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Predicting resistance to stripe (yellow) rust (Puccinia striiformis) in wheat genetic resources using focused identification of germplasm strategy

  • A. BARI (a1), A. AMRI (a1), K. STREET (a1), M. MACKAY (a2), E. DE PAUW (a3), R. SANDERS (a3), K. NAZARI (a4), B. HUMEID (a5), J. KONOPKA (a1) and F. ALO (a5)...

Summary

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a major wheat disease that can inflict yield losses of up to 70% on susceptible varieties under favourable environmental conditions. The timely identification of plant genetic resources likely to possess novel resistance to this disease would facilitate the rapid development of resistant wheat varieties. The focused identification of germplasm strategy (FIGS) approach was used to predict stripe rust resistance in a collection of wheat landraces conserved at ICARDA genebank. Long-term climate data for the collection sites, from which these accessions originated and stripe rust evaluation scores for one group of accessions were presented to three different non-linear models to explore the trait×collection site environment interactions. Patterns in the data detected by the models were used to predict stripe rust resistance in a second and different set of accessions. The results of the prediction were then tested against actual evaluation scores of the disease in the field. The study mimics the real scenario where requests are made to plant genetic resources curators to provide accessions that are likely to possess variation for specific traits such as disease resistance.

The models used were able to identify stripe rust-resistant accessions with a high degree of accuracy. Values as high as 0·75 for area under the curve and 0·45 for Kappa statistics, which quantify the agreement between the models’ predictions and the curator's disease scores, were achieved. This demonstrates a strong environmental component in the geographic distribution of resistance genes and therefore supports the theoretical basis for FIGS. It is argued that FIGS will improve the rate of gene discovery and efficiency of mining genetic resource collections for adaptive traits by reducing the number of accessions that are normally required for evaluation to identify such variation.

