QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster.

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.

Capturing Wheat Phenotypes at the Genome Level.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

A new strategy for using historical imbalanced yield data to conduct genome-wide association studies and develop genomic prediction models for wheat breeding.

Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over 10 years, through testing yield in 40-66 lines each year at 6-14 locations with 38-41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, was used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01287-8.

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.

Population genomic analysis of Aegilops tauschii identifies targets for bread wheat improvement.

Aegilops tauschii, the diploid wild progenitor of the D subgenome of bread wheat, is a reservoir of genetic diversity for improving bread wheat performance and environmental resilience. Here we sequenced 242 Ae. tauschii accessions and compared them to the wheat D subgenome to characterize genomic diversity. We found that a rare lineage of Ae. tauschii geographically restricted to present-day Georgia contributed to the wheat D subgenome in the independent hybridizations that gave rise to modern bread wheat. Through k-mer-based association mapping, we identified discrete genomic regions with candidate genes for disease and pest resistance and demonstrated their functional transfer into wheat by transgenesis and wide crossing, including the generation of a library of hexaploids incorporating diverse Ae. tauschii genomes. Exploiting the genomic diversity of the Ae. tauschii ancestral diploid genome permits rapid trait discovery and functional genetic validation in a hexaploid background amenable to breeding.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments.

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F5:7recombinant inbred lines derived from the cross 'TAM 112'/'TAM 111' was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m-2 and increased test weight by 2.14 and 3.47 kg m-3 with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.

RNA-seq analysis reveals different drought tolerance mechanisms in two broadly adapted wheat cultivars 'TAM 111' and 'TAM 112'.

Wheat cultivars 'TAM 111' and 'TAM 112' have been dominantly grown in the Southern U.S. Great Plains for many years due to their high yield and drought tolerance. To identify the molecular basis and genetic control of drought tolerance in these two landmark cultivars, RNA-seq analysis was conducted to compare gene expression difference in flag leaves under fully irrigated (wet) and water deficient (dry) conditions. A total of 2254 genes showed significantly altered expression patterns under dry and wet conditions in the two cultivars. TAM 111 had 593 and 1532 dry-wet differentially expressed genes (DEGs), and TAM 112 had 777 and 1670 at heading and grain-filling stages, respectively. The two cultivars have 1214 (53.9%) dry-wet DEGs in common, which agreed with their excellent adaption to drought, but 438 and 602 dry-wet DEGs were respectively shown only in TAM 111 and TAM 112 suggested that each has a specific mechanism to cope with drought. Annotations of all 2254 genes showed 1855 have functions related to biosynthesis, stress responses, defense responses, transcription factors and cellular components related to ion or protein transportation and signal transduction. Comparing hierarchical structure of biological processes, molecule functions and cellular components revealed the significant regulation differences between TAM 111 and TAM 112, particularly for genes of phosphorylation and adenyl ribonucleotide binding, and proteins located in nucleus and plasma membrane. TAM 112 showed more active than TAM 111 in response to drought and carried more specific genes with most of them were up-regulated in responses to stresses of water deprivation, heat and oxidative, ABA-induced signal pathway and transcription regulation. In addition, 258 genes encoding predicted uncharacterized proteins and 141 unannotated genes with no similar sequences identified in the databases may represent novel genes related to drought response in TAM 111 or TAM 112. This research thus revealed different drought-tolerance mechanisms in TAM 111 and TAM 112 and identified useful drought tolerance genes for wheat adaption. Data of gene sequence and expression regulation from this study also provided useful information of annotating novel genes associated with drought tolerance in the wheat genome.

Genome wide identification of QTL associated with yield and yield components in two popular wheat cultivars TAM 111 and TAM 112.

Two drought-tolerant wheat cultivars, 'TAM 111' and 'TAM 112', have been widely grown in the Southern Great Plains of the U.S. and used as parents in many wheat breeding programs worldwide. This study aimed to reveal genetic control of yield and yield components in the two cultivars under both dryland and irrigated conditions. A mapping population containing 124 F5:7 recombinant inbred lines (RILs) was developed from the cross of TAM 112/TAM 111. A set of 5,948 SNPs from the wheat 90K iSelect array and double digest restriction-site associated DNA sequencing was used to construct high-density genetic maps. Data for yield and yield components were obtained from 11 environments. QTL analyses were performed based on 11 individual environments, across all environments, within and across mega-environments. Thirty-six unique consistent QTL regions were distributed on 13 chromosomes including 1A, 1B, 1D, 2A, 2D, 3D, 4B, 4D, 6A, 6B, 6D, 7B, and 7D. Ten unique QTL with pleiotropic effects were identified on four chromosomes and eight were in common with the consistent QTL. These QTL increased dry biomass grain yield by 16.3 g m-2, plot yield by 28.1 g m-2, kernels spike-1 by 0.7, spikes m-2 by 14.8, thousand kernel weight by 0.9 g with favorable alleles from either parent. TAM 112 alleles mainly increased spikes m-2 and thousand kernel weight while TMA 111 alleles increased kernels spike-1, harvest index and grain yield. The saturated genetic map and markers linked to significant QTL from this study will be very useful in developing high throughput genotyping markers for tracking the desirable haplotypes of these important yield-related traits in popular parental cultivars.

