Abstract: Few resistance genes providing defence against the major fungal diseases septoria tritici blotch (STB), septoria nodorum blotch, leaf rust (LR), and an emerging wheat blast disease have been identified in durum wheat. We identified sixteen fungal disease-associated QTL through genome-wide association mapping of 180 inbred lines sampled from a durum wheat Composite Cross-population. Two STB resistance-associated QTL mapped to chromosome 3A, one of which colocalizes with Stb6, a known resistance gene previously identified in bread wheat. This partial resistance could be conferred by a new allele of Stb6 or another paralogous gene. The second locus is associated with a reduction in pycnidia density, a recently identified and poorly understood form of resistance. A resistance QTL strongly associated with LR, and colocalizing with Lr61, was observed in a 3.24 Mbp region on chromosome 6B. QTL mapping of LR resistance following treatment by chitin used in the context of inducer treatment was also investigated. Using a combination of resistance alleles at these loci could confer durable resistance to multiple fungal diseases and aid durum wheat breeders in their fight against these fungal pathogens. DOI: 92 10.1007/s10681-020-02631-9

Abstract: This study compares different strategies of genomic prediction to optimize resource allocation in breeding schemes by using information from cheaper correlated traits to predict a more expensive trait of interest. We used bread wheat baking score (BMS) calculated for French registration as a case study. To conduct this project, 398 lines from a public breeding program were genotyped and phenotyped for BMS and correlated traits in 11 locations in France between 2000 and 2016. Single-trait (ST), multi-trait (MT) and trait-assisted (TA) strategies were compared in terms of predictive ability and cost. In MT and TA strategies, information from dough strength (W), a cheaper trait correlated with BMS (r = 0.45), was evaluated in the training population or in both the training and the validation sets, respectively. TA models allowed to reduce the budget allocated to phenotyping by up to 65% while maintaining the predictive ability of BMS. TA models also improved the predictive ability of BMS compared to ST models for a fixed budget (maximum gain: + 0.14 in cross-validation and + 0.21 in forward prediction). We also demonstrated that the budget can be further reduced by approximately one fourth while maintaining the same predictive ability by reducing the number of phenotypic records to estimate BMS adjusted means. In addition, we showed that the choice of the lines to be phenotyped can be optimized to minimize cost or maximize predictive ability. To do so, we extended the mean of the generalized coefficient of determination (CDmean) criterion to the multi-trait context (CDmulti).<br /> DOI: 10.1007/s00122-020-03590-4

Abstract: Wheat grain yield is usually decomposed in the yield components: number of spikes / m2, number of grains / spike, number of grains / m2 and thousand kernel weight (TKW). These are correlated one with another due to yield component compensation. Under optimal conditions, the number of grains per m2 has been identified as the main determinant of yield. However, with increasing occurrences of post-flowering abiotic stress associated with climate change, TKW may become severely limiting and hence a target for breeding. TKW is usually studied at the plot scale as it represents the average mass of a grain. However, this view disregards the large intra-genotypic variance of individual grain mass and its effect on TKW. The aim of this study is to investigate the determinism of the variance of individual grain size. We measured yield components and individual grain size variances of two large genetic wheat panels grown in two environments. We also carried out a genome-wide association study using a dense SNPs array. We show that the variance of individual grain size partly originates from the pre-flowering components of grain yield; in particular it is driven by canopy structure via its negative correlation with the number of spikes per m2. But the variance of final grain size also has a specific genetic basis. The genome-wide analysis revealed the existence of QTL with strong effects on the variance of individual grain size, independently from the other yield components. Finally, our results reveal some interesting drivers for manipulating individual grain size variance either through canopy structure or through specific chromosomal regions. doi: e0230689 10.1371/journal.pone.0230689

Abstract: Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.<br /> doi: 10.1093/jxb/eraa477

Abstract: Understanding the molecular mechanisms controlling the accumulation of grain storage proteins in response to nitrogen (N) and sulfur (S) nutrition is essential to improve cereal grain nutritional and functional properties. Here, we studied the grain transcriptome and metabolome responses to postanthesis N and S supply for the diploid wheat einkorn (Triticum monococcum). During grain filling, 848 transcripts and 24 metabolites were differentially accumulated in response to N and S availability. The accumulation of total free amino acids per grain and the expression levels of 241 genes showed significant modifications during most of the grain filling period and were upregulated in response to S deficiency. Among them, 24 transcripts strongly responded to S deficiency and were identified in coexpression network analyses as potential coordinators of the grain response to N and S supply. Sulfate transporters and genes involved in sulfate and Met metabolism were upregulated, suggesting regulation of the pool of free amino acids and of the grain N-to-S ratio. Several genes highlighted in this study might limit the impact of S deficiency on the accumulation of grain storage proteins. doi: 10.1104/pp.19.00842

Abstract: We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities. DOI: 10.1371/journal.pone.0222733

Abstract: The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection. DOI: 10.34133/2020/3521852

Abstract: In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars’ future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses. DOI: 4876 10.1038/s41467-020-18480-y

Abstract: Single nucleotide polymorphisms (SNPs) are widely used for detecting quantitative trait loci or for searching for causal variants of diseases. Nevertheless, structural variations such as copy-number variants (CNVs) represent a large part of natural genetic diversity and contribute significantly to trait variation. Numerous methods and softwares have been already developed to detect CNVs based on different technologies (amplicons, CGH, tiling, or SNP arrays, or sequencing), but they bypass a wealth of information such as genotyping data from segregating populations, produced e.g. for QTL mapping. Here we propose an original method to both detect and genetically map CNVs using mapping panels. Specifically, we exploit the apparent heterozygous state of duplicated loci: peaks in appropriately defined genome-wide allelic profiles provide highly specific signatures that identify the nature and position of the CNVs. Our original method and software can detect and map automatically up to 33 different predefined types of CNVs based on segregation data only. We validate this approach on simulated and experimental bi-parental mapping panels in two maize and one wheat populations. Most of the events found correspond to having just one extra copy in one of the parental lines but the corresponding allelic value can be that of either parent. We also find cases with two or more additional copies, especially in wheat where these copies locate to homeologues. More generally, our computational tool can be used to give additional value, at no cost, to many datasets produced over the past decade from genetic mapping panels. DOI: 10.1534/genetics.119.302881