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Corresponding author

* To whom all correspondence should be addressed. Email: a.bari@cgiar.org

References

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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, 14651481.
Beard, C., Loughman, R., Thomas, G. & Jayasena, K. (2005). Managing Stripe Rust and Leaf Rust in Wheat. South Perth, Australia: Department of Agriculture and Food. Farmnote 43/2005, replaces 102/01. Available from: http://www.agric.wa.gov.au/PC_92983.html (verified 11 June 2013).
Bhullar, N. K., Street, K., Mackay, M., Yahiaoui, N. & Keller, B. (2009). Unlocking wheat genetic resources for the molecular identification of previously undescribed functional alleles at the Pm3 resistance locus. Proceedings of the National Academy of Sciences USA 106, 95199524.
Bhullar, N. K., Zhang, Z., Wicker, T. & Keller, B. (2010). Wheat gene bank accessions as a source of new alleles of the powdery mildew resistance gene Pm3, a large scale allele mining project. BMC Plant Biology 10, 88.
Bonman, J. M., Bockelman, H. E., Jin, Y., Hijmans, R. J. & Gironella, A. (2007). Geographic distribution of stem rust resistance in wheat landraces. Crop Science 47, 19551963.
Breiman, L. (2001). Random forests. Machine Learning 45, 532.
Brown, A. H. D. & Spillane, C. (1999). Implementing core collections – principles, procedures, progress, problems and promise. In Core Collections for Today and Tomorrow (Eds Johnson, R. C. & Hodgkin, T.), pp. 110. Madison, Wisconsin, USA: Crop Science Society of America.
Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning 20, 273297.
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J. & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology 88, 27832792.
Dedryver, F., Paillard, S., Mallard, S., Robert, O., Trottet, M., Negre, S., Verplancke, G. & Jahier, J. (2009). Characterization of genetic components involved in durable resistance to stripe rust in the bread wheat ‘Renan’. Phytopathology 99, 968973.
De Pauw, E., Göbel, W. & Adam, H. (2000). Agrometeorological aspects of agriculture and forestry in the arid zones. Agricultural and Forest Meteorology 103, 4358.
Díaz-Uriarte, R. & Alvarez De Andrés, S. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3. DOI: 10.1186/1471-2105-7-3.
Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D. & Weingessel, A. (2010). R Library (e1071): Misc Functions of the Department of Statistics. Vienna: The R Foundation for Statistical Computing.
Drake, J. M., Randin, C. & Guisan, A. (2006). Modelling ecological niches with support vector machines. Journal of Applied Ecology 43, 424432.
Duff, C., Hamblin, P. & Poole, N. (2006). Stripe Rust Management in Wheat. Kingston, ACT, Australia: Grains Research & Development Corporation (GRDC).
Duveiller, E., Singh, R. P. & Nicol, J. M. (2007). The challenges of maintaining wheat productivity: pests, diseases, and potential epidemics. Euphytica 157, 417430.
Dwivedi, S. L., Upadhyaya, H. D., Stalker, H. T., Blair, M. W., Bertioli, D. J., Nielen, S. & Ortiz, R. (2008). Enhancing crop gene pools with beneficial traits using wild relatives. In Plant Breeding Reviews vol. 30 (Ed. Janick, J.), pp. 179230. Hoboken, NJ, USA: John Wiley & Sons Inc.
El-Bouhssini, M., Street, K., Joubi, A., Ibrahim, Z. & Rihawi, F. (2009). Sources of wheat resistance to Sunn pest, Eurygaster integriceps Puton, in Syria. Genetic Resources and Crop Evolution 56, 10651069.
El-Bouhssini, M., 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, 9697.
El-Bouhssini, M., Ogbonnaya, F. C., Chen, M., Lhaloui, S., Rihawi, F. & Dabbous, A. (2013). Sources of resistance in primary synthetic hexaploid wheat (Triticum aestivum L.) to insect pests: Hessian fly, Russian wheat aphid and Sunn pest in the Fertile Crescent. Genetic Resources and Crop Evolution 60, 621627.
Endresen, D. T. F., Street, K., Mackay, M., Bari, A. & De Pauw, E. (2011). Predictive association between biotic stress traits and ecogeographic data for wheat and barley landraces. Crop Science 51, 20362055.
Endresen, D. T. F., Street, K., Mackay, M., Bari, A., Amri, A., De Pauw, E., Nazari, K. & Yahyaoui, A. (2012). Sources of resistance to stem rust (ug99) in bread wheat and durum wheat identified using focused identification of germplasm strategy (FIGS). Crop Science 52, 764773.
Evans, N., Baierl, A., Semenov, M. A., Gladders, P. & Fitt, B. D. L. (2008). Range and severity of a plant disease increased by global warming. Journal of the Royal Society: Interface 5, 525531.
FAO (2010). The Second Report on the State of the World's Plant Genetic Resources for Food and Agriculture. Rome, Italy: FAO.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters 27, 861874.
