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Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson's correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson's correlation calculated by fitting a bivariate model was higher than the division of the Pearson's correlation by the squared root of the heritability across traits, by 2.53-11.46%. Across the datasets, the corrected Pearson's correlation was higher than the uncorrected by 5.80-14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

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Summary: High‐throughput genotyping arrays have provided a cost‐effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384‐well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype‐optimized novel SNPs and legacy cross‐platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome‐wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available. [ABSTRACT FROM AUTHOR]

Determining the stability and consistency of grain yield performance requires accurate evaluation of genotypes in different environments. In Mexico, annual national spring wheat irrigated trials (ENTRI) are conducted to assess elite bread (Triticum aestivum L.) and durum (Triticum durum L.) wheat performance in different testing environments (TEs) in the main wheat‐growing areas. These trials provide data supporting release of new cultivars and aim to also address Mexican wheat value chain grain needs. This study analyzed grain yield performance of 30 bread and durum wheat cultivars grown in trials in the 2012–2013 and 2013–2014 growing cycles conducted across TEs in northwest, north, and central Mexico. Environmental variability (location, sowing timing, and irrigation schemes) across the ENTRI enabled genotype by environment interaction to be effectively evaluated. Bread and durum wheat genotypes with high and stable grain yield were also identified and compared across TEs of the wheat‐growing areas of Mexico. The bread wheat cultivars Bacorehuis F2015 and Borlaug100 F2014, and the durum cultivars Barobampo C2015, CONASIST C2015, and Anatoly C2011 were high yielding and gave stable performance in most of the TEs. This analysis demonstrates the utility of multi‐year, multi‐environment testing and analysis to identify improved wheat cultivars to meet wheat production demand in Mexico. It also provides useful testing and analysis methods for selection of suitable broadly as well as locally adapted varieties in other wheat producing regions of the world. Core Ideas: We identified cultivars with high and stable grain yield in Mexico.Environmental variability enabled G × E interaction to be effectively evaluated.Multi‐year, multi‐environment testing and analysis is useful to identify improved wheat cultivars. [ABSTRACT FROM AUTHOR]

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using highthroughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered. [ABSTRACT FROM AUTHOR]

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1–M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15–85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis. [ABSTRACT FROM AUTHOR]

The profit function (net returns minus costs) allows breeders to derive trait economic weights to predict the net genetic merit (H) using the linear phenotypic selection index (LPSI). Economic weight is the increase in profit achieved by improving a particular trait by one unit and should reflect the market situation and not only preferences or arbitrary values. In maize (Zea mays L.) and wheat (Triticum aestivum) breeding programs, only grain yield has a specific market price, which makes application of a profit function difficult. Assuming the traits' phenotypic values have multivariate normal distribution, we used the market price of grain yield and its conditional expectation given all the traits of interest to construct a profit function and derive trait economic weights in maize and wheat breeding. Using simulated and real maize and wheat datasets, we validated the profit function by comparing its results with the results obtained from a set of economic weights from the literature. The criteria to validate the function were the estimated values of the LPSI selection response and the correlation between LPSI and H. For our approach, the maize and wheat selection responses were 1,567.13 and 1,291.5, whereas the correlations were.87 and.85, respectively. For the other economic weights, the selection responses were 0.79 and 2.67, whereas the correlations were.58 and.82, respectively. The simulated dataset results were similar. Thus, the profit function is a good option to assign economic weights in plant breeding. Core Ideas: LPSI is the best linear predictor of the net genetic merit.The main LPSI objective is to predict the net genetic merit and maximize the selection response.The profit function allows breeders to derive trait economic weights to predict H.Economic weight is the increase in profit achieved by improving a particular trait by one unit. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) is a methodology that is revolutionizing plant breeding because it can select candidate genotypes without phenotypic evaluation in the field. However, its practical implementation in hybrid prediction remains challenging since many factors affect its accuracy. The main objective of this study was to research the genomic prediction accuracy of wheat hybrids by adding covariates with the hybrid parental phenotypic information to the model. Four types of different models (MA, MB, MC, and MD) with one covariate (same trait to be predicted) (MA_C, MB_C, MC_C, and MD_C) or several covariates (of the same trait and other correlated traits) (MA_AC, MB_AC, MC_AC, and MD_AC) were studied. We found that the four models with parental information outperformed models without parental information in terms of mean square error by at least 14.1% (MA vs. MA_C), 5.5% (MB vs. MB_C), 51.4% (MC vs. MC_C), and 6.4% (MD vs. MD_C) when parental information of the same trait was used and by at least 13.7% (MA vs. MA_AC), 5.3% (MB vs. MB_AC), 55.1% (MC vs. MC_AC), and 6.0% (MD vs. MD_AC) when parental information of the same trait and other correlated traits were used. Our results also show a large gain in prediction accuracy when covariates were considered using the parental phenotypic information, as opposed to marker information. Finally, our results empirically demonstrate that a significant improvement in prediction accuracy was gained by adding parental phenotypic information as covariates; however, this is expensive since, in many breeding programs, the parental phenotypic information is unavailable. [ABSTRACT FROM AUTHOR]

Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper. [ABSTRACT FROM AUTHOR]

Estimation of the rate of genetic gain over time allows quantification of breeding progress. Here we report on the rate of grain yield and zinc (Zn) concentration increase over 11 yr of targeted wheat (Triticum aestivum L.) biofortification breeding at the International Maize and Wheat Improvement Center (CIMMYT). Data from yield trials evaluated across multiple locations in South Asia and beyond showed that average annual increases in grain yield potential of ∼1.5% and 0.9% per year gains for grain Zn and Fe concentrations. Across locations in all countries, mean yields of the five highest‐yielding entries showed an annual gain of 109 kg ha−1 yr−1 in yield and 0.3 mg kg−1 for grain Fe and Zn as well as 0.66 g for the yield component thousand‐grain weight. There was a strong positive correlation between Fe and Zn (r =.42) across locations, whereas no genetic correlation was observed between grain yield and Zn (r =.05) across locations. Despite the slight negative relationship between yield and Zn, through targeted crossing and development of large segregating populations we were able to identify transgressive segregants combining increased yield and Zn concentrations. Significant differences between lines for grain micronutrient concentrations were detected, and significant location effects on grain Zn and Fe concentrations were observed. These results demonstrate that continuous and simultaneous genetic gain for grain yield and concentrations of Fe and Zn is possible in elite spring bread wheat lines with potential to deliver global impact through identification of superior parents for use by national breeding programs and the release of biofortified wheat cultivars. Core Ideas: Significant genetic gains for grain yield, Zn and Fe concentration, and kernel weight were measured among high‐Zn biofortified lines.Biofortified lines yield gains were at par with other breeding programs showing simultaneous gains for Zn and yield.A strong positive association between grain Zn and Fe showed similar trends in genetic gains.Competitive high‐Zn lines developed at CIMMYT are being released as biofortified cultivars in target countries.As mainstreaming of grain Zn in the CIMMYT wheat breeding program is being implemented, it is expected that a large proportion of CIMMYT elite lines distributed to partners would be Zn enriched. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: The production and availability of food underpins societal stability. In Afghanistan, wheat is the major arable agricultural crop and source of dietary energy. The withdrawal of NATO allies and partner countries from Afghanistan presents numerous well‐documented societal and political challenges and has impacts on immediate and longer‐term food security. Conflict‐impacted irrigation infrastructure coupled with growing climate instability have also contributed markedly to reductions in current food, and specifically wheat, production. Here, we review the status of Afghan wheat improvement and propose a research agenda to support the regeneration of Afghanistan's wheat and agricultural sector. Summary: Afghanistan is a country with diverse natural ecologies in a largely arid and mountainous region. The rural sector is still considered to drive economic potential. Current social, political and economic instability along with climatic challenges are driving food and water insecurity in the wider region. In the short term, it is likely that this and the associated challenges of displacement and unemployment can only be addressed by humanitarian intervention and agrifood and nutrition support. In the medium to long term, drought, and heat, probably linked to climate change, will pose recurrent challenges for agriculture and food security that will require a much broader set of interventions to secure the rural population's livelihoods. The genetic gap, among other major challenges, must be addressed if Afghanistan is to develop its agricultural potential leading to income and livelihood improvements for farmers and stable and accessible supplies for consumers. Only thereby will the country be enabled to reap the important and long‐sought trade and food security benefits derived from self‐sufficiency. Here, we highlight the agricultural challenges facing Afghanistan and propose forward strategies for ensuring the future stability of wheat production, the cornerstone of Afghan agriculture. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