Determining the Efficacy of a Hybridizing Agent in Wheat (Triticum aestivum L.).

Hybrid wheat (Triticum spp.) has the potential to boost yields and enhance production under changing climates to feed the growing global population. Production of hybrid wheat seed relies on male sterility, the blocking of pollen production, to prevent self-pollination. One method of preventing self-pollination in the female plants is to apply a chemical hybridizing agent (CHA). However, some combinations of CHA and genotypes have lower levels of sterility, resulting in decreased hybrid purity. Differences in CHA efficacy are a challenge in producing hybrid wheat lines for commercial and experimental use. Our primary research questions were to estimate the levels of sterility for wheat genotypes treated with a CHA and determine the best way to analyze differences. We applied the CHA sintofen (1-(4-chlorphyl)-1,4-dihydro-5-(2-methoxyethoxy)-4-oxocinnoline-3-carboxylic acid; Croisor 100) to 27 genotypes in replicate. After spraying, we counted seed in bagged female heads to evaluate CHA efficacy and CHA-by-genotype interaction. Using logit and probit models with a threshold of 7 seeds, we found differences among genotypes in 2015. Sterility was higher in 2016 and fewer genotypic differences were found. When CHA-induced sterilization is less uniform as in 2015, zero-inflated and hurdle count models were superior to standard mixed models. These models calculate mean seed number and fit data with limit-bounded scales collected by agronomists and plant breeders to compare genotypic differences. These analyses can assist in selecting parents and identifying where additional optimization of CHA application needs to occur. There is little work in the literature examining the relationship between CHAs and genotypes, making this work fundamental to the future of hybrid wheat breeding.

Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs.

Stable quantitative trait loci (QTL) are important for deployment in marker assisted selection in wheat (Triticum aestivum L.) and other crops. We reported QTL discovery in wheat using a population of 217 recombinant inbred lines and multiple statistical approach including multi-environment, multi-trait and epistatic interactions analysis. We detected nine consistent QTL linked to different traits on chromosomes 1A, 2A, 2B, 5A, 5B, 6A, 6B and 7A. Grain yield QTL were detected on chromosomes 2B.1 and 5B across three or four models of GenStat, MapQTL, and QTLNetwork while the QTL on chromosomes 5A.1, 6A.2, and 7A.1 were only significant with yield from one or two models. The phenotypic variation explained (PVE) by the QTL on 2B.1 ranged from 3.3-25.1% based on single and multi-environment models in GenStat and was pleiotropic or co-located with maturity (days to heading) and yield related traits (test weight, thousand kernel weight, harvest index). The QTL on 5B at 211 cM had PVE range of 1.8-9.3% and had no significant pleiotropic effects. Other consistent QTL detected in this study were linked to yield related traits and agronomic traits. The QTL on 1A was consistent for the number of spikes m-2 across environments and all the four analysis models with a PVE range of 5.8-8.6%. QTL for kernels spike-1 were found in chromosomes 1A, 2A.1, 2B.1, 6A.2, and 7A.1 with PVE ranged from 5.6-12.8% while QTL for thousand kernel weight were located on chromosomes 1A, 2B.1, 5A.1, 6A.2, 6B.1 and 7A.1 with PVEranged from 2.7-19.5%. Among the consistent QTL, five QTL had significant epistatic interactions (additive × additive) at least for one trait and none revealed significant additive × additive × environment interactions. Comparative analysis revealed that the region within the confidence interval of the QTL on 5B from 211.4-244.2 cM is also linked to genes for aspartate-semialdehyde dehydrogenase, splicing regulatory glutamine/lysine-rich protein 1 isoform X1, and UDP-glucose 6-dehydrogenase 1-like isoform X1. The stable QTL could be important for further validation, high throughput SNP development, and marker-assisted selection (MAS) in wheat.

Development and validation of KASP markers for the greenbug resistance gene Gb7 and the Hessian fly resistance gene H32 in wheat.

