Tres MesesYebawyin

RopaSampatti

ARC 15530Dudh Kadar

MimidimGoo Meunag

NahCI 7387-1

Kawin 2ARC 14435

PawhtunBinuhangin

AedalArc 15589

BadoAvo 742

Translation of regionsTranslation of regions

H. Wang


700 Channel 800 Channel

LI-COR

Samples VS. IR64

Samples vs. IR64 Samples vs. NB Samples vs. IR64 Samples vs. NB

samples VS. NB

Agarose

LiCorTrehalose-6-phosphate phosphatase (Chr.2, locus a)


Actin depolymerizing factor (Chr. 2, locus a)700 Channel LiCor

Samples VS. IR64 Samples VS. NB

800 Channel


Agarose

53ADONIS, March 4, 2009Samples VS. IR64 Samples VS. NB

700 Channel 800 ChannelLiCor


Mitogen activated protein kinase (MAPK, Chr. 7)

Agarose


This low-cost SNP detection offers a simple way to track specific genes in breeding pedigrees and segregating populations.

Application in Breeding LinesApplication in Breeding Lines

Detecting parental allele carried by a BC3F3 line for MYB1.P1- SHZ-2 and P2- TXZ-13 (indicas), B-BC3F3 line and Az-Azucena (japonica).SNPs were detected between the B and SHZ2 (indicating the allele carried by B for MYB1 was of TXZ-13 type); SHZ-2 and TXZ–13; and also SHZ-2 and Az. The full length product is 2.3 kb.

B B+P1 B+P2 P1 P2 P1+P2 Az Az+P1

C. Raghavan/L. Bin


Parameter SNP detection on agarose gel

SNP detection using the LiCor Genotyper

DNA Pooling 2 per contrast for EcoTILLING; 8 per pool for TILLING

2 per pool for EcoTILLING; 8 per pool for TILLING

PCR primer Unlabeled Labeled (IRD 700 and 800)

Amplicon size 2.0-3.0 kb 1.2-1.5 kb

Post-CEL I cleavage clean up

None Removal of excess salt and concentrating the sample required

Gel Analysis Agarose / ethidiumbromide

Polyacrylamide gel / LiCorgenotyper

Overall time for electrophoresis

2 hours 5 hours

Resolution of closely spaced SNPs

May not be distinguishable; hence having single amplicon is critical

Distinguishable, less critical to have single amplicon

Relative cost Low High

Properties of the agarose vs. LiCor systems


Mismatch PatternsPrimer No. of putative mismatch patterns

O. sativa O. glaberrima AA genome wild species

vs. IR 64 vs. NB vs. CG14 vs. NB vs. IR 64 vs. NB

ERF4 7 4 11 16

DREB2 6 6 1 2

MAPK2 5 5 2 3 7 11

TPP 7 7 26 26

TPP5082a 2 4

BZIP 2 4 2 3 10 9

EXT 4 6

SUC 8 5

ADFCH2a 7 6 3 4 9 10

ADFCH2b 6 6


Haplotype/SNP Summary Gene Haplotypes SNPs % Transition % Transversion % Indels

TPP 14 17 52.9 29.4 17.6SUC 8 9 44.4 11.1 44.4DREB2 9 12 91.7 8.3 0.0ERF3 11 7 57.1 42.8 0.0BZIP 9 7 57.1 42.8 0.0ADF2b 9 8 75.0 25.0 0.0ADF2a 10 12 50.0 50.0 0.0MAPk2 7 11 72.3 18.2 9.1

1536 O. sativa


Conversion of Mismatches to SNPsAccession number

Haplotype

146 525 572 721 746 747 928 936 939

12731 (NB) 1 T C G A A A A - G76310 1 T C G A A A A - G32389 1 T C G A A A - - *66970 (IR64) 2 T C A G - - - T G25085 2 T C A G - - - T G28134 2 T C A G - - - T G43400 3 T C A G - - - T C19972 3 T C A G - - - T C64086 3 T C A G - - - T C6799 4 T C A G A A - T G33295 4 T C A G * * * T G33085 4 T C A G A A - T G4122 5 T C G G A A A - G50836 5 T C G G A A A - G61467 6 C C G G A A - - G66818 7 T Y A G - - - T G66808 7 T Y A G - - - T G66819 7 T T A G - - - T G61631 8 T C G G A A - T G66831 8 T C G G A A - - G21634 8 T C G G A A - T G6144 8 * C G G A - - T G27138 8 T C G G A - - T G

position relative to Chr7:25430634..25431638 (SUC)