All stage resistance to stripe rust races prevalent in India was investigated in the European winter wheat cultivar 'Acienda'. In order to dissect the genetic basis of the resistance, a backcross population was developed between 'Acienda' and the stripe rust susceptible Indian spring wheat cultivar 'HD 2967'. Inheritance studies revealed segregation for a dominant resistant gene. High density SNP genotyping was used to map stripe rust resistance and marker regression analysis located stripe rust resistance to the distal end of wheat chromosome 1A. Interval mapping located this region between the SNP markers AX-95162217 and AX-94540853, at a LOD score of 15.83 with a phenotypic contribution of 60%. This major stripe rust resistance locus from 'Acienda' has been temporarily designated as Yraci. A candidate gene search in the 2.76 Mb region carrying Yraci on chromosome 1A identified 18 NBS-LRR genes based on wheat RefSeqv1.0 annotations. Our results indicate that as there is no major gene reported in the Yraci chromosome region, it is likely to be a novel stripe rust resistance locus and offers potential for deployment, using the identified markers, to confer all stage stripe rust resistance. [ABSTRACT FROM AUTHOR]

Wheat cultivars differ in their response to nitrogen (N) fertilizer, both in terms of its uptake and utilization. Characterizing this variation is an important step in improving the N use efficiency (NUE) of future cultivars while maximizing production (yield) potential. In this study, we compared the agronomic performance of 48 diverse wheat cultivars released between 1936 and 2016 at low and high N input levels in field conditions to assess the relationship between NUE and its components. Agronomic trait values were significantly lower in the low N treatment, and the cultivars tested showed a significant variation for all traits (apart from the N remobilization efficiency), indicating that response is genotype-dependent, although significant genotype × environment effects were also observed. Overall, we show a varietal improvement in NUE over time of 0.33 and 0.30% year–1 at low and high N, respectively, and propose that this is driven predominantly by varietal selection for increased yield. More complete understanding of the components of these improvements will inform future targeted breeding and selection strategies to support a reduction in fertilizer use while maintaining productivity. [ABSTRACT FROM AUTHOR]

Central to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2 + 12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5 + 10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs. Delorean et al., along with colleagues from the Open Wild Wheat Consortium, present an analysis of Aegilops tauschii genomes to investigate the origins of the Glu-D1 gene in modern wheats. They discover an abundance of novel Glu-D1 gene alleles that could serve as a reservoir for generating higher quality wheat varieties. This is a companion to the Consortium paper which is available in Nature Biotechnology. [ABSTRACT FROM AUTHOR]

Nitrogen is one of the most important macronutrients for crop growth and metabolism. To identify marker-trait associations for complex nitrogen use efficiency (NUE)-related agronomic traits, field experiments were conducted on nested synthetic wheat introgression libraries at three nitrogen input levels across two seasons. The introgression libraries were genotyped using the 35K Axiom® Wheat Breeder's Array and genetic diversity and population structure were examined. Significant phenotypic variation was observed across genotypes, treatments, and their interactions across seasons for all the 22 traits measured. Significant positive correlations were observed among grain yield and yield-attributing traits and root traits. Across seasons, a total of 233 marker-trait associations (MTAs) associated with fifteen traits of interest at different levels of nitrogen (N0, N60, and N120) were detected using 9,474 genome-wide single nucleotide polymorphism (SNP) markers. Of these, 45 MTAs for 10 traits in the N0 treatment, 100 MTAs for 11 traits in the N60 treatment, and 88 MTAs for 11 traits in the N120 treatment were detected. We identified putative candidate genes underlying the significant MTAs which were associated directly or indirectly with various biological processes, cellular component organization, and molecular functions involving improved plant growth and grain yield. In addition, the top 10 lines based on N response and grain yield across seasons and treatments were identified. The identification and introgression of superior alleles/donors improving the NUE while maintaining grain yield may open new avenues in designing next generation nitrogen-efficient high-yielding wheat varieties. [ABSTRACT FROM AUTHOR]