Abstract: Plant breeders evaluate their selection candidates in multi-environment trials to estimate their performance in contrasted environments. The number of genotype/environment combinations that can be evaluated is strongly constrained by phenotyping costs and by the necessity to limit the evaluation to a few years. Genomic prediction models taking the genotype by environment interactions (GEI) into account can help breeders identify combination of (possibly unphenotyped) genotypes and target environments optimizing the traits under selection. We propose a new prediction approach in which a secondary trait available on both the calibration and the test sets is introduced as an environment specific covariate in the prediction model (trait-assisted prediction, TAP). The originality of this approach is that the phenotyping of the test set for the secondary trait is replaced by crop-growth model (CGM) predictions. So there is no need to sow and phenotype the test set in each environment which is a clear advantage over the classical traitassisted prediction models. The interest of this approach, called CGM-TAP, is highest if the secondary trait is easy to predict with CGM and strongly related to the target trait in each environment (and thus capturing GEI). We tested CGM-TAP on bread wheat with heading date as secondary trait and grain yield as target trait. Simple CGMTAP model with a linear effect of heading date resulted in high predictive abilities in three prediction scenarios (sparse testing, or prediction of new genotypes or of new environments). It increased predictive abilities of all reference GEI models, even those involving sophisticated environmental covariates. DOI: 10.3389/fpls.2020.00827

Abstract: The identification of genes of agronomic interest in bread wheat (Triticum aestivum L.) is hampered by its allopolyploid nature (2n = 6x = 42; AABBDD) and its very large genome, which is largely covered by transposable elements. However, owing to this complex structure, aneuploid stocks can be developed in which fragments or entire chromosomes are missing, sometimes resulting in visible phenotypes that help in the cloning of affected genes. In this study, the 2C gametocidal chromosome from Aegilops cylindrica was used to develop a set of 113 deletion lines for chromosome 3D in the reference cultivar Chinese Spring. Eighty-four markers were used to show that the deletions evenly covered chromosome 3D and ranged from 6.5 to 357 Mb. Cytogenetic analyses confirmed that the physical size of the deletions correlated well with the known molecular size deduced from the reference sequence. This new genetic stock will be useful for positional cloning of genes on chromosome 3D, especially for Ph2 affecting homoeologous pairing in bread wheat. DOI: 1756 10.3389/fpls.2019.01756

Abstract: Since its domestication in the Fertile Crescent ~8000 to 10,000 years ago, wheat has undergone a complex history of spread, adaptation, and selection. To get better insights into the wheat phylogeography and genetic diversity, we describe allele distribution through time using a set of 4506 landraces and cultivars originating from 105 different countries genotyped with a high-density single-nucleotide polymorphism array. Although the genetic structure of landraces is collinear to ancient human migration roads, we observe a reshuffling through time, related to breeding programs, with the appearance of new alleles enriched with structural variations that may be the signature of introgressions from wild relatives after 1960. Science Advances, DOI:10.1126/sciadv.aav0536

Abstract: Albumins and globulins (AGs) of wheat endosperm represent about 20% of total grain proteins. Some of these physiologically active proteins can influence the synthesis of storage proteins (SPs) (gliadins and glutenins) and consequently, rheological properties of wheat flour and processing. To identify such AGs, data, (published by Bonnot et al., 2017) concerning abundance in 352 AGs and in the different seed SPs during grain filling and in response to different nitrogen (N) and sulfur (S) supply, were integrated with mixOmics R package. Relationships between AGs and SPs were first unraveled using the unsupervised method sparse Partial Least Square, also known as Projection to Latent Structure (sPLS). Then, data were integrated using a supervised approach taking into account the nutrition and the grain developmental stage. We used the block.splda procedure also referred to as DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies). These approaches led to the identification of discriminant and highly correlated features from the two datasets (AGs and SPs) which are not necessarily differentially expressed during seed development or in response to N or S supply. Eighteen AGs were correlated with the quantity of SPs per grain. A statistical validation of these proteins by genetic association analysis confirmed that 5 out of this AG set were robust candidate proteins able to modulate the seed SP synthesis. In conclusion, this latter result confirmed that the integrative strategy is an adequate way to reduce the number of potentially relevant AGs for further functional validation. DOI: 10.3389/fpls.2019.00832.

Abstract: Background and Aims: Because functional–structural plant models (FSPMs) take plant architecture explicitly into consideration, they constitute a promising approach for unravelling plant–plant interactions in complex canopies. However, existing FSPMs mainly address competition for light. The aim of the present work was to develop a comprehensive FSPM accounting for the interactions between plant architecture, environmental factors and the metabolism of carbon (C) and nitrogen (N).<br /> Methods: We developed an original FSPM by coupling models of (1) 3-D wheat architecture, (2) light distribution within canopies and (3) C and N metabolism. Model behaviour was evaluated by simulating the functioning of theoretical canopies consisting of wheat plants of contrasting leaf inclination, arranged in pure and mixed stands and considering four culm densities and three sky conditions. DOI: 10.1093/aob/mcy208.