Frankel, O. H. (1984). Genetic perspective of germplasm conservation. In Genetic Manipulations: Impact on Man and Society (Eds Arber, W., Limensee, K. Peacock, W. J. & Stralinger, P.), pp. 161170. Cambridge, UK: Cambridge University Press.
Gepts, P. (2006). Plant genetic resources conservation and utilization: the accomplishments and future of a societal insurance policy. Crop Science 46, 22782292.
Glaszmann, J. C., Kilian, B., Upadhyaya, H. D. & Varshney, R. K. (2010). Accessing genetic diversity for crop improvement. Current Opinion in Plant Biology 13, 167173.
Gollin, D., Smale, M. & Skovmand, B. (2000). Searching an ex situ collection of wheat genetic resources. American Journal of Agricultural Economics 82, 812827.
Günther, F. & Fritsch, S. (2010). Neuralnet: training of neural networks. The R Journal 2, 3038.
Guo, Q., Kelly, M. & Graham, C. H. (2005). Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling 182, 7590.
Hakes, A. S. & Cronin, J. T. (2011). Environmental heterogeneity and spatiotemporal variability in plant defense traits. Oikos 120, 452462.
Heywood, J. S. (1991). Spatial analysis of genetic variation in plant populations. Annual Review of Ecology and Systematics 22, 335355.
Hoisington, D., Khairallah, M., Reeves, T., Ribaut, J. M., Skovmand, B., Taba, S. & Warburton, M. (1999). Plant genetic resources: what can they contribute toward increased crop productivity? Proceedings of the National Academy of Sciences USA 96, 59375943.
Hovmøller, M. S., Yahyaoui, A. H., Milus, E. A. & Justesen, A. F. (2008). Rapid global spread of two aggressive strains of a wheat rust fungus. Molecular Ecology 17, 38183826.
Hutchinson, M. F. (1995). Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems 9, 385403.
Hutchinson, M. F. (2000). ANUSPLIN version 4.1. User Guide. Canberra, Australia: Center for Resource and Environmental Studies, Australian National University.
Hutchinson, M. F. & Corbett, J. D. (1995). Spatial interpolation of climatic data using thin plate smoothing splines. In Co-ordination and Harmonisation of Databases and Software for Agroclimatic Applications (Ed. FAO), pp. 211224. FAO Agrometeorology Series no. 13. Rome: FAO.
Karatzoglou, A., Meyer, D. & Hornik, K. (2006). Support vector machines in R. Journal of Statistical Software 15, 128. Available from: http://www.jstatsoft.org/v15/i09 (verified 12 June 2013).
Kilian, B. & Graner, A. (2012). NGS technologies for analyzing germplasm diversity in genebanks. Briefings in Functional Genomics 11, 3850.
Koo, B. & Wright, B. D. (2000). The optimal timing of evaluation of genebank accessions and the effects of biotechnology. American Journal of Agricultural Economics 82, 797811.
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software 28, 126. Available from: http://www.jstatsoft.org/v28 (verified 12 June 2013).
Landis, J. R. & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics 33, 159174.
Li, Q., Chen, X. M., Wang, M. N. & Jing, J. X. (2011). Yr45, a new wheat gene stripe resistance on the long arm of chromosome 3D. Theoretical and Applied Genetics 122, 189197.
Liaw, A. & Wiener, M. (2002). Classification and regression by randomForest. R News 2, 1822.
Lunetta, K. L., Hayward, L. B., Segal, J. & Van Eerdewegh, P. (2004). Screening large-scale association study data: exploiting interactions using random forests. BMC Genetics 5, 32. doi: 10.1186/1471-2156-5-32.
Mackay, M. C. (1990). Strategic planning for effective evaluation of plant germplasm. In Wheat Genetic Resources: Meeting Diverse Needs (Eds Srivastava, J. P. & Damania, A. B.), pp. 2125. Chichester, UK: John Wiley & Sons.
Mackay, M. C. (1995). One core collection or many? In Core Collections of Plant Genetic Resources (Eds Hodgkin, T., Brown, A. H. D., Van Hintum, Th. J. L. & Morales, E. A. V.), pp. 199210. Chichester, UK: John Wiley & Sons.
Mackay, M. C. (2011). Surfing the genepool: the effective and efficient use of plant genetic resources. Ph.D. Thesis, The Swedish University of Agricultural Sciences, Alnarp, Sweden.
Manel, S., Schwartz, M. K., Luikart, G. & Taberlet, P. (2003). Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology and Evolution 18, 189197.
Milus, E. A., Kristensen, K. & Hovmøller, M. S. (2009). Evidence for increased aggressiveness in a recent widespread strain of Puccinia striiformis f. sp. tritici causing stripe rust of wheat. Phytopathology 99, 8994.
Nazari, K., Hodson, D. & Hovmøller, M. (2011). Yellow rust in CWANA in 2010 & 2011: the situation and measures for control. In Proceedings of the Borlaug Global Rust Initiative 2011 Technical Workshop (Ed. McIntosh, R.), p. 24. Saint Paul, Minnesota, USA: The Borlaug Global Rust Initiative.
