Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (Zea mays L.).

Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize-rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.

Maximization through optimization? On the relationship between hybrid performance and parental genetic distance.

Heterosis is the improved performance of hybrids compared with their parental components and is widely exploited in agriculture. According to quantitative genetic theory, genetic distance between parents at heterotic quantitative trait loci is required for heterosis, but how heterosis varies with genetic distance has remained elusive, despite intensive research on the topic. Experimental studies have often found a positive association between heterosis and genetic distance that, however, varied in strength. Most importantly, it has remained unclear whether heterosis increases continuously with genetic distance or whether there is an optimum genetic distance after which heterosis declines again. Here, we revisit the relationship between heterosis and genetic distance and provide perspectives on how to maximize heterosis and hybrid performance in breeding, as well as the consequences for the design of heterotic groups and the utilization of more exotic material and genetic resources.

The genetic architecture of soybean photothermal adaptation to high latitudes.

Soybean is a major plant protein source for both human food and animal feed, but to meet global demands as well as a trend towards regional production, soybean cultivation needs to be expanded to higher latitudes. In this study, we developed a large diversity panel consisting of 1503 early-maturing soybean lines and used genome-wide association mapping to dissect the genetic architecture underlying two crucial adaptation traits, flowering time and maturity. This revealed several known maturity loci, E1, E2, E3, and E4, and the growth habit locus Dt2 as causal candidate loci, and also a novel putative causal locus, GmFRL1, encoding a homolog of the vernalization pathway gene FRIGIDA-like 1. In addition, the scan for quantitative trait locus (QTL)-by-environment interactions identified GmAPETALA1d as a candidate gene for a QTL with environment-dependent reversed allelic effects. The polymorphisms of these candidate genes were identified using whole-genome resequencing data of 338 soybeans, which also revealed a novel E4 variant, e4-par, carried by 11 lines, with nine of them originating from Central Europe. Collectively, our results illustrate how combinations of QTL and their interactions with the environment facilitate the photothermal adaptation of soybean to regions far beyond its center of origin.

Do We Need to Breed for Regional Adaptation in Soybean?-Evaluation of Genotype-by-Location Interaction and Trait Stability of Soybean in Germany.

Soybean is a crop in high demand, in particular as a crucial source of plant protein. As a short-day plant, soybean is sensitive to the latitude of the growing site. Consequently, varieties that are well adapted to higher latitudes are required to expand the cultivation. In this study, we employed 50 soybean genotypes to perform a multi-location trial at seven locations across Germany in 2021. Two environmental target regions were determined following the latitude of the locations. Adaptation and trait stability of seed yield and protein content across all locations were evaluated using Genotype plus Genotype-by-Environment (GGE) biplots and Shukla's stability variance. We found a moderate level of crossing-over type genotype-by-location interaction across all locations. Within the environmental target regions, the genotype-by-location interaction could be minimised. Despite the positive correlation (R = 0.59) of seed yield between the environmental target regions and the same best-performing genotype, the genotype rankings differed in part substantially. In conclusion, we found that soybean can be grown at a wide range of latitudes across Germany. However, the performance of genotypes differed between the northern and southern locations, with an 18.8% higher mean yield in the south. This in combination with the observed rank changes of high-performing genotypes between both environmental target regions suggests that selection targeted towards environments in northern Germany could improve soybean breeding for those higher latitude regions.

Genetic Dissection of Phosphorus Use Efficiency and Genotype-by-Environment Interaction in Maize.

Genotype-by-environment interaction (G-by-E) is a common but potentially problematic phenomenon in plant breeding. In this study, we investigated the genotypic performance and two measures of plasticity on a phenotypic and genetic level by assessing 234 maize doubled haploid lines from six populations for 15 traits in seven macro-environments with a focus on varying soil phosphorus levels. It was found intergenic regions contributed the most to the variation of phenotypic linear plasticity. For 15 traits, 124 and 31 quantitative trait loci (QTL) were identified for genotypic performance and phenotypic plasticity, respectively. Further, some genes associated with phosphorus use efficiency, such as Zm00001eb117170, Zm00001eb258520, and Zm00001eb265410, encode small ubiquitin-like modifier E3 ligase were identified. By significantly testing the main effect and G-by-E effect, 38 main QTL and 17 interaction QTL were identified, respectively, in which MQTL38 contained the gene Zm00001eb374120, and its effect was related to phosphorus concentration in the soil, the lower the concentration, the greater the effect. Differences in the size and sign of the QTL effect in multiple environments could account for G-by-E. At last, the superiority of G-by-E in genomic selection was observed. In summary, our findings will provide theoretical guidance for breeding P-efficient and broadly adaptable varieties.

Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).

Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.

Can we abandon phosphorus starter fertilizer in maize? Results from a diverse panel of elite and doubled haploid landrace lines of maize (Zea mays L.).

The importance of phosphorus (P) in agriculture contrasts with the negative environmental impact and the limited resources worldwide. Reducing P fertilizer application by utilizing more efficient genotypes is a promising way to address these issues. To approach this, a large panel of maize (Zea mays L.) comprising each 100 Flint and Dent elite lines and 199 doubled haploid lines from six landraces was assessed in multi-environment field trials with and without the application of P starter fertilizer. The treatment comparison showed that omitting the starter fertilizer can significantly affect traits in early plant development but had no effect on grain yield. Young maize plants provided with additional P showed an increased biomass, faster growth and superior vigor, which, however, was only the case under environmental conditions considered stressful for maize cultivation. Importantly, though the genotype-by-treatment interaction variance was comparably small, there is genotypic variation for this response that can be utilized in breeding. The comparison of elite and doubled haploid landrace lines revealed a superior agronomic performance of elite material but also potentially valuable variation for early traits in the landrace doubled haploid lines. In conclusion, our results illustrate that breeding for P efficient maize cultivars is possible towards a reduction of P fertilizer in a more sustainable agriculture.

The performance of phenomic selection depends on the genetic architecture of the target trait.

KEY MESSAGE: The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

Do lower nitrogen fertilization levels require breeding of different types of cultivars in triticale?

KEY MESSAGE: The comparably low genotype-by-nitrogen level interaction suggests that selection in early generations can be done under high-input conditions followed by selection under different nitrogen levels to identify genotypes ideally suited for the target environment. Breeding high-yielding, nitrogen-efficient crops is of utmost importance to achieve greater agricultural sustainability. The aim of this study was to evaluate nitrogen use efficiency (NUE) of triticale, investigate long-term genetic trends and the genetic architecture, and develop strategies for NUE improvement by breeding. For this, we evaluated 450 different triticale genotypes under four nitrogen fertilization levels in multi-environment field trials for grain yield, protein content, starch content and derived indices. Analysis of temporal trends revealed that modern cultivars are better in exploiting the available nitrogen. Genome-wide association mapping revealed a complex genetic architecture with many small-effect QTL and a high level of pleiotropy for NUE-related traits, in line with phenotypic correlations. Furthermore, the effect of some QTL was dependent on the nitrogen fertilization level. High correlations of each trait between N levels and the rather low genotype-by-N-level interaction variance showed that generally the same genotypes perform well over different N levels. Nevertheless, the best performing genotype was always a different one. Thus, selection in early generations can be done under high nitrogen fertilizer conditions as these provide a stronger differentiation, but the final selection in later generations should be conducted with a nitrogen fertilization as in the target environment.

High-resolution association mapping with libraries of immortalized lines from ancestral landraces.

KEY MESSAGE: Association mapping with immortalized lines of landraces offers several advantages including a high mapping resolution, as demonstrated here in maize by identifying the causal variants underlying QTL for oil content and the metabolite allantoin. Landraces are traditional varieties of crops that present a valuable yet largely untapped reservoir of genetic variation to meet future challenges of agriculture. Here, we performed association mapping in a panel comprising 358 immortalized maize lines from six European Flint landraces. Linkage disequilibrium decayed much faster in the landraces than in the elite lines included for comparison, permitting a high mapping resolution. We demonstrate this by fine-mapping a quantitative trait locus (QTL) for oil content down to the phenylalanine insertion F469 in DGAT1-2 as the causal variant. For the metabolite allantoin, related to abiotic stress response, we identified promoter polymorphisms and differential expression of an allantoinase as putative cause of variation. Our results demonstrate the power of this approach to dissect QTL potentially down to the causal variants, toward the utilization of natural or engineered alleles in breeding. Moreover, we provide guidelines for studies using ancestral landraces for crop genetic research and breeding.

Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize.

Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits.

KEY MESSAGE: A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package "selectiongain" from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith-Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package "selectiongain" with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.

Genetic Dissection of Phosphorus Use Efficiency in a Maize Association Population under Two P Levels in the Field.

