Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat.

Nested association mapping (NAM) populations emerged as a multi-parental strategy that combines the high statistical power of biparental linkage mapping with greater allelic richness of association mapping. Several statistical models have been developed for marker-trait associations (MTAs) in genome-wide association studies (GWAS), which ranges from simple to increasingly complex models. These statistical models vary in their performance for detecting real association with the avoidance of false positives and false negatives. Furthermore, significant threshold methods play an equally important role for controlling spurious associations. In this study, we compared the performance of seven different statistical models ranging from single to multi-locus models on eight different simulated traits with varied genetic architecture for a NAM population of spring wheat (Triticum aestivum L.). The best identified model was further used to identify MTAs for 11 different agronomic and spectral reflectance traits, which were collected on the NAM population between 2014 and 2016. The "Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK)" model performed better than all other models observed based on QQ plots and detection of real association in a simulated data set. The results from model comparison suggest that BLINK controls both false positives and false negatives under the different genetic architecture of simulated traits. Comparison of multiple significant threshold methods suggests that Bonferroni correction performed superior for controlling false positives and false negatives and complements the performance of GWAS models. BLINK identified 45 MTAs using Bonferroni correction of 0.05 for 11 different phenotypic traits in the NAM population. This study helps identify the best statistical model and significant threshold method for performing association analysis in subsequent NAM population studies.

LMA or vivipary? Wheat grain can germinate precociously during grain maturation under the cool conditions used to induce late maturity alpha-amylase (LMA).

Introduction: This study found that wheat (Triticum aestivum) grain can germinate precociously during the maturation phase of grain development, a phenomenon called vivipary that was associated with alpha-amylase induction. Farmers receive severe discounts for grain with low falling number (FN), an indicator that grain contains sufficiently elevated levels of the starch-digesting enzyme alpha-amylase to pose a risk to end-product quality. High grain alpha-amylase can result from: preharvest sprouting (PHS)/germination when mature wheat is rained on before harvest, or from late maturity alpha-amylase (LMA) when grain experiences cool temperatures during the soft dough stage of grain maturation (Zadoks growth stage 85). An initial LMA-induction experiment found that low FN was associated with premature visible germination, suggesting that cool and humid conditions caused vivipary. Methods: To examine whether LMA and vivipary are related, controlled environment experiments examined the conditions that induce vivipary, whether LMA could be induced without vivipary, and whether the pattern of alpha-amylase expression during vivipary better resembled PHS or LMA. Results: Vivipary was induced in the soft to hard dough stages of grain development (Zadok's stages 83-87) both on agar and after misting of the mother plant. This premature germination was associated with elevated alpha-amylase activity. Vivipary was more strongly induced under the cooler conditions used for LMA-induction (18°C day/7.5°C night) than warmer conditions (25°C day/18°C night). Cool temperatures could induce LMA with little or no visible germination when low humidity was maintained, and susceptibility to vivipary was not always associated with LMA susceptibility in a panel of 8 varieties. Mature grain preharvest sprouting results in much higher alpha-amylase levels at the embryo-end of the kernel. In contrast, vivipary resulted in a more even distribution of alpha-amylase that was reminiscent of LMA. Discussion: Vivipary can occur in susceptible varieties under moist, cool conditions, and the resulting alpha-amylase activity may result in low FN problems when a farm experiences cool, rainy conditions before the crop is mature. While there are genotypic differences in LMA and vivipary susceptibility, overlapping mechanisms are likely involved since they are similarly controlled by temperature and growth stage, and result in similar patterns of alpha-amylase expression.

Investigating the effect of the mean path length on reluctance actuator output force characterization.

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.

Modeling and analysis of reluctance motion system with asymmetrical air gaps.

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.

Biogenic VOCs Emission Profiles Associated with Plant-Pest Interaction for Phenotyping Applications.