Abstract: In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField® platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops’ phenotype. In this paper, we will highlight the PhénoField® production capability using data from 2017-wheat trial. The innovative approach of the PhénoField® platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField® platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question. DOI: 10.3389/fpls.2019.00904

Summary: The quality of wheat grain is mainly determined by the quantity and composition of its grain storage proteins (GSPs). Grain storage proteins consist of low‐ and high‐molecular‐weight glutenins (LMW‐GS and HMW‐GS, respectively) and gliadins. The synthesis of these proteins is essentially regulated at the transcriptional level and by the availability of nitrogen and sulfur. The regulation network has been extensively studied in barley where BLZ1 and BLZ2, members of the basic leucine zipper (bZIP) family, activate the synthesis of hordeins. To date, in wheat, only the ortholog of BLZ2, Storage Protein Activator (SPA), has been identified as playing a major role in the regulation of GSP synthesis. Here, the ortholog of BLZ1, named SPA Heterodimerizing Protein (SHP), was identified and its involvement in the transcriptional regulation of the genes coding for GSPs was analyzed. In gel mobility shift assays, SHP binds cis‐motifs known to bind to bZIP family transcription factors in HMW‐GS and LMW‐GS promoters. Moreover, we showed by transient expression assays in wheat endosperm that SHP acts as a repressor of the activity of these gene promoters. This result was confirmed in transgenic lines overexpressing SHP, which were grown with low and high nitrogen supply. The phenotype of SHP‐overexpressing lines showed a lower quantity of both LMW‐GS and HMW‐GS, while the quantity of gliadin was unchanged, whatever the nitrogen availability. Thus, the gliadin/glutenin ratio was increased, which suggests that gliadin and glutenin genes may be differently regulated. DOI: https://doi.org/10.1111/tpj.14163

Abstract: Wheat spindle streak mosaic virus (WSSMV) is a major concern for cereal crops in Europe and North America. A strong increase in the occurrence of WSSMV has been observed in each French region where susceptible cultivars are cultivated. Most European bread wheat cultivars are resistant, but assessing the status of newly registered cultivars or breeding lines regarding WSSMV resistance is of importance. This paper describes a genome-wide association study carried out on a panel of 163 cultivars and tested for their resistance to WSSMV. Two regions on chromosomes 5B<br /> and 7D showed minor effects on WSSMV resistance. More importantly, a large genomic region on chromosome 2D explained most of the resistance to WSSMV. More than 99% of the cultivars carrying the AA genotype at the most associated marker (Excalibur_c15426_661) were resistant to WSSMV, while 100% of the cultivars showing the GG genotype were susceptible. This large genomic region of 45.8 Mb was found distal to the centromere and showed very high linkage disequilibrium. It is hypothesized that this region may be an alien introgression originating from a wild<br /> related species. This region contains a total of 2605 predicted genes based on the Chinese Spring IWGSC RefSeq v. 1.0 including genes potentially involved in plant disease resistance. A kompetitive allele-specific PCR (KASP) singlenucleotide<br /> polymorphism (SNP) marker was designed in order to identify breeding lines or registered cultivars resistant to WSSMV. DOI: https://onlinelibrary.wiley.com/doi/full/10.1111/ppa.12972

Abstract: As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N-deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N-induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high-throughput phenotyping data of yield-related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N-deficiency tolerant wheat varieties. DOI: 10.1111/tpj.14186.

Abstract: Wheat ear density estimation is an appealing trait for plant breeders. Current manual counting is tedious and inefficient. In this study we investigated the potential of convolutional neural networks (CNNs) to provide accurate ear density using nadir high spatial resolution RGB images. Two different approaches were investigated, either using the Faster-RCNN state-of-the-art object detector or with the TasselNet local count regression network. Both approaches performed very well (rRMSE≈6%) when applied over the same conditions as those prevailing for the calibration of the models. However, Faster-RCNN was more robust when applied to a dataset acquired at a later stage with ears and background showing a different aspect because of the higher maturity of the plants. Optimal spatial resolution for Faster-RCNN was around 0.3 mm allowing to acquire RGB images from a UAV platform for high-throughput phenotyping of large experiments. Comparison of the estimated ear density with in-situ manual counting shows reasonable agreement considering the relatively small sampling area used for both methods. Faster-RCNN and in-situ counting had high and similar heritability (H²≈85%), demonstrating that ear density derived from high resolution RGB imagery could replace the traditional counting method. Keywords: Wheat ear density, Object detection, Object counting, Convolutional neural networks, Phenotyping, Broad-sense heritability. DOI: https://doi.org/10.1016/j.agrformet.2018.10.013

Abstract: GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems. DOI: 10.34133/2019/1671403.

Abstract: Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G ×  E). We found that using a different kinship for the main genetic effect and the G ×  E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G ×  E than climatic covariates. DOI: 10.1007/s00122-019-03432-y.

Abstract: Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G × E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments. Theoretical and Applied Genetics (2019) 132:2859–2880, DOI: https://doi.org/10.1007/s00122-019-03393-2.

Abstract: Bread wheat (Triticum aestivum L.) is a staple food for a significant part of the world’s population. The growing demand on its production can be satisfied by improving yield and resistance to biotic and abiotic stress. Knowledge of the genome sequence would aid in discovering genes and QTLs underlying these traits and provide a basis for genomics-assisted breeding. Physical maps and BAC clones associated with them have been valuable resources from which to generate a reference genome of bread wheat and to assist map-based gene cloning. As a part of a joint effort coordinated by the International Wheat Genome Sequencing Consortium, we have constructed a BAC-based physical map of bread wheat chromosome arm 7DS consisting of 895 contigs and covering 94% of its estimated length. By anchoring BAC contigs to one radiation hybrid map and three high resolution genetic maps, we assigned 73% of the assembly to a distinct genomic position. This map integration, interconnecting a total of 1713 markers with ordered and sequenced BAC clones from a minimal tiling path, provides a tool to speed up gene cloning in wheat. The process of physical map assembly included the integration of the 7DS physical map with a whole-genome physical map of Aegilops tauschii and a 7DS Bionano genome map, which together enabled efficient scaffolding of physical-map contigs, even in the non-recombining region of the genetic centromere. Moreover, this approach facilitated a comparison of bread wheat and its ancestor at BAC-contig level and revealed a reconstructed region in the 7DS pericentromere. Keywords: Triticum aestivum, BAC, BNG map, Aegilops tauschii, Centromere. DOI: https://doi.org/10.1016/j.nbt.2018.03.003

