Predictive characterization methods for accessing and using CWR diversityThormann I, Parra-Quijano M, Iriondo JM, Rubio-Teso ML, Endresen DT, Dias S, van Etten J, Maxted N

ENHANCED GENEPOOL UTILIZATION, Cambridge 16-20 June 2014

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One aim of PGR-Secure: Research novel characterization techniques for CWR + LR high throughput phenotyping metabolomics transcriptomics predictive characterization through FIGS

FIGS (focused identification of germplasm strategy) carries out a predictive characterization of yet uncharacterized germplasm by assigning potential phenotypic or genotypic properties using environmental information from collecting sites or C/E data from already characterized samples as predictor.Environmental profiles are used as filters to increase the likelihood of finding trait of interest when selecting accessions for field trials.

Assumption: different environments generate different selective pressures and genetic differentiation of adaptive value.

PGR-Secure context

WP1WP2

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• Predictive association between trait data and ecogeographic data for Nordic barley landraces • Predictive association between biotic stress traits and ecogeographic data for wheat and barley • Ug99 wheat rust:

– Traditional characterization: 4563 wheat LR screenedfor Ug99 in Yemen 2007 10.2 % resistant accessions

– FIGS predictive characterization: 500 accessions selected from3728 accession 25.8% resistant accessions

• Net blotch - barley• Boron toxicity - wheat• Sunn pest - wheat • Powdery mildew - wheat• Russian wheat aphid• Drought – faba bean

Bari et al 2012; El Bouhssini et al 2011; Endresen 2010; Endresen et al 2011, 2012; Khazaei et al 2013; Mackay and Street 2004; Street et al 2008

Examples of predictive association studies and identification of resistant material through the use of FIGS

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• Predictive association between trait data and ecogeographic data for Nordic barley landraces • Predictive association between biotic stress traits and ecogeographic data for wheat and barley • Ug99 wheat rust:

– Traditional characterization: 4563 wheat LR screenedfor Ug99 in Yemen 2007 10.2 % resistant accessions

– FIGS predictive characterization: 500 accessions selected from3728 accession 25.8% resistant accessions

• Net blotch - barley• Boron toxicity - wheat• Sunn pest - wheat • Powdery mildew - wheat• Russian wheat aphid• Drought – faba bean

Bari et al 2012, El Bouhssini et al 2011; Endresen 2010; Endresen et al 2011, 2012; Khazaei et al 2013; Mackay and Street 2004; Street et al 2008

Examples of predictive association studies and identification of resistant material through the use of FIGS

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Two FIGS methods were adapted to optimize the search for populations and accessions with targeted adaptive traits in LR and CWR in thePGR-Secure genera

Ecogeographical filtering method

Calibration method

The various existing methodsmainly differ in the way in whichthe environmental profile usedas filter is developed and embeddedin the process

FIGS methods used in PGR-Secure project

Target traits identified in PGR Secure project in collaboration with breeders and crop experts

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Major steps

1) Compile + clean occurrence data• Data sources: GRIN, SINGER, EURISCO,

GBIF• Data cleaning• Georeferencing• Quality check of existing geographic coordinates (now through online tool developed in

CAPFITOGEN)

passport data set of occurrences of the target taxon, with a minimum of duplicate records, and with verified geographic coordinates

Ecogeographical filtering method

spatial distributionof the target species

ecogeographical identification of those environments that are likely to impose selection pressure for the target trait

Genus LR all records

CWR all records

Avena 3855 3900Beta 1614 1596Brassica 3606 886Medicago 149 2153

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2) Develop ecogeographical landcharacterization map

• ELC maps represent the adaptive scenarios that are present over the territory studied

• Requires to identify the bioclimatic, edaphic and/or geophysicalvariables that determine the spatial distribution of the species

• Map development now supported by CAPFITOGENtools

Ecogeographical filtering method

Variables identified based on literature and expert knowledge as relevant for the geographical distribution of Avena

Avena ELC map

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Ecogeographical filtering method

Beta ELC map

Variables Bioclimatic Geophysic

BIO3 Isothermality (BIO2/BIO7) (* 100)

NORTHNESS Northness

BIO6 Min temperature of coldest month

ELEVATION Elevation

BIO12 Mean annual precipitation SOLRADOP Global irradiation on an optimal inclination

PRECIP2 Average February precipitation

PRECIP6 Average June precipitation Edaphic

PRECIP7 Average July precipitation MINERALOGY Mineralogical profile of soil

PRECIP8 Average August precipitation WRBCODESTU World reference base for soil resources (WRB) coder for soil typological unit (STU)

TMED1 January mean temperature DEPTHTOROC Depth to rocks

TMED3 March mean temperature DOMPARMAT Dominant parent material (obstacle to roots)

TMED11 November mean temperature

TMIN1 Average January minimum temperature

TMIN12 Average December minimum temperature

Variables identified based on literature and expert knowledge as relevant for the geographical distribution of Beta

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3) Identify the most appropriate variables that describe the environment profile (EP) of sites where the target trait may evolve, and threshold values

• Based on literature research and expert consultations• Data for identified variables is added to the

occurrence data file

Iar-DM value Zone classification

0 - 5 Extremely arid (desert)

5 - 10 Arid (steppic)

10 - 20 Semiarid (mediterranean)

20 - 30 Subhumid

30 - 60 Humid

> 60 Perhumid

Ecogeographical filtering method

De Martonne aridity index, threshold value for Beta: < 10

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4) Filtering in R – environment using the R – script developed for this method

• The script first produces an optimized subset based on ELC map

• Then records are selected based on the EP threshold value

Ecogeographical filtering method

Genus LR all records

CWR all records

LR identified subset

CWR identifiedSubset

Avena 3855 3900 103 171Beta 1614 1596 133 33Brassica 3606 886 121 275Medicago 149 2153 4 54

Results for PGR Secure project genera: Number of total records and number of selected records

Using the R script developed in PGR Secure

Distribution of Beta CWR – selected records in pink

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Major steps 1) Compile occurrence and climate data of uncharacterized

accessions (= test set)

2) Compile C/E and climate data for training and calibration set

3) Run R – script on training set to calibrate model based on relationship identified between trait and environment

4) Fine tune model with calibration set

5) Run test set through model to select occurrences

Insufficient C/E data available for LR and CWR of Avena, Beta, Brassica, Medicago

Calibration method

Existing evaluation data for trait of interest

Climate data specific to the environment at collecting sites

Model relationships between trait and environment

Builds a computer model explaining the crop trait score from the climate data

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Implemented assumption: different environmental conditions generate different selective pressures and genetic differentiation of adaptive value accurate georeferenced information about accessions/populations is required

to allow extraction of climate, edaphic and geophysic data interest in making use of the increasing number of environmental variables

and their quality that are made available globally ELC maps and calibration models correctly reflect the different

environmental conditions EP: correctly assigning an environmental variable (for which we have data on

the territory) that is strongly linked to the environmental conditions that promote a particular targeted trait

Useful for LR + CWR, but not for improved varieties (complex pedigree)

Critical aspects and limitations

Next steps

Publication of guidelines on how to use these FIGS methods, including• Detailed steps• Example data • R – scripts

Application of FIGS methods in new EU – ACP funded project SADC Crop Wild Relatives

Project objective: Enhance link between conservation and use of CWR through• Scientific capacity building• Development of National Strategic Action Plans

for the conservation and use of CWR

Thank you