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The Triticeae-CAP spring wheat nested association mapping population (Reg. No. MP-10, NSL 527060 MAP) consisting of recombinant inbred line (RIL) populations derived from 32 spring wheat (Triticum aestivum L.) accessions each crossed to a common spring wheat parent, ‘Berkut’, has been released. The spring wheat accessions consisted of 29 landraces and three cultivars. Each population consists of approximately 75 lines for a total of 2325 RILs (Reg. Nos. GSTR No. 14701–GSTR 17133). The RILs have all been genotyped with the Illumina wheat iSelect 90K single nucleotide polymorphism array using the Infinium assay method and through genotype-by-sequencing. This nested association mapping population provides a genotyped germplasm resource for the wheat community. A potential strategy for use of the material is to screen the parents for a trait of interest, followed by analysis of RIL of populations that are likely to be segregating for a target trait or sequence.

Inbred cultivars and advanced breeding lines have been subjected to numerous recombination cycles, have strong allelic selection for desired traits, and share important attributes for adaptation and agronomic performance. Genetic variation in elite gene pools captured using molecular markers is immediately useful for cultivar development. The primary goal of this study was to implement a genome-wide association study for 17 agronomic traits using elite inbred lines. A panel consisting of 237 elite hard red spring wheat (Triticum aestivum L.) lines from different wheat breeding institutions in North America were evaluated in 11 locations over 2 yr. A total of 19,192 polymorphic single-nucleotide polymorphism (SNP) markers from the Illumina 90K SNP array and markers linked to major genes controlling plant height, photoperiod sensitivity, and vernalization were used to assay the population. Linkage disequilibrium was observed to decay within a map distance of ~3 cM in the A and B genomes and 7 cM in the D genome. A total of 226 marker-trait associations were identified. Potentially novel associations were detected for grain yield on chromosome 2B and kernels per spike on 1B and 7D, whereas others colocalized with well-known adaptation loci for photoperiod response, vernalization, and plant height. The frequency of positive alleles for specific marker-trait associations differed among the programs, suggesting targets for introgression by the respective breeding programs.

Plant breeding has significantly improved in recent years; however, phenotyping remains a bottleneck, as the process of evaluating and measuring plant traits is often expensive, subjective, and laborious. Although commercial phenotyping systems are available, factors like cost, space, and need for specific controlled-environment conditions limit the affordability of these products. An accurate, user-friendly, adaptive, and high-throughput phenotyping (HTP) system is highly desirable to plant breeders, physiologists, and agronomists. To solve this problem, an automated HTP system and image processing algorithms were developed and tested in this study. The automated platform was an integration of an aluminum framework (including movement and control components), three cameras, and a laptop computer. A control program was developed using LabVIEW to manage operation of the system frame and sensors as a single-unit automated HTP system. Image processing algorithms were developed in MATLAB for high-throughput analysis of images acquired by the system to estimate phenotypes and traits associated with tested plants. The phenotypes extracted were color/spectral, texture, temperature, morphology, and greenness features on a temporal scale. Using two wheat lines with known heat tolerance, the functions of the HTP system were validated. Heat stress tolerance experiments revealed that features such as green leaf area and green normalized difference vegetation index derived from our system showed differences between the control and heat stress treatments, as well as between heat-tolerant and susceptible wheat lines. In another experiment, stripe rust resistance in wheat was assessed. With the HTP system, some potential for detecting qualitative traits, such as disease resistance, was observed, although further validation is needed. In summary, successful development and implementation of an automated system with custom image processing algorithms for HTP in wheat was achieved. Improvement of such systems would further help plant breeders, physiologists, and agronomists to phenotype crops in an efficient, objective, and high-throughput manner. [ABSTRACT FROM AUTHOR]

