Biology subjects

Coupel-Ledru, A.

Publications and source records attributed to Coupel-Ledru, A..

3 recordsLinked to original sources

bioRxiv2026

Genetic control of leaf carbon- and water-related traits in grapevine under greenhouse and field conditions

Understanding the genetic architecture of ecophysiological traits related to carbon gain and water loss is crucial to develop cultivars tolerant to abiotic stresses and that maintain productivity in the context of climate change. This can be achieved both through experiments in the greenhouse on potted plants under controlled conditions and in the field. However, the stability of genetic determinism of ecophysiological traits across environmental conditions has rarely been evaluated. We studied a grapevine diversity panel grown both in a greenhouse and in the field, to investigate the impact of growing conditions on leaf ecophysiological traits related to leaf morphology, fluorescence, gas exchange and water use efficiency. The correlation structure between traits was conserved within each environment even though, for most traits, cultivars ranking was not conserved between both environments. However, a set of cultivars, sharing the same functioning pattern in terms of gas exchange and fluorescence in both environments, was identified. Single- and multi-environment Genome-Wide Association Studies (GWAS), based on a dense genotyping matrix, were used to dissect the genetic architecture of leaf traits. The Quantitative Trait Loci (QTLs) identified through single-environment analyses differed substantially from those detected using multi-environment ones. Multi-environment approaches highlighted shared QTLs across environments for gas exchange and fluorescence traits. Candidate genes associated with carbon- and water-related functions were further identified in these genomic regions. Despite the weak correlation between greenhouse and field trait values, combining both environments enabled the identification of robust genetic regions associated with ecophysiological traits of interest.

plant biology↗

bioRxiv2025

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R{superscript 2} > 0.58), while the poro-fluorometer was efficient for predicting net CO2 assimilation (R{superscript 2} > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R{superscript 2} > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.

plant biology↗

bioRxiv2020

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

genetics↗