Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments.

KEY MESSAGE: Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90 k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions.

Genomic selection can accelerate the biofortification of spring wheat.

KEY MESSAGE: Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding.

Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates.

KEY MESSAGE: A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.

Impact of elevated CO2 and heat stress on wheat pollen viability and grain production.

Periods of high temperature and an expected increase in atmospheric CO2 concentration as a result of global climate change are major threats to wheat (Triticum aestivum L.) production. Developing heat-tolerant wheat cultivars demands improved understanding of the impacts of high temperature and elevated CO2 on plant growth and development. This research investigated the interactive effects of heat stress and CO2 concentration on pollen viability and its relationship to grain formation and yield of wheat in greenhouse conditions. Nineteen wheat genotypes and a current cultivar, Suntop, were heat stressed at either meiosis or anthesis at ambient (400 µL L-1) or elevated (800 µL L-1) CO2. Elevated CO2 and heat stress at meiosis reduced pollen viability, spikelet number and grain yield per spike; however, increased tillering at the elevated CO2 level helped to minimise yield loss. Both heat-tolerant genotypes (e.g. genotype 1, 2, 10 or 12) and heat-sensitive genotypes (e.g. genotype 6 or 9) were identified and response related to pollen sensitivity and subsequent impacts on grain yield and yield components were characterised. A high-throughput protocol for screening wheat for heat stress response at elevated CO2 was established and meiosis was the most sensitive stage, affecting pollen viability, grain formation and yield.

Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping.

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

Genetic Contribution of Emmer Wheat (Triticum dicoccon Schrank) to Heat Tolerance of Bread Wheat.

Rising global temperatures cause substantial yield losses in many wheat growing environments. Emmer wheat (Triticum dicoccon Schrank), one of the first wheat species domesticated, carries significant variation for tolerance to abiotic stresses. This study identified new genetic variability for high-temperature tolerance in hexaploid progeny derived from crosses with emmer wheat. Eight hexaploid and 11 tetraploid parents were recombined in 43 backcross combinations using the hexaploid as the recurrent parent. A total of 537 emmer-based hexaploid lines were developed by producing approximately 10 doubled haploids on hexaploid like BC1F1 progeny and subsequent selection for hexaploid morphology. These materials and 17 commercial cultivars and hexaploid recurrent parents were evaluated under two times of sowing in the field, in 2014-2016. The materials were genotyped using a 90K SNP platform and these data were used to estimate the contribution of emmer wheat to the progeny. Significant phenotypic and genetic variation for key agronomical traits including grain yield, TKW and screenings was observed. Many of the emmer derived lines showed improved performance under heat stress (delayed sowing) compared with parents and commercial cultivars. Emmer derived lines were the highest yielding material in both sowing dates. The emmer wheat parent contributed between 1 and 44% of the genome of the derived lines. Emmer derived lines with superior kernel weight and yield generally had a greater genetic contribution from the emmer parent compared to those with lower trait values. The study showed that new genetic variation for key traits such as yield, kernel weight and screenings can be introduced to hexaploid wheat from emmer wheat. These genetic resources should be explored more systematically to stabilize grain yield and quality in a changing climate.