Trait data mining at European pre-breeding workshop at Alnarp (25 Nov 2009)
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Transcript of Trait data mining at European pre-breeding workshop at Alnarp (25 Nov 2009)
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• U#liza#on of gene#c diversity • Core collec#on subset • Trait mining selec#on (FIGS)
• Computer modeling
• Some examples (FIGS)
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corn, maize
wild tomato
tomato
teosinte 3
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C A
B
Tradi#onal landraces
A A
B
Crop Wild Rela#ves
A A
A
Modern cul#vars
Gene/c bo1lenecks during crop domes/ca/on and during modern plant breeding. The circles represent allelic varia#on. The funnels represents allelic varia#on of genes found in the crop wild rela#ves, but gradually lost during domes#ca#on, tradi#onal cul#va#on and modern plant breeding.
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• Scien#sts and plant breeders want a few hundred germplasm accessions to evaluate for a par#cular trait.
• How does the scien#st select a small subset likely to have the useful trait?
• Example: More than 560 000 wheat accessions in genebanks worldwide.
6 Slide adopted from a slide by Ken Street, ICARDA (FIGS team)
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• The scien#st or the breeder need a smaller subset to cope with the field screening experiments.
• A common approach is to create a so-‐called core collec/on.
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Sir OYo H. Frankel (1900-‐1998) proposed a limited set established from an exis#ng collec#on with
between its entries.
The core collec#on is of limited size and chosen to
of a large collec#on (1984) .
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• Given that the trait property you are looking for is rela#vely rare:
• Perhaps as rare as a unique allele for one single landrace cul#var...
• Geang what you want is largely a ques#on of LUCK!
8 Slide adopted from a slide by Ken Street, ICARDA (FIGS team)
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Objec/ve of this study:
– Explore climate data as a predic#on model for “computer pre-‐screening” of crop traits BEFORE full scale field trials.
– Iden#fica#on of landraces with a higher probability of holding an interes#ng trait property.
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Wild rela#ves are shaped by the environment
Primi#ve cul#vated crops are shaped by local climate and humans
Tradi#onal cul#vated crops (landraces) are shaped by climate and humans
Modern cul#vated crops are mostly shaped by humans (plant breeders)
Perhaps future crops are shaped in the molecular laboratory…? 11
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• Primi#ve crops and tradi#onal landraces are an important source for novel traits for improvement of modern crops.
• Landraces are ohen not well described for the economically valuable traits.
• Iden#fica#on of novel crop traits will ohen be the result of a larger field trial screening project (thousands of individual plants).
• Large scale field trials are very costly, area and human working hours.
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Assump/on: the climate at the original source loca#on, where the landrace was developed during long-‐term tradi#onal cul#va#on, is correlated to the trait score.
Aim: to build a computer model explaining the crop trait score (dependent variables) from the climate data (independent variables).
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1) Landrace samples (genebank seed accessions) 2) Trait observa#ons (experimental design) -‐ High cost data 3) Climate data (for the landrace loca#on of origin) -‐ Low cost data
• The accession iden#fier (accession number) provides the bridge to the crop trait observa#ons. • The longitude, la/tude coordinates for the original collec#ng site of the accessions (landraces) provide the bridge to the environmental data.
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Lima, Peru
Benin
Alnarp, Sweden
Svalbard
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hYp://barley.ipk-‐gatersleben.de
16 Powdery Mildew, Blumeria graminis
Leaf spots Ascochyta sp.
Yellow rust Puccinia strilformis
Black stem rust Puccinia graminis
Faba bean, Finland Field trials, Gatersleben, Germany
Forage crops, Dotnuva, Lithuania Radish (S. Jeppson)
Potato Priekuli Latvia
Linnés äpple
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The climate data is extracted from the WorldClim dataset. hYp://www.worldclim.org/
Data from weather sta#ons worldwide are combined to a con#nuous surface layer.
Climate data for each landrace is extracted from this surface layer.
Precipita#on: 20 590 sta#ons
Temperature: 7 280 sta#ons 17
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FIGS selec#on is a new method to predict crop traits of primi#ve cul#vated material from climate variables by using mul#variate sta#s#cal methods.
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Origin of Concept (1980s): Wheat and barley landraces from marine soils in the Mediterranean region provided genetic variation for boron toxicity.
What is
Slide made by Michael Mackay 1995
hYp://www.figstraitmine.org/
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South Australia
Mediterranean region
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FIGS The FIGS technology takes much of the guess work out of choosing which accessions are most likely to contain the specific characteris#cs being sought by plant breeders to improve plant produc#vity across numerous challenging environments. hYp://www.figstraitmine.org/
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Slide made by Michael Mackay 1995
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– For the ini#al calibra#on or training step.
– Further calibra#on, tuning step – Ohen cross-‐valida#on on the training
set is used to reduce the consump#on of raw data.
– For the model valida#on or goodness of fit tes#ng.
– New external data, not used in the model calibra#on.
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– No model can ever be absolutely correct
– A simula#on model can only be an approxima#on
– A model is always created for a specific purpose
– The simula#on model is applied to make predic#ons based on new fresh data
– Be aware to avoid extrapola#on problems 24
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• No sources of Sunn pest resistance previously found in hexaploid wheat.
• 2 000 accessions screened at ICARDA without result (during last 7 years).
• A FIGS set of 534 accessions was developed and screened (2007, 2008).
• 10 resistant accessions were found! • The FIGS selec#on started from 16 000 landraces from
VIR, ICARDA and AWCC • Exclude origin CHN, PAK, IND were Sunn pest only
recently reported (6 328 acc). • Only accession per collec#ng site (2 830 acc). • Excluding dry environments below 280 mm/year • Excluding sites of low winter temperature below 10
degrees Celsius (1 502 acc)
hYp://dx.doi.org/10.1007/s10722-‐009-‐9427-‐1
Slide adopted from Ken Street, ICARDA (FIGS team)
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27 Priekuli (L) Bjorke (N) Landskrona (S)
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Heading Ripening Length H-‐Index Vol wgt TGW Priekuli (L) Bjorke (N) Landskrona (S)
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Eddy De Pauw Climate data
Harold Bockelman Net blotch data
Ken Street FIGS project leader
Michael Mackay FIGS coordinator
Dag Endresen Data analysis
• Barley (Hordeum vulgare ssp. vulgare) collected from different countries worldwide screened for susceptibility of net blotch infection (1676 greenhouse + 2975 field observations).
• Net blotch is a common disease of barley caused by the fungus Pyrenophora teres.
• Screened at four USDA research stations: North Dakota (Langdon, Fargo), Minnesota (Stephen), Georgia (Athens).
• 1-3 are basically resistant group 1 • 4-6 are intermediate group 2 • 7-9 are susceptible group 3
• Discriminant analysis (DA): • Correctly classified groups: 45.9% in the training set
and 44.4% in the test set. • Work in progress! (SIMCA, D-PLS)
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