Phosphorus (P) deficiency is an important challenge the world faces while having to increase crop yields. It is therefore necessary to select maize (Zea may L.) genotypes with high phosphorus use efficiency (PUE). Here, we extensively analyzed the biomass, grain yield, and PUE-related traits of 359 maize inbred lines grown under both low-P and normal-P conditions. A significant decrease in grain yield per plant and biomass, an increase in PUE under low-P condition, as well as significant correlations between the two treatments were observed. In a genome-wide association study, 49, 53, and 48 candidate genes were identified for eleven traits under low-P, normal-P conditions, and in low-P tolerance index (phenotype under low-P divided by phenotype under normal-P condition) datasets, respectively. Several gene ontology pathways were enriched for the genes identified under low-P condition. In addition, seven key genes related to phosphate transporter or stress response were molecularly characterized. Further analyses uncovered the favorable haplotype for several core genes, which is less prevalent in modern lines but often enriched in a specific subpopulation. Collectively, our research provides progress in the genetic dissection and molecular characterization of PUE in maize.

Genome-wide association study for deoxynivalenol production and aggressiveness in wheat and rye head blight by resequencing 92 isolates of Fusarium culmorum.

BACKGROUND: Fusarium culmorum is an important pathogen causing head blight of cereals in Europe. This disease is of worldwide importance leading to reduced yield, grain quality, and contamination by mycotoxins. These mycotoxins are harmful for livestock and humans; therefore, many countries have strict regulatory limits for raw materials and processed food. Extensive genetic diversity is described among field populations of F. culmorum isolates for aggressiveness and production of the trichothecene mycotoxin deoxynivalenol (DON). However, the causes for this quantitative variation are not clear, yet. We analyzed 92 isolates sampled from different field populations in Germany, Russia, and Syria together with an international collection for aggressiveness and DON production in replicated field experiments at two locations in two years with two hosts, wheat and rye. The 30x coverage whole-genome resequencing of all isolates resulted in the identification of 130,389 high quality single nucleotide polymorphisms (SNPs) that were used for the first genome-wide association study in this phytopathogenic fungus. RESULTS: In wheat, 20 and 27 SNPs were detected for aggressiveness and DON content, respectively, of which 10 overlapped. Additionally, two different SNPs were significantly associated with aggressiveness in rye that were among those SNPs being associated with DON production in wheat. Most of the SNPs explained only a small proportion of genotypic variance (pG), however, four SNPs were associated with major quantitative trait loci (QTLs) with pG ranging from 12 to 48%. The QTL with the highest pG was involved in DON production and associated with a SNP most probably located within the Tri4 gene. CONCLUSIONS: The diversity of 92 isolates of F. culmorum were captured using a heuristic approach. Key phenotypic traits, SNPs, and candidate genes underlying aggressiveness and DON production were identified. Clearly, many QTLs are responsible for aggressiveness and DON content in wheat, both traits following a quantitative inheritance. Several SNPs involved in DON metabolism, among them the Tri4 gene of the trichothecene pathway, were inferred as important source of variation in fungal aggressiveness. Using this information underlying the phenotypic variation will be of paramount importance in evaluating strategies for successful resistance breeding.

Identification and Fine-Mapping of Quantitative Trait Loci Controlling Plant Height in Central European Winter Triticale (×Triticosecale Wittmack).

The quantitatively inherited trait plant height is routinely evaluated in triticale breeding programs as it substantially influences lodging and disease susceptibility, is a main contributor to biomass yield, and is required to improve hybrid seed production by fine-tuning plant height in the female and male parental pools in hybrid breeding programs. In this study, we evaluated a panel of 846 diverse Central European triticale genotypes to dissect the genetic architecture underlying plant height by genome-wide association mapping. This revealed three medium- to large-effect QTL on chromosomes 5A, 4B, and 5R. Genetic and physical fine-mapping of the putative QTL revealed that the QTL on chromosome 5R most likely corresponds to Ddw1 and that the QTL on chromosome 5A is likely to be Rht12. Furthermore, we observed a temporal trend in registered cultivars with a decreasing plant height during the past decades, accompanied by an increasing use of the height-reducing alleles at the identified QTL. In summary, our results shed new light on the genetic control of plant height in triticale and open new avenues for future improvement by breeding.

Genetic architecture underlying the expression of eight α-amylase trypsin inhibitors.

