An Integrated Decision Support System for Genomic Breeding

With the rapidly increasing performance of sequencing and genotyping technology, we are on the dawn of an era in which availability of genome-wide genotypic variation (“variomics”) will become reality for many economically important crops. On the one hand, this will lead to the identification of causal genomic factors that contribute to economically important traits. On the other hand, it will enable control over highly complex traits by selection, using an abundance of markers and without the need for knowledge on the causal biological mechanisms. 

The big data revolution is of great potential benefit for the plant breeding practice. In the coming years, the generation of variomics data for large germplasm collections and materials in breeding programs will become economically feasible. As a consequence, the pace of the breeding process itself will become more and more the limiting factor and time (i.e.breeding cycles) is then the most precious resource to a breeder.

We have developed deterministic algorithms that are capable of optimizing the logistics of breeding cycles and to customize breeding strategies using variomics and phenotypic data. The algorithms have been implemented in an integrated and user-friendly software environment. This allows the user to explore the solution space of possible breeding routes leading towards the next breeding target and to make informed selections of optimal routes based on logistic and biological constraints.