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

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 Cπ, 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.

New protocol for rapid cassava multiplication in field conditions: a perspective on speed breeding

“…Genomic selection involves selecting parents based on genomic estimated breeding values (GEBV) predicted at the seedling stage, without incorporating phenotypic data from the current generation. While this strategy reduces the cassava breeding cycle to 24 months (including the crossing stages), complex traits with a significant influence of non-additive effects, such as fresh root yield, dry root yield, and resistance to diseases like cassava brown streak disease (CBSD), exhibit low predictive accuracy ( Wolfe et al., 2017 ; Andrade et al., 2022 ; Ozimati et al., 2022 ). This is likely due to the fact that genomic prediction models have not been updated with phenotypic data from current generations.…”

Evaluating breeding for broad versus narrow adaptation for cassava in Nigeria using stochastic simulation

“…Dominance effects for all loci were derived as $$$$\begin{equation*}d\;\left( {\bf{x}} \right)\; = \;\mathop \sum \limits_{i\; = \;1}^{{n_{{\mathrm{QTL}}}}} \left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {{\delta _i}\; \times \;\left| {{a_i}} \right|,\;if\;\;{{\bf{x}}_i}\; = \;1}\\ {0,\;otherwise} \end{array} } \right.\end{equation*}$$$$the product of locus‐specific dominance degree (𝛿 i ) and absolute value of its additive effect a i . Dominance degrees were sampled from normal distribution with $${\delta _i}\sim \;N( {{\mu _\delta },\sigma _\delta ^2} )$$, where $${\mu _\delta }$$ is the mean dominance degree set to 0.20 to simulate positive directional dominance and $$\sigma _\delta ^2$$ is the dominance variance equal to 0.1 (de Andrade et al., 2022; Wolfe et al., 2016). A dominance degree of 0 denotes no dominance, and a dominance degree of 1 signifies complete dominance.…”

“…the product of locus-specific dominance degree (𝛿 i ) and absolute value of its additive effect a i . Dominance degrees were sampled from normal distribution with 𝛿 𝑖 ∼ 𝑁(𝜇 𝛿 , 𝜎 2 𝛿 ), where 𝜇 𝛿 is the mean dominance degree set to 0.20 to simulate positive directional dominance and 𝜎 2 𝛿 is the dominance variance equal to 0.1 (de Andrade et al, 2022;Wolfe et al, 2016). A dominance degree of 0 denotes no dominance, and a dominance degree of 1 signifies complete dominance.…”

The Development of Thematic Core Collections in Cassava Based on Yield, Disease Resistance, and Root Quality Traits

“…These attributes are of paramount importance in cassava selection programs. However, it is crucial to note that these traits are influenced by non-additive genetic effects, as highlighted by [32]. Therefore, it becomes imperative to employ selection methods that account for these effects in population improvement and the identification of genotypes for release as cultivars.…”

“…Therefore, it becomes imperative to employ selection methods that account for these effects in population improvement and the identification of genotypes for release as cultivars. In this context, the advancement of selective processes such as genomic selection can play a pivotal role in enabling the early selection of superior parents and clones for further breeding advancements [32]. By incorporating genomic selection strategies, breeders can enhance the efficiency of selection and expedite the development of high-yielding cassava cultivars with improved agronomic attributes.…”