Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.

KEY MESSAGE: Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype. The chromosomal regions possibly involved in these interactions are largely unknown. The objective of this study was to quantify the response of elite bread wheat cultivars to different nitrogen field stress scenarios and identify genomic regions involved in this response. For this purpose, 212 elite bread wheat varieties were grown in a multi-environment trial at different nitrogen levels. Genomic regions associated with grain yield, protein concentration and grain protein deviation responses to nitrogen deficiency were identified. Environments were clustered according to adjusted means for grain yield, yield components and grain protein concentration. Four nitrogen availability scenarios were identified: optimal condition, moderate early deficiency, severe late deficiency, and severe continuous deficiency. A large range of tolerance to nitrogen deficiency was observed among varieties, which were ranked differently in different nitrogen deficiency scenarios. The well-known negative correlation between grain yield and grain protein concentration also existed between their respective tolerance indices. Interestingly, the tolerance indices for grain yield and grain protein deviation were either null or weakly positive meaning that breeding for the two traits should be less difficult than expected. Twenty-two QTL regions were identified for the tolerance indices. By selecting associated markers, these regions may be selected separately or combined to improve the tolerance to N deficiency within a breeding programme.

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.

KEY MESSAGE: Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.

Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection.

KEY MESSAGE: Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.

Bread wheat milling behavior: effects of genetic and environmental factors, and modeling using grain mechanical resistance traits.

KEY MESSAGE: Genetic (Pinb-D1 alleles) and environment (through vitreousness) have important effects on bread wheat milling behavior. SKCS optimal values corresponding to soft vitreous or hard mealy grains were defined to obtain the highest total flour yield. Near-isogenic lines of bread wheat that differ in hardness, due to distinct puroindoline-b alleles (the wild type, Pinb-D1a, or the mutated forms, Pinb-D1b or Pinb-D1d), were grown in different environments and under two nitrogen fertilization levels, to study genetic and environmental effects on milling behavior. Milling tests used a prototype mill, equipped with two break steps, one sizing step, and two reduction steps, and this enabled 21 individual or aggregated milling fractions to be collected. Four current grain characters, thousand grain weight, test weight, grain diameter, and protein content, were measured, and three characters known to influence grain mechanical resistance, NIRS hardness, SKCS hardness index, and grain vitreousness (a character affecting the grain mechanical behavior but generally not studied). As expected, the wild type or mutated forms of Pinb-D1 alleles led to contrasted milling behavior: soft genotypes produced high quantities of break flour and low quantities of reduction flour, whereas reverse quantities were observed for hard genotypes. This different milling behavior had only a moderate influence on total flour production. NIRS hardness and vitreousness were, respectively, the most important and the second most important grain characters to explain milling behavior. However, contrary to NIRS hardness, vitreousness was only involved in endosperm reduction and not in the separation between the starchy endosperm and the outer layers. The highest flour yields were obtained for SKCS values comprised between 30 and 50, which corresponded either to soft vitreous or hard mealy grains. Prediction equations were defined and showed a good accuracy estimating break and reduction flours portions, but should be used more cautiously for total flour.

Relationships between wheat grain physical characteristics studied through near-isogenic lines with distinct puroindoline-b allele.

KEY MESSAGE: Genetic (different forms of puroindoline-b) and environment (through variations in vitreousness), have important effects on wheat grain mechanical properties. The two methods of hardness measurements (NIRS, SKCS) do not give the same information. Bread wheat near-isogenic lines differing in hardness, due to distinct puroindoline-b alleles (the wild type, Pinb-D1a, or the mutated forms, Pinb-D1b or Pinb-D1d), were grown for three years in seven sites and under two nitrogen fertilization levels, to study genetic and environmental effects on grain mechanical properties. Two methods, Near-Infrared Reflectance Spectroscopy (NIRS) and Single Kernel Characterization System (SKCS), currently used for grain hardness characterization, were carried out. Grain vitreousness, which is known to affect the grain mechanical behavior but is generally not studied, was also measured, as well as three other characters (Thousand Grain Weight, Test Weight and protein content). The relationships between the different characters were studied. Results revealed a clear effect of the different Pinb-D1 alleles on NIRS hardness, and a marked impact of the environmental conditions on vitreousness. SKCS hardness was influenced by both Pinb-D1 alleles and environmental conditions. The relationship between SKCS and NIRS hardness was strong when considering together soft and hard genotypes, but moderate within a class of genetical hardness. Vitreousness had only a weak effect on NIRS hardness, whereas vitreousness and SKCS values were strongly correlated, with two distinct regressions for soft and hard genotypes. Vitreousness was positively related to protein content, especially in the case of hard genotypes, which were able to reach high vitreousness values never observed for soft genotypes.

Genome-wide prediction of three important traits in bread wheat.

Five genomic prediction models were applied to three wheat agronomic traits-grain yield, heading date and grain test weight-in three breeding populations, each comprising about 350 doubled haploid or recombinant inbred lines evaluated in three locations during a 3-year period. The prediction accuracy, measured as the correlation between genomic estimated breeding value and observed trait, was in the range of previously published values for yield (r = 0.2-0.5), a trait with relatively low heritability. Accuracies for heading date and test weight, with relatively high heritabilities, were about 0.70. There was no improvement of prediction accuracy when two or three breeding populations were merged into one for a larger training set (e.g., for yield r ranged between 0.11 and 0.40 in the respective populations and between 0.18 and 0.35 in the merged populations). Cross-population prediction, when one population was used as the training population set and another population was used as the validation set, resulted in no prediction accuracy. This lack of cross-population prediction accuracy cannot be explained by a lower level of relatedness between populations, as measured by a shared SNP similarity, since it was only slightly lower between than within populations. Simulation studies confirm that cross-prediction accuracy decreases as the proportion of shared QTLs decreases, which can be expected from a higher level of QTL × environment interactions.

Combined meta-genomics analyses unravel candidate genes for the grain dietary fiber content in bread wheat (Triticum aestivum L.).

Grain dietary fiber content in wheat not only affects its end use and technological properties including milling, baking and animal feed but is also of great importance for health benefits. In this study, integration of association genetics (seven detected loci on chromosomes 1B, 3A, 3D, 5B, 6B, 7A, 7B) and meta-QTL (three consensus QTL on chromosomes 1B, 3D and 6B) analyses allowed the identification of seven chromosomal regions underlying grain dietary fiber content in bread wheat. Based either on a diversity panel or on bi-parental populations, we clearly demonstrate that this trait is mainly driven by a major locus located on chromosome 1B associated with a log of p value >13 and a LOD score >8, respectively. In parallel, we identified 73 genes differentially expressed during the grain development and between genotypes with contrasting grain fiber contents. Integration of quantitative genetics and transcriptomic data allowed us to propose a short list of candidate genes that are conserved in the rice, sorghum and Brachypodium chromosome regions orthologous to the seven wheat grain fiber content QTL and that can be considered as major candidate genes for future improvement of the grain dietary fiber content in bread wheat breeding programs.