Optimizing the breeding scheme of new grapevine disease resistant varieties by genomic and phenomic selections

Summary

French viticulture is currently facing two major challenges: adapting to climate change and reducing the use of phytosanitary products. Among the available strategies, the development of new disease-resistant grapevine varieties represents a promising solution, as demonstrated by the first polygenic-resistant cultivars released through the INRAE-ResDur varietal innovation program. However, the long duration of the breeding cycle, estimated at around fifteen years, remains a major bottleneck limiting the rapid deployment of these innovations. Furthermore, future varietal ideotypes must not only combine disease resistance but also meet agronomic, technological, and oenological requirements adapted to the diversity of regional production contexts. Most traits underlying these ideotypes exhibit a complex genetic architecture, often governed by numerous loci with small effects, which limits the efficiency of marker-assisted selection (MAS). 

The emergence of genomic prediction and related statistical approaches provides new opportunities to accelerate and enhance the reliability of breeding programs. These approaches rely on a training population combining detailed phenotypic data with various types of omics datasets (SNP markers, NIR spectra, metabolites, etc.). Statistical models are then used to establish prediction equations linking omic data to phenotypes, allowing the prediction of genetic values for new candidates based solely on their omic profiles. 

The objectives of this thesis were to exploit populations from the INRAE-ResDur breeding program to (i) identify the genetic determinants of agronomic and oenological traits through QTL mapping and GWAS, (ii) evaluate the accuracy of various genomic, phenomic (NIR), and metabolomic (LC-MS) prediction models across biparental and multi-parental populations, and (iii) compare these approaches with conventional phenotypic selection already implemented in the program. The results demonstrated the feasibility and complementarity of omic-based selection approaches in grapevine breeding.

Based on these findings, a new integrated selection scheme was proposed, combining marker-assisted selection, genomic prediction, and phenomic prediction, reducing the breeding cycle from 15 to 9-10 years, increasing the genetic gain per unit time, and enabling the rapid integration of new resistance sources into grapevine improvement programs.

Contact

• Clementine Borrelli (UMR SVQV)
• Linkedin

Publications

• Borrelli, C., Delannoy, L., Chepca, H., Calcaterra, M., Chedid, E., Arnold, G., Dumas, V., Baltenweck, R., Maia-Gondard, A., Hugueney, P., Merdinoglu, D., Duchêne, E., and Avia, K. Comparative assessment of genomic, phenomic, and metabolomic prediction models in biparental grapevine breeding populations. Soumis à Sciences of plants et disponible sur BioRxiv avec le doi suivant : https://doi.org/10.1101/2025.10.24.684307
• Trapp, O., Avia, K., Borrelli, C., Eibach, R., Merdinoglu, D., & Töpfer, R. (2025). More sustainability in Europe's vineyards–Using resistant grapevine varieties to reduce the input of pesticides. Plants, People, Planet. https://doi.org/10.1002/ppp3.70038 (Soumis)
• Borrelli, C., Prado, E., Dumas, V., Arnold, G., Onimus, C., Butterlin, G., Jaegli, N., Wiedemann-Merdinoglu, S., Lacombe, M-C., Dorne, M-A., Umar-Faruk, A., Chaumonnot, S., Valentin, S., Ley, L., Reynard, J-S., Spring, J-L., Duchêne, E., Schneider, C., Merdinoglu, D., Avia, K., Uncovering the genetic basis of agronomic traits in over 1,000 grapevine genotypes derived from a disease resistance breeding program. Soumis à Horticulture Research L’article est disponible sur BioRxiv avec le doi suivant : https://doi.org/10.1101/2025.10.05.680539