monogram - wgin · cereals, and grasses, cf: ... c4 grasses – brachypodium ... – the adaptation...

1111thth October 2006October 2006 11

MONOGRAMTina Barsby

November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 22

What is MONOGRAM?UK focus for international efforts in small grain cereals, and grasses, cf: (Tritigen, ETGI, ITMI).

A single integrated programme of research, aligning resources across four Institutes.

Pooling resources and expertise.

Linking with Universities.

Managed by Tina Barsby, and a steering group:

– JIC – Graham Moore– RRes – Andy Phillips– SCRI – Robbie Waugh– IGER – Ian King


Species and TopicsSpecies : – Wheat, Barley etc.– Grasses (Lolium, Festuca) with strong links to

C4 grasses– Brachypodium (model)

5 Working Groups:– I Germplasm and Markers– II Physical mapping– III Functional genomics– IV Gene Validation– V Bioinformatics


Working GroupsI Germplasm and Markers:– Molecular marker methods and their high-throughput

implementation and applicationsII Physical mapping:– Physical maps and generating contigs from ESTs, large-scale

sequencing, bioinformatics for assembly and annotation, exploitation of synteny

III Functional genomics:– Transcriptomics platforms, proteomics, metabolomics. – Systems approaches to data integration

IV Gene Validation:– Transformation, VIGS and TILLING

V Bioinformatics:– Integration of current resources and data curation– The adaptation and development of databases in order to

maximise uniformity and accessibility of available data sets. – Provision of tools to make data more readily available and

provision of training and support for users– Web site


WG I - Germplasm and Markers

Scoping

Potential Technologies and topics:–Molecular marker methods and their

high-throughput implementation and applications

–Cereal genetic diversity– Improving the accessibility of data sets

and genotypes held in different institutes to wider group of users


WG II -Physical mappingClear objectives. Most important area

Technologies and topics: – Physical maps and generating contigs from

ESTs– Large-scale sequencing– Bioinformatics for assembly and annotation– Exploitation of synteny

For wheat, engagement with international efforts through imminent funding bid(Ch5, 2??)


WG II -Physical mappingPhysical map is needed because it links the genetic recombinational data to the order of the actual physical chromosomesThereby facilitates access to gene sequences, new gene based markersA step on the way to a genome sequence– And breaks the problem down to a manageable

level. Identifies underlying BACs which can then be sequenced

– Speed and ease of access– Generation of new markers


WG III - Functional genomics

Technologies and topics :–Transcriptomics platforms, proteomics

and metabolomics–Systems approaches to data integration


WG IV - Gene Validation

Forward and reverse genetics tools

Technologies and topics : –Transient and stable transformation–VIGS, RNAi, T-DNA tagging–TILLING–(SNP association genetics)


WG V - BioinformaticsImportant role in map integration– Genetic/physical/gene expression data– Cross species

Technologies and topics : – Integration of current resources and data curation– The adaptation and development of databases in order

to maximise uniformity and accessibility of available data sets.

– Provision of tools to make data more readily available and provision of training and support for users

– Web site


Work package Wheat Barley Grasses Oats BrachypodiumI Germplasm and

Markers

II Physical mapping

III Functional genomics: transcriptomics, proteomics

IV VIGS, Tilling, transformation

V Bioinformatics

Building the picture:Identifying gaps & locating resources


Flagship projects

Likely themes:

– Physical mapsLimits the model to crop translation, and the isolation of genes following genetic mapping

– Genetic recombinationThe single factor which limits cereal and grass research and breedingLimits the practical utility of physical mapsA meeting to be planned to include wide interest groups


Exemplar projectsPossible themes to help demonstrate and upgrade resources:

– QualityDigestibility, starch structure, protein type and content, allergens, phytateand mineral content and availability, fibre content,

– Biotic and abiotic stressNovel disease resistances genes, durable resistance, mycotoxin reduction, genes in the defence response pathways, plant-microbe interactions and gene-for-gene interactionsGenetic components of stress tolerance such as drought, low temperature and salinity,


Developments

Working Groups formed and meetings heldSelection of Flagship projectsImproved accessibility to resourcesWebsite under constructionSmall Grain Cereals (SGC)– To be expanded to include grasses = Small

Grain Cereals and Grasses (SGCG) and come within Monogram, need bid for funding


WGIN and MonogramMonogram is key in the models to crops transitionWGIN is a vehicle for exploitation of Monogram outputsMonogram will work closely with WGIN– Selection of Flagship projects– Improved accessibility to resources

PrioritisationTilling

– Small Grain Cereals (SGCG)


Small Grain Cereals Network (SGC)– Application to extend funding– To be expanded to include grasses– Small Grain Cereals and Grasses (SGCG)– Has a steering committee representative of the wider

community, Institutes, Universities, Industry– This steering committee could be used to advise

Monogram.

1111thth October 2006October 2006 11

MONOGRAM and ETGI - COST Initiative

Tina Barsby


Species and topicsSpecies and topicsMonogram:

– Species : Small Grain cereals: Wheat, Barley, Oats.Grasses (Lolium, Festuca) with strong links to C4 grasses

– 5Working Groups:I Germplasm and

MarkersII Physical mappingIII Functional

genomicsIV Gene ValidationV Bioinformatics

COST/ETGI

– SpeciesWheat barley and rye

– 4 working groupsI Germplasm and

MarkersII Physical mappingIII Functional

genomicsIV Gene ValidationBioinformatics component included in each of the above groups.


