Uncovering the history of recombination and population structure in western Canadian stripe rust populations through mating type alleles.

BACKGROUND: The population structure of crop pathogens such as Puccinia striiformis f. sp. tritici (Pst), the cause of wheat stripe rust, is of interest to researchers looking to understand these pathogens on a molecular level as well as those with an applied focus such as disease epidemiology. Cereal rusts can reproduce sexually or asexually, and the emergence of novel lineages has the potential to cause serious epidemics such as the one caused by the 'Warrior' lineage in Europe. In a global context, Pst lineages in Canada were not well-characterized and the origin of foreign incursions was not known. Additionally, while some Pst mating type genes have been identified in published genomes, there has been no rigorous assessment of mating type diversity and distribution across the species. RESULTS: We used a whole-genome/transcriptome sequencing approach for the Canadian Pst population to identify lineages in their global context and evidence tracing foreign incursions. More importantly: for the first time ever, we identified nine alleles of the homeodomain mating type locus in the worldwide Pst population and show that previously identified lineages exhibit a single pair of these alleles. Consistently with the literature, we find only two pheromone receptor mating type alleles. We show that the recent population shift from the 'PstS1' lineage to the 'PstS1-related' lineage is also associated with the introduction of a novel mating type allele (Pst-b3-HD) to the Canadian population. We also show evidence for high levels of mating type diversity in samples associated with the Himalayan center of diversity for Pst, including a single Canadian race previously identified as 'PstPr' (probable recombinant) which we identify as a foreign incursion, most closely related to isolates sampled from China circa 2015. CONCLUSIONS: These data describe a recent shift in the population of Canadian Pst field isolates and characterize homeodomain-locus mating type alleles in the global Pst population which can now be utilized in testing several research questions and hypotheses around sexuality and hybridization in rust fungi.

Characterization of Brazilian spring wheat germplasm and its potential for increasing wheat genetic diversity in Canada.

In the present era of climate instability, Canadian wheat production has been frequently affected by abiotic stresses and by dynamic populations of pathogens and pests that are more virulent and aggressive over time. Genetic diversity is fundamental to guarantee sustainable and improved wheat production. In the past, the genetics of Brazilian cultivars, such as Frontana, have been studied by Canadian researchers and consequently, Brazilian germplasm has been used to breed Canadian wheat cultivars. The objective of this study was to characterize a collection of Brazilian germplasm under Canadian growing conditions, including the reaction of the Brazilian germplasm to Canadian isolates/pathogens and to predict the presence of certain genes in an effort to increase genetic diversity, improve genetic gain and resilience of Canadian wheat. Over 100 Brazilian hard red spring wheat cultivars released from 1986 to 2016 were evaluated for their agronomic performance in eastern Canada. Some cultivars showed good adaptability, with several cultivars being superior or statistically equal to the highest yielding Canadian checks. Several Brazilian cultivars had excellent resistance to leaf rust, even though only a few of these tested positive for the presence of either Lr34 or Lr16, two of the most common resistance genes in Canadian wheat. Resistance for stem rust, stripe rust and powdery mildew was variable among the Brazilian cultivars. However, many Brazilian cultivars had high levels of resistance to Canadian and African - Ug99 strains of stem rust. Many Brazilian cultivars had good Fusarium head blight (FHB) resistance, which appears to be derived from Frontana. In contrast FHB resistance in Canadian wheat is largely based on the Chinese variety, Sumai-3. The Brazilian germplasm is a valuable source of semi-dwarf (Rht) genes, and 75% of the Brazilian collection possessed Rht-B1b. Many cultivars in the Brazilian collection were found to be genetically distinct from Canadian wheat, making them a valuable resource to increase the disease resistance and genetic variability in Canada and elsewhere.

Systematic Characterization of Multi-Rust Resistance Genes from a 'Parula × Thatcher' Population with a High-Density Genetic Map.

Pyramiding multiple resistant genes has been proposed as the most effective way to control wheat rust diseases globally. Identifying the most effective pyramids is challenged by the large pool of rust resistance genes and limited information about their mechanisms of resistance and interactions. Here, using a high-density genetic map, a double haploid population, and multi-rust field testing, we aimed to systematically characterize the most effective gene pyramids for rust resistance from the durable multi-rust resistant CIMMYT cultivar Parula. We revealed that the Parula resistance gene pyramid contains Lr34/Yr18/Sr57 (Lr34), Lr46/Yr29/Sr58 (Lr46), Lr27/Yr30/Sr2 (Sr2), and Lr68. The efficacy, magnitude of effect, and interactions varied for the three rust diseases. A subpopulation mapping approach was applied to characterize the complex interactions of the resistance genes by controlling for the effect of Lr34. Using this approach, we found that Lr34 and Lr68 have a strong additive effect for leaf rust, whereas no additive effects were observed for any rusts between Lr34 and Lr46. Lr34 combined synergistically with Sr12 from Thatcher for stem rust, whereas the additive effect of Lr34 and Sr2 was dependent on the type of rust and environment. Two novel leaf rust quantitative trait loci (QTLs) from Parula were identified in this study, a stable QTL QLr-7BS and QLr-5AS, which showed Lr34 dependent expression. With these findings, we propose combining two to three high-value genes from Canadian wheat (e.g., Sr12 from Thatcher) with a foundational multi-adult plant resistance cassette for desirable and durable resistance to all three rusts in Canadian wheat.

