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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 33
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 44
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 55
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 66
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??)
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 77
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 88
WG III - Functional genomics
Technologies and topics :–Transcriptomics platforms, proteomics
and metabolomics–Systems approaches to data integration
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 99
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)
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1010
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1111
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1212
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1313
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,
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1414
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1515
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)
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1616
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1818
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.
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 1919
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2020
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2121
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2222
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 ]
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2323
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
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2424
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 (?)
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2525
Proposed meetingsProposed meetings
November 2006November 2006 MONOGRAM MONOGRAM –– Tina BarsbyTina Barsby 2626
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
2. COMPOSITION AND QUALITY
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
2. COMPOSITION AND QUALITY
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
acca
HEr
ewar
d
RiB
and
Lynx
Cad
enZa
Spar
k
V1-
Eno
rm
V3-
Sokr
ates
BAtis
SoLs
tice
ME
rcia
V2-
Petru
s
AR
che
RE
-Cha
blis
Ria
Lto
ELS
Para
gon
Flan
ders
CaP
horn
V4-M
onop
ol
ISen
grai
n
Soi
Sso
ns
AP-
Zyta
Ava
lon
Mar
isW
idge
on
Cap
elle
Des
prez
grai
n yi
eld
(t/ha
at 8
5%D
M)
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
Courtesy: [email protected]
Large-scale phenotyping• Wt and low phytate wheat lines – 20 tonne
multiplication for animal feeding experiments
Courtesy: [email protected]
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
Courtesy: [email protected]
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
Courtesy: [email protected]
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
Courtesy: [email protected]
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