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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]

Long non-coding RNA (lncRNA) research in plants has recently gained momentum taking cues from studies in animals systems. The availability of next-generation sequencing has enabled genome-wide identification of lncRNA in several plant species. Some lncRNAs are inhibitors of microRNA expression and have a function known as target mimicry with the sequestered transcript known as an endogenous target mimic (eTM). The lncRNAs identified to date show diverse mechanisms of gene regulation, most of which remain poorly understood. In this review, we discuss the role of identified putative lncRNAs that may act as eTMs for nutrient-responsive microRNAs (miRNAs) in plants. If functionally validated, these putative lncRNAs would enhance current understanding of the role of lncRNAs in nutrient homeostasis in plants. [ABSTRACT FROM AUTHOR]

Key message: The NIAB_WW_SHW_NAM population, a large nested association mapping panel, is a useful resource for mapping QTL from synthetic hexaploid wheat that can improve modern elite wheat cultivars. The allelic richness harbored in progenitors of hexaploid bread wheat (Triticum aestivum L.) is a useful resource for addressing the genetic diversity bottleneck in modern cultivars. Synthetic hexaploid wheat (SHW) is created through resynthesis of the hybridisation events between the tetraploid (Triticum turgidum subsp. durum Desf.) and diploid (Aegilops tauschii Coss.) bread wheat progenitors. We developed a large and diverse winter wheat nested association mapping (NAM) population (termed the NIAB_WW_SHW_NAM) consisting of 3241 genotypes derived from 54 nested back-cross 1 (BC1) populations, each formed via back-crossing a different primary SHW into the UK winter wheat cultivar ‘Robigus’. The primary SHW lines were created using 15 T. durum donors and 47 Ae. tauschii accessions that spanned the lineages and geographical range of the species. Primary SHW parents were typically earlier flowering, taller and showed better resistance to yellow rust infection (Yr) than ‘Robigus’. The NIAB_WW_SHW_NAM population was genotyped using a single nucleotide polymorphism (SNP) array and 27 quantitative trait loci (QTLs) were detected for flowering time, plant height and Yr resistance. Across multiple field trials, a QTL for Yr resistance was found on chromosome 4D that corresponded to the Yr28 resistance gene previously reported in other SHW lines. These results demonstrate the value of the NIAB_WW_SHW_NAM population for genetic mapping and provide the first evidence of Yr28 working in current UK environments and genetic backgrounds. These examples, coupled with the evidence of commercial wheat breeders selecting promising genotypes, highlight the potential value of the NIAB_WW_SHW_NAM to variety improvement. [ABSTRACT FROM AUTHOR]

Introduction When weight cannot be measured during the management of medical emergencies in children, a convenient, quick and accurate method of weight estimation is required, as many drug doses and other interventions are based on body weight. Many weight estimation methodologies in current use have been shown to be inaccurate, especially in low- and middle-income countries with a high prevalence of underweight children. This meta-analysis evaluated the accuracy of weight estimation systems in children from studies from low- and middle-income countries. Methods Articles from low- and middle-income countries were screened for inclusion to evaluate and compare the accuracy of existing systems and the newer dual length- and habitus-based methods, using standard meta-analysis techniques. Results The 2D systems and parental estimates performed best overall. The PAWPER tape, parental estimates, the Wozniak method and the Mercy method were the most accurate systems with percentage of weight estimates within 10% of actual weight (PW10) accuracies of 86.9%, 80.4%, 72.1% and 71.4% respectively. The Broselow tape (PW10 47.1%) achieved a moderate accuracy and age-based estimates a very low accuracy (PW10 11.8–47.5%). Conclusions The PAWPER tape, the Wozniak method and the Mercy method achieved an acceptable level of accuracy in studies from low- and middle-income countries and should preferentially be used and further advanced for clinical emergency medicine practice. Parental estimates may be considered if the regular caregiver of the child is present and a recent measured weight is known. The Broselow tape and age-based formulas should be abandoned in low- and middle-income country populations as they are potentially dangerously inaccurate. [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]

