Historique de l'amélioration de la canne à sucre et état de l'art des recherches en génétique d'association pour le rendement

La sélection de la canne à sucre est récente, elle commence lors de la création des premiers hybrides obtenus à Java au début du siècle dernier. La nobilisation, une méthode de croisements interspécifiques, permit l'obtention de variétés nettement plus performantes et résistantes aux principaux pathogènes de la canne. Depuis, les centres croisent des variétés élites et la sélection se fait à partir du phénotypage des descendances. Utiliser l'information moléculaire pour la sélection d'élites serait une réelle avancée pour le sélectionneur. Bien que de nombreuses études en génétique d'association soient réalisées, aucun marqueur n'est aujourd'hui utilisé dans les schémas de sélection de la canne à sucre. Les effets des marqueurs ne sont pas estimés de façon assez précise et doivent être validés dans des populations indépendantes. L'association marqueurs-caractères est aujourd'hui complétée par de nouvelles approches telles que la sélection génomique et l'utilisation des modèles écophysiologiques dans la prédiction du rendement. (Résumé d'auteur

Experimental validation of genomic selection in Sugarcane : P0197

Sugarcane cultivars (Saccharum spp.) are interspecific hybrids characterized by highly heterozygous and polyploid genome. Genomic selection (GS) approach is believed to be well suited for complex traits by including all markers in prediction models. Our objective was to test the GS approach in a complex polyploid crop. Predictions of genetic values were carried out on two independent panels, each composed of 167 cultivars and breeding materials covering the worldwide diversity. Accessions were genotyped using 1499 DArT. Phenotyping was carried out in Reunion for one panel and in Guadeloupe for the other one. We considered ten traits relative to sugar and fiber contents, digestibility and composition of the bagasse, plant morphology and disease resistances. We used seven predictive models: Bayesian Regression, Bayesian LASSO, Ridge-regression, BayesA, BayesB, Reproducing Kernel Hilbert Space and Partial Least Square Regression. Accuracies of predictions were assessed through correlations between observed and predicted genetic values, firstly by using a cross-validation within each panel, and secondly by using a cross-validation between panels. Accuracies of predictions were of similar value between the seven GS models for a given trait, while differences were observed among traits. Depending on the trait considered, the average GS accuracy values related to within-panel prediction ranged from 0.29 to 0.61 in the Reunion panel and from 0.13 to 0.5 in the Guadeloupe panel. GS accuracy values based on cross-validations between the two independent panels ranged from 0.13 (smut resistance) to 0.55 (brix). This study represents the first validation of GS approach in sugarcane with experimental data. (Résumé d'auteur

Machine Learning for Predicting Pulmonary Graft Dysfunction After Double-Lung Transplantation: A Single-Center Study Using Donor, Recipient, and Intraoperative Variables

Grade 3 primary graft dysfunction at 72 h (PGD3-T72) is a severe complication following lung transplantation. We aimed to develop an intraoperative machine-learning tool to predict PGD3-T72. We retrospectively analyzed perioperative data from 477 patients who underwent double-lung transplantation at a single center between 2012 and 2019. Data were structured into nine chronological steps, and supervised machine-learning models (XGBoost and logistic regression) were trained to predict PGD3-T72, with hyperparameters optimized via grid search and cross-validation. PGD3-T72 occurred in 83 patients (17.3%). XGBoost outperformed logistic regression, achieving peak performance at second graft implantation with an AUROC of 0.84 IQR: 0.065, p < 0.001, with a sensitivity of 0.81 and a specificity of 0.68. The top predictors included extracorporeal membrane oxygenation (ECMO) use, blood lactate levels, PaO2/FiO2 ratio, and total lung capacity mismatch. Subgroup analyses confirmed robustness across ECMO and non-ECMO cohorts. PGD3-T72 can be reliably predicted intraoperatively, offering potential for early intervention

Experimental assessment of the accuracy of genomic selection in sugarcane

Sugarcane cultivars are interspecific hybrids with an aneuploid, highly heterozygous polyploid genome. The complexity of the sugarcane genome is the main obstacle to the use of marker assisted selection in sugarcane breeding. Given the promising results of recent studies of plant genomic selection, we explored the feasibility of genomic selection in this complex polyploid crop. Genetic values were predicted in two independent panels, each composed of 167 accessions representing sugarcane genetic diversity worldwide. Accessions were genotyped with 1499 DArT markers. One panel was phenotyped in Reunion Island and the other in Guadeloupe. Ten traits concerning sugar and bagasse contents, digestibility and composition of the bagasse, plant morphology and disease resistance were used. We used four statistical predictive models: bayesian LASSO, ridge regression, reproducing kernel Hilbert space and partial least square regression. The accuracy of the predictions was assessed through the correlation between observed and predicted genetic values by cross - validation within each panel and between the two panels. We observed equivalent accuracy among the four predictive models for a given trait, and marked differences were observed among traits. Depending on the trait concerned, within - panel cross validation yielded median correlations ranging from 0.29 to 0.62 in the Reunion Island panel and from 0.11 to 0.5 in the Guadeloupe panel. Cross validation between panels yielded correlations ranging from 0.13 for smut resistance to 0.55 for brix. This level of correlations is promising for future implementations. Our results provide the first validation of genomic selection in sugarcane. (Texte intégral

