Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease of wheat (Triticum aestivum L.). Deployment of resistant wheat cultivars is one of the best strategies to mitigate disease-associated risks. The genotype P2711 possesses effective stripe rust resistance under field conditions in western Canada, however, the genetic basis of this resistance is unknown. To identify resistance, a recombinant inbred line (RIL) population comprised of 252 RILs was developed from the cross AAC Cameron/P2711. This population was evaluated for stripe rust severity at the adult plant stage in Creston, BC (in 2018, 2019, and 2020) and Lethbridge, AB (in 2018 and 2020), and was genotyped using the wheat 90K iSelect single nucleotide polymorphism (SNP) assay. A high-density genetic map comprised of 8,915 markers was constructed covering all the wheat chromosomes. Four resistance quantitative trait loci (QTLs) were contributed by P2711 and three from AAC Cameron. QYr.lrdc-2A.1, corresponding to the Yr17 gene, was the most stable QTL and detected in four out of five environments, whereas QYr.lrdc-2B was the second most stable QTL. These two QTL along with QYr.lrdc-5A showed a significant reduction in stripe rust severity when present together. Where most of the QTLs detected in both locations, QYr.lrdc-1A.1 and QYr.lrdc-1A.2, both were detected only in Creston, BC. Stable QTLs on chromosome 2A, 2B, and 5A, and their closely associated markers identified in this study could be utilized in marker-assisted selection for stripe rust resistant cultivar development.

Stripe rust, caused by Puccinia striiformis Westend. f.sp. tritici Erikss. (Pst), is one of the most devastating diseases of wheat (Triticum aestivum L.) globally. Exploring and utilizing new sources of resistance is essential for breeding resistant wheat cultivars. Thus, a doubled haploid population (n = 291) derived from the cross 'AAC Innova'/'AAC Proclaim' was evaluated to dissect the genetics of resistance in the cultivar 'AAC Innova'. This population was evaluated for stripe rust severity in disease nurseries at Creston, British Columbia (in 2016), and Lethbridge, Alberta (in 2016, and genotyped using the wheat 90 K SNP assay. A highdensity genetic map was constructed which consisted of 7112 SNP markers with an average marker interval of 2.3 cM. Quantitative trait loci (QTL) mapping identified one major (QYr.lrdc-2A) and 10 minor effect (QYr.

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.