2

corn, maize

wild tomato

tomato

teosinte

Traditional landracesCrop Wild Relatives Modern cultivars

Genetic bottlenecks during crop domestication and during modern plant breeding.

The circles represent allelic variation. The funnels represents allelic variation of genes

found in the crop wild relatives, but gradually lost during domestication, traditional

cultivation and modern plant breeding.

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• Scientists and plant breeders want a

few hundred germplasm accessions

to evaluate for a particular trait.

• How does the scientist select a small

subset likely to have the useful trait?

• Example: More than 560 000 wheat

accessions in genebanks worldwide.

6Slide adopted from a slide by Ken Street, ICARDA (FIGS team)

• The scientist or the breeder

need a smaller subset to cope

with the field screening

experiments.

• A common approach is to

create a so-called core

collection.

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• Given that the trait

property you are looking

for is relatively rare:

• Perhaps as rare as a

unique allele for one

single landrace cultivar...

• Getting what you want is

largely a question of

LUCK!

8Slide adopted from a slide by Ken Street, ICARDA (FIGS team)

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Objective of this study:

– Explore climate data as a

prediction model for “computer

pre-screening” of crop traits

BEFORE full scale field trials.

– Identification of landraces with a

higher probability of holding an

interesting trait property.

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Wild relatives are shaped

by the environment

Primitive cultivated crops

are shaped by local

climate and humans

Traditional cultivated crops

(landraces) are shaped by

climate and humans

Modern cultivated crops are

mostly shaped by humans

(plant breeders)

Perhaps future crops are

shaped in the molecular

laboratory…? 11

• Primitive crops and traditional landraces are an important source for novel traits

for improvement of modern crops.

• Landraces are often not well described

for the economically valuable traits.

• Identification of novel crop traits will often 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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Assumption: the climate at the

original source location, where

the landrace was developed

during long-term traditional

cultivation, 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).

1) Landrace samples (genebank seed accessions)

2) Trait observations (experimental design) - High cost data

3) Climate data (for the landrace location of origin) - Low cost data

• The accession identifier (accession number) provides the bridge to the crop trait observations.

• The longitude, latitude coordinates for the original collecting site of the accessions (landraces) provide the

bridge to the environmental data.

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Lima, Peru

Benin

Alnarp, Sweden

Svalbard

http://barley.ipk-gatersleben.de

16Powdery 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

The climate data is extracted from

the WorldClim dataset.

http://www.worldclim.org/

Data from weather stations

worldwide are combined to a

continuous surface layer.

Climate data for each landrace is

extracted from this surface layer.Precipitation: 20 590 stations

Temperature: 7 280 stations

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FIGS selection is a

new method to

predict crop traits of

primitive cultivated

material from

climate variables by

using multivariate

statistical 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 FFocusedocused IIdentificationdentification of GGermplasmermplasm SStrategytrategy

Slide made byMichael Mackay 1995

http://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 characteristics being

sought by plant breeders to improve plant

productivity across numerous challenging

environments. http://www.figstraitmine.org/

FIGS salinity setFIGS salinity set20

Temperature

Salinity score

Elevation

Rainfall

Agro-climatic zone

Disease distribution

F I G SOCUSED DENTIFICATION OF ERMPLASM TRATEGY

Data layers sieve accessions

based on latitude & longitude

Slide made byMichael Mackay 1995

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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 selection 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 collecting site (2 830 acc).• Excluding dry environments below 280 mm/year• Excluding sites of low winter temperature below 10

degrees Celsius (1 502 acc)

http://dx.doi.org/10.1007/s10722-009-9427-1

Slide adopted from Ken Street, ICARDA (FIGS team)

27Priekuli (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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