Background: The recently identified Puccinia graminis f. sp. tritici (Pgt) race TTKSK (Ug99) poses a severe threat to global wheat production because of its broad virulence on several widely deployed resistance genes. Additional virulences have been detected in the Ug99 group of races, and the spread of this race group has been documented across wheat growing regions in Africa, the Middle East (Yemen), and West Asia (Iran). Other broadly virulent Pgt races, such as TRTTF and TKTTF, present further difficulties in maintaining abundant genetic resistance for their effective use in wheat breeding against this destructive fungal disease of wheat. In an effort to identify loci conferring resistance to these races, a genome-wide association study was carried out on a panel of 250 spring wheat breeding lines from the International Maize and Wheat Improvement Center (CIMMYT), six wheat breeding programs in the United States and three wheat breeding programs in Canada. Results: The lines included in this study were grouped into two major clusters, based on the results of principal component analysis using 23,976 SNP markers. Upon screening for adult plant resistance (APR) to Ug99 during 2013 and 2014 in artificial stem rust screening nurseries at Njoro, Kenya and at Debre Zeit, Ethiopia, several wheat lines were found to exhibit APR. The lines were also screened for resistance at the seedling stage against races TTKSK, TRTTF, and TKTTF at USDA-ARS Cereal Disease Laboratory in St. Paul, Minnesota; and only 9 of the 250 lines displayed seedling resistance to all the races. Using a mixed linear model, 27 SNP markers associated with APR against Ug99 were detected, including markers linked with the known APR gene Sr2. Using the same model, 23, 86, and 111 SNP markers associated with seedling resistance against races TTKSK, TRTTF, and TKTTF were identified, respectively. These included markers linked to the genes Sr8a and Sr11 providing seedling resistance to races TRTTF and TKTTF, respectively. We also identified putatively novel Sr resistance genes on chromosomes 3B, 4D, 5A, 5B, 6A, 7A, and 7B. Conclusion: Our results demonstrate that the North American wheat breeding lines have several resistance loci that provide APR and seedling resistance to highly virulent Pgt races. Using the resistant lines and the SNP markers identified in this study, marker-assisted resistance breeding can assist in development of varieties with elevated levels of resistance to virulent stem rust races including TTKSK. [ABSTRACT FROM AUTHOR]

Recycling of unnecessary or dysfunctional cellular structures through autophagy plays a critical role in cellular homeostasis and environmental resilience. Therefore, the autophagy trait may have been unintentionally selected in wheat breeding programs for higher yields in arid climates. This hypothesis was tested by measuring the response of three common autophagy markers, ATG7, ATG8, and NBR1, to a heat wave under reduced soil moisture content in 16 genetically diverse spring wheat landraces originating from different geographical locations. We observed in the greenhouse trials that ATG8 and NBR1 exhibited genotype-specific responses to a 1 h, 40 °C heat wave, while ATG7 did not show a consistent response. Three genotypes from Uruguay, Mozambique, and Afghanistan showed a pattern consistent with higher autophagic activity: decreased or stable abundance of both ATG8 and NBR1 proteins, coupled with increased transcription of ATG8 and NBR1 . In contrast, three genotypes from Pakistan, Ethiopia, and Egypt exhibited elevated ATG8 protein levels alongside reduced or unaltered ATG8 transcript levels, indicating a potential suppression or no change in autophagic activity. Principal component analysis demonstrated a correlation between lower abundance of ATG8 and NBR1 proteins and higher yield in the field trials. We found that (i) the combination of heat and drought activated autophagy only in several genotypes, suggesting that despite being a resilience mechanism, autophagy is a heat-sensitive process; (ii) higher autophagic activity correlates positively with greater yield; (iii) the lack of autophagic activity in some high-yielding genotypes suggests contribution of alternative stress-resilient mechanisms; and (iv) enhanced autophagic activity in response to heat and drought was independently selected by wheat breeding programs in different geographic locations.