Abstract: In wheat endosperm, mannan, is poorly documented. Nevertheless, this hemicellulosic polysaccharide might have a determinant role in wheat grain development since, in Arabidopsis thaliana, mutants with a reduced amount of mannan show an altered seed development. In order to gain knowledge about mannan in wheat, we have determined its biochemical structure in wheat endosperm where mannose content is about 0.2% (dry weight basis). We developed a method of enzymatic fingerprinting and isolated mannan-enriched fractions to decipher its fine structure. Although it is widely accepted that the class of mannan present in grass cell walls is glucomannan, our data indicate that, in wheat endosperm, this hemicellulose is only represented by short unsubstituted chains of 1,4 linked D-mannose residues and is slightly acetylated. Our study provides information regarding the interactions of mannan with other cell wall components and help to progress towards the understanding of monocot cell wall architecture and the mannan synthesis in wheat endosperm. DOI: 115063 10.1016/j.carbpol.2019.115063

Abstract: The Wheat@URGI portal has been developed to provide the international community of researchers and breeders with access to the bread wheat reference genome sequence produced by the International Wheat Genome Sequencing Consortium. Genome browsers, BLAST, and InterMine tools have been established for in-depth exploration of the genome sequence together with additional linked datasets including physical maps, sequence variations, gene expression, and genetic and phenomic data from other international collaborative projects already stored in the GnpIS information system. The portal provides enhanced search and browser features that will facilitate the deployment of the latest genomics resources in wheat improvement. Keywords: Data integration, Information system, Big data, Wheat genomics, genetics and phenomics. DOI: https://doi.org/10.1186/s13059-018-1491-4

Abstract: An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage–related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding. DOI: 10.1126/science.aar7191

Abstract: Important biological, nutritional and technological roles are attributed to cell wall polymers from cereal grains. The composition of cell walls in dry wheat grain has been well studied, however less is known about cell wall deposition and modification in the grain outer layers during grain development. In this study, the composition of cell walls in the outer layers of the wheat grain (Triticum aestivum Recital cultivar) was investigated during grain development, with a focus on cell wall phenolics. We discovered that lignification of outer layers begins earlier than previously reported and long before the grain reaches its final size. Cell wall feruloylation increased in development. However, in the late stages, the amount of ferulate releasable by mild alkaline hydrolysis was reduced as well as the yield of lignin-derived thioacidolysis monomers. These reductions indicate that new ferulate-mediated cross-linkages of cell wall polymers appeared as well as new resistant interunit bonds in lignins. The formation of these additional linkages more specifically occurred in the outer pericarp.<br /> Our results raised the possibility that stiffening of cell walls occur at late development stages in the outer pericarp and might contribute to the restriction of the grain radial growth. Keywords: cell wall, ferulic acid, grain size, lignins, wheat grain, developing pericarp. DOI https://doi.org/10.1016/j.plantsci.2018.08.022

Abstract: Wheat grains are an important source of human food but current production amounts cannot meet world needs. Environmental conditions such as high temperature (above 30°C) could affect wheat production negatively. Plants from two wheat genotypes have been subjected to two growth temperature regimes. One set has been grown at an optimum daily mean temperature of 19°C while the second set of plants has been subjected to warming at 27°C from two to 13 days after anthesis (daa). While warming did not affect mean grain number per spike, it significantly reduced other yield-related indicators such as grain width, length, volume and maximal cell numbers in the endosperm. Whole genome expression analysis identified 6,258 and 5,220 genes, respectively, whose expression was affected by temperature in the two genotypes. Co-expression analysis using WGCNA (Weighted Gene Coexpression Network Analysis) uncovered modules (groups of co-expressed genes) associated with agronomic traits. In particular, modules enriched in genes related to nutrient reservoir and endopeptidase inhibitor activities were found to be positively associated with cell numbers in the endosperm. A hypothetical model pertaining to the effects of warming on gene expression and growth in wheat grain is proposed. Under moderately high temperature conditions, network analyses suggest a negative effect of the expression of genes related to seed storage proteins and starch biosynthesis on the grain size in wheat. DOI 13(6): e0199434

Abstract: Recombination affects the fate of alleles in populations by imposing constraints on the reshuffling of genetic information. Understanding the genetic basis of these constraints is critical for manipulating the recombination process to improve the resolution of genetic mapping, and reducing the negative effects of linkage drag and deleterious genetic load in breeding. Using sequence-based genotyping of a wheat nested association mapping (NAM) population of 2,100 recombinant inbred lines created by crossing 29 diverse lines, we mapped QTL affecting the distribution and frequency of 102 000 crossovers (CO). Genome-wide recombination rate variation was mostly defined by rare alleles with small effects together explaining up to 48.6% of variation. Most QTL were additive and showed predominantly trans-acting effects. The QTL affecting the proximal COs also acted additively without increasing the frequency of distal COs. We showed that the regions with decreased recombination carry more single nucleotide polymorphisms (SNPs) with possible deleterious effects than the regions with a high recombination rate. Therefore, our study offers insights into the genetic basis of recombination rate variation in wheat and its effect on the distribution of deleterious SNPs across the genome. The identified trans-acting additive QTL can be utilized to manipulate CO frequency and distribution in the large polyploid wheat genome opening the possibility to improve the efficiency of gene pyramiding and reducing the deleterious genetic load in the low-recombining pericentromeric regions of chromosomes. DOI: 10.1111/tpj.14009

Abstract: Plant breeding has always sought to develop crops able to withstand environmental stresses, but this is all the more urgent now as climate change is affecting the agricultural regions of the world. It is currently difficult to screen genetic material to determine how well a crop will tolerate various stresses. Multi-environment trials (MET) which include a particular stress condition could be used to train a genomic selection model thanks to molecular marker information that is now readily available. Our study focuses on understanding how and predicting whether a plant is adapted to a particular environmental stress. We propose a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the factorial regression genomic best linear unbiased predictor (FR-gBLUP). Twenty-eight wheat trials in France (3 years, 12 locations, nitrogen or water stress treatments) were split into two METs where different stresses limited grain number and yield. In MET1, drought at flowering was responsible for 46.7% of the genotype-by-environment (G ×E) interactions for yield while in MET2, heat stress during booting was identified as the main factor responsible for G× E interactions, but that explained less of the interaction variance (33.6%). Since drought at flowering explained a fairly large variance in G ×E in MET1, the FR-gBLUP model was more accurate than the additive gBLUP across all types of cross validation. Accuracy gains varied from 2.4% to 12.9% for the genomic regression to drought. In MET2 accuracy gains were modest, varying from −5.7% to 2.4%. When a major stress influencing G ×E is identified, the FR-gBLUP strategy makes it possible to predict the level of adaptation of genotyped individuals to varying stress intensities, and thus to select them in silico. Our study demonstrates how genome-wide selection can facilitate breeding for adaptation. Keywords: Genotype-by-environment interaction, Factorial regression, Genomic prediction, Reaction norm, Drought adaptation. DOI 10.1016/j.fcr.2017.08.020

