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

Summary

French viticulture nowadays faces several challenges, one of which is coping with the consequences of climate change while under growing social pressure to achieve a substantial reduction in the use of plant phytosanitary products. One of the solutions to this challenge is to create new varieties with long-lasting resistance to the most important grape pathogens and, at the same time, with good agronomic and wine quality performance. Such a concept has been shown to be feasible through the INRAE-ResDur breeding program, which has so far produced the first nine grape varieties to carry polygenic resistance against downy and powdery mildews. Although the ResDur program has substantially reduced the length of the breeding cycle, it still takes about 15 years to create a new variety. To accelerate the creation of new grape ideotypes that are disease resistant and adapted to climate change and regional environmental conditions, new approaches are needed, especially since the targeted traits often display complex genetic architectures that classic marker assisted selection (MAS) is unable to address. The recent introduction of breeding value prediction models in agriculture makes it possible to improve current breeding schemes. In brief, these models use a training panel for which phenotypic data as well as “omics” data such as molecular markers, metabolomic profiles, transcription information or near infra-red spectra (NIRS) are obtained. Statistical models are then inferred, linking phenotypic and “omics” data, and prediction equations are constructed and used to predict genetic breeding values in a selection population for which only “omics” data have been obtained. This thesis project aims to take advantage of the extensive corpus of breeding plant material created during the ResDur program, to build breeding value prediction models based on “omics” predictors that incorporate acquired information on the genetic architecture of the targeted traits, and to validate model accuracy by comparing outcomes to those of the ResDur program obtained through classic MAS techniques.

Contact

• Clementine Borrelli (UMR SVQV)
• Linkedin