Marker‐assisted breeding provides an opportunity for wheat breeders to introgress/pyramid genes of interest into breeding lines and to identify genes and/or quantitative trait loci in germplasm to be used as parents. Molecular markers were deployed to assist selection for disease resistance, agronomic and quality traits in several wheat cultivars released for commercial cultivation in Canada. Marker‐assisted breeding is routinely used in most wheat breeding programmes for rust resistance (leaf, stem and stripe rust), orange wheat blossom midge resistance, high grain protein concentration, Fusarium head blight and common bunt resistance. Markers are being used selectively within breeding programmes to target traits that relate to market class or regional adaptation. For example, marker‐assisted breeding for low lipoxygenase activity and low grain cadmium is being performed in durum breeding programmes and for enhancing stem solidness in programmes targeting resistance to the wheat stem sawfly. Markers are also being utilized for ergot resistance in durum wheat. Increased gluten strength is being selected with a marker for the overexpression of the Bx7 high‐molecular‐weight glutenin subunit. Marker‐assisted breeding is also being used to pyramid resistance genes against a group of stem rust races related to TTKS (Ug99), a disease that poses a serious threat to global wheat production. Development of tightly linked diagnostic markers and high‐throughput genotyping with SNP markers will result in more effective molecular wheat breeding in the near future and will open the door to genomic selection.

Triticale (xTriticosecale Wittmack) is an important feed crop which suffers severe yield, grade and end-use quality losses due to Fusarium head blight (FHB). Development of resistant triticale cultivars is hindered by lack of effective genetic resistance sources. To dissect FHB resistance, a doubled haploid spring triticale population produced from the cross TMP16315/AC Ultima using a microspore culture method, was phenotyped for FHB incidence, severity, visual rating index (VRI), deoxynivalenol (DON) and some associated traits (ergot, grain protein content, test weight, yield, plant height and lodging) followed by single nucleotide polymorphism (SNP) genotyping. A high-density map consisting of 5274 SNPs, mapped on all 21 chromosomes with a map density of 0.48 cM/SNP, was constructed. Together, 17 major quantitative trait loci were identified for FHB on chromosomes 1A, 2B, 3A, 4A, 4R, 5A, 5R and 6B; two of incidence loci (on 2B and 5R) also co-located with loci for severity and VRI, and two other loci of VRI (on 1A and 4R) with DON accumulation. Major and minor loci were also identified for all other traits in addition to many epistasis loci. This study provides new insight into the genetic basis of FHB resistance and their association with other traits in triticale.

Leaf and stripe rust are major threats to wheat production worldwide. The effective, multiple rust resistances present in the Brazilian cultivar Toropi makes it an excellent choice for a genetic study of rust resistance. Testing of DNA from different seed lots of Toropi with 2,194 polymorphic 90K iSelect single nucleotide polymorphism markers identified significant genetic divergence, with as much as 35% dissimilarity between seed lots. As a result, further work was conducted with a single plant line derived from Toropi variant Toropi-6.4. A double haploid population with 168 lines derived from the cross Toropi-6.4 × Thatcher was phenotyped over multiple years and locations in Canada, New Zealand, and Kenya, with a total of seven field trials undertaken for leaf rust and nine for stripe rust. Genotyping with the 90K iSelect array, simple sequence repeat and Kompetitive allele-specific polymerase chain reaction markers resulted in a genetic map of 3,043 cM, containing 1,208 nonredundant markers. Significant quantitative trait loci (QTL) derived from Toropi-6.4 were identified in multiple environments on chromosomes 1B (QLr.crc-1BL/QYr.crc-1BL), 3B (QLr.crc-3BS), 4B (QYr.crc-4BL), 5A (QLr.crc-5AL and QYr.crc-5AL), and 5D (QLr.crc-5DS). The QTL QLr.crc-1BL/QYr.crc-1BL colocated with the multi-rust resistance locus Lr46/Yr29, while the QTL QLr.crc-5DS located to the Lr78 locus previously found in a wheat backcross population derived from Toropi. Comparisons of QTL combinations showed QLr.crc-1BL to contribute a significantly enhanced leaf rust resistance when combined with QLr.crc-5AL or QLr.crc-5DS, more so than when QLr.crc-5AL and QLr.crc-5DS were combined. A strong additive effect was also seen when the stripe rust resistance QTL QYr.crc-1BL and QYr.crc-5AL were combined.

Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2–6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54–0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat.

Background: Pre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping. Results: Twenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy. Conclusions: Our results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.