Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses.

KEY MESSAGE: Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.

Dataset for the metabolic and physiological characterization of seeds from oilseed rape (Brassica napus L.) plants grown under single or combined effects of drought and clubroot pathogen Plasmodiophora brassicae.

Faced with the challenges of adapting agriculture to climate change, seed production should have increased resilience to abiotic stress factors and the expected proliferation of pathogens. This concerns both the nutritional quality and seed vigor, two crucial factors in seedling establishment and yield. Both qualities are acquired during seed development, but how environment influences the genetic and physiological determinisms of these qualities remains to be elucidated. With a world production of 71 Mt of seeds per year, oilseed rape (Brassica napus) is the third largest oleaginous crop. But its productivity must cope with several abiotic stresses, among which drought is one of the main constraints in current and future climate scenarios. In addition, clubroot disease, caused by the pathogen Plasmodiophora brassicae, leads to severe yield losses for the Brassica crops worldwide. Clubroot provokes the formation of galls on the infected roots that can restrict the flow of water and nutrients within the plant throughout the growth cycle. In order to get new insights into the impact of single or combined constraints on seed qualities, metabolic profiling assays were run for a collection of 330 seed samples (including developing, mature and imbibed seeds) harvested from plants of two B. napus cultivars ("Express" and "Montego") that were grown under either drought conditions, the presence of P. brassicae, or a combination of both stresses. Metabolites were identified and quantified by UPLC or GC. In addition, monitoring germination traits was conducted for 60 mature seed lots under in vitro conditions using an automated phenotyping platform. The present dataset contains the raw contents for 42 metabolites (nmol.mg-1 of seed dry weight) filtered and analyzed with statistical tests as well as germination speed and percentages. This dataset is available under accession at Data INRAE. These data will contribute to a better understanding of the crosstalk between the plant responses to water deprivation and/or pathogen attack and how it compromises seed quality. A better understanding of the molecular and physiological responses of the seed to (a)biotic stress on a molecular and physiological will be a first step to meet scientific and technological challenges of adapting seeds to their environment.

High throughput phenotyping dataset related to seed and seedling traits of sugar beet genotypes.

Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental conditions. More generally, these traits are also measured in the field of ecology as simple traits that can be correlated to other adaptative traits more difficult to measure on adult plants, as for example traits of the rooting system. Methods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth. The present dataset comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first sugar beet crop ontology. This dataset corresponds to the first automated phenotyping of a population of 198 genotypes and 4 commercial control varieties and is hosted on INRAE public depository under the reference number doi.org/10.15,454/AKNF4Q. The equipment and methods presented here are available on a phenotyping platform opened to collaborative research and adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping values can also be linked to genomic information to study the genetic determinism of the trait values.

Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds.

BACKGROUND: The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination. RESULTS: The results revealed that the LDA models had good accuracy in distinguishing 'Aged' and 'Non-aged' seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, 'Germinated' and 'Non-germinated' seeds with OCC of 81.80, 79.05 and 81.0%, 'Early germinated', 'Medium germinated' and 'Dead' seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give 'Normal' and 'Abnormal' seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner. CONCLUSION: The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality.

Determination of Germination Response to Temperature and Water Potential for a Wide Range of Cover Crop Species and Related Functional Groups.

A wide range of species can be sown as cover crops during fallow periods to provide various ecosystem services. Plant establishment is a key stage, especially when sowing occurs in summer with high soil temperatures and low water availability. The aim of this study was to determine the response of germination to temperature and water potential for diverse cover crop species. Based on these characteristics, we developed contrasting functional groups that group species with the same germination ability, which may be useful to adapt species choice to climatic sowing conditions. Germination of 36 different species from six botanical families was measured in the laboratory at eight temperatures ranging from 4.5-43°C and at four water potentials. Final germination percentages, germination rate, cardinal temperatures, base temperature and base water potential were calculated for each species. Optimal temperatures varied from 21.3-37.2°C, maximum temperatures at which the species could germinate varied from 27.7-43.0°C and base water potentials varied from -0.1 to -2.6 MPa. Most cover crops were adapted to summer sowing with a relatively high mean optimal temperature for germination, but some Fabaceae species were more sensitive to high temperatures. Species mainly from Poaceae and Brassicaceae were the most resistant to water deficit and germinated under a low base water potential. Species were classified, independent of family, according to their ability to germinate under a range of temperatures and according to their base water potential in order to group species by functional germination groups. These groups may help in choosing the most adapted cover crop species to sow based on climatic conditions in order to favor plant establishment and the services provided by cover crops during fallow periods. Our data can also be useful as germination parameters in crop models to simulate the emergence of cover crops under different pedoclimatic conditions and crop management practices.

Cellular changes during Medicago truncatula hypocotyl growth depend on temperature and genotype.

Hypocotyl growth is a key characteristic for plant emergence, influenced by environmental conditions, particularly temperature, and varying among genotypes. Cellular changes in Medicago truncatula hypocotyl were characterized to study the impact of the environment on heterotrophic growth and analyze differences between genotypes. The number and length of epidermal cells, ploidy levels, and sugar contents were measured in hypocotyls grown in the dark at 20 °C and 10 °C using two genotypes with contrasting maximum hypocotyl length. Hypocotyl elongation in the dark was due to cell elongation and not to an increase in cell number. A marked increase in cell ploidy level was observed just after germination and until mid elongation of the hypocotyl under all treatments. Larger ploidy levels were also observed in the genotype with the shorter hypocotyl and in cold conditions, but they were associated with larger cells. The increase in ploidy level and in cell volume was concomitant with a marked increase in glucose and fructose contents in the hypocotyl. Finally, differences in hypocotyl length were mainly due to different number of epidermal cells in the seed embryo, shown as a key characteristic of genotypic differences, whereas temperature during hypocotyl growth affected cell volume.

QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula.

Enhancing the knowledge on the genetic basis of germination and heterotrophic growth at extreme temperatures is of major importance for improving crop establishment. A quantitative trait loci (QTL) analysis was carried out at sub- and supra-optimal temperatures at these early stages in the model Legume Medicago truncatula. On the basis of an ecophysiological model framework, two populations of recombinant inbred lines were chosen for the contrasting behaviours of parental lines: LR5 at sub-optimal temperatures (5 or 10°C) and LR4 at a supra-optimal temperature (20°C). Seed masses were measured in all lines. For LR5, germination rates and hypocotyl growth were measured by hand, whereas for LR4, imbibition and germination rates as well as early embryonic axis growth were measured using an automated image capture and analysis device. QTLs were found for all traits. The phenotyping framework we defined for measuring variables, distinguished stages and enabled identification of distinct QTLs for seed mass (chromosomes 1, 5, 7 and 8), imbibition (chromosome 4), germination (chromosomes 3, 5, 7 and 8) and heterotrophic growth (chromosomes 1, 2, 3 and 8). The three QTL identified for hypocotyl length at sub-optimal temperature explained the largest part of the phenotypic variation (60% together). One digenic interaction was found for hypocotyl width at sub-optimal temperature and the loci involved were linked to additive QTLs for hypocotyl elongation at low temperature. Together with working on a model plant, this approach facilitated the identification of genes specific to each stage that could provide reliable markers for assisting selection and improving crop establishment. With this aim in view, an initial set of putative candidate genes was identified in the light of the role of abscissic acid/gibberellin balance in regulating germination at high temperatures (e.g. ABI4, ABI5), the molecular cascade in response to cold stress (e.g. CBF1, ICE1) and hypotheses on changes in cell elongation (e.g. GASA1, AtEXPA11) with changes in temperatures based on studies at the whole plant scale.