Cereal flour blending can reduce food insecurity risks, as well as contribute to economic and nutrition goals. Yet, the potential for blending has not been realized, and new products have not become scalable commercial propositions. Numerous experiments have shown the potential to produce acceptable foods derived from blended flours of diverse crops including wheat. An important question is whether the incentives, capacities and needs of farmers, processors and consumers have been considered. We argue that technical solutions must be developed within a specific agroecological, commercial, economic, and political environment. Innovations must address the clearly defined objectives of a wheat flour blending policy, if the potential benefits of blending for addressing food insecurity and economic stress are to be achieved. • Dependence on imports of cereals threatens food insecurity in many countries. • Blending wheat with other crops can ameliorate food insecurity and economic stress. • Technical and business challenges should be addressed by coherent blending policies. • Multistakeholder platforms can engage farmers, industry and public sector bodies. • Successful innovations depend on efficient agrifood chains and consumer acceptance. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is a major nutrient limiting productivity in many ecosystems. The large N demands associated with food crop production are met mainly through the provision of synthetic N fertiliser, leading to economic and ecological costs. Optimising the balance between N supply and demand is key to reducing N losses to the environment. Wheat (Triticum aestivum L.) production provides food for millions of people worldwide and is highly dependent on sufficient N supply. The size of the N sink, i.e. wheat grain (number, size, and protein content) is the main driver of high N requirement. Optimal functioning of temporary sinks, in particular the canopy, can also affect N requirement. N use efficiency (i.e. yield produced per unit of N available) tends to be lower under high N conditions, suggesting that wheat plants are more efficient under low N conditions and that there is an optimal functioning yet unattained under high N conditions. Understanding the determinants of low N requirement in wheat would provide the basis for the selection of genetic material suitable for sustainable cereal production. In this review, we dissect the drivers of N requirement at the plant level along with the temporal dynamics of supply and demand. [ABSTRACT FROM AUTHOR]

In this study, we anchored genotyping-by-sequencing data to the International Wheat Genome Sequencing Consortium Reference Sequence v1.0 assembly to generate over 40,000 high quality single nucleotide polymorphism markers on a panel of 376 elite European winter wheat varieties released between 1946 and 2007. We compared association mapping and genomic prediction accuracy for a range of productivity traits with previous results based on lower density dominant DArT markers. The results demonstrate that the availability of RefSeq v1.0 supports higher precision trait mapping and provides the density of markers required to obtain accurate predictions of traits controlled by multiple small effect loci, including grain yield. [ABSTRACT FROM AUTHOR]

Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder's equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125–0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied. [ABSTRACT FROM AUTHOR]

Plant breeding is achieved through the controlled self- or cross-pollination of individuals and typically involves isolation of floral parts from selected parental plants. Paper, cellulose or synthetic materials are used to avoid self pollination or cross contamination. Low seed set limits the rate of breeding progress and increases costs. We hypothesized that a novel ‘non-woven’ fabric optimal for both pollination and seed set in multiple plant species could be developed. After determining the baseline pollen characteristics and usage requirements we established iterative three phase development and biological testing. This determined (1) that white fabric gave superior seed return and informed the (2) development of three non-woven materials using different fibre and layering techniques. We tested their performance in selfing and hybridisation experiments recording differences in performance by material type within species. Finally we (3) developed further advanced fabrics with increased air permeability and tested biological performance. An interaction between material type and species was observed and environmental decoupling investigated, showing that the non-woven fabrics had superior water vapour transmission and temperature regulation compared to controls. Overall, non-woven fabrics outperformed existing materials for both pollination and seed set and we found that different materials can optimize species-specific, rather than species-generic performance. [ABSTRACT FROM AUTHOR]