Common Working GroupsI Germplasm and Markers:– Molecular marker methods and their high-throughput implementation

and applicationsII Physical mapping:– Physical maps and generating contigs from ESTs, large-scale

sequencing, bioinformatics for assembly and annotation, exploitation of synteny

III Functional genomics:– Transcriptomics platforms, proteomics, metabolomics. – Systems approaches to data integration

IV Gene Validation:– Transformation, VIGS and TILLING

Bioinformatics:– Integration of current resources and data curation– The adaptation and development of databases in order to maximise

uniformity and accessibility of available data sets. – Provision of tools to make data more readily available and provision of

training and support for users– Web site


UK RespondentsT. BrownK Edwards T. BarsbyS. BentleyA. GreenlandA.J. FlavellP. IsaacG. MooreW. HarwoodS. GriffithsK. Kanyuka

D. LeaderL. RamsayR. WaughI KingD. HabashG. KingH. JonesA. PhillipsK. Hammond KossackC. RawlingsP. Shewry


Main Objectives of COSTMain Objectives of COST

““Development of technology platforms Development of technology platforms and coordinating projects to identify and coordinating projects to identify and exploit qualitative and QTL alleles and exploit qualitative and QTL alleles for improving wheat barley and ryefor improving wheat barley and rye””Target areas:Target areas:–– Biotic stress resistance Biotic stress resistance –– AbioticAbiotic stress resistancestress resistance–– Harvest quality (incl. starch, protein, fibre, Harvest quality (incl. starch, protein, fibre,

carotenoidscarotenoids, , phytatephytate, gluten , gluten allergenicityallergenicity))–– Agronomic sustainabilityAgronomic sustainability–– Biomass conversionBiomass conversion


Secondary ObjectivesSecondary ObjectivesSurvey and database of existing and planned Survey and database of existing and planned TriticeaeTriticeae genomics research, genomics research, platforms and applications in Europe; production of a list and rplatforms and applications in Europe; production of a list and review eview publication of recommended developmentspublication of recommended developmentsDevelopment of new tools and platforms for Development of new tools and platforms for ““omicomic”” and and bioinformaticbioinformaticanalyses, portal for those recommended.analyses, portal for those recommended.Publications and database regarding new and efficient methods foPublications and database regarding new and efficient methods for linkage r linkage mapping and molecular breedingmapping and molecular breedingCollaborative development of comparative genomics for cereals anCollaborative development of comparative genomics for cereals and d grasses; joint publications and a database.grasses; joint publications and a database.Coordinated development and collaborative application of highCoordinated development and collaborative application of high--resolution resolution mapping populations in the mapping populations in the TriticeaeTriticeae; a publication and database of ; a publication and database of existing and planned populations.existing and planned populations.Coordinated transfer of knowCoordinated transfer of know--how and tools needed to manage, maintain, how and tools needed to manage, maintain, and exploit natural genetic diversity; production of a handbook and exploit natural genetic diversity; production of a handbook on stateon state--ofof--thethe--art methods.art methods.Coordination on highCoordination on high--throughput phenotyping facilities for effective throughput phenotyping facilities for effective association geneticsassociation geneticsDevelopment of a framework for Development of a framework for TriticeaeTriticeae physical mapping.physical mapping.TriticeaeTriticeae Genomics edited book [under separately applied ESF funding ]Genomics edited book [under separately applied ESF funding ]


ITMI Monogram ETGI / COSTCore collections – Reference sets of germplasm markers and phenotypes

I Germplasm and Markers Tools for Assessing and Harvesting Genetic DiversityPeter Isaac, Wayne Powell and Simon GriffithsManchester, SCRI, JIC, NIAB, IGER

Recombinational analysis – Past, present and future genetic studies– Linkage maps, marker trait associations, QTL and mapping populations

Physical analysis – Long range sequencing, physical maps, radiation hybrid mapping – Includes IGROW

II Physical mapping Accessing the Physical Genome for Sustainability and QualityIan King, Robbie WaughSCRI, JIC, IGER

Expression profiling – RNA (microarrays), proteins and metabolites

III Functional genomics: transcriptomics, proteomics

Implementation of Genomics Approaches for Understanding Cereal TraitsPeter Shewry, Robbie WaughIGER, SCRI, JIC, RRES, Bristol

Functional analysis – Transformation, RNAi, VIGS, TIGS, mutagenesis, high throughput in situs

IV Gene validation- VIGS, Tilling, transformation

Functional Genomics for Testing and Validation of Candidate GenesAndy Phillips, Keith EdwardsIGER, SCRI, JIC, RRES

Bioinformatics – Information and links to websites with information on the Triticeae

V Bioinformatics (Bioinformatics is integrated within each working group)

Applied genomics – Transfer of genomics data and technologies to breeding programs

Alignment of programmes


COST - Next StepsFunding approved, (4 years, 19MEuros)

Kick off meeting in Brussels, or Copenhagen, probably week 5 or 7 of 2007 (early-mid February)– Identifying Country coordinators (TB for UK)– Deciding on WG coordinators

Nominations? (HJ for WGIV..)– Clarifying the Scientific Missions (maybe by email early)

and STSM coordinator (s)Nominations

– Deciding on Webmaster(Useful if all post holders are also country coordinators (?)


