Biology subjects

Doligez, A.

Publications and source records attributed to Doligez, A..

7 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↗

bioRxiv2023

Impact of water deficit on single grapevine berry ripening

The effect of water deficit on grapevine fruit ripening has most often been addressed under the assumption that individual berries behave identically to their blend in the future harvest, both kinetically and metabolically. However, mixing unsynchronized berries, whose water and sucrose import pathways critically change according to their own developmental stages, intrinsically blurs the physiological and phenological effects of stress. We investigated the consequences of water deprivation on berry growth and primary metabolites content (glucose, fructose, tartaric and malic acids) on sixteen genetically distant genotypes of Vitis vinifera and fungus-tolerant hybrids submitted to 10 watering regimes, from well-watered to partial leaf shedding. Then, six genotypes were selected for comprehensive single berry analyses. Own-rooted potted plants bearing berries at the late herbaceous plateau stage were subjected to the different water treatments for four weeks in a greenhouse with automated regulation of soil water content. Berry and cluster growth were monitored by image analysis, before performing a final destructive sampling to determine berry weight and composition. Grape phenology was highly dependent on water availability. In some cultivars, ripening was considerably delayed or even prevented under well-watered conditions. These cultivars required an intermediate water deficit to trigger the second berry growth period along with sugar accumulation and malate breakdown, typical of the ripening process. Ripening still occurred in all genotypes upon severe water deprivation, although sugar accumulation and concentration were dramatically impaired, and the second growth period was annihilated or even replaced by shrivelling. Water deficit increased malate breakdown, uncoupling it from sugar accumulation. Single berry analyses suggest that although asynchronicity in berry ripening was reduced upon stress, individual fruits within the same cluster may undergo heterogeneous water budgets, expansion or shrivelling.

physiology↗

bioRxiv2021

Interest of phenomic prediction as an alternative to genomic prediction in grapevine

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of -0.2, thus suggesting that phenomic prediction could be applied for any trait.

genetics↗

bioRxiv2021

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting offspring within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

genetics↗

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↗

bioRxiv2020

Genome-wide association and prediction studies using a grapevine diversity panel give insights into the genetic architecture of several traits of interest

To cope with the challenges faced by agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennials such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait locus (QTL) in bi-parental crosses, we exploited a diverse panel of 279 Vitis vinifera L. cultivars. This panel planted in five blocks in the vineyard was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms (SNPs) by combining an 18K microarray and genotyping-by-sequencing (GBS). The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via GBS markedly increased the proportion of genetic variance explained by SNPs, and two multi-SNP models identified QTLs not found by a SNP-by-SNP model. Overall, 489 reliable QTLs were detected for 41% more response variables than by a SNP-by-SNP model with microarray-only SNPs, many new ones compared to the results from bi-parental crosses. Prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as QTL and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application in breeding are discussed.

genetics↗