New predictive characterization methods for accessing and using crop wild relatives diversity
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The document discusses predictive characterization methods for assessing and utilizing genetic diversity in crop wild relatives (CWR) and landraces (LR), emphasizing the focused identification of germplasm strategy (FIGS) to enhance trait selection based on ecogeographic data. It highlights the relationship between environmental factors and traits, showcasing successful case studies like the increased resistance in wheat accessions to UG99 rust using predictive techniques. The methods developed aim to optimize the identification of accessions with adaptive traits and propose next steps for the use of FIGS methods in new projects.
New predictive characterization methods for accessing and using crop wild relatives diversity
- 1. Predictive characterization methods for accessing and using CWR diversity Thormann 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
- 2. 2 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 WP1 WP2
- 3. 3 • 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 screened for Ug99 in Yemen 2007 10.2 % resistant accessions – FIGS predictive characterization: 500 accessions selected from 3728 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
- 4. 4 • 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 screened for Ug99 in Yemen 2007 10.2 % resistant accessions – FIGS predictive characterization: 500 accessions selected from 3728 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
- 5. 5 Two FIGS methods were adapted to optimize the search for populations and accessions with targeted adaptive traits in LR and CWR in the PGR-Secure genera Ecogeographical filtering method Calibration method The various existing methods mainly differ in the way in which the environmental profile used as filter is developed and embedded in the process FIGS methods used in PGR-Secure project Target traits identified in PGR Secure project in collaboration with breeders and crop experts
- 6. 6 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 distribution of 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 3900 Beta 1614 1596 Brassica 3606 886 Medicago 149 2153
- 7. 7 2) Develop ecogeographical land characterization map • ELC maps represent the adaptive scenarios that are present over the territory studied • Requires to identify the bioclimatic, edaphic and/or geophysical variables that determine the spatial distribution of the species • Map development now supported by CAPFITOGEN tools Ecogeographical filtering method Variables identified based on literature and expert knowledge as relevant for the geographical distribution of Avena Avena ELC map
- 8. 8 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
- 9. 9 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
- 10. 10 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 identified Subset Avena 3855 3900 103 171 Beta 1614 1596 133 33 Brassica 3606 886 121 275 Medicago 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
- 11. 11 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
- 12. 12 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
- 13. 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
- 14. Thank you
Editor's Notes
- #3: And one task was called the predictive characterization Wild relatives are shaped by the environment Add here a sentence about using the link between collecting site, the environment that can be defined based of the location and the assumed link with diversity that is used for core collections and targeted samplling or gap assessment in collections.
- #4: Bari, A., Street, K., Mackay, M., Endresen, D.T.F., de Pauw, E., & Amri A. (2012). Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables. Genetic Resources and Crop Evolution, 59:1465-1481. DOI:10.1007/s10722-011-9775-5 El Bouhssini, M.E., Street, K., Amri, A., Mackay, M., Ogbonnaya, F.C., Omran, A., Abdalla, O., Baum, M., Dabbous, A., & Rihawi, F. (2011). Sources of resistance in bread wheat to Russian wheat aphid (Diuraphis noxia) in Syria identified using the focused identification of germplasm strategy (FIGS). Plant Breeding, 130: 96-97. DOI:10.1111/j.1439-0523.2010.01814.x Endresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw, K. Nazari, and A. Yahyaoui (2012). Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy (FIGS). Crop Science [online first]. doi: 10.2135/cropsci2011.08.0427; Published online 8 Dec 2011. Endresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw (2011). Predictive association between biotic stress traits and ecogeographic data for wheat and barley landraces. Crop Science 51: 2036-2055. DOI: 10.2135/cropsci2010.12.0717 Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Science 50: 2418-2430. DOI: 10.2135/cropsci2010.03.0174 Khazaei, H., Street, K., Bari, A., Mackay, M., & Stoddard, F.L. (2013). The FIGS (focused identification of germplasm strategy) approach identifies traits related to drought adaptation in Vicia faba genetic resources. PLoS ONE, 8(5): e63107. DOI:10.1371/journal.pone.0063107 Mackay, M. C., & Street, K. (2004). Focused identification of germplasm strategy – FIGS. In: Black, C. K., Panozzo, J.F., and Rebetzke, G.J. (Eds), Cereals 2004. Proceedings of the 54th Australian Cereal Chemistry Conference and the 11th Wheat Breeders’ Assembly, 21-24 September 2004, Canberra, Australian Capital Territory (ACT) (pp. 138-141). Cereal Chemistry Division, Royal Australian Chemical Institute, Melbourne, Australia. Street, K., Mackay, M., Zuev, E., Kaul, N., El Bouhssini, M., Konopka, J., & Mitrofanova, O. (2008). Diving into the genepool - a rational system to access specific traits from large germplasm collections. In Appels, R., Eastwood, R., Lagudah, E., Langridge, P., Mackay, M., McIntyre, L., and Sharp, P. (Eds), The 11th International Wheat Genetics Symposium proceedings. Sydney University Press, Sydney, Australia. ISBN: 978-1-920899-14-1. Available at http://hdl.handle.net/2123/3390, verified 18 June 2014.
