Horti-Predict

Horticultural crops face the urgent need for climate adaptation and a shift towards a more sustainable production model. Genetic improvement through conventional breeding is one of the most viable and sustainable solutions yet is hampered by its slow pace. Predictive breeding, and the integration of new omics resources, is on the cutting-edge of breeding science and offers a potential solution to accelerate this process. While effective in major cereal crops, predictive breeding has been underutilised in horticulture. This project will harness this opportunity and develop novel predictive breeding methods by uniquely combining multi-omics data and advanced prediction models to speed up the breeding cycle to increase the rate of genetic improvement. To achieve this the applicant will (1) generate high-quality multi-omics data, (2) develop models incorporating multi-omics data and genotype-by-environment interaction terms in order to increase the predictive ability of the model, (3) validate the models and create haplotype catalogues in order to develop practical breeder-friendly tools. Grapevine and apple are two of the most important horticultural species in Europe and will be explored as use cases in the project, however the methodology and tools developed will also be highly relevant for other crops. Specifically, the project will result in three open-access outcomes (1) An end-to-end framework to produce high-quality omics data, (2) Advanced predictive breeding models, and (3) a deployable breeding tool in the form of a haplotype catalogue. All outcomes contribute to scientific advances in either breeding methodology or improved trait knowledge and collectively work towards increased genetic gain for enhanced adaptation of horticultural crops to future environments.