Dual RNA-sequencing of Fusarium head blight resistance in winter wheat.

Fusarium head blight (FHB) is a devastating fungal disease responsible for significant yield losses in wheat and other cereal crops across the globe. FHB infection of wheat spikes results in grain contamination with mycotoxins, reducing both grain quality and yield. Breeding strategies have resulted in the production of FHB-resistant cultivars, however, the underlying molecular mechanisms of resistance in the majority of these cultivars are still poorly understood. To improve our understanding of FHB-resistance, we performed a transcriptomic analysis of FHB-resistant AC Emerson, FHB-moderately resistant AC Morley, and FHB-susceptible CDC Falcon in response to Fusarium graminearum. Wheat spikelets located directly below the point of inoculation were collected at 7-days post inoculation (dpi), where dual RNA-sequencing was performed to explore differential expression patterns between wheat cultivars in addition to the challenging pathogen. Differential expression analysis revealed distinct defense responses within FHB-resistant cultivars including the enrichment of physical defense through the lignin biosynthesis pathway, and DON detoxification through the activity of UDP-glycosyltransferases. Nucleotide sequence variants were also identified broadly between these cultivars with several variants being identified within differentially expressed putative defense genes. Further, F. graminearum demonstrated differential expression of mycotoxin biosynthesis pathways during infection, leading to the identification of putative pathogenicity factors.

QTL analysis identified two major all-internodes solidness loci from a completely solid-stemmed spring wheat line.

The culms of solid-stemmed wheat cultivars are filled with "pith" - a parenchymatous tissue largely composed of soft, spongy, and compact parenchyma cells. Breeding solid-stemmed cultivars is the most effective way to decrease the detrimental impact of wheat stem sawfly (WSS), Cephus cinctus Norton (Hymenoptera: Cephidae) on wheat production. Although a major solid stem gene has been previously identified from durum wheat, it produces an intermediate level of stem solidness in common wheat which is insufficient to provide the required level of WSS resistance. The maximum resistance is achieved when stems are totally filled with pith. Thus, to identify a secondary source of solidness in common wheat, we developed three mapping populations from wheat cvs. Sadash, 'AAC Innova' and 'AAC Cameron', each crossed separately with P2711, a completely solid-stemmed hexaploid wheat breeding line. All populations were genotyped using either wheat 15K or 90K Infinium iSelect SNP Assay and high-density linkage maps were generated from individual populations along with consensus maps for chromosomes 3B and 3D from all populations. 'Sadash/P2711' and 'AAC Innova/P2711' populations were subjected to extensive phenotyping in ≥3 environments followed by quantitative trait loci (QTL) analyses using population-specific and consensus linkage maps. We identified two major solid stem QTLs in the distal regions of chromosome arms 3BL and 3DL in both populations in addition to several population-specific or common minor QTLs. Internode-specific QTL analyses detected both major QTLs of chromosomes 3B and 3D across internodes, from top to bottom of the stalk, but minor QTLs were largely detected in upper or middle internodes. Our results suggest that both major QTLs are sufficient to develop highly solid-stemmed cvs; however, the minor loci, which additively enhance the pith expression, can be coupled with major genes to achieve a complete solid stem phenotype in common wheat. Comparative and haplotype analyses showed that the 3B locus is homoeologous to 3D, the former being mapped to a 1.1 Mb genomic region. Major QTLs identified in this study can be incorporated in modern wheat cultivars to achieve maximum WSS resistance from high pith expression.

Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat.

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.

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction.

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.

Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat.

KEY MESSAGE: This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.

QTL Mapping of Adult Plant Resistance to Stripe Rust in a Doubled Haploid Wheat Population.

