terclim by ICS banner
IVES 9 IVES Conference Series 9 International Congress on Grapevine and Wine Sciences 9 2ICGWS-2023 9 Identification of important genomic regions controlling resistance to biotic and abiotic stresses in Vitis sp. through QTL meta-analysis

Identification of important genomic regions controlling resistance to biotic and abiotic stresses in Vitis sp. through QTL meta-analysis

Abstract

In the context of global change, the environmental conditions are expected to be more stressful for viticulture. The choice of the rootstock may play a crucial role to improve the adaptation of viticulture to new biotic and abiotic threats (Ollat et al., 2016). However, the selection of interesting traits in rootstock breeding programs is complex because of the combination of multiple targets in a same ideotype. In this sense, the integration of studies about the genetic architecture for desired biotic and abiotic response traits allow us to identify genomic regions to combine and those with interesting pleiotropic effects. In this work we aimed to study the genetic determinism of several traits related to disease resistance and tolerance to abiotic stresses in Vitis sp. with a potential interest to be used as grapevine rootstocks. For this purpose, 30 genetic maps and QTL mapping data, available in the literature, were collected and combined with unpublished QTL for root traits obtained at EGFV lab. This information was used to construct a dense consensus genetic map of Vitis sp.. Then, a QTL meta-analysis was conducted using the software Biomercator. The obtained consensus genetic map, comprising information from different Vitis sp. is a useful genetic resource for translational genetics. In addition, the identified meta-QTLs, that combined information from independent studies, allowed to reduce QTL confidence intervals, notably for tolerance to abiotic stress traits. These results, highlight the interest of QTL meta-analysis to narrow-down the position of loci controlling desired traits for rootstock breeding programs, as previously proved for scions (Delfino et al., 2019).

References:

Ollat N. et al. (2016) Grapevine rootstocks: Origins and perspectives. Acta Horticulturae, 1136: 11-22. 10.17660/ActaHortic.2016.1136.2
Delfino, P. (2019) Selection of candidate genes controlling veraison time in grapevine through integration of meta-QTL and transcriptomic data. BMC Genomics, 20:1. https://doi.org/10.1186/s12864-019-6124-0

DOI:

Publication date: October 6, 2023

Issue: ICGWS 2023

Type: Poster

Authors

Elsa Chedid1*, Pierre Gastou2, Jean-Pascal Tandonnet1, Philippe Vivin1, Sarah Cookson1, Pierre-François Bert1, Nathalie Ollat1, Elisa Marguerit1, Marina de Miguel1

1 EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, 33882 Villenave d’Ornon, France
2 UMR SAVE, INRAE, BSA, ISVV, 33882 Villenave d’Ornon, France

Contact the author*

Keywords

biotic stress, abiotic stress, meta-analysis, QTL, Vitis sp

Tags

2ICGWS | ICGWS | ICGWS 2023 | IVES Conference Series

Citation

Related articles…

Reconstructing ancient microbial fermentation genomes from the wine residues of Herod, Roman king of Judea

The fortress of the Herodium, built towards the end of the first century BCE/ante Cristo, on the orders of Herod the Great, Roman client king of Judea, attests the expansion of Roman influence in the eastern Mediterranean. During archaeological excavations of the Herodium in 2017[1], a winery was discovered on the ground floor of the palace, with an assortment of clay vessels in situ, including large dolia – clay fermentation vessels each capable of fermenting up to 300-400 L of wine. Thanks to the recent progresses in the field of paleogenomics[2], we could analyse the organic material consistent with grape pomace at the bottom of these vessels, by extracting and sequencing the DNA using shotgun metagenomics and targeted capture, aiming for enrichment of DNA from fermentation associated microbes.

Prediction of aromatic attributes of red wines from its colour properties 

Wine perception is a multisensory experience that makes use of the sight, smell, and taste senses. When wine is sensorially assessed, the stimulus received generates multiple signals that tasters convert into organoleptic descriptors. Colour is commonly the first attribute evaluated during wine tasting. Moreover, the colour properties provide the taster with a priori information of the wine’s aroma. This preconceived perception is later confirmed or denied during the aroma evaluation.

Uncovering the interplay between Copper and SO2 tolerance in Saccharomyces cerevisiae

[lwp_divi_breadcrumbs home_text="IVES" use_before_icon="on" before_icon="||divi||400" module_id="publication-ariane" _builder_version="4.20.4" _module_preset="default" module_text_align="center" module_font_size="16px" text_orientation="center"...

Wine odors: chemicals, physicochemical and perceptive processes involved in their perception

The odors of wines are diverse, complex and dynamic and much research has been devoted to the understanding of their chemical bases. However, while the “basic” chemical part of the problem, namely the identity of the chemicals responsible for the different odor nuances, was satisfactorily solved years ago, there are some relevant questions precluding a clear understanding. These questions are related to the physicochemical interactions determining the effective volatilities of the odorants and, particularly, to the perceptual interactions between different odor molecules affecting in different ways to the final sensory outputs.

Analysis of the interaction of melatonin with glycolytic proteins in Saccharomyces cerevisiae during alcoholic fermentation 

Melatonin is a bioactive compound with antioxidant properties, that has been found in many fermented beverages, such as beer and wine [1]. Indeed, it has been shown that yeast can synthesize melatonin during alcoholic fermentation, although its role inside the cell, as well as the metabolic pathway involved in its synthesis, is still unclear [1]. Recent studies showed that during fermentation, melatonin interacts with different proteins of the glycolytic pathway in both Saccharomyces and non-Saccharomyces yeast, for instance glyceraldehyde 3-phosphate dehydrogenase, pyruvate kinase or enolase [2].