Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones - de Andrade, Luciano Rogério Braatz; Sousa, Massaine Bandeira e; Wolfe, Marnin; Jannink, Jean-Luc; de Resende, Marcos Deon Vilela; Azevedo, Camila Ferreira; de Oliveira, Eder Jorge (Frontiers in Plant Science, 2022)
Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield (FRY), dry root yield (DRY), as well as dry matter content (DMC) in cassava roots. The bias and predictive ability of the combinations of prediction methods G-BLUP, Bayes B, Bayes C, and RKHS with additive and additive-dominant genetic models were estimated. FRY and DRY exhibited predominantly dominant heritability, while DMC exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for DMC. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for FRY and DRY, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for FRY and DRY. For DMC, the highest predictive ability was obtained by G-BLUP with the additive genetic model. DMC exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for FRY, DRY, and DMC, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.