12

73

1

73

61

0

32

38

9

66

97

0

25

08

5

28

13

4

43

40

0

19

97

2

64

08

6

67

99

33

29

5

33

08

5

41

22

50

83

6

61

46

7

66

81

8

66

80

8

66

81

9

61

63

1

66

83

1

21

63

4

61

44

27

13

8

1 1 1 2 2 2 3 3 3 4 4 4 5 5 6 7 7 7 8 8 8 8 8

SUC

vs. IR 64

vs. NB


EcoTILLING wild relatives


Code Species IRGC Accno Genome Code Species IRGC Accno GenomeMC1 O. barthii 105507 AA MC25 O. rufipogon 106523 AAMC2 O. barthii 104142 AA MC26 O. rufipogon 105948 AAMC3 O. barthii 104103 AA MC27 O. rufipogon 106157 AAMC4 O. barthii 105612 AA MC28 O. rufipogon 106386 AAMC5 O. barthii 104102 AA MC29 O. rufipogon 106515 AAMC6 O. glumaepatula 100894 AA MC30 O. alta 100161 CCDDMC7 O. glumaepatula 82034 AA MC31 O. australiensis 100882 EEMC8 O. glumaepatula 101960 AA MC32 O. eichingeri 105407 CCMC9 O. longistaminata 92636 AA MC33 O. eichingeri 105414 CCMC10 O. longistaminata 92623 AA MC34 O. grandiglumis 105144 CCDDMC11 O. longistaminata 92607 AA MC35 O. meyeriana 106474 GGMC12 O. meridionalis 103321 AA MC36 O. meyeriana/granulata 104989 GGMC13 O. meridionalis 105279 AA MC37 O. minuta 83825 BBCCMC14 O. meridionalis 105306 AA MC38 O. minuta 105130 BBCCMC15 O. nivara 104612 AA MC39 O. officinalis 80733 CCMC16 O. nivara 106051 AA MC40 O. officinalis 80755 CCMC17 O. nivara 106329 AA MC41 O. officinalis 100949 CCMC18 O. nivara 105740 AA MC42 O. punctata 89163 BBCCMC19 O. nivara 106041 AA MC43 O. punctata 105153 BBCCMC20 O. nivara 105386 AA MC44 O. rhizomatis 105443 CCMC21 O. rufipogon 81900 AA MC45 O. rhizomatis 105445 CCMC22 O. rufipogon 104624 AA MC46 O. ridleyi 100877 HHJJMC23 O. rufipogon 80781 AA MC47 O. schlechteri 82047 HHKKMC24 O. rufipogon 106169 AA Refs. O. sativa (IR64, Nipponbare) 66970, 12731 AA

Extended to 93 diverse Oryza spp. accessions.

Wild Oryza spp. panel (overlaps with GCP core AA genome wild)


