Breeding improves wheat productivity under contrasting agrochemical input levels

Novel PHOTOPERIOD-1 gene variants associate with yield-related and root-angle traits in European bread wheat

KEY MESSAGE

PHOTOPERIOD-1 homoeologous gene copies play a pivotal role in regulation of flowering time in wheat. Here, we show that their influence also extends to spike and shoot architecture and even impacts root development. The sequence diversity of three homoeologous copies of the PHOTOPERIOD-1 gene in European winter wheat was analyzed by Oxford Nanopore amplicon-based multiplex sequencing and molecular markers in a panel of 194 cultivars representing breeding progress over the past 5 decades. A strong, consistent association with an average 8% increase in grain yield was observed for the PpdA1-Hap1 haplotype across multiple environments. This haplotype was found to be linked in 51% of cultivars to the 2NS/2AS translocation, originally introduced from Aegilops ventricosa, which leads to an overestimation of its effect. However, even in cultivars without the 2NS/2AS translocation, PpdA1-Hap1 was significantly associated with increased grain yield, kernel per spike and kernel per m2 under optimal growth conditions, conferring a 4% yield advantage compared to haplotype PpdA1-Hap4. In contrast to Ppd-B1 and Ppd-D1, the Ppd-A1 gene exhibits novel structural variations and a high number of SNPs, highlighting the evolutionary changes that have occurred in this region over the course of wheat breeding history. Additionally, cultivars carrying the photoperiod-insensitive Ppd-D1a allele not only exhibit earlier heading, but also deeper roots compared to those with photoperiod-sensitive alleles under German conditions. PCR and KASP assays have been developed that can be effectively employed in marker-assisted breeding programs to introduce these favorable haplotypes.

Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes

KEY MESSAGE

There is variation in stay-green within barley breeding germplasm, influenced by multiple haplotypes and environmental conditions. The positive genetic correlation between stay-green and yield across multiple environments highlights the potential as a future breeding target. Barley is considered one of the most naturally resilient crops making it an excellent candidate to dissect the genetics of drought adaptive component traits. Stay-green, is thought to contribute to drought adaptation, in which the photosynthetic machinery is maintained for a longer period post-anthesis increasing the photosynthetic duration of the plant. In other cereal crops, including wheat, stay-green has been linked to increased yield under water-limited conditions. Utilizing a panel of diverse barley breeding lines from a commercial breeding program we aimed to characterize stay-green in four environments across two years. Spatiotemporal modeling was used to accurately model senescence patterns from flowering to maturity characterizing the variation for stay-green in barley for the first time. Environmental effects were identified, and multi-environment trait analysis was performed for stay-green characteristics during grain filling. A consistently positive genetic correlation was found between yield and stay-green. Twenty-two chromosomal regions with large effect haplotypes were identified across and within environment types, with ten being identified in multiple environments. In silico stacking of multiple desirable haplotypes showed an opportunity to improve the stay-green phenotype through targeted breeding. This study is the first of its kind to model barley stay-green in a large breeding panel and has detected novel, stable and environment specific haplotypes. This provides a platform for breeders to develop Australian barley with custom senescence profiles for improved drought adaptation.

Breeding progress of nitrogen use efficiency of cereal crops, winter oilseed rape and peas in long-term variety trials

KEY MESSAGE

Grain yield and NUE increased over time while nitrogen yield did not drop significantly despite reduced nitrogen input. Selection for grain and nitrogen yield is equivalent to selection for NUE. Breeding and registration of improved varieties with high yield, processing quality, disease resistance and nitrogen use efficiency (NUE) are of utmost importance for sustainable crop production to minimize adverse environmental impact and contribute to food security. Based on long-term variety trials of cereals, winter oilseed rape and grain peas tested across a wide range of environmental conditions in Germany, we quantified long-term breeding progress for NUE and related traits. We estimated the genotypic, environmental and genotype-by-environment interaction variation and correlation between traits and derived heritability coefficients. Nitrogen fertilizer application was considerably reduced between 1995 and 2021 in the range of 5.4% for winter wheat and 28.9% for spring wheat while for spring barley it was increased by 20.9%. Despite the apparent nitrogen reduction for most crops, grain yield (GYLD) and nitrogen accumulation in grain (NYLD) was increased or did not significantly decrease. NUE for GYLD increased significantly for all crops between 12.8% and 35.2% and for NYLD between 8% and 20.7%. We further showed that the genotypic rank of varieties for GYLD and NYLD was about equivalent to the genotypic rank of the corresponding traits of NUE, if all varieties in a trial were treated with the same nitrogen rate. Heritability of nitrogen yield was about the same as that of grain yield, suggesting that nitrogen yield should be considered as an additional criterion for variety testing to increase NUE and reduce negative environmental impact.

A Catalog of GNI-A1 Genes That Regulate Floret Fertility in a Diverse Bread Wheat Collection

Modifying inflorescence architecture improves grain number and grain weight in bread wheat (Triticum aestivum). Allelic variation in Grain Number Increase 1 (GNI-A1) genes, encoding a homeodomain leucine zipper class I transcription factor, influences grain number and yield. However, allelic information about GNI-A1 in diverse germplasms remains limited. Here, we investigated GNI-A1 alleles in a panel of 252 diverse bread wheat accessions (NBRP core collection and HRO breeder's panel) by target resequencing. Cultivars carrying the reduced-function allele (105Y) were predominant in the NBRP panel, whereas the 105N functional allele was the major type in the HRO panel. Cultivars with the 105Y allele were distributed in Asian landraces but not in European genotypes. Association analysis demonstrated that floret fertility, together with grain size, were improved in cultivars in the NBRP core collection carrying the 105Y allele. These results imply that different alleles of GNI-A1 have been locally selected, with the 105Y allele selected in East Asia and the 105N allele selected in Europe.

Boosting Triticeae crop grain yield by manipulating molecular modules to regulate inflorescence architecture: insights and knowledge from other cereal crops

One of the challenges for global food security is to reliably and sustainably improve the grain yield of cereal crops. One solution is to modify the architecture of the grain-bearing inflorescence to optimize for grain number and size. Cereal inflorescences are complex structures, with determinacy, branching patterns, and spikelet/floret growth patterns that vary by species. Recent decades have witnessed rapid advancements in our understanding of the genetic regulation of inflorescence architecture in rice, maize, wheat, and barley. Here, we summarize current knowledge on key genetic factors underlying the different inflorescence morphologies of these crops and model plants (Arabidopsis and tomato), focusing particularly on the regulation of inflorescence meristem determinacy and spikelet meristem identity and determinacy. We also discuss strategies to identify and utilize these superior alleles to optimize inflorescence architecture and, ultimately, improve crop grain yield.