- #5: Bari, A., Street, K., Mackay, M., Endresen, D.T.F., de Pauw, E., & Amri A. (2012). Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables. Genetic Resources and Crop Evolution, 59:1465-1481. DOI:10.1007/s10722-011-9775-5 El Bouhssini, M.E., Street, K., Amri, A., Mackay, M., Ogbonnaya, F.C., Omran, A., Abdalla, O., Baum, M., Dabbous, A., & Rihawi, F. (2011). Sources of resistance in bread wheat to Russian wheat aphid (Diuraphis noxia) in Syria identified using the focused identification of germplasm strategy (FIGS). Plant Breeding, 130: 96-97. DOI:10.1111/j.1439-0523.2010.01814.x Endresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw, K. Nazari, and A. Yahyaoui (2012). Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy (FIGS). Crop Science [online first]. doi: 10.2135/cropsci2011.08.0427; Published online 8 Dec 2011. Endresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw (2011). Predictive association between biotic stress traits and ecogeographic data for wheat and barley landraces. Crop Science 51: 2036-2055. DOI: 10.2135/cropsci2010.12.0717 Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Science 50: 2418-2430. DOI: 10.2135/cropsci2010.03.0174 Khazaei, H., Street, K., Bari, A., Mackay, M., & Stoddard, F.L. (2013). The FIGS (focused identification of germplasm strategy) approach identifies traits related to drought adaptation in Vicia faba genetic resources. PLoS ONE, 8(5): e63107. DOI:10.1371/journal.pone.0063107 Mackay, M. C., & Street, K. (2004). Focused identification of germplasm strategy – FIGS. In: Black, C. K., Panozzo, J.F., and Rebetzke, G.J. (Eds), Cereals 2004. Proceedings of the 54th Australian Cereal Chemistry Conference and the 11th Wheat Breeders’ Assembly, 21-24 September 2004, Canberra, Australian Capital Territory (ACT) (pp. 138-141). Cereal Chemistry Division, Royal Australian Chemical Institute, Melbourne, Australia. Street, K., Mackay, M., Zuev, E., Kaul, N., El Bouhssini, M., Konopka, J., & Mitrofanova, O. (2008). Diving into the genepool - a rational system to access specific traits from large germplasm collections. In Appels, R., Eastwood, R., Lagudah, E., Langridge, P., Mackay, M., McIntyre, L., and Sharp, P. (Eds), The 11th International Wheat Genetics Symposium proceedings. Sydney University Press, Sydney, Australia. ISBN: 978-1-920899-14-1. Available at http://hdl.handle.net/2123/3390, verified 18 June 2014.
- #10: Important to note that we have developed R scripts that run through these analyses
- #11: Important to note that we have developed R scripts that run through these analyses
- #12: Training set For the initial calibration or training step. Calibration set Further calibration, tuning step Often cross-validation on the training set is used to reduce the consumption of raw data. Test set For the model validation or goodness of fit testing. External data, not used in the model calibration.
- #14: ACP = The Secretariat of the African, Caribbean and Pacific (ACP) Group of States