Putting omics tools to work - WUR

Putting omics tools to work

Wilco Ligterink

Unraveling the Complex Trait of

Seed Quality

by Genetical Genomics

Seed Quality – a complex trait

Seed performance

• germination potential

• seedling establishment

Biotic Stress

Environment

Post Harvesttreatments

Genetic potentialGenetic potential

Harvest

Storage

Biotic Stress

Environment


Harvest

Storage

Ronny Joosen



Seed performance



Biotic Stress

Environment



Harvest

Storage

Biotic Stress

Environment


Harvest

Storage

Noorulah Khan



Seed performance



Biotic Stress

Environment



Harvest

Storage

Biotic Stress

Environment


Harvest

Storage

This talk


Germination potential

QTL analysis

Gene identification

Natural variation: 101 lines S.lycopersicum x S. pimpinellifolium RIL population

Using the power of Physiological Genetical Genomics

AR

Germination potential Metabolomics Transcriptomics

Network reconstruction

QTL analysis

Gene identification



AR 6h

Recombinant Inbred Line (RIL) population of

Solanum lycopersicum (Moneymaker) x Solanum pimpinellifolium

101 lines – F8Genotyping in F7 with 323 AFLP and 69 RFLP markers

Natural variation; a great genetic resource

Natural variation reflects genetic variation important for adaptation to specific conditions

Seedling morphology Normal seedlings

Shoot sizeRoot sizeHypocotyl sizeRoot growth rateRoot architectureUsable plants

Seed longivityGermination after controlled detoriationGermination after aging

Reserve foodSeed size and seed mass

Seed priming

Embryo architectureX-ray imaging

Germination (potential)Germination under normal conditionsGermination under osmotic stressGermination under salt stressGermination under temperature stressGermination under oxidative stressDormancy (after-ripened vs. fresh seeds)

Physiological parameters

= Gmaxu7525

100% -

75% -

50% -

25% -

time

t50

Variation in RIL population (for germination under osmotic stress)

gMAX (%)

0%

20%

40%

60%

80%

100%

9526

495

298

9523

695

244

9523

295

237

9528

695

303

9529

395

217

9522

195

235

9520

895

256

9528

095

300

9524

395

302

9525

995

223

9520

795

272

9522

595

282

9526

595

216

9524

695

291

9521

195

248

9528

995

254

9525

195

229

9520

5

See poster Rashid Kazmi


0%

20%

40%

60%

80%

100%

9526

4

9529

8

9523

6

9524

4

9523

2

9523

7

9528

6

9530

3

9529

3

9521

7

9522

1

9523

5

9520

8

9525

6

9528

0

9530

0

9524

3

9530

2

9525

9

9522

3

9520

7

9527

2

9522

5

9528

2

9526

5

9521

6

9524

6

9529

1

9521

1

9524

8

9528

9

9525

4

9525

1

9522

9

9520

5

PEG, -0.5 MPaPEG, -0.3 MPaNaCl, -0.5 MPaNaCl, -0.3 MPa

36°C12°C

H2O2, 300mM Poster of Rashid Kazmi

See poster Rashid Kazmi

H2O2, 300mM12°C36°C

NaCl, -0.3 MPaPEG, -0.3 MPaPEG, -0.5 MPaNaCl, -0.5 MPa

36°C>GA germ%36°C>dead germ%H2O2 300mM u75253e FreshRoot3e DryRtootFreshShoot

3e FreshShoot3e DryShoot3e SeedSize3e SeedLength3e SeedWeightImbSeedsSizeSeedLengthSeedWeight

PEG-0.5 u7525ImbSeedLength36°C>25°C germ%ImbSeedCircHypoLength

3e SeedCircPEG-0.3 t50PEG-0.3 u7525

3e SeedYieldH2O2 300mM germ%H2O2 300mM t50H2O2 300mM AUC2e Strat germ%NaCl-0.3 germ%12°C germ%3e Strat germ%Fresh germ%Strat germ%

36°C u7525

Fresh u7525Strat u75252e Strat u75252e strat AUC2e Strat t50NaCl-0.3 t50NaCl-0.3 AUCNaCl-0.5 t50NaCl-0.5 AUCPEG-0.5 t50PEG-0.5 AUCPEG-0.3 germ%PEG-0.3 AUCNaCl-0.5 germ%PEG-0.5 germ%