This paper investigates the effect of the flux's mean path length (MPL) on the reluctance actuator's analytical model. It determines the circumstances where the model neglecting the MPL is valid. The analysis is carried out for both C-Core and E-Core reluctance actuators; the analytical results are calculated by using MATrix LABoratory and then validated against a finite element model simulation by using COMputer SOLution Multiphysics. In addition, the experimental results of the magnetic force of C-Core and E-Core reluctance actuators are presented and compared with the analytical model. The comparison is obtained under different input currents and air gaps for two different ferromagnetic materials. It can be concluded that the analytical model is valid only for air gaps with a relatively high air gap displacement and for small air gaps, considering the MPL is necessary for accurate results. This means that whenever the reluctance actuator is proposed for high-precision motion system applications, it is essential that the analysis takes into account the effect of the MPL.

Puccinia striiformis Westend. f. sp. tritici , commonly known as stripe rust, is an economically important pathogen of wheat ( Triticum aestivum L.). The hexaploid club spring wheat cultivar JD contains both all-stage and adult plant resistance (APR) genes and exhibited consistent high resistance to stripe rust in the field. In this study, we aimed to identify the quantitative trait loci (QTL) for stripe rust resistance using a BC 1 F 7 back-cross inbred-line population derived from the cross of JD and the recurrent parental line 'Avocet'. The population was phenotyped in field plots in Washington State at the Spillman Agronomy Farm in Pullman and Mount Vernon Northwest Washington Research and Extension Center in between 2014 and 2016. A major QTL tentatively designated as QYrJD.wsu-1B , conferring all-stage resistance in JD background, was identified and mapped at the telomere region on the short arm of chromosome 1B using the genotyping-by-sequencing method. This QTL was further characterized with simple sequence repeat (SSR) markers and found to have the greatest logarithm-of-the-odds score and phenotypic effect, using SSR marker wmc798 on chromosome 1BS. Seven additional QTLs associated with APR were identified in the JD background on chromosomes 2D, 3A, 3B, 4A, 6B, and 7A with partial phenotypic effects.

To achieve high throughput and efficiency, semiconductor photolithography machines need an actuation system that can meet high acceleration and precision demands on the nanoscale. One available solution is the reluctance actuator, which provides higher acceleration and force output than the standard Lorentz actuator. A floating stage with air-bearings is used to eliminate friction in the photolithography process; however, vibration transfer is not entirely eliminated, leading to potential misalignment and asymmetries between the actuator elements. With asymmetrical offsets between mover elements, the output force can be greatly affected. This paper shows a method for estimating the force of various asymmetrical cases for the C-core reluctance actuator. Analytical models are developed and further improved through polynomial curve fitting using precomputed finite element simulation results from Comsol Multiphysics (COMSOL) to achieve more optimal solutions. An experiment verified the results of the force estimation equations, which were within ∼11% for different cases of asymmetric air gaps. This contribution will lead to a design for a control system that will overcome the issue of asymmetries or other altered states.

Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops., (© 2022. The Author(s).)

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine- and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine- and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and -0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine- and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine- and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine- and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

Stripe rust, or yellow rust (Puccinia striiformis Westend. f. sp. tritic), is a disease of wheat (Triticum aestivum L.) historically causing significant economic losses in cooler growing regions. Novel isolates of stripe rust with increased tolerance for high temperatures were detected in the United States circa 2000. This increased heat tolerance puts geographic regions, such as the soft red winter wheat (SRWW) growing region of the southeastern United States, at greater risk of stripe rust induced losses. In order to identify sources of stripe rust resistance in contemporary germplasm, we conducted genome-wide association (GWA) studies on stripe rust severity measured in two panels. The first consisted of 273 older varieties, landraces, and some modern elite breeding lines and was evaluated in environments in the U.S. Pacific Northwest and the southeastern United States. The second panel consisted of 588 modern, elite SRWW breeding lines and was evaluated in four environments in Arkansas and Georgia. The analyses identified three major resistance loci on chromosomes: 2AS (presumably the 2NS:2AS alien introgression from Aegilops ventricosa Tausch; syn. Ae. caudata L.), 3BS, and 4BL. The 4BL locus explained a greater portion of variance in resistance than either the 2AS or 3BS loci in southeastern environments. However, its effects were unstable across different environments and sets of germplasm, possibly a result of its involvement in epistatic interactions. Relatively few lines carry resistance alleles at all three loci, suggesting that there is a pre-existing reservoir of enhanced stripe rust resistance that may be further exploited by regional breeding programs., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

