Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

Crossa, J and Pérez-Rodríguez, P and Cuevas, J and Montesinos-López, O and Jarquín, D and de los Campos, G and Burgueño, J and González-Camacho, J M and Pérez-Elizalde, S and Beyene, Y and Dreisigacker, S and Singh, R and Zhang, X and Gowda, M and Roorkiwal, M and Rutkoski, J and Varshney, R K (2017) Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends in Plant Science, 22 (11). pp. 961-975. ISSN 13601385

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Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype × environment (G × E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.

Item Type: Article
Divisions: Research Program : Genetic Gains
Uncontrolled Keywords: genomic selection; genomic-enabled prediction accuracy; model complexity; models for genomic genotype × environment interaction; genomic selection and genetic gains in crop breeding populations
Subjects: Others > Food Production
Others > Plant Breeding
Others > Genetics and Genomics
Others > Climate Change
Others > Germplasm
Depositing User: Mr Ramesh K
Date Deposited: 16 Nov 2017 09:59
Last Modified: 28 Aug 2018 04:04
Official URL:
Acknowledgement: The authors are grateful to their colleagues, collaborators, and by using field and lab technician from CIMMYT and ICRISAT, as well as scientists in National Programs who collected the valuable data used in the various studies. The authors would like to thank Kevin Pixley, Daniel Gianola, and four anonymous reviewers for their positive, careful, and detailed reviews of the manuscript; their comments, editing, and suggestions significantly improved the quality and readability of the manuscript. The authors also thank Michael Listman for editing the revised version of the manuscript.
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