IRGC Acc. Species name Origin IRGC Acc. Species name Origin IRGC Acc. Species name Origin100117 O. barthii Guinea 92659 O. longistaminata Mali 106102 O. nivara India100921 O. barthii Indiaia 100930 O. longistaminata Mali 106155 O. nivara Laos100934 O. barthii Mali 101206 O. longistaminata Benin 80541 O. rufipogon India101257 O. barthii Chad 101207 O. longistaminata Ivory Coast 80550 O. rufipogon India101937 O. barthii Senegal 104075 O. longistaminata Nigeria 80714 O. rufipogon India103591 O. barthii Cameroon 105206 O. longistaminata Ethiopia 81989 O. rufipogon Burma104061 O. barthii Nigeria 80432 O. nivara India 81996 O. rufipogon Papua New Guinea105613 O. barthii Botswana 80435 O. nivara India 82041 O. meridionalis Australia106013 O. barthii Mali 80545 O. nivara India 82988 O. rufipogon China106218 O. barthii Mali 80724 O. nivara Burma 100219 O. rufipogon Thailand82031 O. glumaepatula Brazil 80746 O. nivara Burma 100923 O. rufipogon Burma100924 O. glumaepatula Brazil 81845 O. nivara India 100926 O. rufipogon Burma100968 O. glumaepatula SUR 82028 O. nivara India 103848 O. rufipogon India82032 O. glumaepatula Brazil 86489 O. nivara Vietnam 100189 O. rufipogon Malaysia100970 O. glumaepatula Brazil 100195 O. nivara Burma 104602 O. rufipogon Sri Lanka100971 O. glumaepatula Brazil 100593 O. nivara Taiwan 104624 O. rufipogon China101960 O. glumaepatula Brazil 101978 O. nivara India 104657 O. rufipogon Thailand105465 O. glumaepatula French Guyana 102171 O. nivara India 105250 O. rufipogon Thailand82033 O. glumaepatula Brazil 103306 O. nivara Taiwan 105487 O. rufipogon Thailand82034 O. glumaepatula Brazil 103821 O. nivara China 105491 O. rufipogon Malaysia105668 O. glumaepatula Brazil 103824 O. nivara China 105696 O. rufipogon Nepal86537 O. meridionalis Australia 103838 O. nivara Bangladesh 105709 O. rufipogon India101145 O. meridionalis Australia 104650 O. nivara Thailand 105726 O. rufipogon Cambodia104085 O. meridionalis Australia 105391 O. nivara Thailand 105890 O. rufipogon Bangladesh105289 O. meridionalis Australia 105343 O. nivara India 106027 O. rufipogon Thailand105291 O. meridionalis Australia 105428 O. nivara Sri Lanka 106144 O. rufipogon India105302 O. meridionalis Australia 105704 O. nivara Nepal 104057 O. rufipogon China81951 O. longistaminata Zimbabwe 105706 O. nivara Nepal 106163 O. rufipogon Laos83800 O. longistaminata Chad 105717 O. nivara Cambodia 106420 O. rufipogon Vietnam86485 O. longistaminata Botswana 105785 O. nivara Thailand 106453 O. rufipogon Indonesia89161 O. longistaminata Mozambique 105812 O. nivara Thailand 106523 O. rufipogon Papua New Guinea92635 O. longistaminata Mali 105879 O. nivara Bangladesh 66970, 12731 O. sativa (IR64, NB) Philippines, Japan

Extended AA genome Oryza panel


Amplicon production in wild Oryza species

tpp5082a

AA genome species

CCDD EE FF, GG, HHJJ, HHKK

AA BB BBCC CC CCDDAA BB BBCC CC CCDD

CCDD EE, FF, GG, HHJJ, HHKK AA

AA genome species

AA genome species

AA genome species

mapk1


Species Genome CG18_1 CG18_2 TPP TPP5082a TPP4712a ADF2a ADF2b DREB2 ERF3 DREB2 UP MAPK1 MAPK2

O. barthii AA √ √ √ √ √ √ √ √ √ √ √ √

O. glaberrima AA √ √ √ √ X √ √ √ √ √ √ √

O. glumaepatula AA √ √ √ √ X √ √ √ √ √ √ √

O. longistaminataAA √ √ √ √ √ √ √ √ √ √ √ √

O. meridionalis AA * √ √ √ X √ √ √ √ √ √ √

O. nivara AA √ √ √ √ √ √ √ √ √ √ √ √

O. rufipogon AA √ √ √ √ √ √ √ √ √ √ √ √

O. punctata (2x) BB √ √ X √ X √ √ X X X √ √

O. minuta BBCC √ √ X √ X √ √ √ X X √ √

O. officinalis (4x)BBCC √ √ X √ X √ √ √ √ X √ √

O. punctata (4x) BBCC √ √ X √ X √ √ √ √ X √ √

O. eichingeri CC √ √ X √ X √ √ √ √ X X √

O. officinalis (2x)CC √ √ X √ X √ √ √ √ X X √

O. rhizomatis CC √ √ X √ X √ √ √ X X X √

O. alta CCDD √ √ √ √ X √ √ √ √ X √ √

O. grandiglumis CCDD √ √ √ √ X √ √ √ √ X √ √

O. latifolia CCDD √ √ √ √ X √ √ √ √ X √ √

O. australiensis EE √ √ √ √ X √ √ X √ X √ √

O. brachyantha FF √ √ X X X √ X X X X √ √

O. granulata GG X √ √ X X X √ X X X √ √

O. longiglumis HHJJ √ √ √ X X X X X X X X X

O. ridleyi HHJJ √ √ √ X X X X X X X X √

O. schlechteri HHKK X X X X X X X X X X X X

Amplicons produced in wild species using primers for drought candidate genes


Actin depolymerizing factor (ADFCH02a) WILD ORYZA vs. NB700 channel

AA genome spp. non-AA genome spp.