Grain yield trade-offs in spike-branching wheat can be mitigated by elite alleles affecting sink capacity and post-anthesis source activity

Introducing variations in inflorescence architecture, such as the 'Miracle-Wheat' (Triticum turgidum convar. compositum (L.f.) Filat.) with a branching spike, has relevance for enhancing wheat grain yield. However, in the spike-branching genotypes, the increase in spikelet number is generally not translated into grain yield advantage because of reduced grains per spikelet and grain weight. Here, we investigated if such trade-offs might be a function of source-sink strength by using 385 recombinant inbred lines developed by intercrossing the spike-branching landrace TRI 984 and CIRNO C2008, an elite durum (T. durum L.) cultivar; they were genotyped using the 25K array. Various plant and spike architectural traits, including flag leaf, peduncle, and spike senescence rate, were phenotyped under field conditions for 2 consecutive years. On chromosome 5AL, we found a new modifier QTL for spike branching, branched headt3 (bht-A3), which was epistatic to the previously known bht-A1 locus. Besides, bht-A3 was associated with more grains per spikelet and a delay in flag leaf senescence rate. Importantly, favourable alleles, viz. bht-A3 and grain protein content (gpc-B1) that delayed senescence, are required to improve grain number and grain weight in the spike-branching genotypes. In summary, achieving a balanced source-sink relationship might minimize grain yield trade-offs in Miracle-Wheat.

The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Deciphering the evolution and complexity of wheat germplasm from a genomic perspective

Bread wheat provides an essential fraction of the daily calorific intake for humanity. Due to its huge and complex genome, progress in studying on the wheat genome is substantially trailed behind those of the other two major crops, rice and maize, for at least a decade. With rapid advances in genome assembling and reduced cost of high-throughput sequencing, emerging de novo genome assemblies of wheat and whole-genome sequencing data are leading to a paradigm shift in wheat research. Here, we review recent progress in dissecting the complex genome and germplasm evolution of wheat since the release of the first high-quality wheat genome. New insights have been gained in the evolution of wheat germplasm during domestication and modern breeding progress, genomic variations at multiple scales contributing to the diversity of wheat germplasm, and complex transcriptional and epigenetic regulations of functional genes in polyploid wheat. Genomics databases and bioinformatics tools meeting the urgent needs of wheat genomics research are also summarized. The ever-increasing omics data, along with advanced tools and well-structured databases, are expected to accelerate deciphering the germplasm and gene resources in wheat for future breeding advances.

Dynamic gene regulatory networks improving spike fertility through regulation of floret primordia fate in wheat

The developmental process of spike is critical for spike fertility through affecting floret primordia fate in wheat; however, the genetic regulation of this dynamic and complex developmental process remains unclear. Here, we conducted a high temporal-resolution analysis of spike transcriptomes and monitored the number and morphology of floret primordia within spike. The development of all floret primordia in a spike was clearly separated into three distinct phases: differentiation, pre-dimorphism and dimorphism. Notably, we identified that floret primordia with meiosis ability at the pre-dimorphism phase usually develop into fertile floret primordia in the next dimorphism phase. Compared to control, increasing plant space treatment achieved the maximum increasement range (i.e., 50%) in number of fertile florets by accelerating spike development. The process of spike fertility improvement was directed by a continuous and dynamic regulatory network involved in transcription factor and genes interaction. This was based on the coordination of genes related to heat shock protein and jasmonic acid biosynthesis during differentiation phase, and genes related to lignin, anthocyanin and chlorophyll biosynthesis during dimorphism phase. The multi-dimensional association with high temporal-resolution approach reported here allows rapid identification of genetic resource for future breeding studies to realise the maximum spike fertility potential in more cereal crops.

Stage-specific genotype-by-environment interactions determine yield components in wheat

In cereal crops, environmental fluctuations affect different physiological processes during various developmental phases associated with the formation of yield components. Because these effects are coupled with cultivar-specific phenology, studies investigating environmental responses in different cultivars can give contradictory results regarding key phases impacting yield performance. To dissect how genotype-by-environment interactions affect grain yield in winter wheat, we estimated the sensitivities of yield components to variation in global radiation, temperature and precipitation in 220 cultivars across 81 time-windows ranging from double ridge to seed desiccation. Environmental sensitivity responses were prominent in the short-term physiological subphases of spike and kernel development, causing phenologically dependent, stage-specific genotype-by-environment interactions. Here we reconcile contradicting findings from previous studies and show previously undetected effects; for example, the positive impact of global radiation on kernel weight during canopy senescence. This deep insight into the three-way interactions between phenology, yield formation and environmental fluctuations provides comprehensive new information for breeding and modelling cereal crops.

Exploring the trade-off between individual fitness and community performance of wheat crops using simulated canopy shade

The genetic heritage of wheat (Triticum spp.) crops has been shaped by millions of years of predomestication natural selection, often driven by competition among individuals. However, genetic improvements in yield potential are thought to involve selection towards reduced competitiveness, thus enhancing adaptation to the crop environment. We investigated potential trade-offs between individual plant fitness and community performance using a population of introgression lines carrying chromosome segments of wild emmer (nondomesticated) in the background of an elite durum cultivar. We focused on light as a primary factor affecting plant-plant interactions and assessed morphological and biomass phenotypes of single plants grown in mixtures under sunlight and a simulated canopy shade, and the relevance of these phenotypes for the monoculture community in the field. We found that responses to canopy shade resemble responses to high density and contribute to both the individual and the community. Stepwise regressions suggested that grain number per spike and its persistence under shade are essential attributes of productive communities, advocating their use as a breeding target during early-generation selection. Overall, multiple phenotypes attained under shade could better explain community performance. Our novel, applicable, high-throughput set-up provides new prospects for studying and selecting single-plant phenotypes in a canopy-like environment.

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

Delayed development of basal spikelets in wheat explains their increased floret abortion and rudimentary nature

Large differences exist in the number of grains per spikelet across an individual wheat (Triticum aestivum L.) spike. The central spikelets produce the highest number of grains, while apical and basal spikelets are less productive, and the most basal spikelets are commonly only developed in rudimentary form. Basal spikelets are delayed in initiation, yet they continue to develop and produce florets. The precise timing or the cause of their abortion remains largely unknown. Here, we investigated the underlying causes of basal spikelet abortion using shading applications in the field. We found that basal spikelet abortion is likely to be the consequence of complete floret abortion, as both occur concurrently and have the same response to shading treatments. We detected no differences in assimilate availability across the spike. Instead, we show that the reduced developmental age of basal florets pre-anthesis is strongly associated with their increased abortion. Using the developmental age pre-abortion, we were able to predict final grain set per spikelet across the spike, alongside the characteristic gradient in the number of grains from basal to central spikelets. Future efforts to improve spikelet homogeneity across the spike could thus focus on improving basal spikelet establishment and increasing floret development rates pre-abortion.