Abstract: Because of their abundance and their amenability to high-throughput genotyping techniques, Single Nucleotide Polymorphisms (SNPs) are powerful tools for efficient genetics and genomics studies, including characterization of genetic resources, genome-wide association studies and genomic selection. In wheat, most of the previous SNP discovery initiatives targeted the coding fraction, leaving almost 98% of the wheat genome largely unexploited. Here we report on the use of whole-genome resequencing data from eight wheat lines to mine for SNPs in the genic, the repetitive and non-repetitive intergenic fractions of the wheat genome. Eventually, we identified 3.3 million SNPs, 49% being located on the B-genome, 41% on the A-genome and 10% on the D-genome. We also describe the development of the TaBW280K high-throughput genotyping array containing 280,226 SNPs. Performance of this chip was examined by genotyping a set of 96 wheat accessions representing the worldwide diversity. Sixty-nine percent of the SNPs can be efficiently scored, half of them showing a diploid-like clustering. The TaBW280K was proven to be a very efficient tool for diversity analyses, as well as for breeding as it can discriminate between closely related elite varieties. Finally, the TaBW280K array was used to genotype a population derived from a cross between Chinese Spring and Renan, leading to the construction a dense genetic map comprising 83,721 markers. The results described here will provide the wheat community with powerful tools for both basic and applied research. DOI 10.1371/journal.pone.0186329

Abstract: Genomic selection - the prediction of breeding values using DNA polymorphisms - is adisruptive method that has widely been adopted by animal and plant breeders to increase productivity. Itwas recently shown that other sources of molecular variations such as those resulting from transcripts ormetabolites could be used to accurately predict complex traits. These endophenotypes have the advantageof capturing the expressed genotypes and consequently the complex regulatory networks that occur in thedifferent layers between the genome and the phenotype. However, obtaining such omics data at very largescales, such as those typically experienced in breeding, remains challenging. As an alternative, weproposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive toolto indirectly capture endophenotypic variants and compute relationship matrices for predicting complextraits, and coined this new approach”phenomic selection”(PS). We tested PS on two species of economicinterest (Triticum aestivumL. andPopulus nigraL.) using NIRS on various tissues (grains, leaves, wood).We showed that one could reach predictions as accurate as with molecular markers, for developmental,tolerance and productivity traits, even in environments radically different from the one in which NIRS werecollected. Our work constitutes a proof of concept and provides new perspectives for the breeding com-munity, as PS is theoretically applicable to any organism at low cost and does not require any molecularinformation. DOI: 10.1534/g3.118.200760

Abstract: Background: Transposable elements (TEs) are major components of large plant genomes and main drivers of genome evolution. The most recent assembly of hexaploid bread wheat recovered the highly repetitive TE space in an almost complete chromosomal context and enabled a detailed view into the dynamics of TEs in the A, B, and D subgenomes. Results: The overall TE content is very similar between the A, B, and D subgenomes, although we find no evidence for bursts of TE amplification after the polyploidization events. Despite the near-complete turnover of TEs since the subgenome lineages diverged from a common ancestor, 76% of TE families are still present in similar proportions in each subgenome. Moreover, spacing between syntenic genes is also conserved, even though syntenic TEs have<br /> been replaced by new insertions over time, suggesting that distances between genes, but not sequences, are under evolutionary constraints. The TE composition of the immediate gene vicinity differs from the core intergenic regions. We find the same TE families to be enriched or depleted near genes in all three subgenomes. Evaluations at the subfamily level of timed long terminal repeat-retrotransposon insertions highlight the independent evolution of the diploid A, B, and D lineages before polyploidization and cases of concerted proliferation in the AB tetraploid.<br /> Conclusions: Even though the intergenic space is changed by the TE turnover, an unexpected preservation is observed between the A, B, and D subgenomes for features like TE family proportions, gene spacing, and TE enrichment near genes. Keywords: Transposable elements, Wheat genome, Genome evolution, LTR retrotransposons, Polyploidy, Triticum aestivums. DOI: https://doi.org/10.1186/s13059-018-1479-0

Abstract: GnpIS is an information system designed to help scientists working on plants and fungi to decipher the molecular and genetic architecture of trait variations by facilitating the navigation through genetic, genomic, and phenotypic information. The purpose of the present chapter is to illustrate how users can (1) explore datasets from phenotyping experiments in order to build new datasets for studying genotype × environment interactions in traits, (2) browse into the results of other genetic analysis data such as GWAS to generate or check working hypothesis about candidate genes or to identify important alleles and germplasms for breeding programs, and (3) explore the polymorphism in specific area of the genome using InterMine, JBrowse tools embedded in the GnpIS information system. DOI: 10.1007/978-1-4939-6658-5_5