A previous genome-wide association study (GWAS) for leaf rust (caused by Puccinia triticina ) resistance identified 46 resistance quantitative trait loci (QTL) in an elite spring wheat leaf rust resistance diversity panel. With the aim of characterizing the pleiotropic resistance sources to both leaf rust and stripe rust (caused by P. striiformis f. sp. tritici ), stripe rust responses were tested in five U.S. environments at the adult-plant stage and to five U.S. races at the seedling stage. The data revealed balanced phenotypic distributions in this population except for the seedling response to P. striiformis f. sp. tritici race PSTv-37. GWAS for stripe rust resistance discovered a total of 21 QTL significantly associated with all-stage or field resistance on chromosomes 1B, 1D, 2B, 3B, 4A, 5A, 5B, 5D, 6A, 6B, 7A, and 7B. Previously documented pleiotropic resistance genes Yr18/Lr34 and Yr46 / Lr67 and tightly linked genes Yr17 - Lr37 and Yr30-Sr2 - Lr27 were also detected in this population. In addition, stripe rust resistance QTL Yrswp-2B.1 , Yrswp-3B , and Yrswp-7B colocated with leaf rust resistance loci 2B&#95;3 , 3B&#95;t2 , and 7B&#95;4 , respectively. Haplotype analysis uncovered that Yrswp-3B and 3B&#95;t2 were either tightly linked genes or the same gene for resistance to both stripe and leaf rusts. Single nucleotide polymorphism markers IWB35950 , IWB74350 , and IWB72134 for the 3B QTL conferring resistance to both rusts should be useful in incorporating the resistance allele(s) in new cultivars.

The domestication of wild emmer wheat led to the selection of modern durum wheat, grown mainly for pasta production. We describe the 10.45 gigabase (Gb) assembly of the genome of durum wheat cultivar Svevo. The assembly enabled genome-wide genetic diversity analyses revealing the changes imposed by thousands of years of empirical selection and breeding. Regions exhibiting strong signatures of genetic divergence associated with domestication and breeding were widespread in the genome with several major diversity losses in the pericentromeric regions. A locus on chromosome 5B carries a gene encoding a metal transporter (TdHMA3-B1) with a non-functional variant causing high accumulation of cadmium in grain. The high-cadmium allele, widespread among durum cultivars but undetected in wild emmer accessions, increased in frequency from domesticated emmer to modern durum wheat. The rapid cloning of TdHMA3-B1 rescues a wild beneficial allele and demonstrates the practical use of the Svevo genome for wheat improvement.

Fusarium Head Blight (FHB) has emerged in spring wheat production in Pacific Northwest during the last decade due to factors including climate changes, crop rotations, and tillage practices. A breeding population with 170 spring wheat lines was established and screened over a 2-year period in multiple locations for FHB incidence (INC), severity (SEV), and deposition of the mycotoxin, deoxynivalenol (DON). A genome-wide association study suggested that the detectable number of genetic loci and effects are limited for marker-assisted selection. In conjunction with the success of breeding on FHB resistance in other programs, genomic selection (GS) was suggested as a better option. To evaluate the prediction accuracy of GS in the current breeding population, we conducted a variety of validations by varying proportions of testing populations and cohorts based on both FHB resistance and market class, including soft white spring (SWS), hard white spring (HWS), and hard red spring (HRS). We found that INC had higher heritability, higher correlation across years and locations, and higher prediction accuracy than SEV and DON. Prediction accuracy varied among the scenarios that restricted the testing population to a certain cohort. For a small set of newly developed or introduced lines (<17), prediction accuracy will be about 60% if the lines have similar genetic relationships as those among the current 170-line training population. However, we expect a lower prediction accuracy if new lines are selected for a specific characteristic, such as FHB resistance or market class. With the exception of DON in the SWS lines, the current training population is capable of making reasonably accurate predictions for FHB-resistant lines in most of the major market classes. For SWS, adding more lines or further phenotyping is required to improve prediction accuracy. These results demonstrate the potential and challenges of GS, especially for developing FHB-resistant varieties in the SWS market class.