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.

Breeding from 1891 to 2010 did not increase the content of amylase/trypsin-inhibitors in wheat (Triticum aestivum)

The prevalence of hypersensitivities towards wheat has increased in the last decades. Apart from celiac disease these include allergic and other inflammatory reactions summarized under the term non-celiac wheat sensitivity. One suspected trigger is the family of amylase/trypsin-inhibitors (ATIs), non-gluten proteins that are prominent wheat allergens and that activate the toll-like receptor 4 on intestinal immune cells to promote intestinal and extra-intestinal inflammation. We therefore quantified 13 ATIs in 60 German hexaploid winter wheat cultivars originating from 1891 to 2010 and harvested in three years by targeted liquid chromatography-tandem mass spectrometry combined with stable isotope dilution assay using specific marker peptides as internal standards. The total ATI content and that of the two major ATIs 0.19 and CM3 did not change from old cultivars (first registered from 1891 to 1950) to modern cultivars (1951-2010). There were also no significant changes in ATI distribution.

Grain yield and adaptation of spring wheat to Norwegian growing conditions is driven by allele frequency changes at key adaptive loci discovered by genome-wide association mapping

KEY MESSAGE

Adaptation to the Norwegian environment is associated with polymorphisms in the Vrn-A1 locus. Historical selection for grain yield in Nordic wheat is associated with TaGS5-3A and TaCol-5 loci. Grain yields in Norwegian spring wheat increased by 18 kg ha-1 per year between 1972 and 2019 due to introduction of new varieties. These gains were associated with increments in the number of grains per spike and extended length of the vegetative period. However, little is known about the genetic background of this progress. To fill this gap, we conducted genome-wide association study on a panel consisting of both adapted (historical and current varieties and lines in the Nordics) and important not adapted accessions used as parents in the Norwegian wheat breeding program. The study concerned grain yield, plant height, and heading and maturity dates, and detected 12 associated loci, later validated using independent sets of recent breeding lines. Adaptation to the Norwegian cropping conditions was found to be associated with the Vrn-A1 locus, and a previously undescribed locus on chromosome 1B associated with heading date. Two loci associated with grain yield, corresponding to the TaGS5-3A and TaCol-5 loci, indicated historical selection pressure for high grain yield. A locus on chromosome 2A explained the tallness of the oldest accessions. We investigated the origins of the beneficial alleles associated with the wheat breeding progress in the Norwegian material, tracing them back to crosses with Swedish, German, or CIMMYT lines. This study contributes to the understanding of wheat adaptation to the Norwegian growing conditions, sheds light on the genetic basis of historical wheat improvement and aids future breeding efforts by discovering loci associated with important agronomic traits in wheat.

Genetic dissection of root architectural plasticity and identification of candidate loci in response to drought stress in bread wheat

BACKGROUND

The frequency of droughts has dramatically increased over the last 50 years, causing yield declines in cereals, including wheat. Crop varieties with efficient root systems show great potential for plant adaptation to drought stress, however; genetic control of root systems in wheat under field conditions is not yet well understood.

RESULTS

Natural variation in root architecture plasticity (phenotypic alteration due to changing environments) was dissected under field-based control (well-irrigated) and drought (rain-out shelter) conditions by a genome-wide association study using 200 diverse wheat cultivars. Our results revealed root architecture and plasticity traits were differentially responded to drought stress. A total of 25 marker-trait associations (MTAs) underlying natural variations in root architectural plasticity were identified in response to drought stress. They were abundantly distributed on chromosomes 1 A, 1B, 2 A, 2B, 3 A, 3B, 4B, 5 A, 5D, 7 A and 7B of the wheat genome. Gene ontology annotation showed that many candidate genes associated with plasticity were involved in water-transport and water channel activity, cellular response to water deprivation, scavenging reactive oxygen species, root growth and development and hormone-activated signaling pathway-transmembrane transport, indicating their response to drought stress. Further, in silico transcript abundance analysis demonstrated that root plasticity-associated candidate genes were highly expressed in roots across different root growth stages and under drought treatments.

CONCLUSION

Our results suggest that root phenotypic plasticity is highly quantitative, and the corresponding loci are associated with drought stress that may provide novel ways to enable root trait breeding.

Intergenerational and transgenerational effects of drought stress on winter wheat (Triticum aestivum L.)

The environments where the progenitors are grown have the potential to affect the expression of traits in their offspring. Currently, there are various hypotheses regarding the evolutionary and ecological importance of stress memory effects. There is uncertainty regarding its occurrence, persistence, predictability, and adaptive value. In this study, 15 winter wheat cultivars were grown under drought and well-watered (control) treatments for two seasons to produce seeds with all possible combinations of drought exposure histories. A comprehensive analysis to estimate transgenerational (grandparental effects), intergenerational (parental effects), and their combined memory effects on offspring traits under both control and drought moisture treatments, was performed. There were significant memory effects in most of the evaluated traits ranging from +787% to -39.0% changes in both seed quality and plant traits. The expression of stress memory was highly dependent on the generation and number of exposures, traits, and seasons. Under drought treatment, the combination of grandparental and parental stress memories was additive in all traits, but their strengths were variable when considered separately. Stress memory enhanced the performance of offspring under similar stressful conditions: increased plant height, above-ground biomass, number of grains per plant, grain weight per plant and water potential. This study offers valuable new insights into the occurrence of drought stress memory, the complexities of the effects, possible physiological and metabolic alterations explaining the detected differences, and impacts toward a clearer understanding of their generation and context-dependency.

Convergently selected NPF2.12 coordinates root growth and nitrogen use efficiency in wheat and barley

Understanding the genetic and molecular function of nitrate sensing and acquisition across crop species will accelerate breeding of cultivars with improved nitrogen use efficiency (NUE). Here, we performed a genome-wide scan using wheat and barley accessions characterized under low and high N inputs that uncovered the NPF2.12 gene, encoding a homolog of the Arabidopsis nitrate transceptor NRT1.6 and other low-affinity nitrate transporters that belong to the MAJOR FACILITATOR SUPERFAMILY. Next, it is shown that variations in the NPF2.12 promoter correlated with altered NPF2.12 transcript levels where decreased gene expression was measured under low nitrate availability. Multiple field trials revealed a significantly enhanced N content in leaves and grains and NUE in the presence of the elite allele TaNPF2.12^TT grown under low N conditions. Furthermore, the nitrate reductase encoding gene NIA1 was up-regulated in npf2.12 mutant upon low nitrate concentrations, thereby resulting in elevated levels of nitric oxide (NO) production. This increase in NO correlated with the higher root growth, nitrate uptake, and N translocation observed in the mutant when compared to wild-type. The presented data indicate that the elite haplotype alleles of NPF2.12 are convergently selected in wheat and barley that by inactivation indirectly contribute to root growth and NUE by activating NO signaling under low nitrate conditions.

Genomic prediction with haplotype blocks in wheat

Haplotype blocks might carry additional information compared to single SNPs and have therefore been suggested for use as independent variables in genomic prediction. Studies in different species resulted in more accurate predictions than with single SNPs in some traits but not in others. In addition, it remains unclear how the blocks should be built to obtain the greatest prediction accuracies. Our objective was to compare the results of genomic prediction with different types of haplotype blocks to prediction with single SNPs in 11 traits in winter wheat. We built haplotype blocks from marker data from 361 winter wheat lines based on linkage disequilibrium, fixed SNP numbers, fixed lengths in cM and with the R package HaploBlocker. We used these blocks together with data from single-year field trials in a cross-validation study for predictions with RR-BLUP, an alternative method (RMLA) that allows for heterogeneous marker variances, and GBLUP performed with the software GVCHAP. The greatest prediction accuracies for resistance scores for B. graminis, P. triticina, and F. graminearum were obtained with LD-based haplotype blocks while blocks with fixed marker numbers and fixed lengths in cM resulted in the greatest prediction accuracies for plant height. Prediction accuracies of haplotype blocks built with HaploBlocker were greater than those of the other methods for protein concentration and resistances scores for S. tritici, B. graminis, and P. striiformis. We hypothesize that the trait-dependence is caused by properties of the haplotype blocks that have overlapping and contrasting effects on the prediction accuracy. While they might be able to capture local epistatic effects and to detect ancestral relationships better than single SNPs, prediction accuracy might be reduced by unfavorable characteristics of the design matrices in the models that are due to their multi-allelic nature.

Variations in phenological, physiological, plant architectural and yield-related traits, their associations with grain yield and genetic basis

BACKGROUND AND AIMS

Physiological and morphological traits play essential roles in wheat (Triticum aestivum) growth and development. In particular, photosynthesis is a limitation to yield. Increasing photosynthesis in wheat has been identified as an important strategy to increase yield. However, the genotypic variations and the genomic regions governing morphological, architectural and photosynthesis traits remain unexplored.

METHODS

Here, we conducted a large-scale investigation of the phenological, physiological, plant architectural and yield-related traits, involving 32 traits for 166 wheat lines during 2018-2020 in four environments, and performed a genome-wide association study with wheat 90K and 660K single nucleotide polymorphism (SNP) arrays.

KEY RESULTS

These traits exhibited considerable genotypic variations in the wheat diversity panel. Higher yield was associated with higher net photosynthetic rate (r = 0.41, P < 0.01), thousand-grain weight (r = 0.36, P < 0.01) and truncated and lanceolate shape, but shorter plant height (r = -0.63, P < 0.01), flag leaf angle (r = -0.49, P < 0.01) and spike number per square metre (r = -0.22, P < 0.01). Genome-wide association mapping discovered 1236 significant stable loci detected in the four environments among the 32 traits using SNP markers. Trait values have a cumulative effect as the number of the favourable alleles increases, and significant progress has been made in determining phenotypic values and favourable alleles over the years. Eleven elite cultivars and 14 traits associated with grain yield per plot (GY) were identified as potential parental lines and as target traits to develop high-yielding cultivars.

CONCLUSIONS

This study provides new insights into the phenotypic and genetic elucidation of physiological and morphological traits in wheat and their associations with GY, paving the way for discovering their underlying gene control and for developing enhanced ideotypes in wheat breeding.

Food system actor perspectives on future-proofing European food systems through plant breeding

Crop improvement is a key innovation area in the pursuit of sustainable food systems. However, realising its potential requires integration of the needs and priorities of all agri-food chain stakeholders. In this study, we provide a multi-stakeholder perspective on the role of crop improvement in future-proofing the European food system. We engaged agri-business, farm- and consumer-level stakeholders, and plant scientists through an online survey and focus groups. Four of each group's top five priorities were shared and related to environmental sustainability goals (water, nitrogen and phosphorus efficiency, and heat stress). Consensus was identified on issues including considering existing alternatives to plant breeding (e.g. management strategies), minimising trade-offs, and addressing geographical variation in needs. We conducted a rapid evidence synthesis on the impacts of priority crop improvement options, highlighting the urgent need for further research examining downstream sustainability impacts to identify concrete targets for plant breeding innovation as a food systems solution.

More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop

Using in silico experiment in crop model, we identified different physiological regulations of yield and yield stability, as well as quantify the genotype and environment numbers required for analysing yield stability convincingly. Identifying target traits for breeding stable and high-yielded cultivars simultaneously is difficult due to limited knowledge of physiological mechanisms behind yield stability. Besides, there is no consensus about the adequacy of a stability index (SI) and the minimal number of environments and genotypes required for evaluating yield stability. We studied this question using the crop model APSIM-Wheat to simulate 9100 virtual genotypes grown under 9000 environments. By analysing the simulated data, we showed that the shape of phenotype distributions affected the correlation between SI and mean yield and the genotypic superiority measure (P_i) was least affected among 11 SI. P_i was used as index to demonstrate that more than 150 environments were required to estimate yield stability of a genotype convincingly and more than 1000 genotypes were necessary to evaluate the contribution of a physiological parameter to yield stability. Network analyses suggested that a physiological parameter contributed preferentially to yield or P_i. For example, soil water absorption efficiency and potential grain filling rate explained better the variations in yield than in P_i; while light extinction coefficient and radiation use efficiency were more correlated with P_i than with yield. The high number of genotypes and environments required for studying P_i highlight the necessity and potential of in silico experiments to better understand the mechanisms behind yield stability.