Summary: Wheat grain storage proteins (GSPs) make up most of the protein content of grain and determine flour enduse value. The synthesis and accumulation of GSPs depend highly on nitrogen (N) and sulfur (S) availability and it is important to understand the underlying control mechanisms. Here we studied how the einkorn (Triticum monococcum ssp. monococcum) grain proteome responds to different amounts of N and S supply during grain development. GSP composition at grain maturity was clearly impacted by nutrition treatments, due to early changes in the rate of GSP accumulation during grain filling. Large-scale analysis of the nuclear and albumin-globulin subproteomes during this key developmental phase revealed that the abundance of 203 proteins was significantly modified by the nutrition treatments. Our results showed that the grain proteome was highly affected by perturbation in the N:S balance. S supply strongly increased the rate of accumulation of S-rich a/b-gliadin and c-gliadin, and the abundance of several other proteins involved in glutathione metabolism. Post-anthesis N supply resulted in the activation of amino acid metabolism at the expense of carbohydrate metabolism and the activation of transport processes including nucleocytoplasmic transit. Protein accumulation networks were analyzed. Several central actors in the response were identified whose variation in abundance was related to variation in the amounts of many other proteins and are thus potentially important for GSP accumulation. This detailed analysis of grain subproteomes provides information on how wheat GSP composition can possibly be controlled in low-level fertilization condition. Keywords: Triticum monococcum, grain, nitrogen, sulfur, storage proteins, nuclear proteins, albumin-globulin, network. DOI: 10.1111/tpj.13615

Abstract: During meiosis, crossovers (COs) create new allele associations by reciprocal exchange of DNA. In bread wheat (Triticum aestivum L.), COs are almost limited to subtelomeric regions of chromosomes resulting in a substantial loss of breeding efficiency in the proximal regions though these latter carry ~60-70% of the genes. Identifying sequence and/or chromosome features impacting recombination occurrence is thus relevant to improve and drive recombination. Using the recent release of a reference sequence of chromosome 3B and of the draft assemblies of the 20 other wheat chromosomes, we performed a fine-scale mapping of COs and we revealed that 82% of COs located in the distal ends of chromosome 3B representing 19% of the chromosome length. We used 774 SNPs to genotype 180 varieties representative of the Asian and European genetic pools and a segregating population of 1270 F6 lines. We observed a common location for ancestral COs (predicted through Linkage Disequilibrium) and the COs derived from the segregating population. We delineated 73 small intervals ( doi: 10.1016/j.fcr.2015.12.012

Abstract: Simulations using crop models can assist designing ideotypes for current and future agricultural condi-tions. This approach consists in running simulations for different “in silico genotypes” obtained by varyingthe most sensitive genotypic parameters of these models, and analyzing results obtained for differentenvironments, so as to identify the best genotypes for a target population of environments. However,this approach has rarely been used to guide commercial breeding programs so far. In this paper, weattempt to address some of the gaps yet to be filled before this kind of approach can be implemented,and identify some remaining issues that should be addressed in future research. Our focus is on opti-mizing wheat phenology, integrating simulations from a modified version of the ARCWHEAT model ofwheat growth stages with available knowledge on the genetic control of wheat phenology obtained viamolecular markers. Based on simulations, stem extension could be advanced by 10 days in 2025–2049without increasing frost risks, thus opening up opportunities for lengthening the rapid growth period.Analysis of the current genetic variability for major phenology genes in French elite varieties, showedthat the insensitive PpdD1—spring Vrn3 allele combination appears undesirable and current genotypeswith early stem extensions are unstable (i.e. show a strong response to temperature and can start thestem extension very early in case of mild winter temperatures). We finally use a case study on gene-basedmodelling of wheat phenology in France to illustrate how it can be used to dissect the genetic basis of thequantitative nature of the three components of earliness, beyond the effects of major genes. We identifythe need to link the variability for optimized model parameters and the allelic variations at the gene levelas a critical step of this type of approach. DOI: 10.1016/j.fcr.2015.12.012

Abstract: Plant density is useful variable that determines the fate of the wheat crop. The most commonly used method for plant density quantification is based on visual counting from ground level. The objective of this study is to develop and evaluate a method for estimating wheat plant density at the emergence stage based on high resolution imagery taken from UAV at very low altitude with application to high throughput phenotyping in field conditions. A Sony ILCE α5100L RGB camera with 24 Mpixels and equipped with a 60 mm focal length lens was flying aboard an hexacopter at 3 to 7 m altitude at about 1 m/s speed. This allows getting ground resolution between 0.20 mm to 0.45 mm, while providing 59–77% overlap between images. The camera was looking with 45° zenith angle in a compass direction perpendicular to the row direction to maximize the cross section viewed of the plants and minimize the effect of the wind created by the rotors. Agisoft photoscan software was then used to derive the position of the cameras for each image. Images were then projected on the ground surface to finally extract subsamples used to estimate the plant density. The extracted images were first classified to separate the green pixels from the background and the rows were then identified and extracted. Finally, image object (group of connected green pixels) was identified on each row and the number of plants they contain was estimated using a Support Vector Machine whose training was optimized using a Particle Swarm Optimization.</p> <p>Three experiments were conducted in Gréoux, Avignon and Clermont sites with some variability in the sowing dates, densities, genotypes, flight altitude, and growth stage at the time of the image acquisition. The application of the method on the 270 samples available over the three sites provides a RMSE and relative RMSE on estimates of 34.05 plants/m2 and 14.31% with a bias of 9.01 plants/m2. However, differences in performances were observed between the three sites, mostly related to the growth stage at the time of the flight. Plants should have between one to two leaves when images are taken. Further, a specific sensitivity analysis shows that the ground resolution of the images should be better than 0.40 mm. Finally, the repeatability of the method is good especially when images are taken from similar observational geometries. The current limits and possible improvements of the method proposed are finally discussed. doi: 10.1016/j.rse.2017.06.007

Abstract: Background: Plant density and its non-uniformity drive the competition among plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the plant density in wheat crops from plant counting and reach a given precision. Results: Three experiments were conducted in 2014 resulting in 14 plots across varied sowing density, cultivars and environmental conditions. The coordinates of the plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate plant density for a given precision. Results suggest that measuring the length of segments containing 90 plants will achieve a precision better than 10%, independently from the plant density. This approach appears more efficient than the usual method based on fixed length segments where the number of plants are counted: the optimal length for a given precision on the density estimation will depend on the actual plant density. The gamma count model parameters may also be used to quantify the heterogeneity of plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive plants should be measured. Conclusions: This method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row. Keywords: Wheat, Gamma-count model, Density, RGB imagery, Sampling strategy, Plant spacing heterogeneity. DOI 10.1186/s13007-017-0187-1