Paillard, S., Goldringer, I., Enjalbert, J., Trottet, M., David, J., De Vallavieille-Pope, C. & Brabant, P. (2000). Evolution of resistance against powdery mildew in winter wheat populations conducted under dynamic management. II. Adult plant resistance. Theoretical and Applied Genetics 101, 457462.
Pessoa-Filho, M., Rangel, P. H. N. & Ferreira, M. E. (2010). Extracting samples of high diversity from thematic collections of large gene banks using a genetic-distance based approach. BMC Plant Biology 10, 127. DOI: 10.1186/1471-2229-10-127.
Peterson, R. F., Campbell, A. B. & Hannah, A. E. (1948). A diagramatic scale for estimating rust intensity of leaves and stem of cereals. Canadian Journal of Research Section C 26, 496500.
Podolsky, R. H. & Holtsford, T. P. (1995). Population structure of morphological traits in Clarkia dudleyana. I. Comparison of FST between allozymes and morphological traits. Genetics 140, 733744.
Polignano, G. B., Uggenti, P. & Scippa, G. (2001). Diversity analysis and core collection formation in Bari faba bean germplasm. PGR Newsletter: FAO/Bioversity 125, 3338.
Prasad, A. M., Iverson, L. R. & Liaw, A. (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9, 181199.
Principe, J. C., Euliano, N. R. & Lefebvre, W. C. (2000). Neural and Adaptive Systems: Fundamentals through Simulations. New York: John Wiley & Sons.
Qualset, C. O. (1975). Sampling germplasm in a center of diversity: an example of disease resistance in Ethiopian barley. In Crop Genetic Resources for Today and Tomorrow (Eds Frankel, O. H. & Hawkes, J. G.), pp. 8196. Cambridge, UK: Cambridge University Press.
Ramdani, A., Nazari, C., Hodson, D., Lhaloui, S., Abbad-Andaloussi, F. & Nsarellah, N. (2011). Status of wheat diseases in Morocco during the 2009–10 growing season: yellow rust is becoming a more dangerous disease. In Proceedings of the Borlaug Global Rust Initiative 2011 Technical Workshop (Ed. McIntosh, R.), p. 155. Saint Paul, Minnesota, USA: The Borlaug Global Rust Initiative.
Roelfs, A. P., Singh, R. P. & Saari, E. E. (1992). Rust Diseases of Wheat: Concepts and Methods of Disease Management. Mexico, DF: CIMMYT.
Scott, J. M., Heglund, P. J., Morrison, M. L., Haufler, J. B., Raphael, M. G., Wall, W. A. & Samson, F. B. (2002). Predicting Species Occurrences: Issues of Accuracy and Scale. Washington, DC, USA: Island Press.
Sharbel, T. F., Haubold, B. & Mitchell-Olds, T. (2000). Genetic isolation by distance in Arabidopsis thaliana: biogeography and postglacial colonization of Europe. Molecular Ecology 9, 21092118.
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J. & Kattan, M. W. (2010). Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128138.
Street, K., Mackay, M., Zuev, E., Kaur, 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 Proceedings of the 11th International Wheat Genetics Symposium (Eds Appels, R., Eastwood, R., Lagudah, E., Langridge, P., Mackay, M., Mcintyre, L. & Sharp, P.), pp. 2831. Brisbane, Australia: Sydney University Press.
Strobl, C., Malley, J. & Tutz, G. (2009). An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods 14, 323348.
Swets, J. A., Dawes, R. M. & Monahan, J. (2000). Better decisions through science. Scientific American 283, 8287.
Tirelli, T., Pozzi, L. & Pessani, D. (2009). Use of different approaches to model presence/absence of Salmo marmoratus in Piedmont (Northwestern Italy). Ecological Informatics 4, 234242.
Upadhyaya, H. D., Pundir, R. P. S., Dwivedi, S. L. & Gowda, C. L. L. (2009). Mini Core Collections for Efficient Utilization of Plant Genetic Resources in Crop Improvement Programs. Information Bulletin 78. Patancheru, Andhra Pradesh, India: ICRISAT.
Venables, W. N. & Ripley, B. D. (2002). Modern Applied Statistics with S, 4th edn. New York: Springer.
Walter, S., Fejer-Justesen, A., De Vallavieille-Pope, C. & Hovmøller, M. S. (2011). Global distribution of aggressive wheat yellow rust strains. In Proceedings of the Borlaug Global Rust Initiative 2011 Technical Workshop (Ed. McIntosh, R.), p. 155. Saint Paul, Minnesota, USA: The Borlaug Global Rust Initiative.
Warner, B. & Misra, M. (1996). Understanding neural networks as statistical tools. The American Statistician 50, 284293.
Wellings, C. R. (2011). Global status of stripe rust: a review of historical and current threats. Euphytica 179, 129141.
Xu, Y. (2010). Plant genetic resources: Management, evaluation and enhancement. In Molecular Plant Breeding (Ed. Xu, Y.), pp. 151194. Wallingford, UK: CAB International.

Predicting resistance to stripe (yellow) rust (Puccinia striiformis) in wheat genetic resources using focused identification of germplasm strategy

  • A. BARI (a1), A. AMRI (a1), K. STREET (a1), M. MACKAY (a2), E. DE PAUW (a3), R. SANDERS (a3), K. NAZARI (a4), B. HUMEID (a5), J. KONOPKA (a1) and F. ALO (a5)...

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