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding. [ABSTRACT FROM AUTHOR]

Targeted selection and inbreeding have resulted in a lack of genetic diversity in elite hexaploid bread wheat accessions. Reduced diversity can be a limiting factor in the breeding of high yielding varieties and crucially can mean reduced resilience in the face of changing climate and resource pressures. Recent technological advances have enabled the development of molecular markers for use in the assessment and utilization of genetic diversity in hexaploid wheat. Starting with a large collection of 819 571 previously characterized wheat markers, here we describe the identification of 35 143 single nucleotide polymorphism-based markers, which are highly suited to the genotyping of elite hexaploid wheat accessions. To assess their suitability, the markers have been validated using a commercial high-density Affymetrix Axiom® genotyping array (the Wheat Breeders' Array), in a high-throughput 384 microplate configuration, to characterize a diverse global collection of wheat accessions including landraces and elite lines derived from commercial breeding communities. We demonstrate that the Wheat Breeders' Array is also suitable for generating high-density genetic maps of previously uncharacterized populations and for characterizing novel genetic diversity produced by mutagenesis. To facilitate the use of the array by the wheat community, the markers, the associated sequence and the genotype information have been made available through the interactive web site 'CerealsDB'. [ABSTRACT FROM AUTHOR]

Most agriculturally significant crop traits are quantitatively inherited which limits the ease and efficiency of trait dissection. Multi-parent populations overcome the limitations of traditional trait mapping and offer new potential to accurately define the genetic basis of complex crop traits. The increasing popularity and use of nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations raises questions about the optimal design and allocation of resources in their creation. In this paper we review strategies for the creation of multi-parent populations and describe two complementary in silico studies addressing the design and construction of NAM and MAGIC populations. The first simulates the selection of diverse founder parents and the second the influence of multi-parent crossing schemes (and number of founders) on haplotype creation and diversity. We present and apply two open software resources to simulate alternate strategies for the development of multi-parent populations. [ABSTRACT FROM AUTHOR]

The appropriate timing of developmental transitions is critical for adapting many crops to their local climatic conditions. Therefore, understanding the genetic basis of different aspects of phenology could be useful in highlighting mechanisms underpinning adaptation, with implications in breeding for climate change. For bread wheat (Triticum aestivum), the transition from vegetative to reproductive growth, the start and rate of leaf senescence and the relative timing of different stages of flowering and grain filling all contribute to plant performance. In this study we screened under Smart house conditions a large, multi-founder "NIAB elite MAGIC" wheat population, to evaluate the genetic elements that influence the timing of developmental stages in European elite varieties. This panel of recombinant inbred lines was derived from eight parents that are or recently have been grown commercially in the UK and Northern Europe.We undertook a detailed temporal phenotypic analysis under Smart house conditions of the population and its parents, to try to identify known or novel Quantitative Trait Loci associated with variation in the timing of key phenological stages in senescence. This analysis resulted in the detection of QTL interactions with novel traits such the time between "half of ear emergence above flag leaf ligule" and the onset of senescence at the flag leaf as well as traits associated with plant morphology such as stem height. In addition, strong correlations between several traits and the onset of senescence of the flag leaf were identified. This work establishes the value of systematically phenotyping genetically unstructured populations to reveal the genetic architecture underlying morphological variation in commercial wheat. [ABSTRACT FROM AUTHOR]