Abstract: This work investigates the spatial distribution of wheat plants and its consequences on the canopy structure. A set of RGB images were taken from nadir on a total 14 plots showing a range of sowing densities, cultivars and environmental conditions. The coordinates of the plants were extracted from RGB images. Results show that the distance between-plants along the row follows a gamma distribution law, with no dependency between the distances. Conversely, the positions of the plants across rows follow a Gaussian distribution, with strongly interdependent. A statistical model was thus proposed to simulate the possible plant distribution pattern. Through coupling the statistical model with 3D Adel-Wheat model, the impact of the plant distribution pattern on canopy structure was evaluated using emerging properties such as the green fraction (GF) that drives the light interception efficiency. Simulations showed that the effects varied over different development stages but were generally small. For the intermediate development stages, large zenithal angles and directions parallel to the row, the deviations across the row of plant position increased the GF by more than 0.1. These results were obtained with a wheat functionalstructural model that does not account for the capacity of plants to adapt to their local environment. Nevertheless, our work will extend the potential of functional-structural plant models to estimate the optimal distribution pattern for given conditions and subsequently guide the field management practices. Keywords: Plant distribution pattern, Green fraction, FSPMs, Wheat. DOI: 10.1016/j.compag.2017.02.022

Abstract: Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the connected components (objects) are identified. A neural network is then trained to estimate the number of plants in the objects using the object features. The method was evaluated over three experiments showing contrasted conditions with sowing densities ranging from 100 to 600 seeds.m-2. Results demonstrate that the density is accurately estimated with an average relative error of 12%. The pipeline developed here provides an efficient and accurate estimate of wheat plant density at early stages. Keywords: plant density, RGB imagery, neural network, wheat, recursive feature elimination, Hough transform. DOI: 10.3389/fpls.2017.00739

Abstract: The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3 < GAI < 6 and data simulated with the 3D structure model with 1 < GAI < 7.</p> <p>Results confirm that using only the GF provides poor estimates of GAI (0.89 < RMSE < 1.28; 0.22 < rRMSE < 0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48 < RMSE < 0.55; 0.12 < rRMSE < 0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used. DOI: 10.1016/j.agrformet.2017.07.007

Abstract: In plant breeding, one of the major challenges of genomic selection is to account for genotype-by-environment (G × E) interactions, and more specifically how varieties are adapted to various environments. Crop growth models (CGM) were developed to model the response of plants to environmental conditions. They can be used to characterize eco-physiological stresses in relation to crop growth and developmental stages, and thereby help to dissect G × E interactions. Our study aims at demonstrating how environment characterization using crop models can be integrated to improve both the understanding and the genomic predictions of G × E interactions. We evaluated the usefulness of using CGM to characterize environments by comparing basic and CGM-based stress indicators, to assess how much of the G × E interaction can be explained and whether gains in prediction accuracy can be made. We carried out a case study in wheat (Triticum aestivum) to model nitrogen stress in a CGM in 12 environments defined by year × location × nitrogen treatment. Interactions between 194 varieties of a core collection and these 12 different nitrogen conditions were examined by analyzing grain number. We showed that (i) CGM based indicators captured the G × E interactions better than basic indicators and that (ii) genomic predictions were slightly improved by modeling the genomic interaction with the crop model based characterization of nitrogen stress. A framework was proposed to integrate crop model environment characterization into genomic predictions. We describe how this characterization promises to improve the prediction accuracy of adaptation to environmental stresses. DOI: 10.1016/j.fcr.2017.09.024

Abstract: The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE D 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight underestimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H2 > 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H2 > 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed. DOI 10.3389/fpls.2017.02002

Summary: Key message We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Abstract Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed. DOI 10.1007/s00122-017-2922-4

Abstract: Background and Aims Improving crops requires better linking of traits and metabolic processes to whole plant performance. In this paper, we present CN-Wheat, a comprehensive and mechanistic model of carbon (C) and nitrogen (N) metabolism within wheat culms after anthesis. Methods The culm is described by modules that represent the roots, photosynthetic organs and grains. Each of them includes structural, storage and mobile materials. Fluxes of C and N among modules occur through a common pool and through transpiration flow. Metabolite variations are represented by differential equations that depend on the physiological processes occurring in each module. A challenging aspect of CN-Wheat lies in the regulation of these processes by metabolite concentrations and the environment perceived by organs. Key Results CN-Wheat simulates the distribution of C and N into wheat culms in relation to photosynthesis, N-uptake, metabolite turnover, root exudation and tissue death. Regulation of physiological activities by local concentrations of metabolites appears to be a valuable feature for understanding how the behaviour of the whole plant can emerge from local rules. Conclusions The originality of CN-Wheat is that it proposes an integrated view of plant functioning based on a mechanistic approach. The formalization of each process can be further refined in the future as knowledge progresses. This approach is expected to strengthen our capacity to understand plant responses to their environment and investigate plant traits adapted to changes in agronomical practices or environmental conditions. A companion paper will evaluate the model. Key words: Amino acids, carbon, cytokinins, fructans, process-based functional–structural plant model, nitrogen, proteins, plant metabolism and physiology, sink–source relations, sucrose, Triticum aestivum, wheat. DOI: https://doi.org/10.1093/aob/mcw143