Proposed meetingsProposed meetings


COST - Application of tools across species

Integrated control of wheat blossom midge

Dr Toby Bruce

Aims of the project• Characterise level of susceptibility of wheat

varieties• Seek sources of resistance• Develop attractant based traps to monitor

midges• Combine the above in an integrated control

strategy

Impossible to decide in time which fields need treating hence a large area is sprayed for insurance

Pest ecology• Larvae can remain dormant

in the soil for up to 13 years

• Midges mate at emergence site

• Females migrate to other areas if no suitable egg laying sites are available

Pest problem

X XX

Susceptible growth stages

Larvae digest grain and encourage fungal infections e.g. Fusarium graminearum

Damaged grain

Before this project there was no knowledge of the range of susceptibility of British wheats to OWBM

Infestation levels on different varieties

Live Larvae per 100 Grain as % Controls

Vulnerable -escape mechanisms

• Flower before female flight

• Shorter ear emergence period

• Closed flowering habit• Less attractive volatiles released?

Resistant• Females lay eggs, but

larvae die when they start to feed

• A wound plug is formed at the feeding site due to lignification

• Antibiotic action of phenolic acids produced by the grain

Larvae on Welford

Lignification process

Resistant varieties yield better at infested sites

Conclusions (Part 1)

• Some varieties are genetically resistant to WOBM and suffer minimal damage if at all

• Other varieties are vulnerable to infestation

• Individual crops may escape damage due to the timing of ear emergence

• No resistant bread making wheat varieties yet

• In the long term resistance will be bred into quality wheats

• In the short term risk to susceptible crops still needs assessment using pheromone traps

Chemical ecology• Insects use volatile

chemical odours to find a mate and host plants

• These signals can be used in traps

2,7-nonanediyl dibutyrate

Sex pheromone

• Synthesised in lab• Field formulation developed and

optimised

OCOC3H7

OCOC3H7

midges caught on sticky insert

Pheromone trap

Sticky insert

Farm map – many fields

0

50

100

150

200

250

27-May

31 03-Jun

7 10 14 17 21 24 28

mea

n no

. mal

es/tr

ap/3

- 4

days

Gt Knott II

Summerdells II

Summerdells II (ow l end)

New Zealand

Fosters

Long Hoos III

Gt Harpenden II

Gt Harpenden I

Stackyard

Delafield

Broadbalk (margin)

Large variation in trap catch from field to field

susceptible period

Grid site – one field

• grid size: 150 x 180m (2.7ha)• variety grown: Consort

N

Set aside

ROAD

Barley

Wheat

Wheat

Wood

X X X X X

X X X X X

X X X X X

X X X X X

X X X X X

X X X X X

Pheromone traps: field scale trap catch (7-10 June)

N

Infestation level (larvae per ear)

1 1.5 2 2.5 3 3.5 4 4.5 51

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

4

6

8

10

12

N

Pheromone traps: field scale trap catch (7-10 June)

N

Set aside

ROAD

Barley

Wheat

Wheat

Wood

7th June Trap Counts

AVERAGE in grid =

11 per trap21.5

22

2.5

3

Conclusions• Pheromone traps can be used to assess risk to

susceptible wheat crops• Large variation in pheromone trap catch

between fields• Variation in catch within a field is less important• Consideration of movement of females from

adjacent fields helps interpret trap catches• Ultimately resistant varieties will remove the

need for pheromone trap monitoring.

WGIN 2nd Wheat Syndrome

• To what extent are relative performance differences as 2nd wheats due to take-all?

•Resistance or tolerance?

•Possible traits?

HGCA Recommended List 2006/7

8

9

10

9 10 11 121st wheat t/ha

2nd

whe

at t/

ha

Cordiale

Robigus

Equinox

Napier

Istabraq

Claire

Trial design• 6 varieties:

– initial hypothesis 3 ‘good’, 3 ‘poor’ 2nd wheats.

• 2 trials – Cambridge, Rosemaund

• Previous crop:– wheat (2nd wheat)– oats (1st wheat)

• TA inoculation:– 1st wheat +/- inoculum

• 8-10 reps

• Comprehensive fungicide programme for control of eyespot and foliar diseases.