To date, a small number of major flowering time loci have been identified in the related Triticeae crops, bread wheat (Triticum aestivum), durum wheat (T. durum), and barley (Hordeum vulgare). Natural genetic variants at these loci result in major phenotypic changes which have adapted crops to the novel environments encountered during the spread of agriculture. The polyploid nature of bread and durum wheat means that major flowering time loci in which recessive alleles confer adaptive advantage in related diploid species have not been readily identified. One such example is the PPD-H2 flowering time locus encoded by FLOWERING LOCUS T 3 (HvFT3) in the diploid crop barley, for which recessive mutant alleles confer delayed flowering under short day (SD) photoperiods. In autumn-sown barley, such alleles aid the repression of flowering over the winter, which help prevent the development of cold-sensitive floral organs until the onset of inductive long day (LD) photoperiods the following spring. While the identification of orthologous loci in wheat could provide breeders with alternative mechanisms to fine tune flowering time, systematic identification of wheat orthologs of HvFT3 has not been reported. Here, we characterize the FT gene families in six Poaceae species, identifying novel members in all taxa investigated, as well as FT3 homoeologs from the A, B and D genomes of hexaploid (TaFT3) and tetraploid wheat. Sequence analysis shows TaFT3 homoeologs display high similarity to the HvFT3 coding region (95-96%) and predicted protein (96-97%), with conservation of intron/exon structure across the five cereal species investigated. Genetic mapping and comparative analyses in hexaploid and tetraploid wheat find TaFT3 homoeologs map to the long arms of the group 1 chromosomes, collinear to HvFT3 in barley and FT3 orthologs in rice, foxtail millet and brachypodium. Genome-specific expression analyses show FT3 homoeologs in tetraploid and hexaploid wheat are upregulated under SD photoperiods, but not under LDs, analogous to the expression of HvFT3. Collectively, these results indicate that functional wheat orthologs of HvFT3 have been identified. The molecular resources generated here provide the foundation for engineering a novel major flowering time locus in wheat using forward or reverse genetics approaches. [ABSTRACT FROM AUTHOR]

In wheat, a lack of genetic diversity between breeding lines has been recognized as a significant block to future yield increases. Species belonging to bread wheat's secondary and tertiary gene pools harbour a much greater level of genetic variability, and are an important source of genes to broaden its genetic base. Introgression of novel genes from progenitors and related species has been widely employed to improve the agronomic characteristics of hexaploid wheat, but this approach has been hampered by a lack of markers that can be used to track introduced chromosome segments. Here, we describe the identification of a large number of single nucleotide polymorphisms that can be used to genotype hexaploid wheat and to identify and track introgressions from a variety of sources. We have validated these markers using an ultra-high-density Axiom® genotyping array to characterize a range of diploid, tetraploid and hexaploid wheat accessions and wheat relatives. To facilitate the use of these, both the markers and the associated sequence and genotype information have been made available through an interactive web site. [ABSTRACT FROM AUTHOR]

Narrowing the genetic base available for future genetic progress is a major concern to plant breeders. In order to avoid this, strategies to characterize and protect genetic diversity in regional breeding pools are required. In this study, 89 winter wheat cultivars released in Croatia between 1936 and 2006 were genotyped using 1,229 DArT (diversity array technology) markers to assess the diversity and population structure. In order to place Croatian breeding pool (CBP) in a European context, Croatian wheat cultivars were compared to 523 European cultivars from seven countries using a total of 166 common DArT markers. The results show higher genetic diversity in the wheat breeding pool from Central Europe (CE) as compared to that from Northern and Western European (NWE) countries. The most of the genetic diversity was attributable to the differences among cultivars within countries. When the geographical criterion (CE vs. NWE) was applied, highly significant difference between regions was obtained that accounted for 16.19% of the total variance, revealing that the CBP represents genetic variation not currently captured in elite European wheat. The current study emphasizes the important contribution made by plant breeders to maintaining wheat genetic diversity and suggests that regional breeding is essential to the maintenance of this diversity. The usefulness of open-access wheat datasets is also highlighted. [ABSTRACT FROM AUTHOR]

