During breeding segregating populations are created, which prohibit rigorous statistical analyses. To circumvent this issue, we have developed a double haploid (DH) pipeline (in collaboration with PSB’s crop genome facility) which has the power to greatly assisted in the identification of causative gene combinations. The haploid inducer BREEDIT strategy (HI-BREEDIT) allows the generation of fixed multiple gene edited lines of different orders which can be propagated indeterminately and provide a powerful resource for genetical and molecular analyses.

Within BREEDIT most of our plants have their own unique mutation pattern, which prohibits the use of classical statistical analyses. Therefore we turn to modern machine learning technologies to integrate genotypic and phenotypic data to explain how genes work together to influence specific traits. We take special interest in gene redundancy and synergy our analyses.

Next to phenotyping, we also perform genotypic and transcriptomic analyses. We use multiplex amplicon sequencing to identify which genes are successfully edited and what their exact edit pattern is. This allows us to trace back through data analysis which edits are responsible for which phenotypes. 

The populations that result from multiplex genome editing, are being phenotyped and genotyped. For phenotyping, we rely on high-throughput phenotyping from seedlings (in growth rooms) to mature plants (in greenhouses and on the field).

The size control of multicellular organisms is an old biological question that has always fascinated scientists. At present, the question is still far from being solved because multiple molecular pathways are necessary for the formation of a mature organ and their interplay is highly complex. Our long-term goal is therefore to decipher the molecular networks and gene combinations that contribute to agronomic traits.