Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T10:33:33.107Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  17 April 2014

A. BARI ,
A. AMRI ,
K. STREET ,
M. MACKAY ,
E. DE PAUW ,
R. SANDERS ,
K. NAZARI ,
B. HUMEID ,
J. KONOPKA  and
F. ALO
A. BARI*
Affiliation:
International Centre for Agricultural Research in the Dry Areas, BIGM, P. O. Box 6299 Rabat-Instituts, Rabat, Morocco
A. AMRI
Affiliation:
International Centre for Agricultural Research in the Dry Areas, BIGM, P. O. Box 6299 Rabat-Instituts, Rabat, Morocco
K. STREET
Affiliation:
International Centre for Agricultural Research in the Dry Areas, BIGM, P. O. Box 6299 Rabat-Instituts, Rabat, Morocco
M. MACKAY
Affiliation:
Queensland Alliance for Agricultural and Food Innovation, The University of Queensland, St Lucia Qld 4072, Australia
E. DE PAUW
Affiliation:
International Centre for Agricultural Research in the Dry Areas, Amman, Jordan
R. SANDERS
Affiliation:
International Centre for Agricultural Research in the Dry Areas, Amman, Jordan
K. NAZARI
Affiliation:
International Centre for Agricultural Research in the Dry Areas, Ankara, Turkey
B. HUMEID
Affiliation:
International Centre for Agricultural Research in the Dry Areas, Aleppo, Syrian Arab Republic
J. KONOPKA
Affiliation:
International Centre for Agricultural Research in the Dry Areas, BIGM, P. O. Box 6299 Rabat-Instituts, Rabat, Morocco
F. ALO
Affiliation:
International Centre for Agricultural Research in the Dry Areas, Aleppo, Syrian Arab Republic
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 6 , December 2014 , pp. 906 - 916
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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.Google Scholar
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).Google Scholar
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, 9519–9524.Google Scholar
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.Google Scholar
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, 1955–1963.CrossRefGoogle Scholar
Breiman, L. (2001). Random forests. Machine Learning 45, 5–32.Google Scholar
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. 1–10. Madison, Wisconsin, USA: Crop Science Society of America.Google Scholar
Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning 20, 273–297.CrossRefGoogle Scholar
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, 2783–2792.Google Scholar
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, 968–973.Google Scholar
De Pauw, E., Göbel, W. & Adam, H. (2000). Agrometeorological aspects of agriculture and forestry in the arid zones. Agricultural and Forest Meteorology 103, 43–58.Google Scholar
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.Google Scholar
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.Google Scholar
Drake, J. M., Randin, C. & Guisan, A. (2006). Modelling ecological niches with support vector machines. Journal of Applied Ecology 43, 424–432.CrossRefGoogle Scholar
Duff, C., Hamblin, P. & Poole, N. (2006). Stripe Rust Management in Wheat. Kingston, ACT, Australia: Grains Research & Development Corporation (GRDC).Google Scholar
Duveiller, E., Singh, R. P. & Nicol, J. M. (2007). The challenges of maintaining wheat productivity: pests, diseases, and potential epidemics. Euphytica 157, 417–430.Google Scholar
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. 179–230. Hoboken, NJ, USA: John Wiley & Sons Inc.Google Scholar
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, 1065–1069.Google Scholar
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, 96–97.Google Scholar
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, 621–627.Google Scholar
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, 2036–2055.Google Scholar
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, 764–773.Google Scholar
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, 525–531.Google Scholar
FAO (2010). The Second Report on the State of the World's Plant Genetic Resources for Food and Agriculture. Rome, Italy: FAO.Google Scholar
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874.CrossRefGoogle Scholar
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. 161–170. Cambridge, UK: Cambridge University Press.Google Scholar
Gepts, P. (2006). Plant genetic resources conservation and utilization: the accomplishments and future of a societal insurance policy. Crop Science 46, 2278–2292.Google Scholar
Glaszmann, J. C., Kilian, B., Upadhyaya, H. D. & Varshney, R. K. (2010). Accessing genetic diversity for crop improvement. Current Opinion in Plant Biology 13, 167–173.Google Scholar
Gollin, D., Smale, M. & Skovmand, B. (2000). Searching an ex situ collection of wheat genetic resources. American Journal of Agricultural Economics 82, 812–827.Google Scholar
Günther, F. & Fritsch, S. (2010). Neuralnet: training of neural networks. The R Journal 2, 30–38.Google Scholar
Guo, Q., Kelly, M. & Graham, C. H. (2005). Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling 182, 75–90.CrossRefGoogle Scholar
