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ARS Home » Midwest Area » St. Paul, Minnesota » Cereal Disease Lab » Research » Publications at this Location » Publication #379487

Research Project: Cereal Rust: Pathogen Biology and Host Resistance

Location: Cereal Disease Lab

Title: Genomic prediction of rust resistance in tetraploid wheat under field and controlled environment conditions

Author
item AZIZINIA, SHIVA - Agriculture Victoria
item BARIANA, HARBANS - University Of Sydney
item Kolmer, James - Jim
item PASAM, RAJ - Agriculture Victoria
item BHAVANI, SRIDHAR - International Maize & Wheat Improvement Center (CIMMYT)
item CHHETRI, MUMTA - University Of Sydney
item TOOR, ARVINDER - University Of Sydney
item MIAH, HANIF - University Of Sydney
item HAYDEN, MATTHEW - Agriculture Victoria
item DEL CARPIO, DUNIA - Agriculture Victoria
item BANSAL, URMIL - University Of Sydney
item DAETWYLER, HANS - Agriculture Victoria

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2020
Publication Date: 11/23/2020
Citation: Azizinia, S., Bariana, H., Kolmer, J.A., Pasam, R., Bhavani, S., Chhetri, M., Toor, A., Miah, H., Hayden, M.J., Del Carpio, D.P., Bansal, U., Daetwyler, H.D. 2020. Genomic prediction of rust resistance in tetraploid wheat under field and controlled environment conditions. Agronomy. 10. Article e1843. https://doi.org/10.3390/agronomy10111843.
DOI: https://doi.org/10.3390/agronomy10111843

Interpretive Summary: Wheat is attacked by the leaf rust fungus called Puccinia triticina and stripe rust called Puccinia striiformis. Many of the leaf rust resistance genes in wheat cultivars no longer give good resistance to leaf rust and stripe rust, since biotypes of the rust fungi have the ability to overcome the resistance genes, therefore breeding for rust resistance is a continual process. DNA sequencing technologies and bio-informatics analyses can be used to speed up the process of breeding wheat cultivars with improved leaf and stripe rust resistance. In this study, three different methods of analysis of DNA markers and rust resistance testing of durum wheat with seedling plants and adult plants were tested to determine how these methods can be used to select rust resistant cultivars. The results indicated that use of DNA markers and rust resistance data can be used in a bio-informatics analyses in early generations of selection to speed up development of rust resistant cultivars.

Technical Abstract: Genomic selection can increase the rate of genetic gain in crops by shortening the breeding cycle time and reducing phenotyping cost. We performed genomic prediction for wheat rust disease resistance in tetraploid wheat accessions using three cross-validation schemes: 1) leaving one site out, with the objective of predicting rust resistance when individuals are not tested in all environments/locations; 2) leaving one year out for simulating models to predict future performance of lines; and 3) bivariate analysis for predicting adult plant resistance (APR) of lines where all stage resistance (ASR) seedling phenotypes were included as the second trait. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling response for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) resistance in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 SNP markers. Different Bayesian models and GBLUP yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr were with a range of 0.56-0.71 for Lr and 0.51-0.56 for Yr. The use of seedling assays in genomic prediction was underscored by significant genetic correlations between ASR and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased accuracy for all rust types. Our work suggests the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection early in the breeding cycle.