Genomic Selection in Hybrid Breeding



This chapter aims to outline the basic concepts underlying genomic selection (GS) in hybrid breeding. First, the concepts of dominance, heterosis, combining ability and heterotic groups are presented as a special feature of hybrid breeding, giving special attention to the breeding method of recurrent reciprocal selection. Subsequently, the cross-validated predictability is introduced as an evaluation criterion for the performance of GS and the relatedness between estimation and prediction sets is presented as its fundamental influential factor in hybrid breeding. Consequently, cross-validation schemes which consider different levels of relatedness according to particular breeding scenarios are illustratively explained. Later, classical mixed models and Bayesian GS approaches modeling dominance and additive effects receive special treatment in this chapter. Even though classical mixed models are in principle not suited for all genetic architectures, it seems they are preferred because of their relatively straightforward understanding and implementation plus their considerable robust performance. Moreover, modeling dominance in addition to additive effects seems to be beneficial when dominance effects are expected to have an important influence on predicted traits. GS models efficiently accommodating epistasis are available, but they have not received the attention needed to properly evaluate their advantages and limitations for hybrid performance prediction. Furthermore, other GS approaches are briefly introduced. Finally, the implementation of GS as a tool to assist hybrid breeding is dissected as an optimization problem, giving later emphasis to the model recalibration after implementing GS for the early stages of a breeding program.


Hybrid breeding Genomic selection Dominance Heterosis Combining ability Cross-validation Relatedness Predictability Implementation 



Best linear unbiased prediction


Empirical Bayes method


General combining ability


Genomic selection


Linkage disequilibrium


Marker assisted selection


Phenotypic selection


Relative efficiency


Restricted maximum likelihood


Reproducing kernel Hilbert space


Ridge regression best linear unbiased prediction


Recurrent reciprocal selection


Specific combining ability


Single nucleotide polymorphism


Weighted best linear unbiased prediction


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Breeding ResearchLeibniz Institute of Plant Genetics and Crop Plant Research (IPK)GaterslebenGermany

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