ordination of marker-trait association profiles from long- term international wheat trials vivi...
TRANSCRIPT
![Page 1: Ordination of marker-trait association profiles from long- term international wheat trials Vivi Arief, Pieter Kroonenberg, Ian Delacy, Mark Dieters, Jose](https://reader030.vdocuments.mx/reader030/viewer/2022032722/56649cda5503460f949a3fd9/html5/thumbnails/1.jpg)
Ordination of marker-trait association profiles from long-
term international wheat trials
Vivi Arief, Pieter Kroonenberg, Ian Delacy, Mark Dieters, Jose Crossa and Kaye Basford
![Page 2: Ordination of marker-trait association profiles from long- term international wheat trials Vivi Arief, Pieter Kroonenberg, Ian Delacy, Mark Dieters, Jose](https://reader030.vdocuments.mx/reader030/viewer/2022032722/56649cda5503460f949a3fd9/html5/thumbnails/2.jpg)
Outline
• Motivation
• Construction of the Wheat Phenome Atlas
• Three-way Principal Component Analysis of marker-trait association profiles
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The Challenge
• To develop a detailed understanding of the heritable variation in the wheat genome
• To directly translate this knowledge into gains in wheat breeding
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CIMMYT’s Wheat Program
• Targets wheat breeding in 6 agro-ecological regions around the world
• Contains a vast accumulation of knowledge, data and genetic resources
• Is publicly available
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Wheat Trials
• 40 years of trials • With 10 or more international nurseries per
year• And 50 to 400 entries per nursery• At 50 or more locations around the world
→ 17m phenotypic data points on >80 traits across 13k lines evaluated in >10k field trials (data worth >US$500m)
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Pedigree
• Pedigree information tracing the history of all 13k lines in the breeding programs
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Seed Available
• Retained seed in storage from all trials available for low-cost high throughput genotyping (can give 26m marker data points)
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Unique resource
• No other crop (to our knowledge) has this resource publicly available
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Enabling Technologies
• ASREML (in the last 10 years)– An analysis program that can deal with the 17m
unbalanced data points
• ICIS (in the last 5 years)– Database to capture the pedigree, phenotype and
marker data
• DArTs (since 2006)– The first of high through-put affordable marker
systems– US$45 a DNA sample gives ~1,500 data points
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Phenome Map
• Diagrammatic representation of the regions of a genome that influence heritable phenotypic variation for a trait
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Phenome Atlas
• The integration of all phenome maps and a description of the methodologies that were used to produce the maps
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Producing theWheat Phenome Atlas
• Focus on the ESWYTs
Elite Spring Wheat Yield Trials
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Phenotypic dataAdvanced lines with high yield potential• 25 cycles from 1979/80 to 2004/05• 685 unique lines• 1445 trials across 400 locations• Phenotypic data for 20 traits
– 8 agronomic traits (including grain yield)– 3 rusts (leaf, stripe and stem)– 9 other foliar diseases
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The Analysis
• Obtained BLUPs using ASREML• Fitted a separate residual and design for each trial
– ESWYT 1 to 13: RCB– ESWYT 14 to 25: -lattice
• Fitted separate models for combined association analysis and structured association analysis
– Combined association analysis: G model– Family structure: G model– Spatial structure: GGL model– Temporal structure: GGY model
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Genotypic data
DArTs (Diversity Arrays Technology)
• Dominant markers 1; 0; X
• 1447 markers
• ~1.4 million data points
• 645 genotypes 599 unique genotypes (some are replicated)
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The Analysis
Association analyses• Simple t-test
– for each trait– for each marker – for each structure
Marker order• ESWYT disequilibrium map
– No existing map shared more than 50% common markers with ESWYT
– Obtained using ESWYT dataset
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Wheat Phenome Atlas Version 1.0
• Population structuresPedigree data Phenotypic dataMarker data
• Combination of population and environmental structures ESWYT cycle
• Environmental structures Mega-environmentPhenotypic data
10 Phenome maps:• Overall
x 2 analytical methods
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A wheat phenome atlas
• Phenome map: dense QTL map for a trait• Phenome atlas: collection and description
of phenome maps
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Increasing blue colour = increasing significance of positive association Increasing red colour = increasing significance of negative association
3 RustsStem (SR)Leaf (LR)Stripe (YR)
8 Agronomic traitsGrain Yield (GY)Kernel Size (KS)Plant Height (PH)Days to Heading (DH)Test Weight (TW)Grain Protein (GP)Lodging (LG)Shattering (SH)
9 Other foliar diseasesStripe rust on the spike (YS)Septoria tritici blotch (ST)Septoria nodurum blotch (SN)Spot blotch (SB)Powdery mildew (PM)Barley yellow dwarf (BYD)Fusarium leaf blotch (FN)Tan spot (TS)Xanthomonas (XT)
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What did we find?
• Many Trait Associated Markers (TAMs) for a trait
• Multiple traits for a TAM• Association identified depend on:
– what germplasm included– where tested– when examined
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What about the genotypes?
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Phenome maps: markers traits
For selection: genotypes markers traits
Three-way principal component analysis
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A TAM block
Markers in a linkage disequilibrium block showed significant association
log score 4
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Association: positiveMarker : present
Association: negativeMarker : absent
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Illustration 1
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599 lines 288 TAM blocks 16 traits
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599 lines 257 TAM blocks 15 traits
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599 lines 213 TAM blocks 14 traits
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Illustration 2
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ME1 ME2
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599 lines 202 TAM blocks 12 traits
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599 lines 218 TAM blocks 17 traits
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Three-way ordination:• Summarizes genotype TAM block trait
data• Reveals pattern in genotype TAM block
trait data• Parental selection• Genotype screening• Prediction of selection outcome
• Observed patterns depend on the genotype TAM block trait arrays used
Summary
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Our Team
Vivi AriefIan DeLacyHailemichael DesmaeChristopher LambridesJacqueline BatleyDavid EdwardsMark DietersIan GodwinKaye Basford
Jose CrossaSusanne DreisigackerTom PayneRavi SinghEtienne DuveillerGuy DavenportYann ManesMarilyn WarburtonGraham McLarenHans-Joachim BraunJonathan CrouchRodomiro Ortiz
Peter WenzelEric HuttnerAndrzej Kilian
Also