Aims: The Broselow tape is widely used as a weight-estimation device and drug-dosing guide aid, but concerns about its accuracy and its efficacy have emerged in the last decade. The aim of this study was to systematically review the literature to analyse the accuracy of the Broselow tape as a weight estimation device and review evidence of its utility as a drug-dosing guide.Methods: This was a MOOSE-driven systematic review and meta-analysis, which focused on studies evaluating the accuracy of the Broselow tape and studies reviewing its use as a drug-dosing aid.Main Results: The tape has undergone substantial changes over the years, but there was no evidence to show that the changes have improved weight-estimation performance. The weight-estimation accuracy of the tape was suboptimal in all populations, with just over 50% of children receiving an estimation within 10% of their actual weight. The overestimation of weight in low- and middle-income countries was often extreme. This indicated a significant potential for potentially harmful medication errors. The limited available evidence on the value of the tape as a drug-dosing guide indicated that the tape was frequently used incorrectly and contained insufficient information to function without additional resources.Conclusions: The Broselow tape lacked sufficient accuracy as a weight estimation and drug-dosing tool when compared to other available techniques. In addition, the Broselow tape contains insufficient drug-dosing information to function as a complete resuscitation aid without additional material. The frequent rate of incorrect usage of the tape indicated that appropriate training with the tape is mandatory to reduce errors. [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]

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]

Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G× E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G× E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G× E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G× E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G× E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level. [ABSTRACT FROM AUTHOR]

Agriculture is now facing the 'perfect storm' of climate change, increasing costs of fertilizer and rising food demands from a larger and wealthier human population. These factors point to a global food deficit unless the efficiency and resilience of crop production is increased. The intensification of agriculture has focused on improving production under optimized conditions, with significant agronomic inputs. Furthermore, the intensive cultivation of a limited number of crops has drastically narrowed the number of plant species humans rely on. A new agricultural paradigm is required, reducing dependence on high inputs and increasing crop diversity, yield stability and environmental resilience. Genomics offers unprecedented opportunities to increase crop yield, quality and stability of production through advanced breeding strategies, enhancing the resilience of major crops to climate variability, and increasing the productivity and range of minor crops to diversify the food supply. Here we review the state of the art of genomic-assisted breeding for the most important staples that feed the world, and how to use and adapt such genomic tools to accelerate development of both major and minor crops with desired traits that enhance adaptation to, or mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • Evaluating nitrogen(N) responsiveness in crops has many commercial/environmental advantages. • Current lack of knowledge on its physio-genetic basis is a major bottleneck. • We demonstrated that N dependent yield increase is driven by grain number (GN) in S.italica. • GN has strong genetic basis –22 unique SNPs; six exhibiting haplotypes in natural population. • Based on this, we define N responsive and non-responsive accessions with distinct panicle types. • Few genes lying between SNPs with haplotypes show distinct transcript levels in two genotypes. 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. 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. 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. 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. 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. [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]

Children who experience early and extensive child care, especially center-based care, are rated by teachers as having more externalizing behavior problems than are other children. This association is reduced, but not eliminated, when care is of high quality, and it varies by socioeconomic disadvantage and the type of behavior assessed. We examine the processes that may account for the quantity effect, concluding that it occurs primarily among relatively advantaged White non-Hispanic families. It appears primarily for teacher-rated behavior, especially externalizing and low self-control, but is not evident for positive behavior and peer interaction skills. Some of the processes accounting for the relation of quantity to behavior are most likely to be poor caregiver-child relationships and negative peer interactions, not reduced attachment to mothers or lowered maternal sensitivity. Many questions remain about duration of effects, developmental and individual differences, more nuanced conceptualizations of both care quality and social behavior, and variations across cultural and ethnic groups. [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]

Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs. [ABSTRACT FROM AUTHOR]