Sélection assistée par marqueurs pour l'amélioration variétale de la canne à sucre : diversité génétique et phénotypique du germplasm cultivé, association mapping et sélection génomique

La création variétale est une composante majeure de l'intensification de la culture de la canne à sucre. Cette thèse apporte de nouveaux éléments de réponse quant à l'utilisation des marqueurs moléculaires couplés aux outils statistiques dans la sélection de la canne à sucre. Les travaux se basent sur un germplasm de canne représentant la diversité mondiale cultivée. Dans un premier temps, le rendement en saccharose a été décomposé en sept composantes pour analyser leurs contributions. L'influence des fonds génétiques ancestraux a également été étudiée. Nous avons mis en évidence que les contributions des composantes varient selon l'environnement. De plus, la proportion de génome provenant du parent S. spontaneum exerce un effet sur le rendement et ses composantes qui dépend de l'environnement : en conditions de fort rayonnement et de fortes températures, ce fond génétique a un effet négatif sur le rendement en saccharose, alors qu'il n'exerce aucun effet en conditions de faible rayonnement et basses températures. Ces résultats suggèrent que les efforts d'introgression de S. spontaneum ainsi que le choix des critères de sélection doivent s'adapter selon le milieu visé. Dans un deuxième temps, une étude de génétique d'association a été réalisée sur ce germplasm pour 12 caractères d'importance agronomique. La structure génétique de ce germplasm et principalement l'apparentement entre individus crée une inflation des statistiques de tests conduisant à de nombreuses fausses associations marqueur-trait. Des modèles linéaires mixtes utilisant la matrice Q ou des composantes principales comme cofacteurs, ainsi qu'une matrice de similarité en tant que matrice de variance-covariance, permettent de maîtriser ces inflations. Des marqueurs liés au rendement en saccharose, au taux de floraison ainsi qu'à la résistance à la rouille brune ont été détectés. Dans une troisième partie, nous avons évalué l'approche de sélection génomique pour la canne à sucre. Un deuxième germplasm indépendant a été utilisé afin de réaliser des validations croisées entre les deux panels. Quatre modèles de prédiction ont été utilisés. Ces modèles donnent des résultats similaires entre-eux. Les validations croisées intra-panel ont été obtenues avec des précisions allant de 0.11 pour la résistance au charbon à 0.62 pour le brix. Les validations croisées inter-panel ont été obtenues avec des précisions allant de 0.13 à 0.55 respectivement pour ces mêmes caractères. Ces niveaux de précisions sont prometteurs en vue d'une utilisation future pour la sélection de la canne. (Résumé d'auteur

Improvement of yield per se in sugarcane

Sugarcane is the main source of sucrose in the world and has also become one of the main sources of renewable energy, thanks to ethanol and electricity production. The main objectives of breeding programs are improving cane biomass and qualities. The genetics of current sugarcane cultivars (Saccharum spp.) are extremely complex, owing to a high polyploid genome of 10 to 12 homeologous chromosome sets, resulting from the interspecific origin of two polyploid ancestral genomes. A century of breeding efforts based on elite domesticated progenitors improved by wild introgressions has helped build one of the most productive biomass crops. However, in recent decades the percentage increase in annual sugarcane yield has been lower than in other crops. Until now, cultivar improvement has relied on traditional breeding programs that require 12 to 15 years of expensive field evaluations. This article discusses the most recent advances in genomics applications to support traditional breeding. In the past two decades, efforts have been made to construct genetic maps, study quantitative trait loci (QTL), and start association mapping studies. Today, genomic selection (GS) approaches to select individuals for advancement in the breeding process is believed to be appropriate for complex traits such as yield, thanks to improved estimates of marker effects combined with a better grasp of small-effect QTLs. GS is particularly attractive in the highly polyploid context of sugarcane, where the nature of yield genetic determinism is presumably highly quantitative. Frequent genotype x environment interactions add to the challenges associated with QTL detection. The focus in this article is on research areas that are likely to result in concrete improvements through advancing the incorporation of association genetics-based approaches. A strategy to design a breeder-friendly marker system is also presented. Plant growth modeling could provide sounder ecophysiological parameters for describing the complex biological process underlying yield and sucrose elaboration. Such models could overcome traditional problems caused by G x E interactions and consequently improve both QTL detection and GS approaches. (Résumé d'auteur

