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Genomic Selection in Hybrid Breeding

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Genomic Selection for Crop Improvement

Abstract

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.

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Abbreviations

BLUP:

Best linear unbiased prediction

e-Bayes:

Empirical Bayes method

GCA:

General combining ability

GS:

Genomic selection

LD:

Linkage disequilibrium

MAS:

Marker assisted selection

PS:

Phenotypic selection

RE:

Relative efficiency

REML:

Restricted maximum likelihood

RKHS:

Reproducing kernel Hilbert space

RR-BLUP:

Ridge regression best linear unbiased prediction

RRS:

Recurrent reciprocal selection

SCA:

Specific combining ability

SNP:

Single nucleotide polymorphism

W-BLUP:

Weighted best linear unbiased prediction

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Authors and Affiliations

  1. Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466, Gatersleben, Germany

    Albert Wilhelm Schulthess, Yusheng Zhao & Jochen C. Reif

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  1. Albert Wilhelm Schulthess
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  2. Yusheng Zhao
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  3. Jochen C. Reif
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Correspondence to Jochen C. Reif .

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Editors and Affiliations

  1. Center of Excellence in Genomics, Research Program - Genetic Gains, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India

    Rajeev K. Varshney

  2. Center of Excellence in Genomics, Research Program - Genetic Gains, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India

    Manish Roorkiwal

  3. Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA

    Mark E. Sorrells

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Schulthess, A.W., Zhao, Y., Reif, J.C. (2017). Genomic Selection in Hybrid Breeding. In: Varshney, R., Roorkiwal, M., Sorrells, M. (eds) Genomic Selection for Crop Improvement. Springer, Cham. https://doi.org/10.1007/978-3-319-63170-7_7

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