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.

2HiGWAS: a unifying high-dimensional platform to infer the global genetic architecture of trait development.

Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits.

A reaction norm model for genomic selection using high-dimensional genomic and environmental data.

New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17-34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

In search of tetraploid wheat accessions reduced in celiac disease-related gluten epitopes.

Tetraploid wheat (durum wheat) is mainly used for the preparation of pasta. As a result of breeding, thousands of tetraploid wheat varieties exist, but also tetraploid landraces are still maintained and used for local food preparations. Gluten proteins present in wheat can induce celiac disease, a T-cell mediated auto-immune disorder, in genetically predisposed individuals after ingestion. Compared to hexaploid wheat, tetraploid wheat might be reduced in T-cell stimulatory epitopes that cause celiac disease because of the absence of the D-genome. We tested gluten protein extracts from 103 tetraploid wheat accessions (obtained from the Dutch CGN genebank and from the French INRA collection) including landraces, old, modern, and domesticated accessions of various tetraploid species and subspecies from many geographic origins. Those accessions were typed for their level of T-cell stimulatory epitopes by immunoblotting with monoclonal antibodies against the α-gliadin epitopes Glia-α9 and Glia-α20. In the first selection, we found 8 CGN and 6 INRA accessions with reduced epitope staining. Fourteen of the 57 CGN accessions turned out to be mixed with hexaploid wheat, and 5 out of the 8 selected CGN accessions were mixtures of two or more different gluten protein chemotypes. Based on single seed analysis, lines from two CGN accessions and one INRA accession were obtained with significantly reduced levels of Glia-α9 and Glia-α20 epitopes. These lines will be further tested for industrial quality and may contribute to the development of safer foods for celiac patients.

Environment characterisation for the interpretation of environmental effect and genotype x environment interaction.

Increasing attention is being paid to environment characterisation as a means of identifying the environmental factors determining grain protein content (GPC) in durum wheat. New insights in crop physiology and agronomy have led to the development of crop simulation models. Those models can reconstruct plant development for past cropping seasons. One major advantage of these models is that they can also indicate the intensity of limiting factors affecting plants during particular developmental stages. The main environmental factors determining GPC in durum wheat can be investigated by introducing the intensity of limiting factors into genotype x environment (GxE) models. In our case, limiting factors corresponding to water deficit and nitrogen availability were calculated for the development period between booting and heading. These variables were then introduced into a clustering model. This model is an extension of factorial regression applied to discrete environment and genotypic variables. This procedure effectively described the environment main effect: around 30.9% of the sum of squares of the environment main effect was accounted for, using less than 33% of the degrees of freedom. It also partially accounted for GxE interaction. Our methodology, coupling the use of crop simulation and GxE analysis models, is of potential value for improving our understanding of the main development stages and identification of environmental limiting factors for the development of GPC.