Institut für Züchtungsforschung an landwirtschaftlichen Kulturen
Potato starch is a multipurpose industrial ingredient and constitutes a significant component of the national bioeconomic strategy of Germany. Compared to potato traits with mostly monogenic inheritance such as several disease resistance traits, improvement of quantitatively inherited traits like tuber and starch yield remains challenging for potato breeders. Autotetraploidy, low multiplication factor and 50+ traits with relevance for selection further hamper progress in potato breeding. Modern predictive approaches thus promise to increase the gain from selection of quantitative traits in potato. In a previous project phase (PotatoTools) we developed genomic and statistical tools for predictive starch potato breeding that included preliminary prediction models. The major aim of the current project (PotatoPredict) is the application of these resources and models to generate further fundamental insights on the optimal application of these predictive approaches in potato breeding. More specifically, we aim at (i) increasing the representativeness of the set of structural genomic variation by expanding the resequencing panel, (ii) estimating the precision of predictions across germplasm groups with differing relatedness, as well as prediction accuracy of population mean and segregation variance values, (iii) evaluating the prediction accuracy of models incorporating spectral data as predictors instead of molecular genetic data, and (iv) optimizing potato breeding programs with regards to the applied breeding scheme and the optimized balance between short and long-term response to selection. The knowledge generated in this project will further improve predictive models and function as stepping stone for routine application of predictive breeding approached in potato.
Federal Ministry of Food and Agriculture
