Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

An increased awareness of environmental protection and sustainable production raise the necessity of incorporating the selection of low nitrogen-tolerant winter wheat cultivars for high yield and quality in the breeding process. This selection can be assisted by using stress screening indices. Our study aimed to evaluate and compare a number of stress screening indices and to determine and select the most nitrogen deficiency-tolerant winter wheat cultivars for further breeding. The experiment included forty-eight winter wheat cultivars from eight different countries that were grown for two consecutive years at three different locations under low-nitrogen (LN) and high-nitrogen (HN) conditions. The results emphasized the importance of applying the appropriate stress screening indices in evaluating and selecting nitrogen deficiency-tolerant wheat cultivars. The promising stress screening indices were the mean productivity index (MP), geometric mean productivity index (GMP), harmonic mean index (HM), stress tolerance index (STI) and yield index (YI). They identified cultivars Sofru, BC Opsesija and MV-Nemere as the most tolerant cultivars to LN conditions for grain yield. The same indices classified U-1, OS-Olimpija, Forcali, Viktoria and BC Tena cultivars as the most tolerant to LN conditions for the grain protein content. Using the tolerance index (TOL), yield stability index (YSI) and relative stress index (RSI), the Katarina and Ficko cultivars were denoted as LN-tolerant cultivars in terms of the grain yield and Isengrain, Tosunbey, Vulkan and BC Darija in terms of the grain protein content.

Field trials were conducted to evaluate the effect of nitrogen level on wheat protein content and composition in 16 cultivars over two years at three locations. The nitrogen treatment comprised two nitrogen levels, 0 kg ha⁻¹ as low and 100 kg ha⁻¹ as high nitrogen, applied as top dressings of 50 kg nitrogen per ha at tillering and stem extension growth stages. Increased nitrogen level generally enhanced grain protein by 11.3% (11.5% vs. 12.8%). Considering protein composition determined by reversed phase–high-pressure liquid chromatography, higher nitrogen supply generally enhanced the proportion of total gliadins, α-gliadins, γ-gliadins and high-molecular-weight glutenin subunits by 1.1%, 2.0%, 3.7%, 0.6% and 0.9%, respectively, and reduced albumins and globulins, ω-gliadins, total glutenins and low-molecular-weight glutenin subunits by 1.1%, 1.7%, 1.9% and 3.2%. Under a high nitrogen level, the historical cultivars Libellula, San Pastore and U-1 had a higher protein content (13.1–15.2%) with significantly higher total gliadins, which resulted in a significantly higher gliadin/glutenin ratio (1.68–1.92). In the modern cultivars, protein content varied between 11.4% and 14.6% with a well-balanced gliadin/glutenin ratio (1.08–1.50), except for cultivar MV Nemere which had a high gliadin/glutenin ratio at both nitrogen levels (1.81 vs. 1.87). In summary, increasing nitrogen level enhanced grain protein content while the composition of gliadin and glutenin fractions was changed to a lesser extent and was largely cultivar specific and therefore should be considered for wheat baking quality assessment and breeding purposes.

Extended drought affects the production and quality of wheat (Triticum aestivum L.), one of the world’s most important food crops. Breeding for increased drought resistance is becoming increasingly important due to the rising demand for food production. Four old traditional Croatian wheat cultivars were used in the present study to examine the early antioxidant response of flag leaves to desiccant-stimulated drought stress and to identify drought-tolerant cultivars accordingly. The results indicate that the enzymatic antioxidant system plays the most significant role in the early response of adult wheat plants to drought stress and the removal of excessive H₂O₂, particularly GPOD and APX. Nada and Dubrava cultivars revealed the strongest activation of the enzymatic defense mechanism, which prevented H₂O₂ accumulation and lipid peroxidation. Additionally, the Nada cultivar also showed increased synthesis of proline and specific phenolic compounds, which both contribute to the increased stress tolerance. Among the cultivars investigated, cultivar Nada has the broadest genetic base, which may explain why it possesses the ability to activate both enzymatic and non-enzymatic defense mechanisms in an early response to drought stress. This suggests that old traditional wheat cultivars with broad genetic bases can be a valuable source of drought tolerance, which is especially important given the current climate change.

Breeding for end-use quality traits is often challenging since their assessment requires larger quantities of grain and flour samples, which are usually not available early in the breeding process. Using the mixograph as a fast and effective method of evaluating dough quality together with genomic selection (GS) can help in pre-selecting high-performing progenies earlier in the breeding process and achieve a higher gain per unit of time and cost. In the present study, the potential of GS to predict seven end-use quality traits, including mixograph traits, in two biparental wheat populations was investigated. Field trials with both populations were conducted at two locations in Croatia (Osijek and Slavonski Brod) over three years. Results showed that the size of the training population (TP) plays an important role in achieving higher prediction accuracies, while marker density is not a major limitation. Additionally, results of the present study did not support the optimization of TP based on phenotypic variance as a tool to increase prediction accuracy. The performance of eight prediction models was compared and among them elastic net showed the lowest prediction accuracy for all traits. Bayesian models provided slightly higher prediction accuracy than the ridge regression best linear unbiased prediction (RR-BLUP) model, which is negligible considering the time required to perform an analysis. Although RR-BLUP was not the best performing model in all cases, no advantage of using any other model studied here was observed. Furthermore, strong differences between environments in terms of the prediction accuracy achieved were observed, suggesting that environments that are less predictive should be removed from the dataset used to train the prediction model. The prediction accuracies obtained in this study support implementation of GS in wheat breeding for end-use quality, including some mixograph traits.

Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult. In order to reduce drawbacks caused by GEI and make the selection for wheat quality more effective, it is important to properly assess the effects of genotype, environment, and GEI on the trait of interest. In the present study, GEI patterns for the selected quality and mixograph traits were studied using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Two biparental wheat populations consisting of 145 and 175 RILs were evaluated in six environments. The environment was the dominant source of variation for grain protein content (GPC), wet gluten content (WGC), and test weight (TW), accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation has been shown to be trait and population-dependent. Overall, GEI has been shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which, MG124 is the most interesting, being the prevalent “winner” for GPC and WGC, but also the “winner” for non-correlated trait TW in environment SB10.