Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones

Authors: BRAATZ DE ANDRADE, LUCIANO - Universidade Federal De Vicosa; BANDEIRA E SOUSA, MASSAINE - Embrapa; WOLFE, MARNIN - Auburn University; Jannink, Jean-Luc; VILELA DE RESENDE, MARCOS - Universidade Federal De Vicosa; AZEVEDO, CAMILA - Universidade Federal De Vicosa; DE OLIVEIRA, EDER - Embrapa

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/2/2022
Publication Date: 12/16/2022
Citation: Braatz De Andrade, L.R., Bandeira E Sousa, M., Wolfe, M., Jannink, J., Vilela De Resende, M.D., Azevedo, C.F., De Oliveira, E.J. 2022. Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones. Frontiers in Plant Science. 13.1071156. https://doi.org/10.3389/fpls.2022.1071156.
DOI: https://doi.org/10.3389/fpls.2022.1071156

Interpretive Summary: Predicting variety traits from DNA marker data has been promising in situations where measuring traits is expensive, laborious, and/or inefficient. We evaluated several prediction methods with the goal of increasing cassava fresh root yield, dry root yield, and root dry matter content. For yield, models designed to predict more complicated genetic interactions were slightly more accurate. For dry matter content, the highest predictive ability was obtained with the simplest model. The prediction methods allow breeders to shorten the breeding cycle. Because of that, they may increase gain per unit time by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait should be chosen, thereby accelerating the release of new varieties.

Technical Abstract: 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, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cp, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, 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 fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content 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 fresh root yield, dry root yield, and dry matter content, 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.