KEY MESSAGE: Greenbug and Hessian fly are important pests that decrease wheat production worldwide. We developed and validated breeder-friendly KASP markers for marker-assisted breeding to increase selection efficiency. Greenbug (Schizaphis graminum Rondani) and Hessian fly [Mayetiola destructor (Say)] are two major destructive insect pests of wheat (Triticum aestivum L.) throughout wheat production regions in the USA and worldwide. Greenbug and Hessian fly infestation can significantly reduce grain yield and quality. Breeding for resistance to these two pests using marker-assisted selection (MAS) is the most economical strategy to minimize losses. In this study, doubled haploid lines from the Synthetic W7984 × Opata M85 wheat reference population were used to construct linkage maps for the greenbug resistance gene Gb7 and the Hessian fly resistance gene H32 with genotyping-by-sequencing (GBS) and 90K array-based single nucleotide polymorphism (SNP) marker data. Flanking markers were closely linked to Gb7 and H32 and were located on chromosome 7DL and 3DL, respectively. Gb7-linked markers (synopGBS773 and synopGBS1141) and H32-linked markers (synopGBS901 and IWB65911) were converted into Kompetitive Allele Specific PCR (KASP) assays for MAS in wheat breeding. In addition, comparative mapping identified syntenic regions in Brachypodium distachyon, rice (Oryza sativa), and sorghum (Sorghum bicolor) for Gb7 and H32 that can be used for fine mapping and map-based cloning of the genes. The KASP markers developed in this study are the first set of SNPs tightly linked to Gb7 and H32 and will be very useful for MAS in wheat breeding programs and future genetic studies of greenbug and Hessian fly resistance.

Physiology and transcriptomics of water-deficit stress responses in wheat cultivars TAM 111 and TAM 112.

Hard red winter wheat crops on the U.S. Southern Great Plains often experience moderate to severe drought stress, especially during the grain filling stage, resulting in significant yield losses. Cultivars TAM 111 and TAM 112 are widely cultivated in the region, share parentage and showed superior but distinct adaption mechanisms under water-deficit (WD) conditions. Nevertheless, the physiological and molecular basis of their adaptation remains unknown. A greenhouse study was conducted to understand the differences in the physiological and transcriptomic responses of TAM 111 and TAM 112 to WD stress. Whole-plant data indicated that TAM 112 used more water, produced more biomass and grain yield under WD compared to TAM 111. Leaf-level data at the grain filling stage indicated that TAM 112 had elevated abscisic acid (ABA) content and reduced stomatal conductance and photosynthesis as compared to TAM 111. Sustained WD during the grain filling stage also resulted in greater flag leaf transcriptome changes in TAM 112 than TAM 111. Transcripts associated with photosynthesis, carbohydrate metabolism, phytohormone metabolism, and other dehydration responses were uniquely regulated between cultivars. These results suggested a differential role for ABA in regulating physiological and transcriptomic changes associated with WD stress and potential involvement in the superior adaptation and yield of TAM 112.

Transcriptomics of induced defense responses to greenbug aphid feeding in near isogenic wheat lines.

The greenbug aphid, Schizaphis graminum (Rondani) is an important cereal pest, periodically threatening wheat yields in the United States and around the world. The single dominant gene, Gb3-based resistance is highly durable against prevailing greenbug biotypes under field conditions; however, the molecular mechanisms of Gb3-mediated defense responses remain unknown. We used Affymetrix GeneChip Wheat Genome Arrays to investigate the transcriptomics of host defense responses upon greenbug feeding on resistant and susceptible bulks (RB and SB, respectively) derived from two near-isogenic lines. The study identified 692 differentially expressed transcripts and further functional classification recognized 122 transcripts that are putatively associated to mediate biotic stress responses. In RB, Gb3-mediated resistance resulted in activation of transmembrane receptor kinases and signaling-related transcripts involved in early signal transduction cascades. While in SB, transcripts mediating final steps in jasmonic acid biosynthesis, redox homeostasis, peroxidases, glutathione S-transferases, and notable defense-related secondary metabolites were induced. Also transcripts involved in callose and cell wall decomposition were elevated SB, plausibly to facilitate uninterrupted feeding operations. These results suggest that Gb3-mediated resistance is less vulnerable to cell wall modification and the data provides ample tools for further investigations concerning R gene based model of resistance.

Fine genetic mapping of greenbug aphid-resistance gene Gb3 in Aegilops tauschii.