Ghat: an R package for identifying adaptive polygenic traits

Identifying selection on polygenic complex traits in crops and livestock is important for understanding evolution and helps prioritize important characteristics for breeding. Quantitative trait loci (QTL) that contribute to polygenic trait variation often exhibit small or infinitesimal effects. This hinders the ability to detect QTL-controlling polygenic traits because enormously high statistical power is needed for their detection. Recently, we circumvented this challenge by introducing a method to identify selection on complex traits by evaluating the relationship between genome-wide changes in allele frequency and estimates of effect size. The approach involves calculating a composite statistic across all markers that capture this relationship, followed by implementing a linkage disequilibrium-aware permutation test to evaluate if the observed pattern differs from that expected due to drift during evolution and population stratification. In this manuscript, we describe "Ghat," an R package developed to implement this method to test for selection on polygenic traits. We demonstrate the package by applying it to test for polygenic selection on 15 published European wheat traits including yield, biomass, quality, morphological characteristics, and disease resistance traits. Moreover, we applied Ghat to different simulated populations with different breeding histories and genetic architectures. The results highlight the power of Ghat to identify selection on complex traits. The Ghat package is accessible on CRAN, the Comprehensive R Archival Network, and on GitHub.

Transcriptome profiling at the transition to the reproductive stage uncovers stage and tissue-specific genes in wheat

BACKGROUND

The transition from vegetative to floral phase is the result of complex crosstalk of exogenous and endogenous floral integrators. This critical physiological event is the response to environmental interaction, which causes biochemical cascades of reactions at different internal tissues, organs, and releases signals that make the plant moves from vegetative status to a reproductive phase. This network controlling flowering time is not deciphered largely in bread wheat. In this study, a comparative transcriptome analysis at a transition time in combination with genetic mapping was used to identify responsible genes in a stage and tissue-specific manner. For this reason, two winter cultivars that have been bred in Germany showing contrasting and stable heading time in different environments were selected for the analysis.

RESULTS

In total, 670 and 1075 differentially expressed genes in the shoot apical meristem and leaf tissue, respectively, could be identified in 23 QTL intervals for the heading date. In the transition apex, Histone methylation H3-K36 and regulation of circadian rhythm are both controlled by the same homoeolog genes mapped in QTL TaHd112, TaHd124, and TaHd137. TaAGL14 gene that identifies the floral meristem was mapped in TaHd054 in the double ridge. In the same stage, the homoeolog located on chromosome 7D of FLOWERING TIME LOCUS T mapped on chr 7B, which evolved an antagonist function and acts as a flowering repressor was uncovered. The wheat orthologue of transcription factor ASYMMETRIC LEAVES 1 (AS1) was identified in the late reproductive stage and was mapped in TaHd102, which is strongly associated with heading date. Deletion of eight nucleotides in the AS1 promoter could be identified in the binding site of the SUPPRESSOR OF CONSTANS OVEREXPRESSION 1 (SOC1) gene in the late flowering cultivar. Both proteins AS1 and SOC1 are inducing flowering time in response to gibberellin biosynthesis.

CONCLUSION

The global transcriptomic at the transition phase uncovered stage and tissue-specific genes mapped in QTL of heading date in winter wheat. In response to Gibberellin signaling, wheat orthologous transcription factor AS1 is expressed in the late reproductive phase of the floral transition. The locus harboring this gene is the strongest QTL associated with the heading date trait in the German cultivars. Consequently, we conclude that this is another indication of the Gibberellin biosynthesis as the mechanism behind the heading variation in wheat.

Breeding crops for drought-affected environments and improved climate resilience

Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.

Resistance of QYm.nau-2D to wheat yellow mosaic virus was derived from an alien introgression into common wheat

The QYm.nau-2D locus conferring wheat yellow mosaic virus resistance is an exotic introgression and we developed 11 diagnostic markers tightly linked to QYm.nau-2D. Wheat yellow mosaic virus (WYMV) is a serious disease of winter wheat in China. Breeding resistant varieties is the most effective strategy for WYMV control. A WYMV resistant locus QYm.nau-2D on the chromosome arm 2DL has been repeatedly reported but the mapped region is large. In the present study, we screened recombinants using a biparental population and mapped QYm.nau-2D into an 18.8 Mb physical interval. By genome-wide association studies of 372 wheat varieties for WYMV resistance in four environments, we narrowed down QYm.nau-2D into a 16.4 Mb interval. Haplotype analysis indicated QYm.nau-2D were present as six different states due to recombination during hybridization breeding. QYm.nau-2D was finally mapped into a linkage block of 11.2 Mb. Chromosome painting using 2D specific probes and collinearity analysis among the published sequences corresponding to QYm.nau-2D region indicated the block was an exotic introgression. The Illumina-sequenced reads of four diploid Aegilops species were mapped to the sequence of Fielder, a variety having the introgression. The mapping reads were significantly increased at the putative introgression regions of Fielder. Ae. uniaristata (NN) had the highest mapping reads, suggesting that QYm.nau-2D was possibly an introgression from genome N. We investigated the agronomic performances of different haplotypes and observed no linkage drag of the alien introgression for the 15 tested traits. For marker-assisted selection of QYm.nau-2D, we developed 11 diagnostic markers tightly linked to the locus. This research provided a case study of an exotic introgression, which has been utilized in wheat improvement for WYMV resistance.

Unraveling Genomic Regions Controlling Root Traits as a Function of Nitrogen Availability in the MAGIC Wheat Population WM-800

An ever-growing world population demands to be fed in the future and environmental protection and climate change need to be taken into account. An important factor here is nitrogen uptake efficiency (NUpE), which is influenced by the root system (the interface between plant and soil). To understand the natural variation of root system architecture (RSA) as a function of nitrogen (N) availability, a subset of the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting N treatments in a high-throughput phenotyping system at the seedling stage. Fourteen root and shoot traits were measured. Subsequently, these traits were genetically analyzed using 13,060 polymorphic haplotypes and SNPs in a genome-wide association study (GWAS). In total, 64 quantitative trait loci (QTL) were detected; 60 of them were N treatment specific. Candidate genes for the detected QTL included NRT1.1 and genes involved in stress signaling under N-, whereas candidate genes under N+ were more associated with general growth, such as mei2 and TaWOX11b. This finding may indicate (i) a disparity of the genetic control of root development under low and high N supply and, furthermore, (ii) the need for an N specific selection of genes and genotypes in breeding new wheat cultivars with improved NUpE.

Pinpointing genomic loci for drought-induced proline and hydrogen peroxide accumulation in bread wheat under field conditions

BACKGROUND

Proline (Pro) and hydrogen peroxide (H₂O₂) play a critical role in plants during drought adaptation. Genetic mapping for drought-induced Pro and H₂O₂ production under field conditions is very limited in crop plants since their phenotyping with large populations is labor-intensive. A genome-wide association study (GWAS) of a diversity panel comprised of 184 bread wheat cultivars grown in natural field (control) and rain-out shelter (drought) environments was performed to identify candidate loci and genes regulating Pro and H₂O₂ accumulation induced by drought.