In the last decade the breeding technology referred to as ‘genomic selection’ (GS) has been implemented in a variety of species, with particular success in animal breeding. Recent research shows the potential of GS to reshape wheat breeding. Many authors have concluded that the estimated genetic gain per year applying GS is several times that of conventional breeding. GS is, however, a new technology for wheat breeding and many programs worldwide are still struggling to identify the best strategy for its implementation. This article provides practical guidelines on the key considerations when implementing GS. A review of the existing GS literature for a range of species is provided and used to prime breeder-oriented considerations on the practical applications of GS. Furthermore, this article discusses potential breeding schemes for GS, genotyping considerations, and methods for effective training population design. The components of selection intensity, progress toward inbreeding in half- or full-sibs recurrent schemes, and the generation of selection are also presented. [ABSTRACT FROM AUTHOR]

A review of an article "Decline in climate resilience of European wheat" is presented.

Development of experimental mapping populations enables dissection of complex traits in crops and underpins modern plant breeding. Plant Multi-parent Advanced Generation Inter- Cross (MAGIC) populations, as proposed by Mackay & Powell (2007), have now been constructed in many crop species. The advantages of MAGIC, including greater genetic diversity captured from the multiple parents as well as multiple rounds of intercrossing maximising recombination and minimising population structure, make them an ideal resource for QTL (Quantitative Trait Loci) mapping. The 'NIAB Diverse' MAGIC wheat population has recently been developed from 16 founders, chosen to capture the greatest possible genetic diversity of UK adapted wheat varieties. These included both elite and historical varieties originating from several different northern European countries. Analysis based on SNPs genotyped using the Illumina iSelect 90k array show that over 90% of the genetic diversity in a panel of 519 UK wheat varieties is present in the 16 founders. Using the wheat Affymetrix 35k axiom SNP array (Allen et al. 2016), 596 RILs (Recombinant Inbred Lines) have recently been genotyped at the F7 stage, and a genetic map is under development. Preliminary results from the first year of replicated yield trials indicated high power to detect QTLs in heritable traits such as flowering time and height, and further analysis on a larger number of traits including yield and yield components is ongoing. These results confirm the NIAB Diverse MAGIC population as an excellent resource for genetic dissection of complex traits. The population is being developed as an open access resource for the wheat research and breeding community. A replicated yield trial (2x6m plots) will be available for phenotyping by interested parties at NIAB-Cambridge in the 2018 season, and the population is available on request from NIAB. Basic phenotype data and the founder/progeny genotype data will also be available, along with the associated genetic map and QTL analysis pipeline under development at UCL. Please contact nick.fradgley@niab.com for further information. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) in wheat breeding programs is of great interest for predicting the genotypic values of individuals, where both additive and nonadditive effects determine the final breeding value of lines. While several simulation studies have shown the efficiency of rapid-cycling GS strategies for parental selection or population improvement, their practical implementations are still lacking in wheat and other crops. In this study, we demonstrate the potential of rapid-cycle recurrent GS (RCRGS) to increase genetic gain for grain yield (GY) in wheat. Our results showed a consistent realized genetic gain for GY after 3 cycles of recombination (C1, C2, and C3) of bi-parental F1s, when summarized across 2 years of phenotyping. For both evaluation years combined, genetic gain through RCRGS reached 12.3% from cycle C0 to C3 and realized gain was 0.28 ton ha-1 per cycle with a GY from C0 (6.88 ton ha-1) to C3 (7.73 ton ha-1). RCRGS was also associated with some changes in important agronomic traits that were measured (days to heading, days to maturity, and plant height) but not selected for. To account for these changes, we recommend implementing GS together with multi-trait prediction models. [ABSTRACT FROM AUTHOR]