6

7

8

9

10

8 9 10 11 121st wheat t/ha

2nd

whe

at t/

ha

RLCamRos

Co

Co

Co

Ro

Ro

Ro

Na

Na

Na

Is

Is

Is

Eq

Eq

Eq

Cl

Cl

Cl

2nd wheat syndrome trials 2006 - Cambridge, RosemaundHGCA RL 2006 for comparison

0

1

2

3

4

0 20 40 60TA index

yiel

d lo

ss (c

w 1

st w

heat

uni

noc)

t/ha

Cam inocCam 2ndRos inocRos 2nd

Eq

Eq

Eq

Ro

Ro

Is

IsCl

Cl

Na

Na

Co

Co

Co

2nd wheat syndrome trials 2006 - Cambridge, Rosemaund

Ro

Na

Conclusions

• 2nd wheat performance characterised• Yield loss from inoculation small• Indications from RL confirmed in phased rotational position• Significant variety by rotational position interaction at both sites• Robigus high yield loss • Cordiale/Napier low yield loss.• Poor correlation TAI / yield loss• Indications that Cordiale less severely diseased• Differences mainly ‘tolerance’

Current Research on Grain Developmentand Quality Supported by BBSRC and

Other Bodies

1. GRAIN DEVELOPMENT

Control of grain size and shapeBBSRC Support

CSG - cell lineage tagging (RRes)

Crop Science Initiative - grain size and shape(RRes, JIC, Bath, Oxford, University of Manchester, SCRI)

Agri-Food Committee - GA and grain size (RRes)

2. COMPOSITION AND QUALITY

LINK Grants

Defra LINK Breeding and End UseCCFRA, JIC, RRes, UEA, breeders, millers, bakers, HGCA

Defra/BBSRC LINK PMA/PHSNottingham, RRes, JIC, Harper Adams, breeders, millers, bakers, HGCA

Defra LINK GREENGRAINADAS, SCRI, breeders, distillers, HGCA


BBSRC Grants

Ex-Gen InitiativeEffects of environment on end use qualityRRes, JIC, IFR, Reading, Bristol, Cambridge,UEA, RHM, Syngenta

Agri-Food Committee Patterns and mechanisms of gluten protein deposition RRes


EU Grants

HEALTHGRAIN

RRes, IFR + 40 EU partners

Patterns and mechanisms of cell wall(dietary fibre) synthesis

Phenolics as phytochemicals anddeterminants of fibre solubility

3. FUTURE REQUIREMENTS

• Large integrated programmes exploiting genetic resources, “omics” technologies and functionality measurements

• Genes and markers for key processes / quality traits

• Underpinning research to allow the architecture and composition to be redesigned for specific end uses

BBSRC-INRA (IN-BB-06; BB/E527147/1)Traits and markers to reduce the N requirement and improve the grain protein % of winter wheat

John Foulkes

John Snape

Micha Semenov

Jacques LeGouis (Clermont-Ferrand)

Pierre Martre (Clermont-Ferrand)

& Liaison with BWB (Peter Jack)

Objectives & HypothesesObjectives

1. To identify physiological traits associated with lower fertiliser N requirement and higher and more stable GPC, and to assess their genetic variability.

2. To identify QTL associated with lower fertiliser N requirement and higher and more stable GPC.

3. To identify mutant lines for NUE traits to investigate their inheritance and to identify mutants for candidate genes using TILLING to identify further allelic diversity.

4. To predict the environmental stability of QTL for fertiliser N requirement and GPC using new modelling approaches.

Objectives & HypothesesHypotheses:1.a Low fertiliser N requirement in feed wheats is correlated with low stem N.1.b High & stable GPC in bread-making wheats is correlated with high leaf N.

2. Phenotypic variation for NUE and GPC is under genetic control and can be localised within the genome.

3. Analysis of mutants will provide candidate genes involved in NUE and GPC. Comparison to QTL will reduce the candidate gene pool stillfurther.

4. Crop simulation models can be linked with genetic analysis to predict the environmental stability of QTL for NUE and GPC.

Objective 2

Objective 1 Objective 3

Objective 4

Trait identification Candidate genes

QTL detection QTL modeling

- characterize genetic variation - priorirtise traits- validation of parents

- screening 2 mutant pops for senescence related traits- inheritance studies-TILLING/PCR to identify allelic diversity

- predict trait interactions and G*E - identify stable QTLs

- phenotyping 2 DH pops at low and high N levels

- QTL for NUE components and sub-traits

Data analysis

Plant breeding

- selection criteria:traits, QTL

Relationship between work packages

Sites: Nottingham, JIC*, INRA Mons and INRA Clermont-Ferrand.

Design: Split plot

Main plot: Nitrogen (2)

1. Optimal N 2. 20% Optimal N

Sub-plot: Genotype (16)

1. Alchemy 9. CF91072. Beaver 10. Québon3. Consort 11. CF991024. Paragon 12. Toisondor5. Rialto 13. Perfector6. Robigus 14. VM9601/Arche†7. Savannah 15. Renan8. Soissons 16. Récital

*Low N treatment only†VM9601 at INRA Mons; Arche at Nottingham and INRA Clermont Ferrand.

Objective 1: Trait identification Parental phenotyping 2006/7 & 2007/8

Canopy N partitioning (flowering) and functions

Leaf lamina 45% (photosynthesis)mainly as Rubisco & chlorophyll

Leaf sheath 9% (photosynthesis)mainly as chlorophyll & Rubisco

True stem 23% (structural, transport, metabolic, storage)

structural - mostly lignin with low N

transport: nitrate, amino acids in xylem

metabolic: cytochrome oxidase, ATP

storage: - specific storage for reproductive growth (Nair et al., 1978)- luxury uptake (Grindlay, 1997)

Ear 23%

Variety: Claire

Data: SB 2005

Objective 1: Trait identification

Measurements:-- Developmental rate- DWt and N content of plant organs, radiation interception (ceptometer) at GS31, GS61, GS75 and grain maturity

- leaf ps activity (gas exchange analyzer) and N analysis on different phytomers.

