12 research outputs found

    Data & Agriculture. What's going on ?

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    Genetic bases of variation in plant architecture and rhizobial partner choice along the pea domestication gradient

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    International audienceLegumes play an increasingly important role in sustainable agriculture due to their ability to form a beneficial symbiotic interaction with nitrogen-fixing Rhizobium bacteria. Legumes are also a valuable source of protein for both feed and food, but are not grown as extensively as expected in Europe due to their high yield variability. Many genomic approaches are being developed to improve stress tolerance traits. However, to date, little attention has been paid to improving the interaction between symbiotic partners. The establishment of the symbiotic interaction is a complex evolutionary process in which the interests of both partners are not always aligned. No evidence was found in pea for co-selection of competitiveness for nodulation and nitrogen (N) fixation efficiency (Bourion et al., 2018). Furthermore, several data indicated that N fixation and plant growth could be suboptimal in fields where pea is exposed to populations of heterogenous rhizobial strains with contrasting effects on nodule, root and shoot development (Laguerre et al., 2007). We performed Genome-Wide Association Studies to decipher the genetic determinants and relationships between the complex trait of pea choice between rhizobial strains in mixture and plant architecture. A large panel of 340 pea accessions including very diverse cultivars, wild accessions and landraces, all inoculated with the same mixture of 28 diverse rhizobial strains, was grown in two successive experiments, on a high throughput non-destructive phenotyping platform. The proportion of each strain in the nodules of each pea at harvest was determined by DNA metabarcoding, and 20 variables of nodulated root architecture or plant growth traits were estimated by image analysis or measured. The results highlighted differential variation and largely uncoupled genetic bases between rhizobial partner choice and architectural or growth traits, along the pea domestication gradient

    A genetic and molecular approach to identify transcription factors controlling maize root adaptive response to water deficit

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    International audienceWater stress is recognized as the most severe abiotic stress for agricultural productivity. Root traits play a key role in tolerance to water stress but have largely been neglected in selection schemes. In order to identify the maize genetic bases of the root adaptive responses to water deficit (WD), we used a MAGIC mapping population of 400 lines based on the intercrossing of 16 genotypes. The fine phenotyping of the different genotypes was performed under contrasting water supply on the French root phenotyping platform (4PMI). On the 16 founder genotypes, in addition of phenotyping, we sampled different root tissues daily over 7 days after irrigation arrest and performed RNAseq. On the basis of these 448 transcriptomes, we identified 6945 differentially expressed genes between axial and lateral roots and in response to WD and inferred a regulatory gene network to identify transcription factors (TF). Using a hierarchical clustering, we split the network in 35 clusters homogeneous in their expression pattern. Fine analysis of individual cluster pointed out, without prior knowledge, already known FTs responding to WD and identified new candidates. Functional validation of Arabidopisis orthologues has been initiated and many genotypes have an altered root developmental response to in vitro osmotic stress. In parallel, the phenotyping and a transcriptomic analysis by RNAseq of the genotypes of the mapping population under optimal conditions and water deficit enabled a GWAS and an eQTL analysis. Both approaches identified polymorphisms in genes of interest and identified SNPs colocating near transcription factors also identified by the gene network approach. Taken together all the data identified candidate genes and alleles potentially controlling adaptive root development that can be interesting target for breeding. This work was supported by the European Research Council (ERC) (HyArchi to CM; grant agreement No 788553

    A genetic and molecular approach to identify transcription factors controlling maize root adaptive response to water deficit

    No full text
    International audienceWater stress is recognized as the most severe abiotic stress for agricultural productivity. Root traits play a key role in tolerance to water stress but have largely been neglected in selection schemes. In order to identify the maize genetic bases of the root adaptive responses to water deficit (WD), we used a MAGIC mapping population of 400 lines based on the intercrossing of 16 genotypes. The fine phenotyping of the different genotypes was performed under contrasting water supply on the French root phenotyping platform (4PMI). On the 16 founder genotypes, in addition of phenotyping, we sampled different root tissues daily over 7 days after irrigation arrest and performed RNAseq. On the basis of these 448 transcriptomes, we identified 6945 differentially expressed genes between axial and lateral roots and in response to WD and inferred a regulatory gene network to identify transcription factors (TF). Using a hierarchical clustering, we split the network in 35 clusters homogeneous in their expression pattern. Fine analysis of individual cluster pointed out, without prior knowledge, already known FTs responding to WD and identified new candidates. Functional validation of Arabidopisis orthologues has been initiated and many genotypes have an altered root developmental response to in vitro osmotic stress. In parallel, the phenotyping and a transcriptomic analysis by RNAseq of the genotypes of the mapping population under optimal conditions and water deficit enabled a GWAS and an eQTL analysis. Both approaches identified polymorphisms in genes of interest and identified SNPs colocating near transcription factors also identified by the gene network approach. Taken together all the data identified candidate genes and alleles potentially controlling adaptive root development that can be interesting target for breeding. This work was supported by the European Research Council (ERC) (HyArchi to CM; grant agreement No 788553

    Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits

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    International audienceRoots are essential for water and nutrient uptake but are rarely the direct target of breeding efforts. To characterize the genetic variability of wheat root architecture, the root and shoot traits of 200 durum and 715 bread wheat varieties were measured at a young stage on a high-throughput phenotyping platform. Heritability of platform traits ranged from 0.40 for root biomass in durum wheat to 0.82 for the number of tillers. Field phenotyping data for yield components and SNP genotyping were already available for all the genotypes. Taking differences in earliness into account, several significant correlations between root traits and field agronomic performances were found, suggesting that plants investing more resources in roots in some stressed environments favored water and nutrient uptake, with improved wheat yield. We identified 100 quantitative trait locus (QTLs) of root traits in the bread wheat panels and 34 in the durum wheat panel. Most colocalized with QTLs of traits measured in field conditions, including yield components and earliness for bread wheat, but only in a few environments. Stress and climatic indicators explained the differential effect of some platform QTLs on yield, which was positive, null, or negative depending on the environmental conditions. Modern breeding has led to deeper rooting but fewer seminal roots in bread wheat. The number of tillers has been increased in bread wheat, but decreased in durum wheat, and while the root-shoot ratio for bread wheat has remained stable, for durum wheat it has been increased. Breeding for root traits or designing ideotypes might help to maintain current yield while adapting to specific drought scenarios
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