Wheat photosystem II heat tolerance: evidence for genotype-by-environment interactions.

High temperature stress inhibits photosynthesis and threatens wheat production. One measure of photosynthetic heat tolerance is Tcrit - the critical temperature at which incipient damage to photosystem II (PSII) occurs. This trait could be improved in wheat by exploiting genetic variation and genotype-by-environment interactions (GEI). Flag leaf Tcrit of 54 wheat genotypes was evaluated in 12 thermal environments over 3 years in Australia, and analysed using linear mixed models to assess GEI effects. Nine of the 12 environments had significant genetic effects and highly variable broad-sense heritability (H2 ranged from 0.15 to 0.75). Tcrit GEI was variable, with 55.6% of the genetic variance across environments accounted for by the factor analytic model. Mean daily growth temperature in the month preceding anthesis was the most influential environmental driver of Tcrit GEI, suggesting biochemical, physiological and structural adjustments to temperature requiring different durations to manifest. These changes help protect or repair PSII upon exposure to heat stress, and may improve carbon assimilation under high temperature. To support breeding efforts to improve wheat performance under high temperature, we identified genotypes superior to commercial cultivars commonly grown by farmers, and demonstrated potential for developing genotypes with greater photosynthetic heat tolerance.

WGNAM: whole-genome nested association mapping.

KEY MESSAGE: A powerful QTL analysis method for nested association mapping populations is presented. Based on a one-stage multi-locus model, it provides accurate predictions of founder specific QTL effects. Nested association mapping (NAM) populations have been created to enable the identification of quantitative trait loci (QTL) in different genetic backgrounds. A whole-genome nested association mapping (WGNAM) method is presented to perform QTL analysis in NAM populations. The WGNAM method is an adaptation of the multi-parent whole genome average interval mapping approach where the crossing design is incorporated through the probability of inheriting founder alleles for every marker across the genome. Based on a linear mixed model, this method provides a one-stage analysis of raw phenotypic data, molecular markers, and crossing design. It simultaneously scans the whole-genome through an iterative process leading to a model with all the identified QTL while keeping the false positive rate low. The WGNAM approach was assessed through a simulation study, confirming to be a powerful and accurate method for QTL analysis for a NAM population. This novel method can also accommodate a multi-reference NAM (MR-NAM) population where donor parents are crossed with multiple reference parents to increase genetic diversity. Therefore, a demonstration is presented using a MR-NAM population for wheat (Triticum aestivum L.) to perform a QTL analysis for plant height. The strength and size of the putative QTL were summarized enhancing the understanding of the QTL effects depending on the parental origin. Compared to other methods, the proposed methodology based on a one-stage analysis provides greater power to detect QTL and increased accuracy in the estimation of their effects. The WGNAM method establishes the basis for accurate QTL mapping studies for NAM and MR-NAM populations.

Proteomics reveals commitment to germination in barley seeds is marked by loss of stress response proteins and mobilisation of nutrient reservoirs.

Germination is a critical process in the reproduction and propagation of flowering plants, and is also the key stage of industrial grain malting. Germination commences when seeds are steeped in water, followed by degradation of the endosperm cell walls, enzymatic digestion of starch and proteins to provide nutrients for the growing plant, and emergence of the radicle from the seed. Dormancy is a state where seeds fail to germinate upon steeping, but which prevents inappropriate premature germination of the seeds before harvest from the field. This can result in inefficiencies in industrial malting. We used Sequential Window Acquisition of all THeoretical ions Mass Spectrometry (SWATH-MS) proteomics to measure changes in the barley seed proteome throughout germination. We found a large number of proteins involved in desiccation tolerance and germination inhibition rapidly decreased in abundance after imbibition. This was followed by a decrease in proteins involved in lipid, protein and nutrient reservoir storage, consistent with induction and activation of systems for nutrient mobilisation to provide nutrients to the growing embryo. Dormant seeds that failed to germinate showed substantial biochemical activity distinct from that of seeds undergoing germination, with differences in sulfur metabolic enzymes, endogenous alpha-amylase/trypsin inhibitors, and histone proteins. We verified our findings with analysis of germinating barley seeds from two commercial malting facilities, demonstrating that key features of the dynamic proteome of germinating barley seeds were conserved between laboratory and industrial scales. The results provide a more detailed understanding of the changes in the barley proteome during germination and give possible target proteins for testing or to inform selective breeding to enhance germination or control dormancy. SIGNIFICANCE: Germination is critical to the reproduction and propagation of flowering plants, and in industrial malting. Dormancy, where seeds fail to germinate upon steeping, can result in inefficiencies in industrial malting. Our DIA/SWATH-MS proteomics analyses identified key changes during germination, including an initial loss of proteins involved in desiccation tolerance and germination inhibition, followed by decreases in lipid, protein and nutrient reservoir storage. These changes were consistent between laboratory and industrial malting scales, and therefore demonstrate the utility of laboratory-scale barley germination as a model system for industrial malt house processes. We also showed that dormant seeds that failed to germinate showed substantial biochemical activity distinct from that of seeds undergoing germination, consistent with dormancy being an actively regulated state. Our results provide a more detailed understanding of the changes in the barley proteome during germination and give possible target proteins for testing or to inform selective breeding to enhance germination or control dormancy.

