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Genome-wide markers can predict adult plant resistance to wheat stem rust

By: Rutkoski, J.
Contributor(s): McIntosh, R | Sorrells, M [coaut.] | Huerta-Espino, J [coaut.] | Sukhwinder-Singh [coaut.] | Singh, R.P [coaut.] | Bhavani, S [coaut.].
Material type: materialTypeLabelBookAnalytics: Show analyticsPublisher: 2011Description: p. 190.Online resources: Access only for CIMMYT Staff Summary: Genomic selection (GS) prediction models for stem rust adult plant resistance (APR) were created and validated using a training population consisting of lines from the 2nd SRRSN (Stem Rust Resistance Screening Nursery), selected candidates of the 5th and 6th SRRSN, and 3 biparental APR mapping populations. The lines were phenotyped for adult plant resistance to stem rust race Ug99 and/derivatives at KARI, Njoro, Kenya. Lines with low reactions characteristic of major gene resistance were removed from the training population. Diversity Arrays Technology (DArT) markers were used for genome-wide genotyping of the training population. Marker imputation, implemented in MACH 1.0, a Markov Chain based haplotyper, was used to unite datasets genotyped with different sets of markers. Four different statistical models were applied and compared for prediction accuracy. Accuracies were calculated using 5-fold cross validation, where accuracy was measured as the Pearson?s correlation between the genomic estimated breeding values (GEBVs) and best linear unbiased predictors (BLUPs) of the phenotypes. Removal of 10 empirically identified outlying individuals led to gains of 0.1 in accuracy. Prediction accuracies ranged from 0.2 to 0.8 with a mean of 0.6, indicating that GS would lead to an increase in gain from selection per unit time compared to phenotypic selection.
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Conference proceedings CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

CIMMYT Staff Publications Collection CIS-6341 (Browse shelf) Available
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Abstract only

Genomic selection (GS) prediction models for stem rust adult plant resistance (APR) were created and validated using a training population consisting of lines from the 2nd SRRSN (Stem Rust Resistance Screening Nursery), selected candidates of the 5th and 6th SRRSN, and 3 biparental APR mapping populations. The lines were phenotyped for adult plant resistance to stem rust race Ug99 and/derivatives at KARI, Njoro, Kenya. Lines with low reactions characteristic of major gene resistance were removed from the training population. Diversity Arrays Technology (DArT) markers were used for genome-wide genotyping of the training population. Marker imputation, implemented in MACH 1.0, a Markov Chain based haplotyper, was used to unite datasets genotyped with different sets of markers. Four different statistical models were applied and compared for prediction accuracy. Accuracies were calculated using 5-fold cross validation, where accuracy was measured as the Pearson?s correlation between the genomic estimated breeding values (GEBVs) and best linear unbiased predictors (BLUPs) of the phenotypes. Removal of 10 empirically identified outlying individuals led to gains of 0.1 in accuracy. Prediction accuracies ranged from 0.2 to 0.8 with a mean of 0.6, indicating that GS would lead to an increase in gain from selection per unit time compared to phenotypic selection.

Genetic Resources Program|Global Wheat Program

English

Lucia Segura

INT2843|INT0610|INT3098

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