800 channel

AA genome spp.non-AA genome spp.


100593 N

80550 R

106163 R

80541 R

106144 R

104057 R

103848 R

104624 R

82028 N

105343 N

80545 N

80432 N

80435 N

106102 N

80714 R

101978 N

105879 N

106420 R

106027 R

105250 R

81996 R

105704 N

102171 N

105706 N

81845 N

106523 R

106453 R

105709 R

105428 N

105487 R

100189 R

86489 N

100195 N

104657 R

105465 G

100968 G

100924 G

105668 G

100970 G

82034 G

82032 G

82031 G

82033 G

100971 G

101960 G

104061 B

101257 B

106013 B

100921 B

103591 B

105613 B

101937 B

100117 B

106218 B

100934 B

92659 L

104075 L

92635 L

101207 L

105206 L

101206 L

89161 L

83800 L

86485 L

81951 L

100930 L

86537 M

104085 M

101145 M

82041 M

105289 M

105302 M

105291 M

106155 N

105812 N

105717 N

103306 N

80724 N

105491 R

105785 N

104650 N

105391 N

100923 R

100219 R

100926 R

80746 N

103821 N

103824 N

103838 N

81989 R

104602 R

105890 R

105726 R

82988 R

105696 R

100

70

46

32

39

35

28

28

37

96

52

56

42

88

86

60

43

39

34

25

9

10

30

84

68

67

54

40

48

48

36

25

81

76

76

68

68

53

60

56

55

49

47

46

46

43

41

40

39

36

34

32

32

29

26

25

25

24

24

21

20

20

19

19

16

15

14

14

14

13

13

11

8

6

6

5

4

3

3

2

1

1

1

0

0

O. glumaepatula

O. barthii

O. longistaminata

O. meridionalis

O. rufipogon / O. nivara


vs. IR 64

81845 N

105709 R

105428 N

105890 R

103306 N

103824 N

103821 N

105717 N

106155 N

105812 N

106027 R

100926 R

104057 R

80541 R

106144 R

106163 R

80550 R

81996 R

86489 N

100195 N

81989 R

105391 N

104650 N

105785 N

100219 R

105491 R

105696 R

82988 R

104657 R

105487 R

103848 R

106523 R

106453 R

105726 R

106420 R

106102 N

100923 R

103838 N

104602 R

105879 N

104624 R

80714 R

80432 N

80545 N

101978 N

80435 N

80746 N

80724 N

105343 N

100189 R

82028 N

100593 N

86537 M

101145 M

105289 M

104085 M

105302 M

105291 M

82041 M

105250 R

104075 L

101206 L

105206 L

101207 L

100930 L

86485 L

81951 L

83800 L

92659 L

92635 L

89161 L

106013 B

100921 B

101257 B

100934 B

100117 B

106218 B

103591 B

101937 B

104061 B

105613 B

105465 G

100970 G

100924 G

82034 G

82033 G

82032 G

82031 G

100968 G

100971 G

105668 G

101960 G

105704 N

102171 N

105706 N

98

58

54

94

79

66

38

29

24

21

17

48

85

82

66

65

58

46

54

38

33

68

79

71

57

38

29

45

36

26

24

19

65

62

60

61

60

59

57

48

28

44

43

38

42

40

38

35

31

34

27

27

26

24

22

21

19

18

17

17

17

14

14

13

13

12

9

8

6

6

6

6

5

5

4

2

1

0

0

0

0

0

0


O. meridionalis

O. longistaminata

O. barthii

O. glumaepatula

vs. NBM. Gamalinda/E. Naredo

Analysis of SNP mismatch patterns generated by EcoTilling at 7 loci on AA genome wild species


Replicated Replicated phenotypingphenotyping forfordroughtdrought


Phenotyping Drought Response in 1536 rice germplasm

• Scored traits:leaf rolling, leaf drying, relative water content, leaf conductivity, SPAD reading,

flowering date, maturity date, recovery, plant height, biomass, grain yield, etc.

• Green house test• 144 varieties selected from the first field test were re-screened in green house with 5 replicates, 5 plants per row.

• Field test•300 varieties have been screened in field with 3 replicates, 5 plants per row.•another set of 300 lines is being tested.

