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Sources of resistance to stem rust (Ug99) in bread wheat and durum wheat identified using focused identification of germplasm strategy

By: Endresen, D.T.F.
Contributor(s): Amri, A | Bari, A [coaut.] | De Pauw, E [coaut.] | Mackay, M [coaut.] | Nazari, K [coaut.] | Street, K [coaut.] | Yahyaoui, A.H [coaut.].
Material type: materialTypeLabelArticlePublisher: 2012ISSN: 1435-0653 (Revista en electrónico); 0011-183X. In: Crop Science v. 52, no. 2, p. 764-773Summary: The focused identification of germplasm strategy (FIGS) has been validated using predictive computer models in simulation studies to predict a priori known trait scores. This study was designed as a ?blind? study where the person calculating the computer model did not know the actual trait scores. This study design provides a more realistic test of the predictive capacity of the FIGS approach compared to previous studies. Furthermore this study also explored the suitability of FIGS for the identification of resistance in bread wheat (Triticum aestivum L. subsp. aestivum) and durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.] to Ug99?a strain of stem rust (Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) and typified to race TTKSK. The predictions were validated against a dataset with the screening of wheat accessions conducted in Yemen in 2008. Only a small training set representing 20% of the trait screening results was disclosed to the person conducting the data analysis for the calibration of the prediction model. The hit rate for identification of Ug99-resistant accessions was more than two times higher when using the FIGS approach compared to a random selection of accessions. These results suggested that FIGS was well suited for the identification of samples with resistance to fungal pathogens. It is therefore recommended that FIGS approach be used as a complement to expert knowledge and experience when selecting accessions for plant breeding and crop research activities.Collection: Reprints Collection
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Article CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

Reprints Collection Available
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Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0011-183X

The focused identification of germplasm strategy (FIGS) has been validated using predictive computer models in simulation studies to predict a priori known trait scores. This study was designed as a ?blind? study where the person calculating the computer model did not know the actual trait scores. This study design provides a more realistic test of the predictive capacity of the FIGS approach compared to previous studies. Furthermore this study also explored the suitability of FIGS for the identification of resistance in bread wheat (Triticum aestivum L. subsp. aestivum) and durum wheat [Triticum turgidum L. subsp. durum (Desf.) Husn.] to Ug99?a strain of stem rust (Puccinia graminis Pers. f. sp. tritici Eriks. & Henn.) and typified to race TTKSK. The predictions were validated against a dataset with the screening of wheat accessions conducted in Yemen in 2008. Only a small training set representing 20% of the trait screening results was disclosed to the person conducting the data analysis for the calibration of the prediction model. The hit rate for identification of Ug99-resistant accessions was more than two times higher when using the FIGS approach compared to a random selection of accessions. These results suggested that FIGS was well suited for the identification of samples with resistance to fungal pathogens. It is therefore recommended that FIGS approach be used as a complement to expert knowledge and experience when selecting accessions for plant breeding and crop research activities.

Global Wheat Program

English

No CIMMYT affiliation|Crop Science Society of America (CSSA)

Lucia Segura

INT3301

Reprints Collection

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