RESULTS

The GWAS identified top significant marker-trait associations (MTAs) on 1A and 2A chromosomes, respectively for Pro and H₂O₂ in response to drought. Similarly, MTAs for stress tolerance index (STI) of Pro and H₂O₂ were identified on 5B and 1B chromosomes, respectively. Total 143 significant MTAs were identified including 36 and 71 were linked to drought and 2 and 34 were linked to STI for Pro and H₂O_2, respectively. Next, linkage disequilibrium analysis revealed minor alleles of significant single-markers and haplotypes were associated with higher Pro and H₂O₂ accumulation under drought. Several putative candidate genes for Pro and H₂O₂ content encode proteins with kinase, transporter or protein-binding activities.

CONCLUSIONS

The identified genetic factors associated with Pro and H₂O₂ biosynthesis underlying drought adaptation lay a fundamental basis for functional studies and future marker-assisted breeding programs.

Global crop yields can be lifted by timely adaptation of growing periods to climate change

Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers' decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO₂ fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments.

Insights into breeding history, hotspot regions of selection, and untapped allelic diversity for bread wheat breeding

Breeding has increasingly altered the genetics of crop plants since the domestication of their wild progenitors. It is postulated that the genetic diversity of elite wheat breeding pools is too narrow to cope with future challenges. In contrast, plant genetic resources (PGRs) of wheat stored in genebanks are valuable sources of unexploited genetic diversity. Therefore, to ensure breeding progress in the future, it is of prime importance to identify the useful allelic diversity available in PGRs and to transfer it into elite breeding pools. Here, a diverse collection consisting of modern winter wheat cultivars and genebank accessions was investigated based on reduced-representation genomic sequencing and an iSelect single nucleotide polymorphism (SNP) chip array. Analyses of these datasets provided detailed insights into population structure, levels of genetic diversity, sources of new allelic diversity, and genomic regions affected by breeding activities. We identified 57 regions representing genomic signatures of selection and 827 regions representing private alleles associated exclusively with genebank accessions. The presence of known functional wheat genes, quantitative trait loci, and large chromosomal modifications, i.e., introgressions from wheat wild relatives, provided initial evidence for putative traits associated within these identified regions. These findings were supported by the results of ontology enrichment analyses. The results reported here will stimulate further research and promote breeding in the future by allowing for the targeted introduction of novel allelic diversity into elite wheat breeding pools.

Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale

Crop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time. Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype × environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars × environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions.

Linking quantitative genetics with community-level performance: Are there operational models for plant breeding?

Plant breeding is focused on the genotype and population levels while targeting effects at higher levels of biodiversity, from crop covers to agroecosystems. Making predictions across nested levels of biodiversity is therefore a major challenge for the development of intercropping practices. New prediction tools and concepts are required to design breeding strategies with desirable outcomes at the crop community level. We reviewed theoretical advances in the field of evolutionary ecology to identify potentially operational ways of predicting the effects of artificial selection on community-level performances. We identified three main types of approaches differing in the way they model interspecific indirect genetic effects (IIGEs) at the community level: (1) The community heritability approach estimates the variance for IIGE induced by a focal species at the community level; (2) the joint phenotype approach quantifies genetic constraints between direct genetic effects and IIGE for a set of interacting species; (3) the community-trait genetic gradient approach decomposes the IIGE for a focal species across a multivariate set of its functional traits. We discuss the potential operational capacities of these approaches and stress that each is a special case of a general multitrait and multispecies selection index. Choosing one therefore involves assumptions and goals regarding the breeding target and strategy. Obtaining reliable quantitative, community-level predictions at the genetic level is constrained by the size and complexity of the experimental designs usually required. Breeding strategies should instead be compared using theoretically informed qualitative predictions. The need to estimate genetic covariances between traits measured both within and among species (for IIGE) is another obstacle, as the two are not determined by the exact same biological processes. We suggest future research directions and strategies to overcome these limits. Our synthesis offers an integrative theoretical framework for breeders interested in the genetic improvement of crop communities but also for scientists interested in the genetic bases of plant community functioning.

Connecting the dots between cell surface- and intracellular-triggered immune pathways in plants

Plants can detect microbial molecules via surface-localized pattern-recognition receptors (PRRs) and intracellular immune receptors from the nucleotide-binding, leucine-rich repeat receptor (NLR) family. The corresponding pattern-triggered (PTI) and effector-triggered (ETI) immunity were long considered separate pathways, although they converge on largely similar cellular responses, such as calcium influx and overlapping gene reprogramming. A number of studies recently uncovered genetic and molecular interconnections between PTI and ETI, highlighting the complexity of the plant immune network. Notably, PRR- and NLR-mediated immune responses require and potentiate each other to reach an optimal immune output. How PTI and ETI connect to confer robust immunity in different plant species, including crops will be an exciting future research area.

Functional phenomics for improved climate resilience in Nordic agriculture

The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.

Chromosome 3A harbors several pleiotropic and stable drought-responsive alleles for photosynthetic efficiency selected through wheat breeding

Water deficit is the most severe stress factor in crop production threatening global food security. In this study, we evaluated the genetic variation in photosynthetic traits among 200 wheat cultivars evaluated under drought and rainfed conditions. Significant genotypic, treatments, and their interaction effects were detected for chlorophyll content and chlorophyll fluorescence parameters. Drought stress reduced the effective quantum yield of photosystem II (YII) from the anthesis growth stage on. Leaf chlorophyll content measured at anthesis growth stages was significantly correlated with YII and non-photochemical quenching under drought conditions, suggesting that high throughput chlorophyll content screening can serve as a good indicator of plant drought tolerance status in wheat. Breeding significantly increased the photosynthetic efficiency as newer released genotypes had higher YII and chlorophyll content than the older ones. GWAS identified a stable drought-responsive QTL on chromosome 3A for YII, while under rainfed conditions, it detected another QTL on chromosome 7A for chlorophyll content across both growing seasons. Molecular analysis revealed that the associated alleles of AX-158576783 (515.889 Mbp) on 3A co-segregates with the NADH-ubiquinone oxidoreductase (TraesCS3A02G287600) gene involved in ATP synthesis coupled electron transport and is proximal to WKRY transcription factor locus. This allele on 3A has been positively selected through breeding and has contributed to increasing the grain yield.

