Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization. It allows likely causal markers to account for a certain portion of genetic variance independently from the remaining markers. We systematically tested a list of 5110 GO terms for their predictive performance for physiological (platform traits) and productivity traits (field grain yield) in a maize (Zea mays L.) panel using genomic features best linear unbiased prediction (GFBLUP) model. Predictive abilities were compared to the classical genomic best linear unbiased prediction (GBLUP). Predictive gains with categorizing markers based on a given GO term strongly depend on the trait and on the growth conditions, as a term can be useful for a given trait in a given condition or somewhat similar conditions but not useful for the same trait in a different condition. Overall, results of all GFBLUP models compared to GBLUP show that the former might be less efficient than the latter. Even though we could not identify a prior criterion to determine which GO terms can offer benefit to a given trait, we could a posteriori find biological interpretations of the results, meaning that GFBLUP could be helpful if more about the gene functions and their relationships with the growth conditions was known.