Pest attacks on plants can substantially change plants' volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and associated resistance. Furthermore, the identification of biomarkers-specific biogenic VOCs-associated with the resistance can serve as a non-destructive and rapid tool for phenotyping applications. This research aims to compare the VOCs emission profiles under diverse conditions to identify constitutive (also referred to as green VOCs) and induced (resulting from biotic/abiotic stress) VOCs released in potatoes and wheat. In the first study, wild potato Solanum bulbocastanum (accession# 22; SB22) was inoculated with Meloidogyne chitwoodi race 1 (Mc1), and Mc1 pathotype Roza (SB22 is resistant to Mc1 and susceptible to pathotype Roza), and VOCs emission profiles were collected using gas chromatography-flame ionization detection (GC-FID) at different time points. Similarly, in the second study, the VOCs emission profiles of resistant ('Hollis') and susceptible ('Alturas') wheat cultivars infested with Hessian fly insects were evaluated using the GC-FID system. In both studies, in addition to variable plant responses (susceptibility to pests), control treatments (non-inoculated or non-infested) were used to compare the VOCs emission profiles resulting from differences in stress conditions. The common VOC peaks (constitutive VOCs) between control and infected/infested samples, and unique VOC peaks (induced VOCs) presented only in infected/infested samples were analyzed. In the potato-nematode study, the highest unique peak was found two days after inoculation (DAI) for SB22 inoculated with Mc1 (resistance response). The most common VOC peaks in SB22 inoculated with both Mc1 and Roza were found at 5 and 10 DAI. In the wheat-insect study, only the Hollis showed unique VOC peaks. Interestingly, both cultivars released the same common VOCs between control and infected samples, with only a difference in VOC average peak intensity at 22.4 min retention time where the average intensity was 4.3 times higher in the infested samples of Hollis than infested samples of Alturas. These studies demonstrate the potential of plant VOCs to serve as a rapid phenotyping tool to assess resistance levels in different crops.

Reliable DNA Markers for a Previously Unidentified, Yet Broadly Deployed Hessian Fly Resistance Gene on Chromosome 6B in Pacific Northwest Spring Wheat Varieties.

Hessian fly [Mayetiola destructor (Say)] is a major pest of wheat (Triticum aestivum L.) throughout the United States and in several other countries. A highly effective and economically feasible way to control Hessian fly is with resistant cultivars. To date, over 37 Hessian fly resistance genes have been discovered and their approximate locations mapped. Resistance breeding is still limited, though, by the genes' effectiveness against predominant Hessian fly biotypes in a given production area, genetic markers that are developed for low-throughput marker systems, poorly adapted donor germplasm, and/or the inadequacy of closely linked DNA markers to track effective resistance genes in diverse genetic backgrounds. The purposes of this study were to determine the location of the Hessian fly resistance gene in the cultivar "Kelse" (PI 653842) and to develop and validate Kompetitive Allele Specific PCR (KASP) markers for the resistance locus. A mapping population was genotyped and screened for Hessian fly resistance. The resulting linkage map created from 2,089 Single Nucleotide Polymorphism SNP markers placed the resistance locus on the chromosome 6B short arm, near where H34 has been reported. Three flanking SNPs near the resistance locus were converted to KASP assays which were then validated by fine-mapping and testing a large panel of breeding lines from hard and soft wheat germplasm adapted to the Pacific Northwest. The KASP markers presented here are tightly linked to the resistance locus and can be used for marker-assisted selection by breeders working on Hessian fly resistance and allow confirmation of this Hessian fly resistance gene in diverse germplasm.

Identification and Mapping of Quantitative Trait Loci Associated with Stripe Rust Resistance in Spring Club Wheat Cultivar JD.

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 BC1F7 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.

Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation.

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.

Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.

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.

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014-2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program.

Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014-2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder's toolkit for use in large scale breeding programs.

Identifying Loci Conferring Resistance to Leaf and Stripe Rusts in a Spring Wheat Population (Triticum aestivum) via Genome-Wide Association Mapping.

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_3, 3B_t2, and 7B_4, respectively. Haplotype analysis uncovered that Yrswp-3B and 3B_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.

Introgression of a Novel Ug99-Effective Stem Rust Resistance Gene into Wheat and Development of Dasypyrum villosum Chromosome-Specific Markers via Genotyping-by-Sequencing (GBS).

Dasypyrum villosum is a wild relative of common wheat (Triticum aestivum L.) with resistance to Puccinia graminis f. tritici, the causal agent of stem rust, including the highly virulent race TTKSK (Ug99). In order to transfer resistance, T. durum-D. villosum amphiploids were initially developed and used as a bridge to create wheat-D. villosum introgression lines. Conserved ortholog set (COS) markers were used to identify D. villosum chromosome introgression lines, which were then subjected to seedling P. graminis f. tritici resistance screening with race TTKSK. A COS marker-verified line carrying chromosome 2V with TTKSK resistance was further characterized by combined genomic in situ and fluorescent in situ analyses to confirm a monosomic substitution line MS2V(2D) (20″ + 1' 2V[2D]). This is the first report of stem rust resistance on 2V, which was temporarily designated as SrTA10276-2V. To facilitate the use of this gene in wheat improvement, a complete set of previously developed wheat-D. villosum disomic addition lines was subjected to genotyping-by-sequencing analysis to develop D. villosum chromosome-specific markers. On average, 350 markers per chromosome were identified. These markers can be used to develop diagnostic markers for D. villosum-derived genes of interest in wheat improvement.

Evaluation of the Potential for Genomic Selection to Improve Spring Wheat Resistance to Fusarium Head Blight in the Pacific Northwest.

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.

The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping.

Recombination affects the fate of alleles in populations by imposing constraints on the reshuffling of genetic information. Understanding the genetic basis of these constraints is critical for manipulating the recombination process to improve the resolution of genetic mapping, and reducing the negative effects of linkage drag and deleterious genetic load in breeding. Using sequence-based genotyping of a wheat nested association mapping (NAM) population of 2,100 recombinant inbred lines created by crossing 29 diverse lines, we mapped QTL affecting the distribution and frequency of 102 000 crossovers (CO). Genome-wide recombination rate variation was mostly defined by rare alleles with small effects together explaining up to 48.6% of variation. Most QTL were additive and showed predominantly trans-acting effects. The QTL affecting the proximal COs also acted additively without increasing the frequency of distal COs. We showed that the regions with decreased recombination carry more single nucleotide polymorphisms (SNPs) with possible deleterious effects than the regions with a high recombination rate. Therefore, our study offers insights into the genetic basis of recombination rate variation in wheat and its effect on the distribution of deleterious SNPs across the genome. The identified trans-acting additive QTL can be utilized to manipulate CO frequency and distribution in the large polyploid wheat genome opening the possibility to improve the efficiency of gene pyramiding and reducing the deleterious genetic load in the low-recombining pericentromeric regions of chromosomes.

A Genome-Wide Association Study of Field and Seedling Response to Individual Stem Rust Pathogen Races Reveals Combinations of Race-Specific Genes in North American Spring Wheat.

Stem rust of wheat caused by the fungal pathogen Puccinia graminis f. sp. tritici historically caused major yield losses of wheat worldwide. To understand the genetic basis of stem rust resistance in contemporary North American spring wheat, genome-wide association analysis (GWAS) was conducted on an association mapping panel comprised of 250 elite lines. The lines were evaluated in separate nurseries each inoculated with a different P. graminis f. sp. tritici race for 3 years (2013, 2015, and 2016) at Rosemount, Minnesota allowing the evaluation of race-specificity separate from the effect of environment. The lines were also challenged with the same four races at the seedling stage in a greenhouse facility at the USDA-ARS Cereal Disease Laboratory. A total of 22,310 high-quality SNPs obtained from the Infinium 90,000 SNPs chip were used to perform association analysis. We observed often negative and sometimes weak correlations between responses to different races that highlighted the abundance of race-specific resistance and the inability to predict the response of the lines across races. Markers strongly associated with resistance to the four races at seedling and field environments were identified. At the seedling stage, the most significant marker-trait associations were detected in the regions of known major genes (Sr6, Sr7a, and Sr9b) except for race QFCSC where a strong association was detected on chromosome arm 1AL. We postulated the presence of Sr2, Sr6, Sr7a, Sr8a, Sr9b, Sr11, Sr12, Sr24, Sr25, Sr31, and Sr57 (Lr34) in this germplasm based on phenotypic and marker data. We found over half of the panel possessed three or more Sr genes, and most commonly included various combinations of Sr6, Sr7a, Sr8a, Sr9b, Sr11, Sr12, and Sr57. Most of these genes confer resistance to specific P. graminis f. sp. tritici races accounting for the prevalent stem rust resistance in North American spring wheat.

Genome-Wide Association Mapping of Loci for Resistance to Stripe Rust in North American Elite Spring Wheat Germplasm.

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.

Characterization of molecular diversity and genome-wide mapping of loci associated with resistance to stripe rust and stem rust in Ethiopian bread wheat accessions.