A Multiparameter and Multiscale Dataset to Study Sediments and Particle‐Bound Pesticide Dynamics of Hydric Transfers in a Wine‐Dominated Catchment

International audienceSediment transport plays a crucial role in water quality at the catchment scale. Yet, access to comprehensive datasets for research on sediment quantity and quality at different spatial scales remains limited. This paper introduces a comprehensive hydro‐sedimentological dataset on the Ardières‐Morcille catchment and scientific observatory (Beaujolais vineyard, France) available for the period 2020–2023. The observatory was monitored at three nested scales: the Saint‐Joseph plot (0.28 ha), the Morcille sub‐catchment (3.9 km$^2$), and the Ardières catchment (143 km$^ 2$). This dataset includes continuous monitoring of rainfall, water level, and turbidity at the three sites, from which discharge and suspended solids concentrations are derived. In addition, discontinuous yet integrated over time samples were collected for SPM grain size, major elements, and particle‐bound pesticides analysis. This dataset has made it possible to assess orders of magnitude for sediment and particle‐bound pesticides transfers and to interpret scale effects in time and space responsible for these transfers. We expect this dataset to be a valuable resource for the research community, supporting investigations into sediment transport processes, contaminant fluxes, and hydrological dynamics across multiple scales

Genome wide association mapping of agro-morphological and disease resistance traits in sugarcane

The objectives of the study were to assess genome wide association study (GWAS) for sugarcane on a panel of 183 accessions and to evaluate the impact of population structure and family relatedness on QTL detection. The panel was genotyped with 3327 AFLP, DArT and SSR markers and phenotyped for 13 traits related to agro-morphology, sugar yield, bagasse content and disease resistances. Marker-trait associations were detected using (i) general linear models that took population structure into account with either a Q matrix from STRUCTURE software or principal components from a principal component analysis added as covariates, and (ii) mixed linear models that took into account both population structure and family relatedness estimated using a similarity matrix K* computed using Jaccard's coefficient. With general linear models analysis, test statistics were inflated in most cases, while mixed linear models analysis allowed the inflation of test statistics to be controlled in most cases. When only detections in which both population structure and family relatedness were correctly controlled were considered, only 11 markers were significantly associated with three out of the 13. Among these 11 markers, six were linked to the major resistance gene Bru1, which has already been identified. Our results confirm that the use of GWAS is feasible for sugarcane in spite of its complex polyploid genome but also underline the need to take into account family relatedness and not only population structure. The small number of significant associations detected suggests that a larger population and/or denser genotyping are required to increase the statistical power of association detection. (Résumé d'auteur

Site and Saccharum spontaneum introgression level drive sugarcane yield component traits and their impact on sucrose yield in contrasted radiation and thermal conditions in La Réunion

Improving sucrose yield is one of the main objectives of sugarcane breeding. Splitting this complex traitinto yield components should make this task easier, as each component may be influenced in its own wayby environmental factors and by genetic background. Abiotic conditions experienced by sugarcane acrossits cropping areas differ in many respects; among them, water availability and photo-thermal conditionsparticularly affect sucrose yield formation.In this study, sucrose yield was divided into seven component traits and studied in a panel of 155 sugar-cane accessions phenotyped at two sites under contrasting photo-thermal conditions: one in low altitudeand the other in higher altitude. The accessions were hybrids developed during the last century and rep-resenting the worldwide cultivated genetic diversity. The proportion of Saccharum spontaneum genomein the genome of each accession was estimated by analyzing the genetic structure of the panel associ-ated with two outgroups formed by 19 S. spontaneum and 29 S. officinarum accessions genotyped with419 DArT markers and using a Bayesian clustering method implemented in STRUCTURE software. A K = 2number of clusters clearly separated S. spontaneum from S. officinarum, while the estimated proportionsof the S. spontaneum genome in the genome of hybrid accessions ranged from 0.5 to 0.Multivariate mixed model of log transformed yield components was adjusted to estimate each com-ponent's contribution to sucrose yield genetic variance, taking into account interrelationships amongcomponents. Each component's contribution to sucrose yield variance was site-dependent. On the lowaltitude site with high photo-thermal conditions, stalk section was the main contributor to yield variance,while on the high altitude site with low photo-thermal conditions, stalk height was the main contribu-tor. A linear regression showed that the estimated proportion of S. spontaneum genome in the hybrids'genome had significant effects on sucrose yield and its components. These effects also varied with the site:under low altitude conditions, the estimated proportion of S. spontaneum genome in the hybrid's genomeexerted a significant negative effect on sucrose yield, whereas no significant effect was found under highaltitude conditions. These results suggest that both efforts toward introgression and selection on yieldcomponents for sugarcane breeding purposes should depend on the targeted cropping environment. (Résumé d'auteur