Bayesian optimisation for breeding schemes - Diot, Julien; Iwata, Hiroyoshi (Frontiers in Plant Science, 2023)
Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation. The results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms naive optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints; this suggests that determining a general "rule of thumb" for breeding optimisation may be difficult and that considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme.