It describes the merit of indirect selection as a function of the selection cycle lengths.
Therefore, the sum of all SNP effects (direct genomic value, DGV) will be a good predictor of the genetic merit of selection candidates and will enable selection decisions as soon as the genomic information of those individuals is available [ 4].
These methods imply selection on quadratic indices as the selection merit of a particular individual is a quadratic function of its estimated breeding value.
The merit of indirect selection per unit time, relative to the merit of direct selection, can be described as the indirect selection response (CR X) divided by the direct selection response (R X).
Correlation-based Feature Subset (CFS) selection [29], a multivariate feature selection that evaluates the merit of a probe set subset by measuring the individual predictive power of each probe set along with the redundancy within that subset, was then used.
Conceptually, it involves two phases, a training phase where the genotypic or haplotypic effects are estimated, typically as random effects, in a mixed model scenario, followed by an application phase where the genomic merit of selection candidates is predicted from the knowledge on their genotypes and previously estimated effects from the training phase.
Some authors [ 21, 22] reported a slight advantage of using a multi-breed TP to evaluate the genetic merit of animals under selection.
Genomic prediction refers to the prediction of genetic merit of selection candidates based on genome-wide marker genotypes using information from a reference population of individuals with both phenotypes and genotypes [ 1].
This is expected to increase the prediction accuracy, and thereby the merit of selection based on genomic predictions, even further.
Animal breeding applications commonly involve the fitting of linear mixed models in order to estimate genetic and phenotypic variation or to predict the genetic merit of selection candidates.
Progeny testing is a strategy that is commonly used to increase the accuracy of the predicted genetic merit of selection candidates but it increases generation intervals from 2.5 years when using DGV to about 5.5 years [ 28].