Stripe rust, caused by Puccinia striiformis f. sp. tritici, is a major yield-limiting foliar disease of wheat (Triticum aestivum) worldwide. In this study, the genetic variability of elite spring wheat germplasm from North America was investigated to characterize the genetic basis of effective all-stage and adult plant resistance (APR) to stripe rust. A genome-wide association study was conducted using 237 elite spring wheat lines genotyped with an Illumina Infinium 90K single-nucleotide polymorphism array. All-stage resistance was evaluated at seedling stage in controlled conditions and field evaluations were conducted under natural disease pressure in eight environments across Washington State. High heritability estimates and correlations between infection type and severity were observed. Ten loci for race-specific all-stage resistance were confirmed from previous mapping studies. Three potentially new loci associated with race-specific all-stage resistance were identified on chromosomes 1D, 2A, and 5A. For APR, 11 highly significant quantitative trait loci (QTL) (false discovery rate < 0.01) were identified, of which 3 QTL on chromosomes 3A, 5D, and 7A are reported for the first time. The QTL identified in this study can be used to enrich the current gene pool and improve the diversity of resistance to stripe rust disease.

Harnessing diversity from germplasm collections is more feasible today because of the development of lower-cost and higher-throughput genotyping methods. However, the cost of phenotyping is still generally high, so efficient methods of sampling and exploiting useful diversity are needed. Genomic selection (GS) has the potential to enhance the use of desirable genetic variation in germplasm collections through predicting the genomic estimated breeding values (GEBVs) for all traits that have been measured. Here, we evaluated the effects of various scenarios of population genetic properties and marker density on the accuracy of GEBVs in the context of applying GS for wheat ( L.) germplasm use. Empirical data for adult plant resistance to stripe rust ( f. sp. ) collected on 1163 spring wheat accessions and genotypic data based on the wheat 9K single nucleotide polymorphism (SNP) iSelect assay were used for various genomic prediction tests. Unsurprisingly, the results of the cross-validation tests demonstrated that prediction accuracy increased with an increase in training population size and marker density. It was evident that using all the available markers (5619) was unnecessary for capturing the trait variation in the germplasm collection, with no further gain in prediction accuracy beyond 1 SNP per 3.2 cM (∼1850 markers), which is close to the linkage disequilibrium decay rate in this population. Collectively, our results suggest that larger germplasm collections may be efficiently sampled via lower-density genotyping methods, whereas genetic relationships between the training and validation populations remain critical when exploiting GS to select from germplasm collections., (Copyright © 2017 Crop Science Society of America.)

Stripe rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst) remains one of the most significant diseases of wheat worldwide. We investigated stripe rust resistance by genome-wide association analysis (GWAS) in 959 spring wheat accessions from the United States Department of Agriculture-Agricultural Research Service National Small Grains Collection, representing major global production environments. The panel was characterized for field resistance in multi-environment field trials and seedling resistance under greenhouse conditions. A genome-wide set of 5,619 informative SNP markers were used to examine the population structure, linkage disequilibrium and marker-trait associations in the germplasm panel. Based on model-based analysis of population structure and hierarchical Ward clustering algorithm, the accessions were clustered into two major subgroups. These subgroups were largely separated according to geographic origin and improvement status of the accessions. A significant correlation was observed between the population sub-clusters and response to stripe rust infection. We identified 11 and 7 genomic regions with significant associations with stripe rust resistance at adult plant and seedling stages, respectively, based on a false discovery rate multiple correction method. The regions harboring all, except three, of the QTL identified from the field and greenhouse studies overlap with positions of previously reported QTL. Further work should aim at validating the identified QTL using proper germplasm and populations to enhance their utility in marker assisted breeding.