Data used to: -- test the hypotheses 1a and 1 b,- confirm traits and pops for QTL detection/gene discovery (Obj 2 & 3). - parameterize and extend the Sirius model (Obj 4).

Objective 2 QTL detection

UK DH population

- Rialto x Savannah

- QTL for traits linked to lower fertiliser N requirement in feed wheats

French DH population

Half diallel cross Six parents showing positive (cv Quebon and CF9107 & CF99102) or negative (cv Toisondor, Perfector & VM9602) departures from the relationship between grain yield and protein concentration.

- QTL for high and stable grain protein concentration

Rialto x Savannah DH population

• Rialto low NutE WGIN 04; Savannah highest NutE DEFRA Desk Study

• 130 lines available via JIC agreement with Syngenta.

• A map of 120 SSRs available via JIC agreement with Syngenta.

• Extended map developed by Advanta in DEFRA LINK Lodging project

Identification of wheat cultivars outside thenegative relationship GY – GPC (F.X. Oury 2003)

80 85 90 95 100

1011

1213

rendement (q/ha)

tene

ur e

n pr

otei

nes

(%)

80 85 90 95 100

1011

1213

CF9103RE9201

RE9204

RE9205RE9209

VM9202

VM9203 VM9205

VM9207

VM9209

CF9309

DI9304

CF9414

DI9403DI9404

DI9428

VM9401

VM9409RE9510 VM9509VM9510

VM9516

VM9517CF9608

CF9621

RE9607

VM9601

CF9703CF9717

DI9714

RE9707CF9804

CF9825

DI9812RE9819

CF99005

CF99031EM99006

EM99012

EM99017RE99001

RE99009

RE99017

CF99075

CF99105

CF00189

CF00193

DI00010DI00024

EM00002

EM00015EM00018

CF99351

RE01002

seuil = 1.96 effectif = 54 ; coeff. de correlation = -0.71moyenne rdt = 91.9 moyenne prot = 11.8 y = 21.06 + -0.101 x

INTER-STATIONS 1991-2002 (moyennes sur au moins 8 resultats)

CF9107

VM9014

VM9402

RE99003RE99004

CF99102

VM9601

isengrain

apache

Grain Yield, Unit: 0.1T /ha

GPC

Objective 2 QTL detectionYear 1(2006/7) : Extend UK genetic map & develop NILs at JIC

Year 2 (2007/8): UK DH pop phenotyped under N± at 2 sites INRA DH pop (1500 lines) phenotyped under N+ at 1 siteDevelop NILs at JIC

Year 3 (2008/9): UK DH pop phenotyped under N± at 2 sites INRA DH pop (200 lines) phenotyped under N± at 4 sites 200 INRA DH lines genotyped at INRA Clermont-FerrandNILs for leaf-senescence QTLs phenotyped under N± JIC QTL analysis

Year 4 (2009/10): QTL analysis

Objective 3 Candidate gene analysisFocus on senescence-related traits underlying NUE and GPC.

Analysis of cv. Paragon mutant population at JIC

Year 1: 5000 M5 lines under N- scored for LAD. Year 2: Extended phenotyping for 1000+ lines under N-

Selected lines crossed to tester line.Year 3: Progeny of tester lines crosses scored (inheritance studies)

Selected lines evaluated for candidate genes (TILLING).

Objective 3 Candidate gene analysisFocus on senescence-related traits underlying NUE and GPC.

Analysis of cv Renan mutant population at INRA Clermont-Ferrand

Year 1 : Multiplication to M3Year 2 : 4500 lines (1 row / line) under N- in Clermont

Visual scoring for shorter or longer LADYear 3 : Up to 1000 visually identified mutant lines

Extended phenotyping under N-Year 4: PCR for 100 selected lines / 20 candidate genes

Objective 4: QTL modelling

Sensitivity analysis of genotype-specific parameters (Yr 1 & 2)

• N storage capacity of stem and leaves• Vertical pattern of N distribution• Duration of selected phenological stages (e.g. grain filling duration) • Rate/amount of N re-translocation to the grains.

Development of a QTL-based model (Yr 3 & 4)

• QTL for traits (genotypic parameters) detected. • QTL modelling to predict G x E x N interactions.

Deconvoluting NUE in wheat (Sirius)

Phenology parameters:• Phyllochron• Duration of grain filling period

N Storage• Leaf N concentration • Capacity of stem N storage

Parameters related to N distribution with leaf layer• Vertical pattern of N distribution and the local leaf irradiance• Crop photo-assimilation per unit N

Stem N storage:

- Up to 30 % of total canopy Nis in the true stem.

- Role is not well understood.

- Possible functions are structural, transport, reserve (including storage) and metabolic.

- May be possible to reduce stem N withoutreducing canopy photosynthesis.

N concentration in leaf layers (g/m2)

Flag leaf : 1.80

Leaf 2 : 1.42

Leaf 3 : 1. 09

Remaining leaves : 0.95

Data for Claire.