35°C u7525

35°C germ%36°C germ%NaCl-0.5 u7525NaCl-0.3 u752512°C u7525

3e Strat u75253e Strat t503e Strat AUC

DryShootFreshRootDryRoot

SeedSize

SeedCirc

SeedYield

36°C t50

Fresh AUCFresh t50Strat t50Strat AUC

36°C AUC

35°C t5035°C AUC

12°C t5012°C AUC

19°C t5019°C u7525

LinesTraits

+


0%

20%

40%

60%

80%

100%

9526

4

9529

8

9523

6

9524

4

9523

2

9523

7

9528

6

9530

3

9529

3

9521

7

9522

1

9523

5

9520

8

9525

6

9528

0

9530

0

9524

3

9530

2

9525

9

9522

3

9520

7

9527

2

9522

5

9528

2

9526

5

9521

6

9524

6

9529

1

9521

1

9524

8

9528

9

9525

4

9525

1

9522

9

9520

5

X

-3

-2

-1

0

1

2

3

1 2 3 97 8 10 11 124 5 6

0

1

2

3 1 2 3 97 8 10 11 124 5 6

Money

Pimp

MoneyPimp

1 2 3 97 8 10 11 124 5 6

MoneyPimpGlobal overview of QTLs found for different traits

36>25_germ%36°C_germ%36°C_AUCCircularityHLCirc12°C_t5012°C_AUC12°C_germ%12°C_u752535°C_germ%t50Seedlingu7525SeedlingPEG-0.3_germ%NaCl-0.3_germ%NaCl-0.3_u752535°C_u7525NaCl-0.5_u75252eStrat_u75252estrat_AUC2eStrat_t50NaCl-0.3_t50NaCl-0.3_AUCPEG-0.5_t50PEG-0.5_AUCPEG-0.3_AUCNaCl-0.5_t50NaCl-0.5_AUC35°C_t5035°C_AUCFresh_AUCFresh_t50Strat_t50Strat_AUCFresh_u7525Strat_u7525NaCl-0.5_germ%PEG-0.5_germ%36°C_t50PEG-0.3_t5036°C_u7525PEG-0.3_u7525Fresh_germ%LRdens_42eStrat_t502eStrat_u7525Strat_germ%2eStrat_germ%YieldH2O2_300mM_germ%H2O2_300mM_t50H2O2_300mM_AUC36>GA_germ%36>dead_germ%PEG-0.5_u7525FerretFrRtWtDRtWtAreaSeedWeightSWLengthFreshShFrShWtDryShDShWtSSH2O2_300mM_u7525LRnr_4LRnr_5FreshRtDryRtTRS_4TRS_5MRL_4MRL_5

N r


QTL analysis

Gene identification



AR 6h

Transcriptomics : eQTLProteomics : pQTLMetabolomics: mQTL

Omics data can be treated like phenotypic data, The transcript, protein or metabolite amounts can be used to map a QTL position Perform an omics analysis for all individual RIL lines in the population

Linkage between phenotypic QTL and omics-QTL can help to dissect the genetics of complex traits

Genetical genomics – principle of omics-QTL mapping

Affymetrix pepper tiling array

Environment 100 RIL lines

Standard eQTL approach:

Li, 2008. Trends in Genetics. 24

Option 1) 2 different environments, reduced number of RIL lines

50 RILs

Option 2) 2 different environments, 2 different sets of selected RIL lines

50 RILs

50 RILs

eQTL studies are expensive because of the large amount of microarray hybridizations

Maximize the amount of information that can be extracted from a single experiment