Stripe rust of wheat, caused by Puccinia striiformis f. sp. tritici ( Pst ), is a global concern for wheat production, and has been increasingly destructive in Ethiopia, as well as in the United States and in many other countries. As Ethiopia has a long history of stripe rust epidemics, its native wheat germplasm harbors potentially valuable resistance loci. Moreover, the Ethiopian germplasm has been historically underutilized in breeding of modern wheat worldwide and thus the resistance alleles from the Ethiopian germplasm represent potentially novel sources. The objective of this study was to identify loci conferring resistance to predominant Pst races in Ethiopia and the United States. Using a high-density 90 K wheat single nucleotide polymorphism array, a genome-wide association analysis (GWAS) was conducted on 182 durum wheat landrace accessions and contemporary varieties originating from Ethiopia. Landraces were detected to be more resistant at the seedling stage while cultivars were more resistant at the adult-plant stages. GWAS identified 68 loci associated with seedling resistance to one or more races. Six loci on chromosome arms 1AS, 1BS, 3AS, 4BL, and 5BL were associated with resistance against at least two races at the seedling stage, and five loci were previously undocumented. GWAS analysis of field resistance reactions identified 12 loci associated with resistance on chromosomes 1A, 1B, 2BS, 3BL, 4AL, 4B and 5AL, which were detected in at least two of six field screening nurseries at the adult-plant stage. Comparison with previously mapped resistance loci indicates that six of the 12 resistance loci are newly documented. This study reports effective sources of resistance to contemporary races in Ethiopia and the United States and reveals that Ethiopian durum wheat landraces are abundant in novel Pst resistance loci that may be transferred into adapted cultivars to provide resistance against Pst .

Key Message: Genome-wide association analysis in tetraploid wheat revealed novel and diverse loci for seedling and field resistance to stripe rust in elite spring durum wheat accessions from worldwide. Improving resistance to stripe rust, caused by Puccinia striiformis f. sp. tritici, is a major objective for wheat breeding. To identify effective stripe rust resistance loci, a genome-wide association study (GWAS) was conducted using 232 elite durum wheat (Triticum turgidum ssp. durum) lines from worldwide breeding programs. Genotyping with the 90 K iSelect wheat single nucleotide polymorphism (SNP) array resulted in 11,635 markers distributed across the genome. Response to stripe rust infection at the seedling stage revealed resistant and susceptible accessions present in rather balanced frequencies for the six tested races, with a higher frequency of susceptible responses to United States races as compared to Italian races (61.1 vs. 43.1% of susceptible accessions). Resistance at the seedling stage only partially explained adult plant resistance, which was found to be more frequent with 67.7% of accessions resistant across six nurseries in the United States. GWAS identified 82 loci associated with seedling stripe rust resistance, five of which were significant at the false discovery rate adjusted P value <0.1 and 11 loci were detected for the field response at the adult plant stages in at least two environments. Notably, Yrdurum-1BS.1 showed the largest effect for both seedling and field resistance, and is therefore considered as a major locus for resistance in tetraploid wheat. Our GWAS study is the first of its kind for stripe rust resistance in tetraploid wheat and provides an overview of resistance in elite germplasm and reports new loci that can be used in breeding resistant cultivars.

