Search

Wheat was one of the crops domesticated in the Fertile Crescent region approximately 10,000 years ago. Despite undergoing recent polyploidization, hull-to-free-thresh transition events, and domestication bottlenecks, wheat is now grown in over 130 countries and accounts for a quarter of the world's cereal production. The main reason for its widespread success is its broad genetic diversity that allows it to thrive in different environments. To trace historical selection and hybridization signatures, genome scans were performed on two datasets: approximately 113K SNPs from 921 predominantly bread wheat accessions and approximately 110K SNPs from about 400 wheat accessions representing all ploidy levels. To identify environmental factors associated with the loci, a genome–environment association (GEA) was also performed. The genome scans on both datasets identified a highly differentiated region on chromosome 4A where accessions in the first dataset were dichotomized into a group (n = 691), comprising nearly all cultivars, wild emmer, and most landraces, and a second group (n = 230), dominated by landraces and spelt accessions. The grouping of cultivars is likely linked to their potential ancestor, bread wheat cv. Norin-10. The 4A region harbored important genes involved in adaptations to environmental conditions. The GEA detected loci associated with latitude and temperature. The genetic signatures detected in this study provide insight into the historical selection and hybridization events in the wheat genome that shaped its current genetic structure and facilitated its success in a wide spectrum of environmental conditions. The genome scans and GEA approaches applied in this study can help in screening the germplasm housed in gene banks for breeding, and for conservation purposes. [ABSTRACT FROM AUTHOR]

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the distribution of total yellow pigments, total phenolic compounds, and their antioxidant activities in various durum wheat milling fractions. Carotenoid composition of yellow pigment extract was also examined using UPLC. The ABTS radical scavenging activity of the milling fractions decreased in the order of short bran/bran > feed flour > flour/semolina in both total phenolic and total yellow pigment extracts. Yellow pigments extracts from bran, short bran, and feed flour exhibited 5.6–15.4% higher antioxidant activity than those of total phenolic extracts from the corresponding milling fractions. The UPLC results showed a non-carotenoid peak at Rt 0.47 min which was present in fractions of the grain outer layers but absent in semolina and flour. This peak absorbed in the UV range of 271 to 327 nm. These observations suggest that the unknown peak could be composed of phenolic compounds co-extracted in their free form with carotenoids in the polar water-saturated butanol solvent. The compounds in this peak could result in overestimation of carotenoid content and antioxidant activity in bran, short bran and feed flour as the peak contributed to 18.3–26.0% of total carotenoids if it was taken into account. [ABSTRACT FROM AUTHOR]

In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the 'Cutler' × 'AC Barrie' population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the 'Attila' × 'CDC Go' population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the 'Peace' × 'Carberry' population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the 'Peace' × 'CDC Stanley' population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization. [ABSTRACT FROM AUTHOR]

The destructive wheat powdery mildew disease is caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt). PmG3M, derived from wild emmer wheat Triticum dicoccoides accession G305-3M, is a major gene providing a wide-spectrum resistance against Bgt. PmG3M was previously mapped to wheat chromosome 6B using an F6 recombinant inbred line (RIL) mapping population generated by crossing G305-3M with the susceptible T. durum wheat cultivar Langdon (LDN). In the current study, we aimed to explore the defense mechanisms conferred by PmG3M against Bgt. Histopathology of fungal development was characterized in artificially inoculated leaves of G305-3M, LDN, and homozygous RILs using fluorescence and light microscopy. G305-3M exhibited H2O2 accumulation typical of a hypersensitive response, which resulted in programmed cell death (PCD) in Bgt-penetrated epidermal cells, while LDN showed well-developed colonies without PCD. In addition, we observed a post-haustorial resistance mechanism that arrested the development of fungal feeding structures and pathogen growth in both G305-3M and resistant RIL, while LDN and a susceptible RIL displayed fully developed digitated haustoria and massive accumulation of fungal biomass. In contrast, both G305-3M and LDN exhibited callose deposition in attempt to prevent fungal invasion, supporting this as a mechanism of a basal defense response not associated with PmG3M resistance mechanism per se. The presented results shed light on the resistance mechanisms conferred by PmG3M against wheat powdery mildew. [ABSTRACT FROM AUTHOR]

The domestication and subsequent genetic improvement of wheat led to the development of large-seeded cultivated wheat species relative to their smaller-seeded wild progenitors. While increased grain weight (GW) continues to be an important goal of many wheat breeding programs, few genes underlying this trait have been identified despite an abundance of studies reporting quantitative trait loci (QTL) for GW. Here we perform a QTL analysis for GW using a population of recombinant inbred lines (RILs) derived from the cross between wild emmer wheat accession 'Zavitan' and durum wheat variety 'Svevo'. Identified QTLs in this population were anchored to the recent Zavitan reference genome, along with previously published QTLs for GW in tetraploid wheat. This genome-based, meta-QTL analysis enabled the identification of a locus on chromosome 6A whose introgression from wild wheat positively affects GW. The locus was validated using an introgression line carrying the 6A GW QTL region from Zavitan in a Svevo background, resulting in >8% increase in GW compared to Svevo. Using the reference sequence for the 6A QTL region, we identified a wheat ortholog to OsGRF4, a rice gene previously associated with GW. The coding sequence of this gene (TtGRF4-A) contains four single nucleotide polymorphisms (SNPs) between Zavitan and Svevo, one of which reveals the Zavitan allele to be rare in a core collection of wild emmer and completely absent from the domesticated emmer genepool. Similarly, another wild emmer accession (G18-16) was found to carry a rare allele of TtGRF4-A that also positively affects GW and is characterized by a unique SNP absent from the entire core collection. These results exemplify the rich genetic diversity of wild wheat, posit TtGRF4-A as a candidate gene underlying the 6A GW QTL, and suggest that the natural Zavitan and G18-16 alleles of TtGRF4-A have potential to increase wheat yields in breeding programs. [ABSTRACT FROM AUTHOR]