Abstract: Background: Restless legs syndrome (RLS) is characterised by an urge to move in response to unusual sensations in the legs. Patients experience difficulty describing their RLS sensations, resulting in a diverse range of descriptors which have not been fully categorised. The purpose of this study was to describe RLS sensations and to evaluate the accuracy of current diagnostic descriptors. Methods: Forty-one RLS participants completed an interview which involved: providing spontaneous descriptions of RLS sensations, completing the McGill Pain Questionnaire (MPQ), and selecting descriptors from a list of previously published RLS terms (prompted descriptors). Results: The most frequent spontaneous descriptors were: “irritating” (17%), “painful” (17%), and “urge to move” (24%); prompted descriptors were: “restless” (88%), “uncomfortable” (78%), and “need to stretch” (76%); and MPQ words were: “tingling” (56%) and “jumping” (54%). Discussion: The most frequently cited descriptors in this study differ from the terminology used in the RLS diagnostic criteria. Inclusion of these frequently used descriptors may improve the diagnostic accuracy of RLS. Our data emphasise the need for an international, large scale, multicultural study to determine the most accurate diagnostic descriptors to define RLS more clearly. [Copyright &y& Elsevier]

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]

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 multitrait 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. [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]

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]

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]

Vernalization, photoperiod and the relatively poorly defined earliness per se ( eps) genes regulate flowering in plants. We report here the validation of a major eps quantitative trait locus (QTL) located on wheat 1DL using near isogenic lines (NILs). We used four independent pairs of NILs derived from a cross between Spark and Rialto winter wheat varieties, grown in both the field and controlled environments. NILs carrying the Spark allele, defined by QTL flanking markers Xgdm111 and Xbarc62, consistently flowered 3-5 days earlier when fully vernalized relative to those with the Rialto. The effect was independent of photoperiod under field conditions, short days (10-h light), long days (16-h light) and very long days (20-h light). These results validate our original QTL identified using doubled haploid (DH) populations. This QTL represents variation maintained in elite north-western European winter wheat germplasm. The two DH lines used to develop the NILs, SR9 and SR23 enabled us to define the location of the 1DL QTL downstream of marker Xgdm111. SR9 has the Spark 1DL arm while SR23 has a recombinant 1DL arm with the Spark allele from Xgdm111 to the distal end. Our work suggests that marker assisted selection of eps effects is feasible and useful even before the genes are cloned. This means eps genes can be defined and positionally cloned in the same way as the photoperiod and vernalization genes have been. This validation study is a first step towards fine mapping and eventually cloning the gene directly in hexaploid 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]

The challenges of securing future food security will require deployment of innovative technologies to accelerate crop production. Plant phenotyping methods have advanced significantly, spanning low-cost hand-held devices to large-scale satellite imaging. Field-based phenotyping aims to capture plant response to the environment, generating data that can be used to inform breeding and selection requirements. This in turn requires access to multiple representative locations and capacities for collecting useful information. In this paper we identify the current challenges in access to field phenotyping in multiple locations in Europe based on stakeholder feedback. We present a map of current infrastructure and propose opportunities for greater integration of existing facilities for meeting different user requirements. We also review the currently available technology and data requirements for effective multi-location field phenotyping and provide recommendations for ensuring future access and co-ordination. Taken together we provide an overview of the current status of European field phenotyping capabilities and provides a roadmap for their future use to support crop improvement. This provides a wider framework for the analysis and planning of field phenotyping activities for crop improvement worldwide. • Field phenotyping is a common challenge within the area of crop phenotyping. • Capturing infrastructure availability and accessibility supports co-ordination. • Multi-site crop examples provide a framework for future recommendations. • New technologies, common access and data policies will ensure long-term value. • Co-ordinated field phenotyping will support future crop research and breeding. [ABSTRACT FROM AUTHOR]

High-throughput phenotyping (HTP) provides new opportunities for efficiently dissecting the genetic basis of drought-adaptive traits, which is essential in current wheat breeding programs. The combined use of HTP and genome-wide association (GWAS) approaches has been useful in the assessment of complex traits such as yield, under field stress conditions including heat and drought. The aim of this study was to identify molecular markers associated with yield (YLD) in elite durum wheat that could be explained using hyperspectral indices (HSIs) under drought field conditions in Mediterranean environments in Southern Spain. The HSIs were obtained from hyperspectral imagery collected during the pre-anthesis and anthesis crop stages using an airborne platform. A panel of 536 durum wheat lines were genotyped by sequencing (GBS, DArTseq) to determine population structure, revealing a lack of genetic structure in the breeding germplasm. The material was phenotyped for YLD and 19 HSIs for six growing seasons under drought field conditions at two locations in Andalusia, in southern Spain. GWAS analysis identified 740 significant marker-trait associations (MTAs) across all the durum wheat chromosomes, several of which were common for YLD and the HSIs, and can potentially be integrated into breeding programs. Candidate gene (CG) analysis uncovered genes related to important plant processes such as photosynthesis, regulatory biological processes, and plant abiotic stress tolerance. These results are novel in that they combine high-resolution hyperspectral imaging at the field scale with GWAS analysis in wheat. They also support the use of HSIs as useful tools for identifying chromosomal regions related to the heat and drought stress response in wheat, and pave the way for the integration of field HTP in wheat breeding programs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision, (Copyright © 2024 Mérida-García, Gálvez, Solís, Martínez-Moreno, Camino, Soriano, Sansaloni, Ammar, Bentley, Gonzalez-Dugo, Zarco-Tejada and Hernandez.)