A Novel Approach to the Design and Analysis of Field Experiments to Study Variation in the Tolerance and Resistance of Cultivars to Root Lesion Nematodes (Pratylenchus spp.).

The root lesion nematode (RLN) species Pratylenchus thornei and P. neglectus are widely distributed within cropping regions of Australia and have been shown to limit grain production. Field experiments conducted to compare the performance of cultivars in the presence of RLNs investigate management options for growers by identifying cultivars with resistance, by limiting nematode reproduction, and tolerance, by yielding well in the presence of nematodes. A novel experimental design approach for RLN experiments is proposed where the observed RLN density, measured prior to sowing, is used to condition the randomization of cultivars to field plots. This approach ensured that all cultivars were exposed to consistent ranges of RLN in order to derive valid assessments of relative cultivar tolerance and resistance. Using data from a field experiment designed using the conditioned randomization approach and conducted in Formartin, Australia, the analysis of tolerance and resistance was undertaken in a linear mixed model framework. Yield response curves were derived using a random regression approach and curves modeling change in RLN densities between sowing and harvest were derived using splines to account for nonlinearity. Groups of cultivars sharing similar resistance levels could be identified. A comparison of slopes of yield response curves of cultivars belonging to the same resistance class identified differing tolerance levels for cultivars with equivalent exposures to both presowing and postharvest RLN densities. As such, the proposed design and analysis approach allowed tolerance to be assessed independently of resistance.

Using Yield Response Curves to Measure Variation in the Tolerance and Resistance of Wheat Cultivars to Fusarium Crown Rot.

The disease crown rot, caused predominantly by the fungal pathogen Fusarium pseudograminearum, is a major disease of winter cereals in many regions of the world, including Australia. A methodology is proposed, using response curves, to robustly estimate the relationship between grain yield and increasing crown rot pathogen burdens. Using data from a field experiment conducted in northern New South Wales, Australia in 2016, response curves were derived for five commercial wheat cultivars exposed to six increasing rates of crown rot inoculum, where the rates served to establish a range of crown rot pathogen burdens. In this way, the response curve methodology is fundamentally different from alternate approaches that rely on genetic or environmental variation to establish a range in pathogen burdens over which yield loss relationships are estimated. By manipulating only the rates of crown rot inoculum and, thus, pathogen burden directly, the number of additional confounding factors and interactions are minimized, enabling the robust estimation of the rate of change in yield due to increasing crown rot pathogen burdens for each cultivar. The methodology revealed variation in the rate of change in yield between cultivars, along with the extent of crown rot symptoms expressed by the cultivars. Variation in the rate of change in yield between cultivars provides definitive evidence of differences in the tolerance of commercial Australian wheat cultivars to crown rot caused by F. pseudograminearum, while variation in the extent of crown rot symptoms signifies differences in the resistance of the cultivars to this disease. The response curve methodology also revealed variation in how the different mechanisms of tolerance and resistance act to limit yield losses due to crown rot for different cultivars.

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.