Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat.

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.

A data-driven simulation platform to predict cultivars' performances under uncertain weather conditions.

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.

Functional mapping of N deficiency-induced response in wheat yield-component traits by implementing high-throughput phenotyping.

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.

Epistatic determinism of durum wheat resistance to the wheat spindle streak mosaic virus.

KEY MESSAGE: The resistance of durum wheat to the Wheat spindle streak mosaic virus (WSSMV) is controlled by two main QTLs on chromosomes 7A and 7B, with a huge epistatic effect. Wheat spindle streak mosaic virus (WSSMV) is a major disease of durum wheat in Europe and North America. Breeding WSSMV-resistant cultivars is currently the only way to control the virus since no treatment is available. This paper reports studies of the inheritance of WSSMV resistance using two related durum wheat populations obtained by crossing two elite cultivars with a WSSMV-resistant emmer cultivar. In 2012 and 2015, 354 recombinant inbred lines (RIL) were phenotyped using visual notations, ELISA and qPCR and genotyped using locus targeted capture and sequencing. This allowed us to build a consensus genetic map of 8568 markers and identify three chromosomal regions involved in WSSMV resistance. Two major regions (located on chromosomes 7A and 7B) jointly explain, on the basis of epistatic interactions, up to 43% of the phenotypic variation. Flanking sequences of our genetic markers are provided to facilitate future marker-assisted selection of WSSMV-resistant cultivars.

Bread Wheat (Triticum aestivum L.) Grain Protein Concentration Is Related to Early Post-Flowering Nitrate Uptake under Putative Control of Plant Satiety Level.

The strong negative correlation between grain protein concentration (GPC) and grain yield (GY) in bread wheat complicates the simultaneous improvement of these traits. However, earlier studies have concluded that the deviation from this relationship (grain protein deviation or GPD) has strong genetic basis. Genotypes with positive GPD have an increased ability to uptake nitrogen (N) during the post-flowering period independently of the amount of N taken up before flowering, suggesting that genetic variability for N satiety could enable the breakage of the negative relationship. This study is based on two genotypes markedly contrasted for GPD grown under semi-hydroponic conditions differentiated for nitrate availability both before and after flowering. This allows exploration of the genetic determinants of post-flowering N uptake (PANU) by combining whole plant sampling and targeted gene expression approaches. The results highlights the correlation (r² = 0.81) with GPC of PANU occurring early during grain development (flowering-flowering + 250 degree-days) independently of GY. Early PANU was in turn correlated (r² = 0.80) to the stem-biomass increment after flowering through its effect on N sink activity. Differences in early PANU between genotypes, despite comparable N statuses at flowering, suggest that genetic differences in N satiety could be involved in the establishment of the GPC. Through its strong negative correlation with genes implied in N assimilation, root nitrate concentration appears to be a good marker for evaluating instantaneous plant N demand, and may provide valuable information on the genotypic N satiety level. This trait may help breeders to identify genotypes having high GPC independently of their GY.

Post-flowering nitrate uptake in wheat is controlled by N status at flowering, with a putative major role of root nitrate transporter NRT2.1.

In bread wheat (Triticum aestivum L.), the simultaneous improvement of both yield and grain protein is difficult because of the strong negative relationship between these two traits. However, some genotypes deviate positively from this relationship and this has been linked to their ability to take up nitrogen (N) during the post-flowering period, regardless of their N status at flowering. The physiological and genetic determinants of post-flowering N uptake relating to N satiety are poorly understood. This study uses semi-hydroponic culture of cv. Récital under controlled conditions to explore these controls. The first objective was to record the effects of contrasting N status at flowering on post-flowering nitrate (NO3⁻) uptake under non-limiting NO3⁻ conditions, while following the expression of key genes involved in NO3⁻ uptake and assimilation. We found that post-flowering NO3⁻ uptake was strongly influenced by plant N status at flowering during the first 300-400 degree-days after flowering, overlapping with a probable regulation of nitrate uptake exerted by N demand for growth. The uptake of NO3⁻ correlated well with the expression of the gene TaNRT2.1, coding for a root NO3⁻ transporter, which seems to play a major role in post-flowering NO3⁻ uptake. These results provide a useful knowledge base for future investigation of genetic variability in post-flowering N uptake and may lead to concomitant gains in both grain yield and grain protein in wheat.

Efficiently tracking selection in a multiparental population: the case of earliness in wheat.

Multiparental populations are innovative tools for fine mapping large numbers of loci. Here we explored the application of a wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population for QTL mapping. This population was created by 12 generations of free recombination among 60 founder lines, following modification of the mating system from strict selfing to strict outcrossing using the ms1b nuclear male sterility gene. Available parents and a subset of 380 SSD lines of the resulting MAGIC population were phenotyped for earliness and genotyped with the 9K i-Select SNP array and additional markers in candidate genes controlling heading date. We demonstrated that 12 generations of strict outcrossing rapidly and drastically reduced linkage disequilibrium to very low levels even at short map distances and also greatly reduced the population structure exhibited among the parents. We developed a Bayesian method, based on allelic frequency, to estimate the contribution of each parent in the evolved population. To detect loci under selection and estimate selective pressure, we also developed a new method comparing shifts in allelic frequency between the initial and the evolved populations due to both selection and genetic drift with expectations under drift only. This evolutionary approach allowed us to identify 26 genomic areas under selection. Using association tests between flowering time and polymorphisms, 6 of these genomic areas appeared to carry flowering time QTL, 1 of which corresponds to Ppd-D1, a major gene involved in the photoperiod sensitivity. Frequency shifts at 4 of 6 areas were consistent with earlier flowering of the evolved population relative to the initial population. The use of this new outcrossing wheat population, mixing numerous initial parental lines through multiple generations of panmixia, is discussed in terms of power to detect genes under selection and association mapping. Furthermore we provide new statistical methods for use in future analyses of multiparental populations.

Modelling the effect of wheat canopy architecture as affected by sowing density on Septoria tritici epidemics using a coupled epidemic-virtual plant model.

BACKGROUND AND AIMS: The relationship between Septoria tritici, a splash-dispersed disease, and its host is complex because of the interactions between the dynamic plant architecture and the vertical progress of the disease. The aim of this study was to test the capacity of a coupled virtual wheat-Septoria tritici epidemic model (Septo3D) to simulate disease progress on the different leaf layers for contrasted sowing density treatments. METHODS: A field experiment was performed with winter wheat 'Soissons' grown at three contrasted densities. Plant architecture was characterized to parameterize the wheat model, and disease dynamic was monitored to compare with simulations. Three simulation scenarios, differing in the degree of detail with which plant variability of development was represented, were defined. KEY RESULTS: Despite architectural differences between density treatments, few differences were found in disease progress; only the lower-density treatment resulted in a slightly higher rate of lesion development. Model predictions were consistent with field measurements but did not reproduce the higher rate of lesion progress in the low density. The canopy reconstruction scenario in which inter-plant variability was taken into account yielded the best agreement between measured and simulated epidemics. Simulations performed with the canopy represented by a population of the same average plant deviated strongly from the observations. CONCLUSIONS: It was possible to compare the predicted and measured epidemics on detailed variables, supporting the hypothesis that the approach is able to provide new insights into the processes and plant traits that contribute to the epidemics. On the other hand, the complex and dynamic responses to sowing density made it difficult to test the model precisely and to disentangle the various aspects involved. This could be overcome by comparing more contrasted and/or simpler canopy architectures such as those resulting from quasi-isogenic lines differing by single architectural traits.