Harnessing adult-plant resistance genes to deploy durable disease resistance in crops

Adult-plant resistance (APR) is a type of genetic resistance in cereals that is effective during the later growth stages and can protect plants from a range of disease-causing pathogens. Our understanding of the functions of APR-associated genes stems from the well-studied wheat-rust pathosystem. Genes conferring APR can offer pathogen-specific resistance or multi-pathogen resistance, whereby resistance is activated following a molecular recognition event. The breeding community prefers APR to other types of resistance because it offers broad-spectrum protection that has proven to be more durable. In practice, however, deployment of new cultivars incorporating APR is challenging because there is a lack of well-characterised APRs in elite germplasm and multiple loci must be combined to achieve high levels of resistance. Genebanks provide an excellent source of genetic diversity that can be used to diversify resistance factors, but introgression of novel alleles into elite germplasm is a lengthy and challenging process. To overcome this bottleneck, new tools in breeding for resistance must be integrated to fast-track the discovery, introgression and pyramiding of APR genes. This review highlights recent advances in understanding the functions of APR genes in the well-studied wheat-rust pathosystem, the opportunities to adopt APR genes in other crops and the technology that can speed up the utilisation of new sources of APR in genebank accessions.

Long-Amplicon Single-Molecule Sequencing Reveals Novel, Trait-Associated Variants of VERNALIZATION1 Homoeologs in Hexaploid Wheat

The gene VERNALIZATION1 (VRN1) is a key controller of vernalization requirement in wheat. The genome of hexaploid wheat (Triticum aestivum) harbors three homoeologous VRN1 loci on chromosomes 5A, 5B, and 5D. Structural sequence variants including small and large deletions and insertions and single nucleotide polymorphisms (SNPs) in the three homoeologous VRN1 genes not only play an important role in the control of vernalization requirement, but also have been reported to be associated with other yield related traits of wheat. Here we used single-molecule sequencing of barcoded long-amplicons to assay the full-length sequences (∼13 kbp plus 700 bp from the promoter sequence) of the three homoeologous VRN1 genes in a panel of 192 predominantly European winter wheat cultivars. Long read sequences revealed previously undetected duplications, insertions and single-nucleotide polymorphisms in the three homoeologous VRN1 genes. All the polymorphisms were confirmed by Sanger sequencing. Sequence analysis showed the predominance of the winter alleles vrn-A1, vrn-B1, and vrn-D1 across the investigated cultivars. Associations of SNPs and structural variations within the three VRN1 genes with 20 economically relevant traits including yield, nodal root-angle index and quality related traits were evaluated at the levels of alleles, haplotypes, and copy number variants. Cultivars carrying structural variants within VRN1 genes showed lower grain yield, protein yield and biomass compared to those with intact genes. Cultivars carrying a single vrn-A1 copy and a unique haplotype with a high number of SNPs were found to have elevated grain yield, kernels per spike and kernels per m2 along with lower grain sedimentation values. In addition, we detected a novel SNP polymorphism within the G-quadruplex region of the promoter of vrn-A1 that was associated with deeper roots in winter wheat. Our findings show that multiplex, single-molecule long-amplicon sequencing is a useful tool for detecting variants in target genes within large plant populations, and can be used to simultaneously assay sequence variants among target multiple gene homoeologs in polyploid crops. Numerous novel VRN1 haplotypes and alleles were identified that showed significantly associations to economically important traits. These polymorphisms were converted into PCR or KASP assays for use in marker-assisted breeding.

Wheat Rhizosphere Microbiota Respond to Changes in Plant Genotype, Chemical Inputs, and Plant Phenotypic Plasticity

Modern wheat varieties that were selected since the Green Revolution are generally grown with synthetic chemical inputs, and ancient varieties released before1960 without. Thus, when changes occur in rhizosphere microbiota structure, it is not possible to distinguish if they are due to (i) changes in wheat genotypes by breeding, (ii) modifications of the environment via synthetic chemical inputs, or (iii) phenotypic plasticity, the interaction between wheat genotype and the environment. Using a crossed factorial design in the field, we evaluated the effects of either modern or ancient wheat varieties grown with or without chemical inputs (a N fertilizer, a fungicide, and an herbicide) on “microbiome as a phenotype.” We analyzed the rhizosphere microbiota by bacterial and fungal amplicon sequencing, coupled with microscope observations of mycorrhizal associations. We found that plant genotype and phenotypic plasticity had the most influence on rhizosphere microbiota, whereas inputs had only marginal effects. Phenotypic plasticity was particularly important in explaining diversity variations in bacteria and fungi but had no impact on the mycorrhizal association. Our results show an interest in considering the interaction between wheat genotype and the environment in breeding programs, by focusing on genes involved in the phenotypic plasticity of plant-microbe interactions.

Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions

Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha^-1 year^-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.

Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations

Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. Finding rare diversity requires a deep understanding of biological interactions between the genetic makeup of one genotype and its environmental conditions. Most modern breeding programs still rely on linear regression models to solve this problem, generalizing the complex genotype by phenotype interactions through manually constructed linear features. However, the identification of positive alleles vs. background can be addressed using deep learning approaches that have the capacity to learn complex nonlinear functions for the inputs. Machine learning (ML) is an artificial intelligence (AI) approach involving a range of algorithms to learn from input data sets and predict outcomes in other related samples. This paper describes a variety of techniques that include supervised and unsupervised ML algorithms to improve our understanding of nonlinear interactions from plant breeding data sets. Feature selection (FS) methods are combined with linear and nonlinear predictors and compared to traditional prediction methods used in plant breeding. Recent advances in ML allowed the construction of complex models that have the capacity to better differentiate between positive alleles and the genetic background. Using real plant breeding program data, we show that ML methods have the ability to outperform current approaches, increase prediction accuracies, decrease the computing time drastically, and improve the detection of important alleles involved in qualitative or quantitative traits.

Crucial factors for the feasibility of commercial hybrid breeding in food crops

There is an ongoing societal debate about plant breeding systems and their impact on stakeholders in food systems. Hybrid breeding and hybrid seed have become controversial topics as they are believed to mostly serve high-tech agricultural systems. This article focuses on the perspective of commercial plant breeders when developing new cultivars of food crops. Arguably, hybrid breeding is the most effective breeding system for genetic improvement of crops, enhancing yields, improving product quality and increasing resistance against (a)biotic stresses. Nonetheless, hybrid breeding is not commercially applied in all crops. We analyse how biological and economic factors determine whether a commercial plant breeder opts for the hybrid system or not. We show that the commercial feasibility of hybrid breeding depends on the crop and business case. In conclusion, the commercial application of hybrid breeding in crops seems to be hampered mostly by high costs of seed production. Case studies regarding the hybrid transitions in maize, wheat and potato are included to illustrate these findings.

