Genomic selection: genome-wide prediction in plant improvement

• Genome selection (GS) considers marker effects across the whole genome.
• The use of high-density markers is one of the features of GS.
• GS is based on two distinct and related groups: training and breeding populations.
• Phenotyping is a key informant in GS to build up accuracy of statistical models.
• GS may revolutionize plant and tree breeding practices.

Association analysis is used to measure relations between markers and quantitative trait loci (QTL). Their estimation ignores genes with small effects that trigger underpinning quantitative traits. By contrast, genome-wide selection estimates marker effects across the whole genome on the target population based on a prediction model developed in the training population (TP). Whole-genome prediction models estimate all marker effects in all loci and capture small QTL effects. Here, we review several genomic selection (GS) models with respect to both the prediction accuracy and genetic gain from selection. Phenotypic selection or marker-assisted breeding protocols can be replaced by selection, based on whole-genome predictions in which phenotyping updates the model to build up the prediction accuracy.