Name Function Reference Glutamine Synthetase (GS)

Major assimilatory enzyme which reassimilates ammonia as a result of photorespiration and the breakdown products of leaf proteins

Hirel et al. 2001 (maize); Yamaya et al. 2002 (rice)

Chlorophyllase Catalyzes the initial step in the degration of chlorophyll (hydrolysis of chlorophyll to chlorophylllide and phytol)

Arkus et al. 2005 (wheat)

Rubisco Small subunit (rbcS)

Gene is highly down-regulated during senescence

Demirevska-Kepova et al. 2005 (wheat)

Pheide A Oxygenase (PAO)

Catalyzes key reaction in chlorophyll breakdown, the conversion of pheophorbide a to a fluorescent catabolite (pFCC)

Pruzinska et al. 2005 (Arabidopsis)

Candidate senescence-related genes

Candidate UK DHs

Beaver x SoissonsBeaver high NutE WGIN 04, DEFRA desk Study; Soissonslow NutE WGIN 04, DEFRA Desk Study

Rialto x SavannahRialto low NutE WGIN 04; Savannah highest NutE DEFRA Desk Study

Robigus x AlchemyRobigus ranked equal highest for NutE of 149 NL/RL varieties examined under moderate N in the DEFRA desk study. We have no data on Alchemy.

Savannah x ConsortSavannah (high NutE desk study; WGIN 04) x Consort (intermediate NutE desk study).

Objective 2 QTL detection

- French DH population

Half diallel cross Six parents showing positive (cv Quebon and CF9107 & CF99102) or negative (cv Toisondor, Perfector & VM9602) departures from the relationship between grain yield and protein concentration.

- Identify QTL for high and stable grain protein concentration

N economy characters

DH Population Source No lines

Molecular markers nos

NUE kg DM/kg

NupE NutE

N1-3 N 4-8 N1-3 N 4-8 N1-3 N 4-8

Beaver x Soissons JIC/UoN/ADAS

65 65 lines mapped,181 (SSR and AFLP)

BeaverSoissons

36.032.2

28.525.60

0.9880.953

0.7780.754

36.733.9

34.534.0

Rialto x Spark JIC/UoN/ADAS

144 Only 40 mapped with a few SSR

RialtoSpark

36.732.6

28.226.0

1.0410.985

0.8210.769

34.833.7

34.533.7

Avalon Cadenza JIC/UoN/ADAS

204 74 SSR and STMP on 60 lines

Avalon Cadenza

29.235.4

23.826.8

0.8741.025

0.7150.782

33.634.5

33.234.3

Arina x Riband JIC 120 200+ (SSR + AFLP)

ArinaRibandRiband

-36.1

-28.1

-0.949

-0.743

-38.2

-38.0

Lehmi x Claire JIC 100 LehmiClaireClaire

-37.0

-29.0

-0.994

-0.775

-37.6

-37.5

MinMaxMeanSED

26.140.135.71.32

22.331.528.10.70

0.801.110.990.082

0.680.880.780.022

31.739.736.10.55

32.039.828.10.70

DEFRA Desk Study: Results from analysis of NL and RL trials 1998-2003

WGIN NUE trial 2003/4: N utilization efficiency under different N levels

N-uptake efficiency (kg N uptake/kg N available)

N-utilization efficiency (kg grain DM/kg N uptake)

Fertilizer N 50 kg N/ha 200 kg N/ha 50 kg N/ha 200 kg N/ha Beaver 0.79 (9) 0.59 (7) 68.1 (1) 39.1 (5) Soissons 0.91 (2) 0.46 (28) 46.9 (31) 29.4 (27) LSD (5%) 0.197 6.33

0

2

4

6

8

10

12

14

Scor

pion

Bea

ver

Opu

s

Xi1

9

Eins

tein

PBI

S

Mal

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350 200 100 0

WGIN NUE trial 2003/4: grain yield under different N levels

WGIN NUE trial 2003/4: N utilization efficiency under different N levels

NutE = yield/N uptake5%LSD 6.33

85 90 95 100 105

11.0

12.0

13.0

rendement (q/ha)

tene

ur e

n pr

otei

nes

(%)

85 90 95 100 105

11.0

12.0

13.0

Azimut

6917

6918

Rosario

Alcazar

6928

6931

69326941 6954

6955

6962

Perfector

7020

Bosphor

7026

Kleber

7028

7030Hourra

7033

Mendel

Chagall

AstuceToisondor

Melkior

7061

Ephoros

7069

RessorSoissons

Shango

Crousty

Isengrain

ChargerApache

seuil = 1.96 effectif = 36 ; coeff. de correlation = -0.69moyenne rdt = 97.2 moyenne prot = 12 y = 22.13 + -0.105 x

6944

Quebon

69606965

Sankara

Recital

ESSAI CTPS Nord 2002-03

90 95 100 105 110

10.5

11.0

11.5

12.0

rendement (q/ha)

tene

ur e

n pr

otei

nes

(%)

90 95 100 105 110

10.5

11.0

11.5

12.0

PR22R28

Capnor

322Intense

325

Swing

343

345

359464

465

467

Soissons

seuil = 1.78 effectif = 13 ; coeff. de correlation = -0.99moyenne rdt = 100.4 moyenne prot = 11.2 y = 19.6 + -0.084 x