R. Lafitte/H. Wang

2004DS

2004WS


Lowland Drought Screening 2005DS 600 mini-core accessions, 30 days vegetative stage stress

Mudgo Plot 245Kun Min Tsieh Hunan Plot 280

G. Atlin


Managed Drought Screening

• Upland drought DS2005o 300 accessions (R. Lafitte/J. Cairns)

• Upland drought DS2006o 900 accessions (R. Serraj/J. Cairns)

• Lowland drought DS2006o 900 accessions (G. Atlin)

• Upland/Lowland drought DS2007o 300 accessions (J. Cairns/R. Serraj/A. Kumar)


HaplotypeHaplotype Association Association


hap4

hap2hap5hap3

hap1

hap6

hap8

hap7 hap9

999

998

869595

38

1

10

7

7

5

0 1

2 2

4

12

1

japonicaaromatic

aus/boro

indica

Bootstrap valuesStep values

ERF4 haplotype groups31 sites from 1800 bp (up+CDS)Maximum Parsimony (Phylip 3.65)

node1 CCCCCCTCACGTRAGTA-GGCTTC-CTG--Gnode1_node2 CCCCCCTCACGTGAGTA-GGCTTC-CTG--Gnode2_node3 CCCCCCCCGCGTGAGAA-GGCTCC-TTGTTGnode3_node4 TCCCCCCCGTTTGTGAA-GGCTCC-TAGTTGnode4_node5 TCCCCCCCGTTTGTGAAAGGCTCC-TAGTTGnode5_node6 TCCCCCCCGTTTGTGAAAGGCTCC-TAGTTGnode6_hap1n TCCCCCCCGTTTGTGAAAGGCTCC-TAGTTG

hap1n TCCCCCCCGTTTGTGAAAGGCTCC-TAGTTGnode6_hap5n TCCCCTCTGTTTGTGAAAGGCTCC-TAGTTG

hap5n TCCCCTCTGTTTGTGAAAGGCTCC-TAGTTGnode5_hap3n TCCCCCCCGTTTGTGAAAGGCGCT-TAGTTG

hap3n TCCCCCCCGTTTGTGAAAGGCGCT-TAGTTGnode4_node7 TCTCCCCCGTTTGTG-A-CGCTCCTTAGTTGnode7_hap4n TCTCCCCCGTTTGTG-A-CGCGCTTTAGTTG

hap4n TCTCCCCCGTTTGTG-A-CGCGCTTTAGTTGnode7_hap2n TCTCCCCCGTTTGTG-A-CGCTCCTTAATTG

hap2n TCTCCCCCGTTTGTG-A-CGCTCCTTAATTGnode3_hap6n CCCTTCCCGCGAGAAAA-G-GTCC-TTGTTT

hap6n CCCTTCCCGCGAGAAAA-G-GTCC-TTGTTTnode2_hap7n CCCCCCTCACGTGAGTA-GGCTTC-CTG--G

hap7n CCCCCCTCACGTGAGTA-GGCTTC-CTG--Gnode2_hap8n CTCCCCTCACGTGAGTA-GGCTTC-CTG--G

hap8n CTCCCCTCACGTGAGTA-GGCTTC-CTG--Gnode1_hap9n CCCCCCTCACGTAAGTA-GGCTTC-CTG--G

hap9n CCCCCCTCACGTAAGTA-GGCTTC-CTG--G

H. Wang


Haplotype 1 2 3 4 5 6 7 8 91 (indica) 0.00

2 0.29 0.00

3 0.11 0.40 0.00

4 0.34 0.17 0.23 0.00

5 0.11 0.46 0.23 0.46 0.00

6 (aus) 0.69 0.92 0.80 0.92 0.80 0.00

7 (jap/aro) 0.52 0.69 0.69 0.75 0.69 0.63 0.00

8 0.75 0.75 0.75 0.80 0.75 0.69 0.06 0.00

9 0.75 0.75 0.75 0.80 0.75 0.69 0.06 0.11 0.00

aSNP freq% = (SNPs between haplotypes) / (1742 bp for ERF4 region) X 100

SNP frequenciesa between haplotypes


Yield Stability

0

2

4

6

8

10

1 2 5 7 8 9

Haplotype

yiel

d de

clin

e (g

/hill

)

04DS

05DS

Indica subgroup Japonica subgroup

b

d

e

aA

B

Association of ERF4 haplotypes with Yield

Yield decline in the 2004 and 2005 dry season. Letters a, b indicate significant differences between haplotypes within the indica group; Letters d, e indicate significant differences between haplotypes within the japonica group; The letter case differentiates the data obtained from different seasons; bars without letters are not significantly different from the others within the specific subgroup at P=0.05.

