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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #304166
Title: Genomic selection for quantitative adult plant stem rust resistance in wheat

Author
item RUTKOSKI, JESSICA - Cornell University
item SORRELLS, MARK - Cornell University
item Poland, Jesse
item SINGH, RAVI - International Maize & Wheat Improvement Center (CIMMYT)
item HUERTA-ESPINO, JULIO - Instituto Nacional De Investigaciones Forestales Y Agropecuarias (INIFAP)
item BHAVANI, SRIDHAR - International Maize & Wheat Improvement Center (CIMMYT)
item BARBIER, HUGUES - Cornell University
item Rouse, Matthew - Matt
item Jannink, Jean-Luc

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2014
Publication Date: 5/7/2014
Citation: Rutkoski, J.E., Sorrells, M., Poland, J.A., Singh, R.P., Huerta-Espino, J., Bhavani, S., Barbier, H., Rouse, M.N., Jannink, J. 2014. Genomic selection for quantitative adult plant stem rust resistance in wheat. The Plant Genome. DOI: 10.3835/plantgenome2014.02.0006.

Interpretive Summary: Quantitative adult plant resistance (APR) to stem rust in wheat is caused by many genes and the stem rust pathogen (Puccinia graminis f. sp. tritici) may evolve virulence against it more slowly than against resistance caused by single genes. Because APR is caused by many genes, genomic selection (GS), which uses genome-wide markers to help identify the best experimental lines in a breeding program, may be an effective improvement method. Prior study has identified a few specific loci that have noticeable effects on APR. To evaluate the relative importance of known APR loci in applying genomic selection, we characterized a set of germplasm at important APR loci and genome-wide. Using this germplasm, we evaluated prediction models for APR using data from international stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with standard GS prediction models. We found the Sr2 region of the wheat genome to play an important role in APR in this germplasm. A model using Sr2 linked markers in addition to the genome-wide markers was 10% more accurate than ordinary a model using genome-wide markers alone. This difference however, was not significant. Incorporating seedling phenotype information also led to a slight but non-significant increase in accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, adding these genotypes to the model can lead to better predictions.

Technical Abstract: Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying genomic selection, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequencing. Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G-BLUP), Bayesian LASSO (BL), and Bayes Cp (BCp). We found the Sr2 region to play an important role in APR in this germplasm. A model using Sr2 linked markers as fixed effects in G-BLUP was 32% more accurate than MLR with Sr2 lined markers, and 9.8% more accurate than ordinary G-BLUP; however, these differences were not significant. Incorporating seedling phenotype information as fixed effects in G-BLUP led to a slight but non-significant increase in accuracy over other GS models tested. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, modeling these genotypes as fixed effects can lead to better predictions.