BACKGROUND: The narrow genetic basis of resistance in modern wheat cultivars and the strong selection response of pathogen populations have been responsible for periodic and devastating epidemics of the wheat rust diseases. Characterizing new sources of resistance and incorporating multiple genes into elite cultivars is the most widely accepted current mechanism to achieve durable varietal performance against changes in pathogen virulence. Here, we report a high-density molecular characterization and genome-wide association study (GWAS) of stripe rust and stem rust resistance in 190 Ethiopian bread wheat lines based on phenotypic data from multi-environment field trials and seedling resistance screening experiments. A total of 24,281 single nucleotide polymorphism (SNP) markers filtered from the wheat 90 K iSelect genotyping assay was used to survey Ethiopian germplasm for population structure, genetic diversity and marker-trait associations. RESULTS: Upon screening for field resistance to stripe rust in the Pacific Northwest of the United States and Ethiopia over multiple growing seasons, and against multiple races of stripe rust and stem rust at seedling stage, eight accessions displayed resistance to all tested races of stem rust and field resistance to stripe rust in all environments. Our GWAS results show 15 loci were significantly associated with seedling and adult plant resistance to stripe rust at false discovery rate (FDR)-adjusted probability (P) <0.10. GWAS also detected 9 additional genomic regions significantly associated (FDR-adjusted P < 0.10) with seedling resistance to stem rust in the Ethiopian wheat accessions. Many of the identified resistance loci were mapped close to previously identified rust resistance genes; however, three loci on the short arms of chromosomes 5A and 7B for stripe rust resistance and two on chromosomes 3B and 7B for stem rust resistance may be novel. CONCLUSION: Our results demonstrate that considerable genetic variation resides within the landrace accessions that can be utilized to broaden the genetic base of rust resistance in wheat breeding germplasm. The molecular markers identified in this study should be useful in efficiently targeting the associated resistance loci in marker-assisted breeding for rust resistance in Ethiopia and other countries.

Genome-wide association mapping reveals a rich genetic architecture of stripe rust resistance loci in emmer wheat (Triticum turgidum ssp. dicoccum).

KEY MESSAGE: SNP-based genome scanning in worldwide domesticated emmer germplasm showed high genetic diversity, rapid linkage disequilibrium decay and 51 loci for stripe rust resistance, a large proportion of which were novel. Cultivated emmer wheat (Triticum turgidum ssp. dicoccum), one of the oldest domesticated crops in the world, is a potentially rich reservoir of variation for improvement of resistance/tolerance to biotic and abiotic stresses in wheat. Resistance to stripe rust (Puccinia striiformis f. sp. tritici) in emmer wheat has been under-investigated. Here, we employed genome-wide association (GWAS) mapping with a mixed linear model to dissect effective stripe rust resistance loci in a worldwide collection of 176 cultivated emmer wheat accessions. Adult plants were tested in six environments and seedlings were evaluated with five races from the United States and one from Italy under greenhouse conditions. Five accessions were resistant across all experiments. The panel was genotyped with the wheat 90,000 Illumina iSelect single nucleotide polymorphism (SNP) array and 5106 polymorphic SNP markers with mapped positions were obtained. A high level of genetic diversity and fast linkage disequilibrium decay were observed. In total, we identified 14 loci associated with field resistance in multiple environments. Thirty-seven loci were significantly associated with all-stage (seedling) resistance and six of them were effective against multiple races. Of the 51 total loci, 29 were mapped distantly from previously reported stripe rust resistance genes or quantitative trait loci and represent newly discovered resistance loci. Our results suggest that GWAS is an effective method for characterizing genes in cultivated emmer wheat and confirm that emmer wheat is a rich source of stripe rust resistance loci that can be used for wheat improvement.

Multi-Locus Mixed Model Analysis Of Stem Rust Resistance In Winter Wheat.

Genome-wide association mapping is a powerful tool for dissecting the relationship between phenotypes and genetic variants in diverse populations. With the improved cost efficiency of high-throughput genotyping platforms, association mapping is a desirable method of mining populations for favorable alleles that hold value for crop improvement. Stem rust, caused by the fungus f. sp. is a devastating disease that threatens wheat ( L.) production worldwide. Here, we explored the genetic basis of stem rust resistance in a global collection of 1411 hexaploid winter wheat accessions genotyped with 5390 single nucleotide polymorphism markers. To facilitate the development of resistant varieties, we characterized marker-trait associations underlying field resistance to North American races and seedling resistance to the races TTKSK (Ug99), TRTTF, TTTTF, and BCCBC. After evaluating several commonly used linear models, a multi-locus mixed model provided the maximum statistical power and improved the identification of loci with direct breeding application. Ten high-confidence resistance loci were identified, including SNP markers linked to and and at least three newly discovered resistance loci that are strong candidates for introgression into modern cultivars. In the present study, we assessed the power of multi-locus association mapping while providing an in-depth analysis for its practical ability to assist breeders with the introgression of rare alleles into elite varieties.