Main Conclusion: Association analysis identified 77 marker-trait associations (MTAs) for PUE traits, of which 10 were high-confidence MTAs. Candidate-gene mining and in-silico expression analysis identified 13 putative candidate genes for PUE traits. Bread wheat (Triticum aestivum L.) is a major cereal crop affected by phosphorus (P) deficiency, which affects root characteristics, plant biomass, and other attributes related to P-use efficiency (PUE). Understanding the genetic mechanisms of PUE traits helps in developing bread wheat cultivars that perform well in low-P environments. With this objective, we evaluated a bread wheat panel comprising 304 accessions for 14 PUE traits with high-throughput phenotyping under low-P and optimum-P treatments and observed a significant genetic variation among germplasm lines for studied traits. Genome-wide association study (GWAS) using 14,025 high-quality single-nucleotide polymorphisms identified 77 marker-trait associations (MTAs), of which 10 were chosen as high-confidence MTAs as they had > 10% phenotypic variation with logarithm of odds (LOD) scores of more than five. Candidate-gene (CG) mining from high-confidence MTAs identified 180 unique gene models, of which 78 were differentially expressed (DEGs) with at least twofold change in expression under low-P over optimum-P. Of the 78-DEGs, 13 were thought to be putative CGs as they exhibited functional relevance to PUE traits. These CGs mainly encode for important proteins and their products involved in regulating root system architecture, P uptake, transport, and utilization. Promoter analysis from 1500 bp upstream of gene start site for 13 putative CGs revealed the presence of light responsive, salicylic-acid responsive, gibberellic-acid (GA)-responsive, auxin-responsive, and cold responsive cis-regulatory elements. High-confidence MTAs and putative CGs identified in this study can be employed in breeding programs to improve PUE traits in bread wheat., Competing Interests: Declarations Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Ethical approval and consent to participate Not applicable. Consent to participate Not applicable. Consent for publication Not applicable., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Nitrogen (N) is a macronutrient limiting crop productivity with varied requirements across species and genotypes. Understanding the mechanistic basis of N responsiveness by comparing contrasting genotypes could inform the development and selection of varieties with lower N demands, or inform agronomic practices to sustain yields with lower N inputs. Given the established role of millets in ensuring climate-resilient food and nutrition security, we investigated the physiological and genetic basis of nitrogen responsiveness in foxtail millet (Setaria italica L.). We had previously identified genotypic variants linked to N responsiveness, and here we dissect the mechanistic basis of the trait by examining the physiological and molecular behaviour of N responsive (NRp-SI58) and non-responsive (NNRp-SI114) accessions at high and low N. Under high N, NRp-SI58 allocates significantly more biomass to nodes, internodes and roots, more N to developing grains, and is more effective at remobilizing flag leaf N compared with NNRp-SI114. Post-anthesis flag leaf gene expression suggests that differences in N induce much higher transcript abundance in NNRp-SI114 than NRp-SI58, a large proportion of which is potentially regulated by APETALA2 (AP2) transcription factors. Overall, the study provides novel insights into the regulation and manipulation of N responsiveness in S. italica., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8-10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re-synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so-called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of "hybrid prediction"-originally developed for the prediction of wheat hybrids generated from different heterotic groups - to a situation of allopolyploidization. Our use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in 'traditional' elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross-validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks., (© 2024 International Maize and Wheat Improvement Center (CIMMYT). The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)