OPTIMIZING EUCALYPTS BREEDING STRATEGIES: SIMULATIONS OF GENOMIC AND PHENOTYPIC APPROACHES
Eucalyptus improvement; Genomic selection; Optimal cross selection (OCS); Stochastic simulation; Genetic gain
The search for more efficient strategies to manage diversity/variability and increase selection gains has driven the adoption of approaches such as genomic selection (GS) and optimal-cross selection (OCS) in plant breeding. In the context of forest tree breeding, these tools have great potential, since the late expression of traits of interest imposes long selection cycles. This study aimed to evaluate the application of genomic tools in comparison with classical breeding strategies through in silico simulations. We investigated the impact of key parameters on the recalibration of genomic models (including previous generations, number of repetitions, and the percentage of phenotyped individuals for recalibration) for traits of varying complexity. This complexity was simulated through two heritability scenarios and four traits combining different genetic architectures (additive and dominance effects) and numbers of QTLs. Subsequently, recurrent phenotypic selection strategies such as the traditional progeny test (TPT), the cloned progeny test (CPT), and their combinations with GS and OCS were compared. The results indicate that the number of individuals and the quality of phenotypes are the most decisive factors for the long-term performance of genomic selection models. Regarding the strategies, the use of genomic tools provided advantages over traditional phenotypic selection, regardless of the evaluated trait. The combination of CPT and OCS showed a good balance between gain and cost. However, the results emphasize the need to tailor selection strategies according to the specific objectives and operational constraints of each breeding program.