Hakes, A. S. & Cronin, J. T. (2011). Environmental heterogeneity and spatiotemporal variability in plant defense traits. Oikos 120, 452–462.Google Scholar
Heywood, J. S. (1991). Spatial analysis of genetic variation in plant populations. Annual Review of Ecology and Systematics 22, 335–355.Google Scholar
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, 5937–5943.CrossRefGoogle ScholarPubMed
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, 3818–3826.Google Scholar
Hutchinson, M. F. (1995). Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems 9, 385–403.Google Scholar
Hutchinson, M. F. (2000). ANUSPLIN version 4.1. User Guide. Canberra, Australia: Center for Resource and Environmental Studies, Australian National University.Google Scholar
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. 211–224. FAO Agrometeorology Series no. 13. Rome: FAO.Google Scholar
Karatzoglou, A., Meyer, D. & Hornik, K. (2006). Support vector machines in R. Journal of Statistical Software 15, 1–28. Available from: http://www.jstatsoft.org/v15/i09 (verified 12 June 2013).CrossRefGoogle Scholar
Kilian, B. & Graner, A. (2012). NGS technologies for analyzing germplasm diversity in genebanks. Briefings in Functional Genomics 11, 38–50.Google Scholar
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, 797–811.Google Scholar
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software 28, 1–26. Available from: http://www.jstatsoft.org/v28 (verified 12 June 2013).Google Scholar
Landis, J. R. & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics 33, 159–174.Google Scholar
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, 189–197.Google Scholar
Liaw, A. & Wiener, M. (2002). Classification and regression by randomForest. R News 2, 18–22.Google Scholar
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.Google Scholar
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. 21–25. Chichester, UK: John Wiley & Sons.Google Scholar
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. 199–210. Chichester, UK: John Wiley & Sons.Google Scholar
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.Google Scholar
Manel, S., Schwartz, M. K., Luikart, G. & Taberlet, P. (2003). Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology and Evolution 18, 189–197.Google Scholar
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, 89–94.Google Scholar
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.Google Scholar
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, 457–462.Google Scholar
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.Google Scholar
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, 496–500.Google Scholar
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, 733–744.Google Scholar
Polignano, G. B., Uggenti, P. & Scippa, G. (2001). Diversity analysis and core collection formation in Bari faba bean germplasm. PGR Newsletter: FAO/Bioversity 125, 33–38.Google Scholar
Prasad, A. M., Iverson, L. R. & Liaw, A. (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9, 181–199.CrossRefGoogle Scholar
Principe, J. C., Euliano, N. R. & Lefebvre, W. C. (2000). Neural and Adaptive Systems: Fundamentals through Simulations. New York: John Wiley & Sons.Google Scholar
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. 81–96. Cambridge, UK: Cambridge University Press.Google Scholar
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.Google Scholar
Roelfs, A. P., Singh, R. P. & Saari, E. E. (1992). Rust Diseases of Wheat: Concepts and Methods of Disease Management. Mexico, DF: CIMMYT.Google Scholar
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.Google Scholar
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, 2109–2118.Google Scholar
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, 128–138.Google Scholar
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. 28–31. Brisbane, Australia: Sydney University Press.Google Scholar
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, 323–348.Google Scholar
Swets, J. A., Dawes, R. M. & Monahan, J. (2000). Better decisions through science. Scientific American 283, 82–87.Google Scholar
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, 234–242.CrossRefGoogle Scholar
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.Google Scholar
Venables, W. N. & Ripley, B. D. (2002). Modern Applied Statistics with S, 4th edn. New York: Springer.Google Scholar
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.Google Scholar
Warner, B. & Misra, M. (1996). Understanding neural networks as statistical tools. The American Statistician 50, 284–293.Google Scholar
Wellings, C. R. (2011). Global status of stripe rust: a review of historical and current threats. Euphytica 179, 129–141.CrossRefGoogle Scholar
Xu, Y. (2010). Plant genetic resources: Management, evaluation and enhancement. In Molecular Plant Breeding (Ed. Xu, Y.), pp. 151–194. Wallingford, UK: CAB International.Google Scholar