287307

313

Parador

Balance

Mitchel

334

335

Caphorn

351360

361

Vulcain

369

Boston

HynoRenta

HynoQuinta507

Recital

Tremie

Crousty

Isengrain

ESSAI CTPS Nord 1999-00

Relationship between stem N at flowering and NutE (yield / N uptake)

WGIN NUE 05 Yields

WGIN 05 Grain yield (t/ha, 85%DM) 5%LSD=0.84Variety Code 0 kg-N/ha 200 kg-N/ha (50/150)Avalon AV 3.78 9.91Batis BA 3.96 9.65Cadenza CA 3.69 9.75Claire CL 3.81 10.36Hereward HE 3.73 9.54Hurley HU 3.55 10.30Istabraq IS 4.58 10.84Lynx LY 4.40 10.40Malacca MA 3.86 9.77Maris widgeon MW 3.40 7.53Monopol MO 3.38 8.04Paragon PA 3.25 8.77Riband RI 4.46 10.10Robigus RO 4.36 10.78Savannah SA 4.66 10.70Shamrock SH 4.18 9.83Soissons SS 3.57 10.20Sokrates SK 3.45 10.32Solstice SL 4.58 10.90Xi19 XI 4.78 11.36

Pop (NABIM group) Parent Parent Owner** Type of pop Lines Project actvity Seed availability (6kg) MarkersBeaver (4) x Soissons (2) Beaver Soissons JIC/UoN/ADAS DH 65 lines WGIN In hand 181 SSRs and AFLPs

Robigus (3) x Alchemy (4) Robigus Alchemy Advanta DH 126 lines* HFN, LINKNeed to bulk - decision required soon!

Will be fully genotyped for HFN LINK - skeleton map dec 06.

Rialto (2) x Savannah (4) Rialto Savannah Advanta DH 130 lines Lodging, LINK Advanta agreed to supplyMapping ongoing Lodging LINK (200+ SSR)

Savannah (4) x Consort (3) Savannah Consort RAGT SSD-derivedF5 177 lines NoneNeed to bulk - decision required soon!

100 SSRs; more can be added by RAGT as needed.

27 Alchemy x Robigus50 (Alchemy x Robigus) x Robigus49 (Alchemy x Robigus) x Alchemy

* Mix of 2 way (27 lines) & 3 way (99 lines) crosses as follows:

**IP will have to be agreed by owners on 1:1 basis. Generally BWB will wish to help projects provided their own commercial interests are not compromised. Thus RAGT and Advanta may be prepared to make materials available providing MTA agreed, that data is not disseminated outside project during life of project and possibly short period thereafter - 1 year? One way of getting around marker data is for owner to do QTL analysis and release QTL information but not full segregation dataset.

Relationship between leaf N at flowering and grain N at harvest for 5 varieties at 2 N levels

SB 2005

Av

Wi

Is

At

SoAt

Is

So Av

Wi

y = 1.7177x + 17.943R2 = 0.9243

0

10

20

30

40

50

60

0 5 10 15 20 25

N leaves 1-3 (mg)

N g

rain

(mg)

N leaves 1-3 (mg) per shoot

N g

rain

(mg

) per

sho

ot

Laperche et al : TAG paper (in press)Heritabilities by N treatment

GY grain yield; TKW thousand kernel weight; GPA grains per area; ADM aerial dry matter; HI harvest index; GPY grain protein yield, NSA straw N per area; NTA total N per area, GPC grain protein content; NS% nitrogen straw content, PH plant height, DTH hading date

Low N High N

•222 DH lines from Arche (tolerant) x Recital (sensitive)

• Four locations in 2000, and three in 2001, under high (N+) and low (N) nitrogen supplies.

• Heritabilities of yield and nitrogen traits for both nitrogen supplies were always above 0.6.

• When N stress increased, heritabilities decreased and G x N interaction variances increased. The decrease in heritability was mainly explained by a decrease in genetic variance.

Laperche et al : TAG paper (in press)

Wider impact of WGIN in the community – a view from NIAB

Donal O’Sullivan

WIGN Management Meeting, RRes, 29th Nov 2006

Wheat research @ NIAB• LINK

– Low Phytate– Ergot– SBCMV– REFAM

• CSI (2006-2011)– Ppd pre-breeding– MAGIC– Smart CHO Center

• NIAB Trust (2006-2011)– Synthetic pre-breeding– Transformation

• Other– Bioarcheology (NERC)– Allelic imbalance (GCP)– UK vs EU Diversity (Pioneer)– Cytokinin oxidase PhD (BBSRC CASE)– Rht PhD (NIAB)– Association Genetics PhD (HGCA)– NIAB Fellowships (NIAB Trust)

All taking place alongsidecore activities

DUSNL/RL trialsUKCPVSDisease diagnosticsSeed health testingSeed certification

Specialist Phenotyping – e.g. ergot

Courtesy: [email protected]

Specialist Phenotyping – e.g. rust


Large-scale phenotyping• Wt and low phytate wheat lines – 20 tonne

multiplication for animal feeding experiments


Pre-breeding flowchart

Synthetics Maintain

Subset

Backcross Adapted? ‘Converted’germplasm

Yield test?

Breeding progs.Evaluate

Interesting trait?

‘Enhanced’germplasmStill there?