Abstract: Background and Aims Simulating resource allocation in crops requires an integrated view of plant functioning and the formalization of interactions between carbon (C) and nitrogen (N) metabolisms. This study evaluates the functional–structural model CN-Wheat developed for winter wheat after anthesis. Methods In CN-Wheat the acquisition and allocation of resources between photosynthetic organs, roots and grains are emergent properties of sink and source activities and transfers of mobile metabolites. CN-Wheat was calibrated for field plants under three N fertilizations at anthesis. Model parameters were taken from the literature or calibrated on the experimental data. Key Results The model was able to predict the temporal variations and the distribution of resources in the culm. Thus, CN-Wheat accurately predicted the post-anthesis kinetics of dry masses and N content of photosynthetic organs and grains in response to N fertilization. In our simulations, when soil nitrates were non-limiting, N in grains was ultimately determined by availability of C for root activity. Dry matter accumulation in grains was mostly affected by photosynthetic organ lifespan, which was regulated by protein turnover and C-regulated root activity. Conclusions The present study illustrates that the hypotheses implemented in the model were able to predict realistic dynamics and spatial patterns of C and N. CN-Wheat provided insights into the interplay of C and N metabolism and how the depletion of mobile metabolites due to grain filling ultimately results in the cessation of resource capture. This enabled us to identify processes that limit grain mass and protein content and are potential targets for plant breeding. Key words: Amino acids, carbon, cytokinins, fructans, process-based functional–structural plant model, nitrogen, proteins, plant metabolism and physiology, sink-source relations, sucrose, Triticum aestivum, wheat. DOI: https://doi.org/10.1093/aob/mcw144

Background: In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC–MS, LC–MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained. Aim of review (i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding. Key message Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance. Keywords: Breeding, Metabolic marker, Metabolomics, Plant performance, Prediction. DOI: 10.1007/s11306-016-1099-1

Abstract: The genome sequences of many important Triticeae species, including bread wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), remained uncharacterized for a long time because their high repeat content, large sizes, and polyploidy. As a result of improvements in sequencing technologies and novel analyses strategies, several of these have recently been deciphered. These efforts have generated new insights into Triticeae biology and genome organization and have important implications for downstream usage by breeders, experimental biologists, and comparative genomicists. transPLANT (http://www.transplantdb.eu) is an EU-funded project aimed at constructing hardware, software, and data infrastructure for genome-scale research in the life sciences. Since the Triticeae data are intrinsically complex, heterogenous, and distributed, the transPLANT consortium has undertaken efforts to develop common data formats and tools that enable the exchange and integration of data from distributed resources. Here we present an overview of the individual Triticeae genome resources hosted by transPLANT partners, introduce the objectives of transPLANT, and outline common developments and interfaces supporting integrated data access. DOI: 10.3835/plantgenome2015.06.0038

Abstract: The nuclear proteome of the grain of the two cultivated wheat species Triticum aestivum (hexaploid wheat; genomes A, B, and D) and T. monococcum (diploid wheat; genome A) was analyzed in two early stages of development using shotgun-based proteomics. A procedure was optimized to purify nuclei, and an improved protein sample preparation was developed to efficiently remove nonprotein substances (starch and nucleic acids). A total of 797 proteins corresponding to 528 unique proteins were identified, 36% of which were classified in functional groups related to DNA and RNA metabolism. A large number (107 proteins) of unknown functions and hypothetical proteins were also found. Some identified proteins may be multifunctional and may present multiple localizations. On the basis of the MS/MS analysis, 368 proteins were present in the two species, and in two stages of development, some qualitative differences between species and stages of development were also found. All of these data illustrate the dynamic function of the grain nucleus in the early stages of development. Keywords: cereal, grain development, bread wheat (Triticum aestivum), einkorn wheat (Triticum monococcum), LC−MS/MS, nuclear proteome. DOI: 10.1021/acs.jproteome.5b00446

Abstract: Wheat grain end-use value is determined by complex molecular interactions that occur during grain development, including those in the cell nucleus. However, our knowledge of how the nuclear proteome changes during grain development is limited. Here, we analyzed nuclear proteins of developing wheat grains collected during the cellularization, effective grain-filling, and maturation phases of development, respectively. Nuclear proteins were extracted and separated by two-dimensional gel electrophoresis. Image analysis revealed 371 and 299 reproducible spots in gels with first dimension separation along pH 4–7 and pH6–11 isoelectric gradients, respectively. The relative abundance of 464 (67%) protein spots changed during grain development. Abundance profiles of these proteins clustered in six groups associated with the major phases and phase transitions of grain development. Using nano liquid chromatography-tandem mass spectrometry to analyse 387 variant and non-variant protein spots, 114 different proteins were identified that were classified into 16 functional classes. We noted that some proteins involved in the regulation of transcription, like HMG1/2-like protein and histone deacetylase HDAC2, were most abundant before the phase transition from cellularization to grain-filling, suggesting that major transcriptional changes occur during this key developmental phase. The maturation period was characterized by high relative abundance of proteins involved in ribosome biogenesis. Data are available via ProteomeXchange with identifier PXD002999. Keywords: wheat, developing grain, nuclear proteins, 2D gel electrophoresis, LC-MS/MS. DOI: 10.3389/fpls.2015.00905

With the increasing amount of –omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a weboriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5. Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental –omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (Triticum aestivum L.) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered.We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of –omics data. The results are promising since they are consistent with what was previously established by the scientific community. DOI: 10.1371/journal.pone.0127127

Depuis son émergence à la fin du XIXème siècle, le secteur des semences a connu des évolutions importantes conduisant à une réorganisation générale de la recherche en amélioration des plantes. Le premier fait marquant de cette évolution concerne le positionnement relatif de la recherche publique et de la recherche privée. L'effort privé en recherche a augmenté suite aux évolutions réglementaires (DHS, VAT), à la mise en place de droits de propriété (COV) et, dans certains cas, au développement des semences hybrides. La recherche publique s'est alors repositionnée sur les domaines pour lesquels il existait des défaillances du marché (recherche amont, recherche méthodologique, segments orphelins). Le deuxième fait marquant de cette évolution concerne la structure interne du secteur des semences. Bien qu'il soit encore globalement peu concentré, ce secteur a vu progressivement émerger des acteurs majeurs ayant des positions fortes à la fois sur les semences et dans le domaine des biotechnologies, ces positions étant renforcées par le développement de brevets sur le vivant. Cette concentration croissante s'explique également par les coûts (fixes) croissants liés à la recherche, la réglementation, et la gestion de la propriété intellectuelle. Mots clefs: amélioration des plantes, biotechnologie, structure industrielle, recherche publique.