Generalizing Genetical Genomics – a new eQTL approach

A B

Expr

essi

on

N=100

Li, 2008. Trends in Genetics. 24

All RILs

Condition 1 50 linesdry fresh seeds

Condition 2 50 lines6 hrs imbibed seeds

Generalizing Genetical Genomics – a new eQTL approach

All RILs

sophisticated pools

A B

Expr

essi

on

N=100

Generalizing Genetical Genomics – the statistics

Classical traitsYi = Genoi + Error

Metabolites in 2 conditionsYi = Envi + Genoi + Genoi:Envi + Error

mQTL study : Analyse of primary metabolite profiles by GC-TOF MS

Dry6H

Results:4725 Mass peaks163 Centrotypes / Metabolites54 identified

Generalizing Genetical Genomics – testing the concept

-20

-15

-10

-5

0

5

10

15

20

LOD

sco

re

Genetic effectGen x Env effect

GCMS metabolite RI_2854: main effect QTL

-5

-4

-3

-2

-1

0

1

2

3

4

5

LOD

scor

e

Genetic effectGen x Env effect

GCMS metabolite RI_1135: Gen x Env interaction effect QTL

GCMS datagenetic effects

Money PimpMoneyPimp

RI_3434RI_1507RI_1616RI_1755RI_1501RI_1236GlutamineRI_2672RI_2956RI_2804SucroseRI_3584RI_2542RI_2801RI_1180RI_2394RI_2939RI_2442RI_2572RI_1086RI_2238RI_1494RI_3292RI_3011RI_1914RI_1940RI_1463RI_1912RI_1939GlucarateNicotinateRI_2776RI_1126OxalateRI_968RI_1601HydrouracilRI_968_2RI_1166RI_1735Quinic acidRI_1241Phosphoric AcidRI_1423RI_1239RI_2381RI_2404RI_2669RI_2668HydroxalimineRI_973RI_1122RI_959RI_984MonomethylphosphateRI_2196MalateHydoxytryptamineRI_1947RI_2974RI_3293RI_980RI_1379HypotaurineRI_1868GalactarateRI_1858HexitolRI_1772RI_1957RI_16442-Hydroxyisobutyric acidPentonateGlycolic AcidGlycerateErythronic acid SuccinateCitric acidRI_1960RI_1795RI_1801GlucoseFructoseSorboseRI_3227Threonic AcidRI_1568RI_992RI_1718RI_2351Di galactosylglycerolRI_2955Myo inositolRI_2953MelibioseRI_2898Glycerol-3-phosphateGuanosineRI_1909RI_2979RI_1463_2RI_2087RI_2336RI_1703RI_1279RI_1282RI_2863RI_1135Palmitic AcidOctadecadienoic AcidRI_2213RI_967RI_1807ValineRI_1263Iso Leucine (ile)ProlineMethionioneRI_2365RI_2470RI_989RI_1153Pyroglutamic acidRI_2392BenzoateRI_2210RI_2210_2FumarateTyrosineRI_1022EthanolamineGlycineRI_1113ThreonineAlanineSerineRI_2866GABAN-acetylglutamic acidGlutamateRI_1975RI_3012Glucose-6-phosphateRI_997RI_1008RI_2854PhenylalalineRI_2490RI_3449RI_2491RI_2561RI_3413RI_1157RI_1421RI_2458RI_2436RI_2692GluconateAllantoinRI_2071Aspartic acidAsparagine

QTL locations across the genome

Guanosine Guanosine

Guanosine

GlutamineGlutamic AcidN-acetylglutamic acidEthanolamineGaba

Oxalic acidHydoxytryptamine

Di galactosylglycerolPhenylalanineAlanine

High temperature stress

t50 + AUC stress, seed weight

t50 + AUC and high temperature stress


Network reconstruction

QTL analysis

Gene identification



AR 6h

AcknowledgementsPromotors

Linus van der Plas – WUR (RJ)

Harro Bouwmeester – WUR (RK, NK)

Advice

Maarten Koornneef - MPI Cologne

Joost Keurentjes – WUR

Statistics

Danny Arends

Yang Li

Ritsert Jansen – RUG

Microarray analysis

Nicholas Provart – Toronto

Mapping populations

Sjaak van Heusden – PRI

Olivier Loudet - INRA

www.wageningenseedlab.nl

AcknowledgementsPromotors

Linus van der Plas – WUR (RJ)

Harro Bouwmeester – WUR (RK, NK)

Advice

Maarten Koornneef - MPI Cologne

Joost Keurentjes – WUR

Statistics

Danny Arends

Yang Li

Ritsert Jansen – RUG

Microarray analysis

Nicholas Provart – Toronto

Mapping populations

Sjaak van Heusden – PRI

Olivier Loudet - INRA

www.wageningenseedlab.nl

Leo Willems

Henk Hilhorst

Ronny JoosenNoorulah Khan Rashid Kazmi

Putting omics tools to work - WUR

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