COMPARISON OF LINEAR MIXED MODELS FOR MULTIPLE ENVIRONMENT PLANT BREEDING TRIALS

Evaluations of multiple environment trials (MET) often reveal substantial genotype by environment interactions, and the effects of genotypes within environments are often estimated using cell means, i.e. the simple mean of the observations of each genotype in each environment. However, these estimates are inaccurate, especially for unreplicated or partially replicated trials, so alternative methods of analysis are necessary. One possible approach utilizes information, often from pedigree data, about relationships among the tested genotypes through the use of a genetic relationship matrix (GRM). Predictive accuracy may also be improved by the use of factor analytic (FA) structures for environmental covariances. In this study, data were simulated to resemble results from a range of MET. These simulated data sets covered a range of scenarios with varying numbers of nvironments and genotypes, environmental relationship patterns, field trial designs, and magnitudes of experimental error. The simulated data were used to evaluate 20 mixed models, ten of which included GRMs and ten which did not. The models included ten structures for environmental covariances including structures with no environmental correlation, structures with constant correlation among environments, and six FA structures. These models were compared to each other and to cell means and Additive Main effects and Multiplicative Interaction (AMMI) methods in terms of successful convergence and predictive accuracy. For most of the scenarios, models which included a GRM and a compound symmetric, constant variance structure produced the most accurate estimates. Models with GRM and FA structures were more accurate only when used to evaluate scenarios simulated with Toeplitz patterns of relationships and more than 25 genotypes or five environments. Unfortunately, the improved accuracy with the FA structures in these scenarios came at the cost of reduced convergence rates, so FA structures may not be reliable enough for some uses

Mapping Stripe Rust Resistance in a BrundageXCoda Winter Wheat Recombinant Inbred Line Population

A recombinant inbred line (RIL) mapping population developed from a cross between winter wheat (Triticum aestivum L.) cultivars Coda and Brundage was evaluated for reaction to stripe rust (caused by Puccinia striiformis f. sp. tritici). Two hundred and sixty eight RIL from the population were evaluated in replicated field trials in a total of nine site-year locations in the U.S. Pacific Northwest. Seedling reaction to stripe rust races PST-100, PST-114 and PST-127 was also examined. A linkage map consisting of 2,391 polymorphic DNA markers was developed covering all chromosomes of wheat with the exception of 1D. Two QTL on chromosome 1B were associated with adult plant and seedling reaction and were the most significant QTL detected. Together these QTL reduced adult plant infection type from a score of seven to a score of two reduced disease severity by an average of 25% and provided protection against race PST-100, PST-114 and PST-127 in the seedling stage. The location of these QTL and the race specificity provided by them suggest that observed effects at this locus are due to a complementation of the previously known but defeated resistances of the cultivar Tres combining with that of Madsen (the two parent cultivars of Coda). Two additional QTL on chromosome 3B and one on 5B were associated with adult plant reaction only, and a single QTL on chromosome 5D was associated with seedling reaction to PST-114. Coda has been resistant to stripe rust since its release in 2000, indicating that combining multiple resistance genes for stripe rust provides durable resistance, especially when all-stage resistance genes are combined in a fashion to maximize the number of races they protect against. Identified molecular markers will allow for an efficient transfer of these genes into other cultivars, thereby continuing to provide excellent resistance to stripe rust

Identification of genetic factors controlling kernel hardness and related traits in a recombinant inbred population derived from a soft 3 ‘extra-soft’ wheat (Triticum aestivum L.) cross

Kernel hardness or texture, used to classify wheat (Triticum aestivum L.) into soft and hard classes, is a major determinant of milling and baking quality. Wheat genotypes in the soft class that are termed 'extra-soft' (with kernel hardness in the lower end of the spectrum) have been associated with superior end-use quality. In order to better understand the relationship between kernel hardness, milling yield, and various agronomic traits, we performed quantitative trait mapping using a recombinant inbred line population derived from a cross between a common soft wheat line and a genotype classified as an 'extra-soft' line. A total of 47 significant quantitative trait loci (QTL) (LOD ≥ 3.0) were identified for nine traits with the number of QTL affecting each trait ranging from three to nine. The percentage of phenotypic variance explained by these QTL ranged from 3.7 to 50.3%. Six QTL associated with kernel hardness and break flour yield were detected on chromosomes 1BS, 4BS, 5BS, 2DS, 4DS, and 5DL. The two most important QTL were mapped onto orthologous regions on chromosomes 4DS (Xbarc1118-Rht-D1) and 4BS (Xwmc617-Rht-B1). These results indicated that the 'extra-soft' characteristic was not controlled by the Hardness (Ha) locus on chromosome 5DS. QTL for eight agronomic traits occupied two genomic regions near semi-dwarf genes Rht-D1 on chromosome 4DS and Rht-B1 on chromosome 4BS. The clustering of these QTL is either due to the pleiotropic effects of single genes or tight linkage of genes controlling these various traits.Keywords: Seedling resistance, Lloci, Bread making quality, Spring wheat, Crown rot, Yield, Backcross QTL analysis, Markers, Grain protein content, Dwarfing genesKeywords: Seedling resistance, Lloci, Bread making quality, Spring wheat, Crown rot, Yield, Backcross QTL analysis, Markers, Grain protein content, Dwarfing gene