H. Wang/R. Lafitte/J. Cairns


Changes of putativ e ci s-actin g factors a Haplotypes Regio n Insertio ns Deletio ns 1 2 3 4 5 6

IBOXCORE , G ATABOX -300ELEMENT , GT1GMSC AM4 + + + + + -

NAPINM OTIFBN GTGAN TG10 - + - + - - - GT1M OTIFPSRBCS - - - - - +

POLLEN 1LE LAT52 , SEBFCO NSSTPR10A ,

TGTCACACMC UCUMISIN

GT1CONSENSUS , ASF1M OTIFCAMV - - - - - +

POLASIG2 INRNTP SADB , CA ATBO X1 - - - - + -

CC A ATBOX1 INRNTP SADB + + + + + + CARGCW8GAT, CARGCW8GAT

- - - - - + -

CARGCW8GAT, CARGCW8GAT

MA RTBOX , RO OTM OTI FTAPOX1 + + + + + +

DOFCOREZM PALBOXAPC + + + + + - RO OTM OTI FTAPOX1 NODCON1GM ,

OSE1RO OTNODULE , DOFCOREZM ,

POLASIG1

- - - - - +

- SEF4M OTIFGM7S , ARR 1AT + + + + + -

MA RTBOX - + - + - + - WBO XATNPR1 ,

WRKY71OS GTGAN TG10 - - - - - +

- GT1GMSC AM4 , GT1CONSENSUS + + + + + +

5’ non -

co d ing region

GT1GMSC AM4 , GT1CONSENSUS

- - - + + - -

DOFCOREZM , NODCON2GM ,

OSE2RO OTNODULE

- + + + + + + 3’ UTR

- SEF4M OTIFGM7S - - - - - +

Changes in putative cis-elements in ERF4 (between haplotypes and japonica)

H. Wang


Summary

• Defined population structure for representative collection

• Identified candidate genes• Implemented targeted SNP detection by

EcoTILLING• Increased throughput by optimizing agarose-based

detection• Replicated phenotyping undertaken for vegetative

stage drought• Applying association tests for drought traits to

verified haplotypes


Ma. Elizabeth Naredo Sheila Mae Quilloy Xiangqian ZhaoMario Rodriguez Michael Gamalinda Belinda CaspilloJanice Rayco Joseph Ramos Jessica ReyMillicent Sanciangco Alicia Perez Minerva MacatangayReneeliza Melgar Reflinur Basyirin Rosalyn Angeles Jeffrey Detras Genelou Atienza Rhodesia ManzanoMerlyn Rodonzo Vanica Lacorte Maricris ZaidemMonique Barile Gilbert Mamiit Leanilyn CastanarJyoti Kaur Hehe Wang Htay Htay AungChay Bounphanousay Eric Canicosa *Amita Juliano, RIP*Don Pabale Catherine Aquino Jeffe CadionAnselme Fournier Zoe Lawson Xie YongOlivia Listanco

N.R. Sackville Hamilton (GRC) Ma. Celeste Banaticla Ma. Socorro AlmazanFlora de Guzman Renato Reano Teresita SantosHei Leung (PBGB) Bin Liu Chitra RaghavanFulin Qiu Gary Atlin Arvind KumarR. Lafitte (CESD) Rachid Serraj Jill Cairns

EcoTillersAcknowledgementsAcknowledgements

CIRAD: L. Benoit R. Rivallan B. Courtois C. BillotCIAT: A. Garavito M. Lorieux C.P. MartinezEMBRAPA: T. Borba R.V. Brondani C. BrondaniWARDA: M. Cissoko M.-N. NdjiondjopCornell: A. Famoso S.R. McCouchCAAS: L.Z. Han Y.Y. Zhang

GCP SP1

Ken McNally - ITQBsaibo/ADONIS/talks/Ken... · 3/4/2009 · Ken McNally Current position: •...

Documents

Transcript of Ken McNally - ITQBsaibo/ADONIS/talks/Ken... · 3/4/2009 · Ken McNally Current position: •...