The greenbug, Schizaphis graminum (Rondani), is an important aphid pest of small grain crops especially wheat (Triticum aestivum L., 2n = 6x = 42, genomes AABBDD) in many parts of the world. The greenbug-resistance gene Gb3 originated from Aegilops tauschii Coss. (2n = 2x = 14, genome D(t)D(t)) has shown consistent and durable resistance against prevailing greenbug biotypes in wheat fields. We previously mapped Gb3 in a recombination-rich, telomeric bin of wheat chromosome arm 7DL. In this study, high-resolution genetic mapping was carried out using an F(2:3) segregating population derived from two Ae. tauschii accessions, the resistant PI 268210 (original donor of Gb3 in the hexaploid wheat germplasm line 'Largo') and susceptible AL8/78. Molecular markers were developed by exploring bin-mapped wheat RFLPs, SSRs, ESTs and the Ae. tauschii physical map (BAC contigs). Wheat EST and Ae. tauschii BAC end sequences located in the deletion bin 7DL3-0.82-1.00 were used to design STS (sequence tagged site) or CAPS (Cleaved Amplified Polymorphic Sequence) markers. Forty-five PCR-based markers were developed and mapped to the chromosomal region spanning the Gb3 locus. The greenbug-resistance gene Gb3 now was delimited in an interval of 1.1 cM by two molecular markers (HI067J6-R and HI009B3-R). This localized high-resolution genetic map with markers closely linked to Gb3 lays a solid foundation for map based cloning of Gb3 and marker-assisted selection of this gene in wheat breeding.

Population- and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.).

BACKGROUND: Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. Here, we used a genome-wide set of 1536 SNPs to study linkage disequilibrium (LD) and population structure in a panel of 478 spring and winter wheat cultivars (Triticum aestivum) from 17 populations across the United States and Mexico. RESULTS: Most of the wheat oligo pool assay (OPA) SNPs that were polymorphic within the complete set of 478 cultivars were also polymorphic in all subpopulations. Higher levels of genetic differentiation were observed among wheat lines within populations than among populations. A total of nine genetically distinct clusters were identified, suggesting that some of the pre-defined populations shared significant proportion of genetic ancestry. Estimates of population structure (F(ST)) at individual loci showed a high level of heterogeneity across the genome. In addition, seven genomic regions with elevated F(ST) were detected between the spring and winter wheat populations. Some of these regions overlapped with previously mapped flowering time QTL. Across all populations, the highest extent of significant LD was observed in the wheat D-genome, followed by lower LD in the A- and B-genomes. The differences in the extent of LD among populations and genomes were mostly driven by differences in long-range LD ( > 10 cM). CONCLUSIONS: Genome- and population-specific patterns of genetic differentiation and LD were discovered in the populations of wheat cultivars from different geographic regions. Our study demonstrated that the estimates of population structure between spring and winter wheat lines can identify genomic regions harboring candidate genes involved in the regulation of growth habit. Variation in LD suggests that breeding and selection had a different impact on each wheat genome both within and among populations. The higher extent of LD in the wheat D-genome versus the A- and B-genomes likely reflects the episodes of recent introgression and population bottleneck accompanying the origin of hexaploid wheat. The assessment of LD and population structure in this assembled panel of diverse lines provides critical information for the development of genetic resources for genome-wide association mapping of agronomically important traits in wheat.

Biotypic diversity in greenbug (Hemiptera: Aphididae): microsatellite-based regional divergence and host-adapted differentiation.

Nineteen isolates of the cereal aphid pest greenbug, Schizaphis graminum (Rondani) (Hemiptera: Aphididae), were collected from wheat, Triticum aestivum L.; barley, Hordeum vulgare L.; or noncultivated grass hosts in five locations from Colorado and Wyoming. Parthenogenetic colonies were established. Biotypic profiles of the 19 isolates were determined based on their abilities to damage a set of host plant differentials, and 13 new biotypes were identified. Genetic diversity among the 19 isolates and five previously designated greenbug biotypes (E, G, H, I, and K) was examined with 31 cross-species transferable microsatellite (simple sequence repeat) markers. Neighbor-joining clustering analysis of marker data revealed host-adapted genetic divergence as well as regional differentiation of greenbug populations. Host associated biotypic variation seems to be more obvious in "agricultural biotypes," whereas isolates collected from noncultivated grasses tend to show more geographic divergence. It seems that the biotype sharing the most similar biotypic profiles and the same geographic region with current prevailing one may have the greatest potential to become the new prevailing biotype. Close monitoring of greenbug population dynamics especially biotypic variation on both crop plants and noncultivated grasses in small grain production areas may be a useful strategy for detecting potentially new prevailing virulent biotypes of the greenbug.

Spatial and temporal distribution of induced resistance to greenbug (Homoptera: Aphididae) herbivory in preconditioned resistant and susceptible near isogenic plants of wheat.

