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Financement:  Plant2Pro Institut Carnot

Partenariat: CNRGV (William Marande)

Période : janvier 2023 > janvier 2025 (2 ans)

Contact EGFV: Marina de Miguel Vega

Coordination: EGFV Marina de Miguel Vega

Résumé: Viticulture is facing new challenges in the context of global change, where we can expect more stressful environmental conditions. The choice of rootstock is a potential leverage for vineyard adaptation to future environmental conditions as we can improve plant performance without modifying the scion variety, and consequently wine typicity. However, most of the rootstocks currently used today were selected more than a century ago and these rootstocks have a narrow genetic background, which could compromise their adaptation potential. Thus, there is a scope for in grapevine rootstock varieties by investigating wild Vitis species. The grapevine rootstock breeding program relies on traditional methods to screen the desired traits (i.e. biotic and abiotic stress resistance and rooting and grafting ability). Phenotyping these traits is a real challenge for a below-ground organ in a perennial crop. The objective of this project is to identify the molecular determinants (at genomic and transcriptomic level) of key traits which could be used for breeding grapevine rootstocks using a wider range of previously unexplored wild Vitis species. In this project, reference genomes for highly heterozygous wild grapevine species will be produced. This will allow us to survey genome structural variation and genetic polymorphisms in the regions of interest (identified through a QTL meta-analysis and RNA-seq study in roots under drought conditions). In addition, we will propose intermediate traits (transcripts) linked to complex root phenotypes. The results of this project will provide tools to accelerate the screening of desirable traits in rootstocks. The knowledge gained in this project will be of interest for screening rootstock performance in breeding programs and technical institutes. In addition, the molecular proxies could be used as performance diagnosis tool for wine growers and be inacorporated in yield prediction models.