Perspectives for Genomic Selection Applications and Research in Plants

ABSTRACT Genomic selection (GS) has created a lot of excitement and expectations in the animal‐ and plant‐breeding research communities. In this review, we briefly describe how genomic prediction can be integrated into breeding efforts and point out achievements and areas where more research is need...

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Bibliographic Details
Published in:Crop science 2015-01, Vol.55 (1), p.1-12
Main Authors: Heslot, Nicolas, Jannink, Jean‐Luc, Sorrells, Mark E.
Format: Article
Language:English
Subjects:
Accuracy
Animal breeding
Breeding of animals
Experimental design
Genetic improvement
Genomes
Genomics
Phenotypes
Plant breeding
Resource allocation
Selective breeding
Statistical methods
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Summary:ABSTRACT Genomic selection (GS) has created a lot of excitement and expectations in the animal‐ and plant‐breeding research communities. In this review, we briefly describe how genomic prediction can be integrated into breeding efforts and point out achievements and areas where more research is needed. Genomic selection provides many opportunities to increase genetic gain in plant breeding per unit time and cost. Early empirical and simulation results are promising, but for GS to deliver genetic gains, careful consideration of the problem of optimal resource allocation is needed. Consideration of the cost‐benefit balance of using markers for each trait and stage of the breeding cycle is needed, moving beyond only focusing on recurrent selection with GS on a few complex traits, using prediction on unphenotyped individuals. With decreasing marker cost, phenotype data is quickly becoming the most valuable asset and marker‐assisted selection strategies should focus on making the most of scarce and expensive phenotypes. It is important to realize that markers can also improve accuracy of selection for phenotyped individuals. Use of markers as an aid to phenotype analysis suggests a number of new strategies in terms of experimental design and multi‐trait models. GS also provides new ways to analyze and deal with genotype by environment interactions. Lastly, we point to some recent results showing that new models are needed to improve predictions particularly with respect to the use of distantly related individuals in the training population.
ISSN:0011-183X
1435-0653
DOI:10.2135/cropsci2014.03.0249