Stripe rust caused by Puccinia striiformis Westend. f. sp. tritici. is a major bread wheat disease worldwide with yield losses of up to 100% under severe disease pressure. The deployment of resistant cultivars with adult plant resistance to the disease provides a long-term solution to stripe rust of wheat. An advanced line from the International Winter Wheat Improvement Program (IWWIP) 130675 (Avd/Vee#1//1-27-6275/Cf 1770/3/MV171-C-17466) showed a high level of adult plant resistance to stripe rust in the field. To identify the adult plant resistance genes in this elite line, a mapping population of 190 doubled haploid (DH) lines was developed from a cross between line 130675 and the universal stripe rust-susceptible variety Avocet S. The DH population was evaluated at precision wheat stripe rust phenotyping platform, in Izmir during 2019, 2020, and 2021 cropping seasons under artificial inoculations. Composite interval mapping (CIM) identified two stable QTLs QYr.rcrrc-3B.1, and QYr.rcrrc-3B.2, which were detected in multiple years. In addition to these two QTLs, five more QTLs, QYr.rcrrc-1B, QYr.rcrrc-2A, QYr.rcrrc-3A, QYr.rcrrc-5A, and QYr.rcrrc-7D, were identified, which were specific to the cropping year (environment). All QTLs were derived from the resistant parent, except QYr.rcrrc-3A. The significant QTLs explained 3.4-20.6% of the phenotypic variance. SNP markers flanking the QTL regions can be amenable to marker-assisted selection. The best DH lines with high yield, end-use quality, and stripe rust resistance can be used for further selection for improved germplasm. SNP markers flanking the QTL regions can aid in identifying such lines.

Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat.

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.

Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems.

KEY MESSAGE: Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0M) or environments (CV0), a subset of lines that have been evaluated in either management (CV2M) or some environments (CV2), and the performance of newly developed lines in either management (CV1M) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.

Mapping pre-harvest sprouting resistance loci in AAC Innova × AAC Tenacious spring wheat population.

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.

Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population.

Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

Physical mapping of QTL associated with agronomic and end-use quality traits in spring wheat under conventional and organic management systems.

KEY MESSAGE: Using phenotypic data of four biparental spring wheat populations evaluated at multiple environments under two management systems, we discovered 152 QTL and 22 QTL hotspots, of which two QTL accounted for up to 37% and 58% of the phenotypic variance, consistently detected in all environments, and fell within genomic regions harboring known genes. Identification of the physical positions of quantitative trait loci (QTL) would be highly useful for developing functional markers and comparing QTL results across multiple independent studies. The objectives of the present study were to map and characterize QTL associated with nine agronomic and end-use quality traits (tillering ability, plant height, lodging, grain yield, grain protein content, thousand kernel weight, test weight, sedimentation volume, and falling number) in hard red spring wheat recombinant inbred lines (RILs) using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. We evaluated a total of 698 RILs from four populations derived from crosses involving seven parents at 3-8 conventionally (high N) and organically (low N) managed field environments. Using the phenotypic data combined across all environments per management, and the physical map between 1058 and 6526 markers per population, we identified 152 QTL associated with the nine traits, of which 29 had moderate and 2 with major effects. Forty-nine of the 152 QTL mapped across 22 QTL hotspot regions with each region coincident to 2-6 traits. Some of the QTL hotspots were physically located close to known genes. QSv.dms-1A and QPht.dms-4B.1 individually explained up to 37% and 58% of the variation in sedimentation volume and plant height, respectively, and had very large LOD scores that varied from 19.0 to 35.7 and from 16.7 to 55.9, respectively. We consistently detected both QTL in the combined and all individual environments, laying solid ground for further characterization and possibly for cloning.

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness.

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.

Genetic Characterization of Multiple Components Contributing to Fusarium Head Blight Resistance of FL62R1, a Canadian Bread Wheat Developed Using Systemic Breeding.

Fusarium head blight (FHB) is a devastating fungal disease of small-grain cereals that results in severe yield and quality losses. FHB resistance is controlled by resistance components including incidence, field severity, visual rating index, Fusarium damaged kernels (FDKs), and the accumulation of the mycotoxin deoxynivalenol (DON). Resistance conferred by each of these components is partial and must be combined to achieve resistance sufficient to protect wheat from yield losses. In this study, two biparental mapping populations were analyzed in Canadian FHB nurseries and quantitative trait loci (QTL) mapped for the traits listed above. Nine genomic loci, on 2AS, 2BS, 3BS, 4AS, 4AL, 4BS, 5AS, 5AL, and 5BL, were enriched for the majority of the QTL controlling FHB resistance. The previously validated FHB resistance QTL on 3BS and 5AS affected resistance to severity, FDK, and DON in these populations. The remaining seven genomic loci colocalize with flowering time and/or plant height QTL. The QTL on 4B was a major contributor to all field resistance traits and plant height in the field. QTL on 4AL showed contrasting effects for FHB resistance between Eastern and Western Canada, indicating a local adapted resistance to FHB. In addition, we also found that the 2AS QTL contributed a major effect for DON, and the 2BS for FDK, while the 5AL conferred mainly effect for both FDK/DON. Results presented here provide insight into the genetic architecture underlying these resistant components and insight into how FHB resistance in wheat is controlled by a complex network of interactions between genes controlling flowering time, plant height, local adaption, and FHB resistance components.