Increasing nitrogen fertilizer applications have sustained a growing world population in the 20th century. However, to avoid any further associated environmental damage, new sustainable agronomic practices together with new cultivars must be developed. To date the concept of nitrogen use efficiency (NUE) has been useful in quantifying the processes of nitrogen uptake and utilization, but we propose a shift in focus to consider nitrogen responsiveness as a more appropriate trait to select varieties with lower nitrogen requirements. We provide a roadmap to integrate the regulation of nitrogen uptake and assimilation into varietal selection and crop breeding programs. The overall goal is to reduce nitrogen inputs by farmers growing crops in contrasting cropping systems around the world, while sustaining yields and reducing greenhouse gas (GHG) emissions. Current practice for managing nitrogen (N) use for cereal production are not environmentally sustainable. Overuse of N fertilizers is a global problem for millions of farmers who must decide on N applications whether, when and how much. A combination of improved advice on N management for specific cropping regimes is required, together with a breeding target of new commercial crop varieties with sustainable yields and a low N requirement. While N use efficiency (NUE) has been a useful concept for quantifying the genetic differences in N uptake and utilization, the concept of an economic N optimum derived from N yield dose–response curves may provide new insights for lowering the N requirement. [ABSTRACT FROM AUTHOR]

Wheat is grown on more than 240 million hectares globally, more than any other commercial crop. Variation for nitrogen-use efficiency (NUE) in wheat germplasm is very important as efficiency of nitrogen fertilizers use can have significant effect on overall consumption of N. In the present study we used nested synthetic hexaploid wheat introgression libraries to detect genetic variation associated with NUE and related traits. The libraries were genotyped with 9474 SNP markers and used for genome-wide association mapping. Significant phenotypic variation was observed for all measured and derived traits. We detected 10 Quantitative trait locus (QTL) and 19 marker-trait associations (MTAs) possibly involved in improving NUE. Of these, 5 QTL and 8 MTAs detected under nitrogen-limited conditions have potential for use in breeding to increase nitrogen-deficiency tolerance. Nitrate transporter genes collocated with detected MTAs showed significant changes in expression of TaNRT2 genes in response to N-starvation and N-recovery. The identified promising breeding lines with stable yield, better NUE and acceptable protein content may constitute an important genetic resource in improving NUE of modern wheat varieties. • To meet future wheat production demands, improving nitrogen use while maintaining grain yield is vital. • We identified marker-trait associations improving nitrogen use efficiency while maintaining grain yield under varying nitrogen levels. • We also identified promising breeding lines with significant genetic variations and carrying the trait-associated markers or candidate genes. • These may serve as potential donors to be exploited further in genomics-assisted breeding programs targeting improved NUE while maintaining grain yield in wheat. [ABSTRACT FROM AUTHOR]

Genetic biofortification through breeding offers sustainable solution to the micronutrient malnutrition problems in the target countries. Great progress has been made in the past decade in transferring alleles for high-zinc (Zn) and iron (Fe) from diverse genetic resources into elite wheat breeding lines. However, the major challenge is to maintain simultaneous and high rates of genetic gains for grain yield and grain Zn to meet the food and nutritional security demands through the continuous delivery of biofortified varieties that are competitive to replace non-biofortified varieties successfully. Although a few intermediate effect QTL regions are identified for grain Zn, both yield and Zn content are quantitatively inherited. Increased breeding efforts and new approaches are therefore required to combine them in high frequency in CIMMYT's elite germplasm, ensuring that Zn levels are steadily increased to the required levels across the CIMMYT breeding pipelines. The addition of Zn as a core-trait will be achieved through significant acceleration in the breeding cycle, expanding population sizes, extensive Zn phenotyping, yield testing, phenotyping for biotic and abiotic stresses, molecular-assisted selection and genomic selection. While continuing to increase agronomic performance, high Zn alleles has been added as a core-trait. Eventually Zn content will be increased in the elite lines annually along with the frequency of elite lines with high yield and other agronomic traits that have potential to be released by partners. A genomics assisted "rapid cycle recurrent selection" scheme achieved through rapid generation advancement approaches are expected to enable CIMMYT wheat breeding program to mainstream grain Zn in the majority of elite lines in about 10 years. • Significant genetic variation has been observed for grain Fe and Zn among elite bread wheat lines. • Speed breeding would enable rapid cycling of high Zn and high yield parents faster. • Genomic selection combined with selection index would advance genetic gains for complex traits. • CIMMYT wheat breeding program to mainstream grain Zn in all the elite lines in about 10 years. [ABSTRACT FROM AUTHOR]