Interactions between biotype E greenbugs, Schizaphis graminum (Rodani), and two near isogenic lines of the greenbug resistance gene Gb3 of wheat, Triticum aestivum L., were examined for 62 d after infestation. By comparing aphid performance and host responses on control and greenbug-preconditioned plants, we demonstrated that systemic resistance to greenbug herbivory was inducible in the resistant genotype with varying intensities and effectiveness in different parts of the plants. Preconditioning of susceptible plants resulted in modification of within-plant aphid distribution and reduction of cumulative greenbug densities, but it showed no effect on reducing greenbug feeding damage to host plant. Preconditioning of resistant plants altered greenbug population dynamics by reducing the size and buffering the fluctuation of the aphid population. Preconditioning in the first (oldest) leaf of the resistant plant had no phenotypically detectable effect in the stem and induced susceptibility locally in the first leaf within the first 2 d after infestation. The preconditioning-induced resistance reduced greenbug density, delayed aphid density peaks and extended the life of younger leaves in resistant plants. Expression of induced resistance was spatially and temporally dynamic within the plant, which occurred more rapidly, was longer in duration, and stronger in intensity in younger leaves. Host resistance gene-mediated induced resistance was effective in lowering greenbug performance and reducing damage from greenbug herbivory in host plants. Results from this study supported the optimal defense theory regarding within-plant defense allocation.

Identification and molecular tagging of a gene from PI 289824 conferring resistance to leaf rust (Puccinia triticina) in wheat.

Host-plant resistance is the most economically viable and environmentally responsible method of control for Puccinia triticina, the causal agent of leaf rust in wheat (Triticum aestivum L.). The identification and utilization of new resistance sources is critical to the continued development of improved cultivars as shifts in pathogen races cause the effectiveness of widely deployed genes to be short lived. The objectives of this research were to identify and tag new leaf rust resistance genes. Forty landraces from Afghanistan and Iran were obtained from the National Plant Germplasm System and evaluated under field conditions at two locations in Texas. PI 289824, a landrace from Iran, was highly resistant under field infection. Further evaluation revealed that PI 289824 is highly resistant to a broad spectrum of leaf rust races, including the currently prevalent races of leaf rust in the Great Plains area of the USA. Eight F1 plants, 176 F2 individuals and 139 F2:3 families of a cross between PI 289824 and T112 (susceptible) were evaluated for resistance to leaf rust at the seedling stage. Genetic analysis indicated resistance in PI 289824 is controlled by a single dominant gene. The AFLP analyses resulted in the identification of a marker (P39 M48-367) linked to resistance. The diagnostic AFLP band was sequenced and that sequence information was used to develop an STS marker (TXW200) linked to the gene at a distance of 2.3 cM. The addition of microsatellite markers allowed the gene to be mapped to the short arm of Chromosome 5B. The only resistance gene to be assigned to Chr 5BS is Lr52. The Lr52 gene was reported to be 16.5 cM distal to Xgwm443 while the gene in PI 289824 mapped 16.7 cM proximal to Xgwm443. Allelism tests are needed to determine the relationship between the gene in PI 289824 and Lr52. If the reported map positions are correct, the gene in PI 289824 is unique.

Phenotypic mechanisms of host resistance against greenbug (Homoptera: Aphididae) revealed by near isogenic lines of wheat.

Interactions between biotype E greenbug, Schizaphis graminum (Rondani), and wheat, Triticum aestivum L., were investigated using resistant and susceptible near isogenic lines of the greenbug resistance gene Gb3. In an antixenosis test, the greenbugs preferred susceptible plants to resistant ones when free choice of hosts was allowed. Aphid feeding resulted in quick and severe damage to susceptible plants, which seemed to follow a general pattern spatially and was affected by the position where the greenbugs were initially placed. Symptom of damage in resistant plants resembled senescence. Within-plant distribution of aphids after infestation was clearly different between the two genotypes. Significantly more greenbugs fed on the first (oldest) leaf than on the stem in resistant plants, but this preference was reversed in the susceptible one. After reaching its peak, aphid population on the susceptible plants dropped quickly. All susceptible plants were dead in 10-14 d after infestation due to greenbug feeding. Aphid population dynamics on resistant plants exhibited a multipeak curve. After the first peak, the greenbug population declined slowly. More than 70% of resistant plants were killed 47 d after infestation. Performance of both biotype E and I greenbugs on several Gb3-related wheat germplasm lines were also examined. It seems that the preference-on-stem that was characteristic of biotype E greenbugs on the susceptible plants was aphid biotype- and host genotype-dependent. Results from this study suggested that antixenosis, antibiosis, and tolerance in the resistant plants of wheat might all contribute to resistance against greenbug feeding.