Evaluate

Interesting trait?

Backcross

NIAB

PLANT BREEDERS

450


Multi-parentAdvancedGenerationInterCrossPopulations

ELITE : Hereward Claire Soissons Robigus Brigadier Alchemy Xi19 Rialto

Multiple rounds of random mating maintaining pop size >= 100

MAGIC PopulationExtract ‘highly recombined’ RI

lines of composition ABCDEFGH

16 x DIVERSE : Holfast Steadfast Banco Staring Gladiator Flamingo Kloka CamaMaris Fundin Copain Stetson Slepjner Cordiale Bersee Brigadier Soissons


CSI MAGIC populations

Exploiting wheat-rice syntenyInvestment in :custom bioinformatics tools and datasetsbioinformatics and molecular genetics skills– Targeted marker

enrichment– Identification of candidate

genes – Development of genome/

chromosome specific markers


Association vs Classical QTL Mapping

0

5

10

15lo

d

1 3 5 6 7 8 9 11 14 16 19 21

SBCMV score class frequency for 200Gediflux variety Wiltshire trial 2006

05

101520253035

15 45 75 105

135

165

195

225

255

285

315

345

More

SBCMV Score BinFr

eque

ncy

BARC110 (5DL) 100.0 0.013 0.087 1.000 0.025 0.121 0.168 1.000 0.136

wmc161 (5DL) 106.5 0.747 0.229 0.258 0.639 0.890 0.342 0.419 0.803

wmc765 (5DL) 109.7 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000

BARC144 (5DL) 112.7 0.000 0.078 0.089 0.001 0.008 0.155 0.208 0.013gwm272 (5DL) 119.0 0.347 0.135 0.134 0.365 0.657 0.233 0.273 0.613

Adjusted P-values (GC/DC)P-value for association with SBCMV resist

Sbm1

Sbm2

Sbm1

Resources of Interest to Community• HTP genomics facility and extensive marker

databases for cultivated varieties • Extensive phenotypic databases• Association genetics capability• Field-based phenotyping capability• Specialist expert phenotyping capability

(pathology, seeds)• Rust, mildew collections and UKCPVS data• Ongoing pre-breeding programme (synthetics,

photoperiod sensitivity)

Major NIAB-WGIN Interactions• Management Group participation• Take-all trials• SBCMV LINK

– Availability of AxC seed– Co-ordinated marker coverage

• Great potential to harness NIAB’s unique phenotyping capability in exploitation of Watkins collection and mutagenised populations

• Great opportunities to build up trait and allele mining/ association genetics approaches in future

International Wheat Genome Sequencing Consortium

The mission of is to advance agricultural research for wheat production and utilization

by developing DNA-based tools and resources that result from the complete

sequence of the common (hexaploid) wheat genome

Organization

o

Catherine Feuillet, Bikram Gill, Rudi Appels

Kellye Eversole

Approaches

Parallel approaches:

• Physical map of the whole genome using the available UK-French complete genome BAC libraries. Develop a new library to complement the existing library

• Construct and assess the quality of chromosome-specific, and chromosome-arm specific BAC libraries (Developed by Dolezel, CR), develop BAC fingerprint contig maps for each chromosome

• Physical maps as a basis for systematic sequencing of the three wheat genome

Initial Outcomes

• Complete physical map • Anchored contigs• BAC end sequence

• Assess the distribution of genes across the genome;• Investigate the ability to differentiate homologous

sequences; and• Develop bioinformatics tools for a semi-automated

annotation of large sequences.

Progress

INRA:

• Generated 11 Mb of random BAC end sequence from chromosome 3B

• Sequence is 86% repetitive elements, 3% genic regions, 11% unknown

• 6,000 genes estimated for 3B

• New repetitive elements

New Projects underway

USA:

• Random BAC sequencing (200) (Jeff Bennetzen, KatrienDevos; NSF)

• 3A/3D physical maps (Bikram Gill, Jan Dvorak; USDA)

• Comparative sequence analysis between Brachypodium, Aeg tauschii D genome and wheat D genome (KatrienDevos, Olin Anderson, NSF)

Proposed Projects• Group 1: 1A & 1B -- EU FP7 Submission pending (Feuillet)• 1D (US) - Proposal submitted

• Group 2: (Orphan)

• Group 3: 3A (US) (3AS funded; 3AL submission pending, Gill)• 3B (France) - Funded• 3D -- EU FP7 Submission pending

• Group 4: 4D (US) Proposal submitted• 4A & 4B -- Orphans

• Group 5: 5A (Italy) - Submission to CRA (Catavelli)• 5B (UK Planned)• 5D (Turkey) - Proposals submitted to Turkey & EU (Budak)

• Group 6: 6A & 6B - Orphans• 6D (US) Proposal submitted

• Group 7: (7A, B, & D) -- Australia - Proposal submitted.

UK InvolvementCo-ordinated by Monogram (Tina)Mike Bevan to take the lead for UK

• Contribute to BAC library

• Develop physical map

• Contribute to sequencing – 5B particular target, 2D?

Linked activities

European Triticeae COST Action

International Brachypodium sequencing consortium

monogram - wgin · cereals, and grasses, cf: ... c4 grasses – brachypodium ... – the adaptation...

Documents