Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. 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 responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that 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 in early generations of the breeding cycle.

Global wheat production is constrained by rust diseases and deployment of combinations of genetically diverse sources of resistance in new cultivars is accepted as a preferred means of rust control. This investigation was planned to identify new genomic regions contributing towards resistance to three rust diseases under field conditions in a W195/ BT-Schomburgk Selection (BTSS) RIL population. The RIL population was assessed for variation in stripe rust, leaf rust and stem rust responses at two locations during 2012 and 2013 crop seasons and was subjected to DArTseq genotyping. Linkage map was constructed using 3439 DArTseq markers with an average marker density of 2.7 cM. Composite interval mapping detected three QTL each for stripe rust, leaf rust and stem rust resistance in W195/BTSS RIL population. Two consistent QTL for stripe rust, QYr.sun-3BS and QYr.sun-4DL, were contributed by W195 and one inconsistent QTL, QYr.sun-7AS, by BTSS. In the case of leaf rust QLr.sun-2BS and QLr.sun-3BS were contributed by BTSS and QLr.sun-4DL by W195. Based on seedling tests, QLr.sun-2BS was demonstrated to be Lr23. BTSS carried stem rust QTL; QSr.sun-2BL and QSr.sun-6AS and QSr.sun-4DL was contributed by W195. QSr.sun-6AS corresponded to stem rust resistance gene Sr8a carried by BTSS. The collocated QTL for all three rust diseases in chromosome 4D corresponded to the plieotropic locus Lr67/Yr46/Sr55/Pm46 based on genotyping with a linked SNP marker. Interaction among QTL to condition lower rust responses was also demonstrated. QYr.sun-3BS, QLr.sun-3BS and QSr.sun-2BL represent new rust resistance loci.

No abstract

No abstract

No abstract