Relationship Between Climatic Factors and Distribution of Pratylenchus spp. in the Dryland Wheat-Production Areas of Eastern Washington

Field surveys were conducted by collecting soil samples to estimate nematode densities in soil from winter wheat, spring wheat, spring barley, and spring legumes (lentil, chickpea, and pea) fields during 2010 and 2011. Pratylenchus spp. were observed in 60% of sampled fields. However, nematodes were detected in nearly all of the survey fields in high numbers where crops were grown every year. To identify climatic variables associated with density of Pratylenchus spp. in soil, correlation and regression analyses were performed using climate data of survey sites from 1979 to 2010. Fifty-seven climate variables were significantly correlated with densities of Pratylenchus spp. All precipitation variables were significantly positively correlated with nematode abundance. Summer maximum air temperature was negatively correlated and winter minimum air temperature was positively correlated with nematode densities. In addition, both years’ nematode densities were significantly correlated with cropping intensity. Five multivariate regression models for 2010 and seven models for 2011 nematode abundance levels were developed. The majority of the climate variables selected in the models were related to precipitation. Knowledge of root-lesion nematode distribution in the dryland region of eastern Washington and associated climate variables may be helpful to determine risk and apply management practices to minimize crop damage

Construction and characterization of a full-length cDNA library for the wheat stripe rust pathogen

Puccinia striiformis is a plant pathogenic fungus causing stripe rust, one of the most important diseases on cereal crops and grasses worldwide. However, little is know about its genome and genes involved in the biology and pathogenicity of the pathogen. We initiated the functional genomic research of the fungus by constructing a full-length cDNA and determined functions of the first group of genes by sequence comparison of cDNA clones to genes reported in other fungi

Evidence of varietal adaptation to organic farming systems

Consumer demand regarding the impacts of conventional agriculture on the environment and human health have spurred the growth of organic farming systems; however, organic agriculture is often criticized as low-yielding and unable to produce enough food to supply the world’s population. Using wheat as a model crop species, we show that poorly adapted cultivars are partially responsible for the lower yields often found in organic farming systems when compared with conventional farming systems. Our results demonstrate that the highest yielding soft white winter wheat genotypes in conventional systems are not the highest yielding genotypes in organic systems. An analysis of variance for yield among 35 genotypes between paired organic and conventional systems showed highly significant (P \u3c 0.001) genotype X system interactions in four of five locations. Genotypic ranking analysis using Spearman’s rank correlation coefficient (RS) showed no correlation between genotypic rankings for yield in four of five locations; however, the ranks were correlated for test weight at all five locations. This indicates that increasing yield in organic systems through breeding will require direct selection within organic systems rather than indirect selection in conventional systems. Direct selection in organic systems produced yields 15%, 7%, 31% and 5% higher than the yields resulting from indirect selection for locations 1–4, respectively.With crop cultivars bred in and adapted to the unique conditions inherent in organic systems, organic agriculture will be better able to realize its full potential as a high-yielding alternative to conventional agriculture

Construction and characterization of a full-length cDNA library for the wheat stripe rust pathogen (Puccinia striiformis f. sp. tritici)

BACKGROUND: Puccinia striiformis is a plant pathogenic fungus causing stripe rust, one of the most important diseases on cereal crops and grasses worldwide. However, little is know about its genome and genes involved in the biology and pathogenicity of the pathogen. We initiated the functional genomic research of the fungus by constructing a full-length cDNA and determined functions of the first group of genes by sequence comparison of cDNA clones to genes reported in other fungi. RESULTS: A full-length cDNA library, consisting of 42,240 clones with an average cDNA insert of 1.9 kb, was constructed using urediniospores of race PST-78 of P. striiformis f. sp. tritici. From 196 sequenced cDNA clones, we determined functions of 73 clones (37.2%). In addition, 36 clones (18.4%) had significant homology to hypothetical proteins, 37 clones (18.9%) had some homology to genes in other fungi, and the remaining 50 clones (25.5%) did not produce any hits. From the 73 clones with functions, we identified 51 different genes encoding protein products that are involved in amino acid metabolism, cell defense, cell cycle, cell signaling, cell structure and growth, energy cycle, lipid and nucleotide metabolism, protein modification, ribosomal protein complex, sugar metabolism, transcription factor, transport metabolism, and virulence/infection. CONCLUSION: The full-length cDNA library is useful in identifying functional genes of P. striiformis