Virulence shifts in populations of Puccinia striiformis f. sp. tritici (Pst), the causal pathogen of wheat stripe rust, are a major challenge to resistance breeding. The majority of known resistance genes are already ineffective against current races of Pst, necessitating the identification and introgression of new sources of resistance. Germplasm core collections that reflect the range of genetic and phenotypic diversity of crop species are ideal platforms for examining the genetic architecture of complex traits such as resistance to stripe rust. We report the results of genetic characterization and genome-wide association analysis (GWAS) for resistance to stripe rust in a core subset of 1175 accessions in the National Small Grains Collection (NSGC) winter wheat germplasm collection, based on genotyping with the wheat 9K single nucleotide polymorphism (SNP) iSelect assay and phenotyping of seedling and adult plants under natural disease epidemics in four environments. High correlations among the field data translated into high heritability values within and across locations. Population structure was evident when accessions were grouped by stripe rust reaction. GWAS identified 127 resistance loci that were effective across at least two environments, including 20 with significant genome-wide adjusted P-values. Based on relative map positions of previously reported genes and QTL, five of the QTL with significant genome-wide adjusted P-values in this study represent potentially new loci. This study provides an overview of the diversity of Pst resistance in the NSGC winter wheat germplasm core collection, which can be exploited for diversification of stripe rust resistance in breeding programs., (Copyright © 2016 Bulli et al.)

Background: The cereal rust fungi are destructive pathogens that affect grain production worldwide. Although the genomic and transcript sequences for three Puccinia species that attack wheat have been released, the functions of large repertories of genes from Puccinia still need to be addressed to understand the infection process of these obligate parasites. Host-induced gene silencing (HIGS) has emerged a useful tool to examine the importance of rust fungus genes while growing within host plants. In this study, HIGS was used to test genes from Puccinia with transcripts enriched in haustoria for their ability to interfere with full development of the rust fungi., Results: Approximately 1200 haustoria enriched genes from Puccinia graminis f. sp. tritici (Pgt) were identified by comparative RNA sequencing. Virus-induced gene silencing (VIGS) constructs with fragments of 86 Puccinia genes, were tested for their ability to interfere with full development of these rust fungi. Most of the genes tested had no noticeable effects, but 10 reduced Pgt development after co-inoculation with the gene VIGS constructs and Pgt. These included a predicted glycolytic enzyme, two other proteins that are probably secreted and involved in carbohydrate or sugar metabolism, a protein involved in thiazol biosynthesis, a protein involved in auxin biosynthesis, an amino acid permease, two hypothetical proteins with no conserved domains, a predicted small secreted protein and another protein predicted to be secreted with similarity to bacterial proteins involved in membrane transport. Transient silencing of four of these genes reduced development of P. striiformis (Pst), and three of also caused reduction of P. triticina (Pt) development., Conclusions: Partial suppression of transcripts involved in a large variety of biological processes in haustoria cells of Puccinia rusts can disrupt their development. Silencing of three genes resulted in suppression of all three rust diseases indicating that it may be possible to engineer durable resistance to multiple rust pathogens with a single gene in transgenic wheat plants for sustainable control of cereal rusts.

A severe stem rust epidemic occurred in southern Ethiopia during November 2013 to January 2014, with yield losses close to 100% on the most widely grown wheat cultivar, 'Digalu'. Sixty-four stem rust samples collected from the regions were analyzed. A meteorological model for airborne spore dispersal was used to identify which regions were most likely to have been infected from postulated sites of initial infection. Based on the analyses of 106 single-pustule isolates derived from these samples, four races of Puccinia graminis f. sp. tritici were identified: TKTTF, TTKSK, RRTTF, and JRCQC. Race TKTTF was found to be the primary cause of the epidemic in the southeastern zones of Bale and Arsi. Isolates of race TKTTF were first identified in samples collected in early October 2013 from West Arsi. It was the sole or predominant race in 31 samples collected from Bale and Arsi zones after the stem rust epidemic was established. Race TTKSK was recovered from 15 samples from Bale and Arsi zones at low frequencies. Genotyping indicated that isolates of race TKTTF belongs to a genetic lineage that is different from the Ug99 race group and is composed of two distinct genetic types. Results from evaluation of selected germplasm indicated that some cultivars and breeding lines resistant to the Ug99 race group are susceptible to race TKTTF. Appearance of race TKTTF and the ensuing epidemic underlines the continuing threats and challenges posed by stem rust not only in East Africa but also to wider-scale wheat production.