Genotyping-by-Sequencing Based Molecular Genetic Diversity of Pakistani Bread Wheat (Triticum aestivum L.) Accessions

Spring wheat (Triticum aestivum L.) is one of the most imperative staple food crops, with an annual production of 765 million tons globally to feed ∼40% world population. Genetic diversity in available germplasm is crucial for sustainable wheat improvement to ensure global food security. A diversity panel of 184 Pakistani wheat accessions was genotyped using 123,596 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing with 42% of the SNPs mapped on B, 36% on A, and 22% on D sub-genomes of wheat. Chromosome 2B contains the most SNPs (9,126), whereas 4D has the least (2,660) markers. The mean polymorphic information content, genetic diversity, and major allele frequency of the population were 0.157, 0.1844, and 0.87, respectively. Analysis of molecular variance revealed a higher genetic diversity (80%) within the sub-population than among the sub-populations (20%). The genome-wide linkage disequilibrium was 0.34 Mbp for the whole wheat genome. Among the three subgenomes, A has the highest LD decay value (0.29 Mbp), followed by B (0.2 Mbp) and D (0.07 Mbp) genomes, respectively. The results of population structure, principal coordinate analysis, phylogenetic tree, and kinship analysis also divided the whole population into three clusters comprising 31, 33, and 120 accessions in group 1, group 2, and group 3, respectively. All groups were dominated by the local wheat accessions. Estimation of genetic diversity will be a baseline for the selection of breeding parents for mutations and the genome-wide association and marker-assisted selection studies.

Progenitor species hold untapped diversity for potential climate-responsive traits for use in wheat breeding and crop improvement

Climate change will have numerous impacts on crop production worldwide necessitating a broadening of the germplasm base required to source and incorporate novel traits. Major variation exists in crop progenitor species for seasonal adaptation, photosynthetic characteristics, and root system architecture. Wheat is crucial for securing future food and nutrition security and its evolutionary history and progenitor diversity offer opportunities to mine favourable functional variation in the primary gene pool. Here we provide a review of the status of characterisation of wheat progenitor variation and the potential to use this knowledge to inform the use of variation in other cereal crops. Although significant knowledge of progenitor variation has been generated, we make recommendations for further work required to systematically characterise underlying genetics and physiological mechanisms and propose steps for effective use in breeding. This will enable targeted exploitation of useful variation, supported by the growing portfolio of genomics and accelerated breeding approaches. The knowledge and approaches generated are also likely to be useful across wider crop improvement.

Over-expression of TaDWF4 increases wheat productivity under low and sufficient nitrogen through enhanced carbon assimilation

There is a strong pressure to reduce nitrogen (N) fertilizer inputs while maintaining or increasing current cereal crop yields. We show that overexpression of TaDWF4-B, the dominant shoot expressed homoeologue of OsDWF4, in wheat can increase plant productivity by up to 105% under a range of N levels on marginal soils, resulting in increased N use efficiency (NUE). We show that a two to four-fold increase in TaDWF4 transcript levels enhances the responsiveness of genes regulated by N. The productivity increases seen were primarily due to the maintenance of photosystem II operating efficiency and carbon assimilation in plants when grown under limiting N conditions and not an overall increase in photosynthesis capacity. The increased biomass production and yield per plant in TaDWF4 OE lines could be linked to modified carbon partitioning and changes in expression pattern of the growth regulator Target Of Rapamycin, offering a route towards breeding for sustained yield and lower N inputs.

In wheat, overexpression of TaDWF4 overrides normal nutrient sensing allowing for increased biomass when grown under limiting nutrient conditions. This maintenance of growth is associated with modified carbon partitioning and changes in expression of growth regulator TaTOR, offering a route towards breeding for sustained yields with lower nitrogen inputs.

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

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.

Detecting major introgressions in wheat and their putative origins using coverage analysis

Introgressions from crop wild relatives (CWRs) have been used to introduce beneficial traits into cultivated plants. Introgressions have traditionally been detected using cytological methods. Recently, single nucleotide polymorphism (SNP)-based methods have been proposed to detect introgressions in crosses for which both parents are known. However, for unknown material, no method was available to detect introgressions and predict the putative donor species. Here, we present a method to detect introgressions and the putative donor species. We demonstrate the utility of this method using 10 publicly available wheat genome sequences and identify nine major introgressions. We show that the method can distinguish different introgressions at the same locus. We trace introgressions to early wheat cultivars and show that natural introgressions were utilised in early breeding history and still influence elite lines today. Finally, we provide evidence that these introgressions harbour resistance genes.

GWAS identifies genetic loci underlying nitrogen responsiveness in the climate resilient C4 model Setaria italica (L.)

INTRODUCTION

N responsiveness is the capacity to perceive and induce morpho-physiological adaptation to external and internal Nitrogen (N). Crop productivity is propelled by N fertilizer and requires the breeding/selection of cultivars with intrinsically high N responsiveness. This trait has many advantages in being more meaningful in commercial/environmental context, facilitating in-season N management and not being inversely correlated with N availability over processes regulating NUE. Current lack of its understanding at the physio-genetic basis is an impediment to select for cultivars with a predictably high N response.

OBJECTIVES

To dissect physio-genetic basis of N responsiveness in 142 diverse population of foxtail millet, Setaria italica (L.) by employing contrasting N fertilizer nutrition regimes.

METHODS

We phenotyped S. italica accessions for major yield related traits under low (N10, N25) and optimal (N100) growth conditions and genotyped them to subsequently perform a genome-wide association study to identify genetic loci associated with nitrogen responsiveness trait. Groups of accessions showing contrasting trait performance and allelic forms of specific linked genetic loci (showing haplotypes) were further accessed for N dependent transcript abundances of their proximal genes.

RESULTS

Our study show that N dependent yield rise in S. italica is driven by grain number whose responsiveness to N availability is genetically underlined. We identify 22 unique SNP loci strongly associated with this trait out of which six exhibit haplotypes and consistent allelic variation between lines with contrasting N dependent grain number response and panicle architectures. Furthermore, differential transcript abundances of specific genes proximally linked to these SNPs in same lines is indicative of their N dependence in a genotype specific manner.

CONCLUSION

The study demonstrates the value/ potential of N responsiveness as a selection trait and identifies key genetic components underlying the trait in S. italica. This has major implications for improving crop N sustainability and food security.