Use of spectral reflectance for indirect selection of yield potential and stability in Pacific Northwest winter wheat

•Yield showed significant genetic correlations with spectral reflectance indices.•Response to selection was generally high in moist cool rain-fed condition.•Predictive power of yield models using selected indices ranged from 41 to 82%.•Integrated use of reflectance indices and grain yield increased selection efficiency. The use of canopy spectral reflectance as a high throughput selection method has been recommended to augment genetic gain from yield based selection in highly variable environments. The objectives of this study were to estimate genotypic correlations between grain yield and spectral reflectance indices (SRIs), and estimate heritability, expected response to selection, relative efficiency of indirect selection, and accuracy of yield predictive models in Pacific Northwest winter wheat (Triticum aestivum L.) under a range of moisture regimes. A diversity panel of 402 winter wheat genotypes (87 hard and 315 soft) was grown in rain-fed and irrigated conditions across the eastern Washington in 2012 and 2013. Canopy spectral reflectance measured at heading, milk, soft dough, and hard dough stages were used to derive several SRIs which generally had higher broad sense heritability (H2) than yield per se. Grain yield and SRIs showed generally high genetic variability and response to selection in moist-cool rain-fed condition. Efficiency of indirect selection for yield using SRIs was high in drought environment and exceeded efficiency of yield-based selection in the soft winter subgroup. Normalized water band index (NWI) showed consistent response to selection across environments, higher genetic correlation with yield (0.51–0.80, p<0.001), and highest indirect selection efficiency (up to 143%). A yield predictive model with one or more SRIs explained 41–82% of total variation in grain yield (p<0.001). The repeatability of genotypic performance between years increased when selection was conducted based on both SRIs and grain yield compared to selection based on yield or SRI alone. The generally high heritability of SRIs and their significant genotypic correlation with grain yield highlight the possibility to improve yield and yield stability in winter wheat through remotely sensed phenotyping approaches

Evaluation of agronomic traits and spectral reflectance in Pacific Northwest winter wheat under rain-fed and irrigated conditions

•Variations in moisture and temperature explained 86% of total yield variation.•Grain yield was significantly correlated with spectral reflectance indices.•Subpopulations showed differentiation for agronomic and remotely sensed traits.•Earliness didn’t show net yield advantage in Pacific Northwest drought condition. The US Pacific Northwest (PNW) is characterized by high latitude and Mediterranean climate where wheat production is predominantly rain-fed and often subject to low soil moisture. As a result, selection for drought-adaptive traits in modern cultivars has been an integral component of the regional breeding programs. The goal of this research was to evaluate phenotypic associations of morpho-physiological traits and their response to soil moisture variation in winter wheat germplasm adapted to the PNW. A panel of 402 winter wheat accessions (87 hard and 315 soft) was evaluated for spectral reflectance indices (SRIs), canopy temperature (CT), plant stature, phenology, grain yield, and yield components under rain-fed and irrigated conditions in 2012–2014. Variation in soil moisture and temperature cumulatively explained 86% of total yield variation across years and locations. The phenotypic associations of yield with phenology, plant height, and CT were environment dependent. Various SRIs related to biomass, stay green, pigment composition, and hydration status showed consistent patterns of response to drought and strong correlations with yield (p<0.001). The compensatory interaction of grain number and weight was indicated in the negative correlation between thousand kernel weight and grain number per spike across moisture regimes. Area under vegetation index curve (AUVIC) explained 53–88% of the total variation in stay green estimated from visual score of flag leaf senescence (p<0.001). Principal component analysis revealed three major clusters that explained more than 76% of interrelations among traits. The market classes within the study population showed differentiation with respect to these traits. This study highlights the potential use of spectral radiometry in field screening of winter wheat for grain yield and drought adaptation in Mediterranean-like environments

Kernel Morphology Variation in a Population Derived from a Soft by Hard Wheat Cross and Associations with End-Use Quality Traits

Physical attributes, including kernel morphology, are used to grade wheat, and indicate wheat milling and baking quality (MBQ). Using a recombinant inbred population derived from a soft by hard wheat cross, this study quantified kernel traits\u27 sources of variation, studied their heritability, and relationships between morphological and MBQ traits. Transgressive segregation occurred for all traits. Thousand-kernel weight (TKW) and kernel texture (NIR-T) were primarily influenced by genotype and test weight (TW) mainly by year. NIR-T had the highest heritability. Low genetic correlation (GCOR) between kernel length (LEN) and width WID) suggest independent inheritance. NIR-T and LEN, or WID, showed low CCOR. Thus, it is genetical& feasible to produce cultivars with any kernel texture and LEN, or WID, combination. No GCOR was found between TW and flour milling yield (FY), TKW, NIR-T or kernel morphology. GCOR showed that harder wheats had greater FY. Traits’ low correlations call for studies clarifying the efficacy of using kernel traits in wheat classification or end-use quality prediction