The availability of high-density low-cost marker genotyping platforms in wheat and other crops has enabled a paradigm shift in plant breeding, by making genomic prediction and selection feasible. Genomic selection (GS) enables the prediction of breeding values of progeny lines without costly phenotyping, saving time and money, increasing intensity of selection as well as accuracy of trait prediction.
The current GS approach used by many national and international breeding programs is based on utilising a large number of genome-wide markers for obtaining genomic estimated breeding values (GEBV) to make selection decisions. While being useful, prediction accuracies from this approach are fairly low for complex disease resistances like stripe rust and Fusarium Head Blight (FHB), two of the most devastating plant diseases affecting European and North American wheat production.
WheatSustain uses the huge established knowledgebase of biologically relevant data, quantitative trait loci (QTL) and marker-trait relationships to improve prediction accuracy by including prior knowledge into the GS models.
New modeling concepts that improves genomic selection for disease resistance in wheat, focuing on major diseases like fusarium head blight (FHB) and stripe rust.
*At the time of the proposal. Please consider this data as an accurate estimate; it may vary during the project’s lifespan.
Total costs include in kind contribution by grant holders and can therefore be higher than the total requested funding.