HYWHEAT: Genomic and metabolomic prediction of hybrid performance in soft winter wheat

Hybrid breeding in maize was a tremendous success story of a knowledge-driven increase and stabilization of grain yield. Hybrid breeding in wheat is currently hampered by the time- and resource-demanding production and testing of hybrid combinations and the lack of accurate methods to predict hybrid performance before evaluating crosses in intensive field trials. Advances in the development of high-throughput SNP technology and metabolite profiling facilitate now many innovative strategies towards an efficient genomics- and metabolomics based prediction of hybrid performance in wheat. Thus, we will establish hybrid prediction models based on high dense marker maps and metabolomic data. These prediction models will be validated with experimental data of > 1500 hybrids and their 120 parents within the project.