Conventional breeding, marker-assisted selection, genomic selection and inbreeding in clonally propagated crops: a case study for cassava

Abstract

Key messageConsolidates relevant molecular and phenotypic information on cassava to demonstrate relevance of heterosis, and alternatives to exploit it by integrating different tools. Ideas are useful to other asexually reproduced crops.AbstractAsexually propagated crops offer the advantage that all genetic effects can be exploited in farmers’ production fields. However, non-additive effects complicate selection because, while influencing the performance of the materials under evaluation, they cannot be transmitted efficiently to the following cycle of selection. Cassava can be used as a model crop for asexually propagated crops because of its diploid nature and the absence of (known) incompatibility effects. New technologies such as genomic selection (GS), use of inbred progenitors based on doubled haploids and induction of flowering can be employed for accelerating genetic gains in cassava. Available information suggests that heterosis, non-additive genetic effects and within-family variation are relatively large for complex traits such as fresh root yield, moderate for dry matter or starch content in the roots, and low for defensive traits (pest and disease resistance) and plant architecture. The present article considers the potential impact of different technologies for maximizing gains for key traits in cassava, and highlights the advantages of integrating them. Exploiting heterosis would be optimized through the implementation of reciprocal recurrent selection. The advantages of using inbred progenitors would allow shifting the current cassava phenotypic recurrent selection method into line improvement, which in turn would allow designing outstanding hybrids rather than finding them by trial and error.

Sequencing wild and cultivated cassava and related species reveals extensive interspecific hybridization and genetic diversity

“…Largescale breeding efforts, such as the NextGen Cassava program 40,41 , will need to incorporate the impact of common introgressions in predictive genotype-phenotype models to realize the full power of genome-enabled approaches. npg r e s o u r c e METHODS Methods and any associated references are available in the online version of the paper.…”

Marker-based estimates reveal significant non-additive effects in clonally propagated cassava (Manihot esculenta): implications for the prediction of total genetic value and the selection of varieties

“…Prediction with additive models has recently been evaluated (Oliveira et al 2012;Ly et al 2013) and genomic selection using standard models is currently being tested (http://www.nextgencassava.org). Vegetatively propagated crop (e.g., cassava) breeding can exploit nonadditive genetic effects by identifying superior clones as varieties (Ceballos et al 2015).…”

“…Diallelic studies in cassava indicate that nonadditive genetic effects (e.g., specific combining ability) are strong, particularly for root yield traits (Cach et al 2005(Cach et al , 2006Calle et al 2005;Jaramillo et al 2005;Pérez et al 2005a,b;Zacarias and Labuschagne 2010;Kulembeka et al 2012;Tumuhimbise et al 2014;Ceballos et al 2015;Chalwe et al 2015). If the limited number of parents tested thus far represents the broader cassava breeding germplasm, genetic gains, especially for already lowheritability root yield traits, will be slow regardless of the breeding scheme employed (e.g., phenotypic vs. pedigree vs. genomic selection).…”

Perspectives on the Application of Next-generation Sequencing to the Improvement of Africa’s Staple Food Crops

“…This reduced breeding cycles and the cost of multi-environment testing. Strategies such as GS also allow simultaneous selection for multiple traits through a selection index [52,[116][117][118][119].…”

“…Given the long cycle of breeding, African staple crops such as cassava are set to benefit from GS approaches [117,118,120], where preliminary results have indicated reduced time of breeding cycle and reasonable prediction accuracy in some traits. Various ways of refining the prediction models via repeated phenotypic evaluations are being considered.…”