Mapping of Major Fusarium Head Blight Resistance from Canadian Wheat cv. AAC Tenacious.

Fusarium head blight (FHB) is one of the most devastating wheat disease due to its direct detrimental effects on grain-yield, quality and marketability. Resistant cultivars offer the most effective approach to manage FHB; however, the lack of different resistance resources is still a major bottleneck for wheat breeding programs. To identify and dissect FHB resistance, a doubled haploid wheat population produced from the Canadian spring wheat cvs AAC Innova and AAC Tenacious was phenotyped for FHB response variables incidence and severity, visual rating index (VRI), deoxynivalenol (DON) content, and agronomic traits days to anthesis (DTA) and plant height (PHT), followed by single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) marker genotyping. A high-density map was constructed consisting of 10,328 markers, mapped on all 21 chromosomes with a map density of 0.35 cM/marker. Together, two major quantitative trait loci for FHB resistance were identified on chromosome 2D from AAC Tenacious; one of these loci on 2DS also colocated with loci for DTA and PHT. Another major locus for PHT, which cosegregates with locus for low DON, was also identified along with many minor and epistatic loci. QTL identified from AAC Tenacious may be useful to pyramid FHB resistance.

Histology and RNA Sequencing Provide Insights Into Fusarium Head Blight Resistance in AAC Tenacious.

Fusarium head blight (FHB) is a serious fungal disease affecting wheat and other cereals worldwide. This fungus causes severe yield and quality losses from a reduction in grain quality and contamination of grain with mycotoxins. Intensive breeding efforts led to the release of AAC Tenacious, which was the first spring wheat cultivar registered in Canada with a resistant (R) rating to FHB. To elucidate the physiological mechanisms of resistance, we performed histological and transcriptomic analyses of AAC Tenacious and a susceptible control Roblin after inoculation with Fusarium graminearum (Fg). The spikelet and rachis of infected wheat spikes were hand sectioned and monitored by confocal and fluorescent microscopy. Visible hyphae were observed within the inoculated spikelets for AAC Tenacious; however, the infection was largely restricted to the point of inoculation (POI), whereas the adjacent florets in Roblin were heavily infected. Significant cell wall thickening within the rachis node below the POI was evident in AAC Tenacious compared to Roblin in response to Fg inoculation. Rachis node and rachilla tissues from the POI and the rachis node below the POI were collected at 5 days post inoculation for RNAseq. Significant changes in gene expression were detected in both cultivars in response to infection. The rachis node below the POI in AAC Tenacious had fewer differentially expressed genes (DEGs) when compared to the uninoculated control, likely due to its increased disease resistance. Analysis of DEGs in Roblin and AAC Tenacious revealed the activation of genes and pathways in response to infection, including those putatively involved in cell wall modification and defense response.

MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens.

BACKGROUND: Effective disease management depends on timely and accurate diagnosis to guide control measures. The capacity to distinguish between individuals in a pathogen population with specific properties such as fungicide resistance, toxin production and virulence profiles is often essential to inform disease management approaches. The genomics revolution has led to technologies that can rapidly produce high-resolution genotypic information to define individual variants of a pathogen species. However, their application to complex fungal pathogens has remained limited due to the frequent inability to culture these pathogens in the absence of their host and their large genome sizes. RESULTS: Here, we describe the development of Mobile And Real-time PLant disEase (MARPLE) diagnostics, a portable, genomics-based, point-of-care approach specifically tailored to identify individual strains of complex fungal plant pathogens. We used targeted sequencing to overcome limitations associated with the size of fungal genomes and their often obligately biotrophic nature. Focusing on the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (Pst), we demonstrate that our approach can be used to rapidly define individual strains, assign strains to distinct genetic lineages that have been shown to correlate tightly with their virulence profiles and monitor genes of importance. CONCLUSIONS: MARPLE diagnostics enables rapid identification of individual pathogen strains and has the potential to monitor those with specific properties such as fungicide resistance directly from field-collected infected plant tissue in situ. Generating results within 48 h of field sampling, this new strategy has far-reaching implications for tracking plant health threats.

Genetic Characterization of Leaf and Stripe Rust Resistance in the Brazilian Wheat Cultivar Toropi.

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.