KEY MESSAGE: Wheat cultivars largely differ in the content and composition of ATI proteins, but heritability was quite low for six out of eight ATIs. The genetic architecture of ATI proteins is built up of few major and numerous small effect QTL. Amylase trypsin inhibitors (ATIs) are important allergens in baker's asthma and suspected triggers of non-celiac wheat sensitivity (NCWS) inducing intestinal and extra-intestinal inflammation. As studies on the expression and genetic architecture of ATI proteins in wheat are lacking, we evaluated 149 European old and modern bread wheat cultivars grown at three different field locations for their content of eight ATI proteins. Large differences in the content and composition of ATIs in the different cultivars were identified ranging from 3.76 pmol for ATI CM2 to 80.4 pmol for ATI 0.19, with up to 2.5-fold variation in CM-type and up to sixfold variation in mono/dimeric ATIs. Generally, heritability estimates were low except for ATI 0.28 and ATI CM2. ATI protein content showed a low correlation with quality traits commonly analyzed in wheat breeding. Similarly, no trends were found regarding ATI content in wheat cultivars originating from numerous countries and decades of breeding history. Genome-wide association mapping revealed a complex genetic architecture built of many small, few medium and two major quantitative trait loci (QTL). The major QTL were located on chromosomes 3B for ATI 0.19-like and 6B for ATI 0.28, explaining 70.6 and 68.7% of the genotypic variance, respectively. Within close physical proximity to the medium and major QTL, we identified eight potential candidate genes on the wheat reference genome encoding structurally related lipid transfer proteins. Consequently, selection and breeding of wheat cultivars with low ATI protein amounts appear difficult requiring other strategies to reduce ATI content in wheat products.

Training set design in genomic prediction with multiple biparental families.

Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.

Optimizing the P balance: How do modern maize hybrids react to different starter fertilizers?

Phosphorus (P) is an essential macronutrient for plants, but also a limited resource worldwide. Strict regulations for fertilizer applications in the European Union are a consequence of the negative environmental effects in case of improper use. Maize is typically grown with the application of P starter fertilizer, which, however, might be reduced or even omitted if suitable varieties were available. This study was performed with the 20 commercially most important maize hybrids in Germany evaluated in multi-location field trials with the aim to investigate the potential to breed for high-performing maize hybrids under reduced P starter fertilizer. At the core location, three starter fertilizers with either phosphate (triple superphosphate, TSP), ammonium nitrate (calcium ammonium nitrate, CAN), or a combination of ammonium and phosphate (diammonium phosphate, DAP) were evaluated relative to a control and traits from youth development to grain yield were assessed. Significant differences were mainly observed for the DAP starter fertilizer, which was also reflected in a yield increase of on average +0.67 t/ha (+5.34%) compared to the control. Correlations among the investigated traits varied with starter fertilizer, but the general trends remained. As expected, grain yield was negatively correlated with grain P concentration, likely due to a dilution effect. Importantly, the genotype-by-starter fertilizer interaction was always non-significant in the multi-location analysis. This indicates that best performing genotypes can be identified irrespective of the starter fertilizer. Taken together, our results provide valuable insights regarding the potential to reduce starter fertilizers in maize cultivation as well as for breeding maize for P efficiency under well-supplied conditions.

Location and Variety but Not Phosphate Starter Fertilization Influence the Profiles of Fatty Acids, Carotenoids, and Tocochromanols in Kernels of Modern Corn (Zea mays L.) Hybrids Cultivated in Germany.

Phosphate is a limiting plant nutrient and essential for corn growth and development. Thus, the impact of phosphate fertilization, location, and the variety of modern corn (Zea mays L.) hybrids on the profiles of fatty acids, carotenoids, and tocochromanols (vitamin E) was assessed in corn grains. Eight different corn hybrids were grown with (52.9 kg of phosphorus per ha) or without starter fertilizer at three experimental sites in Germany. Location (p < 0.05) and genetics (p < 0.001) but not phosphate fertilization significantly altered the concentrations of individual saturated and unsaturated fatty acids, carotenoids, and tocochromanols. Significant (p < 0.05) interaction effects on the concentrations were mainly observed between the variety and the location. In conclusion, the choice of the corn variety had a more significant impact on the biosynthesis of fatty acids, carotenoids, and tocochromanols than the location or phosphate application on phosphate-sufficient soils.

High-resolution proteomics reveals differences in the proteome of spelt and bread wheat flour representing targets for research on wheat sensitivities.

Wheat consumption can trigger celiac disease, allergic reactions and non-celiac wheat sensitivity (NCWS) in humans. Some people with NCWS symptoms claim a better tolerability of spelt compared to bread wheat products. We therefore investigated potential differences in the proteomes of spelt and bread wheat flour using nano LC-ESI-MS/MS on a set of 15 representative varieties for each of the two species. Based on the bread wheat reference, we detected 3,050 proteins in total and for most of them the expression was mainly affected by the environment. By contrast, 274 and 409 proteins in spelt and bread wheat, respectively, had a heritability ≥ 0.4 highlighting the potential to influence their expression level by varietal choice. We found 84 and 193 unique proteins for spelt and bread wheat, respectively, and 396 joint proteins, which expression differed significantly (p ≤ 0.05) when comparing both species. Thus, about one third of proteins differed significantly between spelt and bread wheat. Of them, we identified 81 proteins with high heritability, which therefore might be interesting candidates for future research on wheat hypersensitivities.