New races of Puccinia striiformis f. sp. tritici (Pst), the causal pathogen of wheat stripe rust, show high virulence to previously deployed resistance genes and are responsible for large yield losses worldwide. To identify new sources of resistance we performed a genome-wide association study (GWAS) using a worldwide collection of 1000 spring wheat accessions. Adult plants were evaluated under field conditions in six environments in the western United States, and seedlings were tested with four Pst races. A single-nucleotide polymorphism (SNP) Infinium 9K-assay provided 4585 SNPs suitable for GWAS. High correlations among environments and high heritabilities were observed for stripe rust infection type and severity. Greater levels of Pst resistance were observed in a subpopulation from Southern Asia than in other groups. GWAS identified 97 loci that were significant for at least three environments, including 10 with an experiment-wise adjusted Bonferroni probability < 0.10. These 10 quantitative trait loci (QTL) explained 15% of the phenotypic variation in infection type, a percentage that increased to 45% when all QTL were considered. Three of these 10 QTL were mapped far from previously identified Pst resistance genes and QTL, and likely represent new resistance loci. The other seven QTL mapped close to known resistance genes and allelism tests will be required to test their relationships. In summary, this study provides an integrated view of stripe rust resistance resources in spring wheat and identifies new resistance loci that will be useful to diversify the current set of resistance genes deployed to control this devastating disease., (Copyright © 2015 Maccaferri et al.)

Recent advances in our understanding of the nature of resistance genes and rust fungus genomics are providing some insight into the basis of resistance and susceptibility to rust diseases in our cereal crops. Characterized rust resistance genes, for the most part, resemble other resistance genes that interact with effectors intracellularly, but some have unique features. Characterization of rust effectors is just beginning but genomic information and technical advances in rust functional genomics will accelerate their characterization. The ephemeral nature of resistance in past varieties has made the design of cultivars with durable resistance a major focus for geneticists and cereal breeders. This includes strategies for deploying race-specific resistance genes that prolong their effects and methods of predicting which will be difficult for the pathogen to defeat. Identification of resistance genes with race-nonspecific effects is another strategy where recent breakthroughs have been made. Routinely combining the numerous genes required for complex resistance, whether specific or nonspecific, in elite cultivars remains a primary constraint to realizing durable resistance in most programs.

Domesticated crops experience strong human-mediated selection aimed at developing high-yielding varieties adapted to local conditions. To detect regions of the wheat genome subject to selection during improvement, we developed a high-throughput array to interrogate 9,000 gene-associated single-nucleotide polymorphisms (SNP) in a worldwide sample of 2,994 accessions of hexaploid wheat including landraces and modern cultivars. Using a SNP-based diversity map we characterized the impact of crop improvement on genomic and geographic patterns of genetic diversity. We found evidence of a small population bottleneck and extensive use of ancestral variation often traceable to founders of cultivars from diverse geographic regions. Analyzing genetic differentiation among populations and the extent of haplotype sharing, we identified allelic variants subjected to selection during improvement. Selective sweeps were found around genes involved in the regulation of flowering time and phenology. An introgression of a wild relative-derived gene conferring resistance to a fungal pathogen was detected by haplotype-based analysis. Comparing selective sweeps identified in different populations, we show that selection likely acts on distinct targets or multiple functionally equivalent alleles in different portions of the geographic range of wheat. The majority of the selected alleles were present at low frequency in local populations, suggesting either weak selection pressure or temporal variation in the targets of directional selection during breeding probably associated with changing agricultural practices or environmental conditions. The developed SNP chip and map of genetic variation provide a resource for advancing wheat breeding and supporting future population genomic and genome-wide association studies in wheat.