Further reductions in computation time could be achieved by performing a typical BLUP (best linear unbiased prediction) analysis with fewer iterations to estimate breeding values, using predetermined variance components.
Here, we compare the accuracy of prediction of genome-wide breeding values (GW-BV) for a sib-evaluated trait in a typical aquaculture population, assuming either IBS or IBD genomic relationship matrices, and by varying marker density and size of the training dataset.
In a typical multi-stage genomic procedure, such as that described by VanRaden [ 9], traditional breeding values are calculated using best linear unbiased prediction (as opposed to best linear unbiased n) (BLUP) methodology [ 10] for animals with genotyping information.
Using stochastic simulations, assuming a full-sib data structure typical for aquaculture breeding schemes, we compared the two designs with respect to accuracy of breeding values and their ability to produce accurate estimates of (co variance components of direct and social effects.
The W-FAM strategy uses the typical family-based aquaculture breeding structure with large full-sib families, to obtain accurate conventional breeding values for each family and combine them with genomic within-family breeding values, only using molecular markers to estimate the deviation from the family mean for each candidate.
Traditional quantitative trait loci (QTL -based prediction (QTL -basedker-assisted selection (MAS) is defined as a two-stepredictionto predict breeding values (BVs) of untested lines for traits of interest based on QPL inentified from a typical biparental plant breeding population.
To avoid bias problems typical of animal threshold models [ 20], genetic (co variance components were estimated with an algorithm that was based on parental breeding values only [ 21], while all other dispersion and location parameters were estimated as in a standard animal threshold model.
Estimated breeding values from routine breeding estimation were used for fertility traits.
It gives higher accuracy of breeding values and does not require a complete restructuring of the breeding program.
The availability of genomic estimated breeding values (GEBV) allows for possible modifications to existing dairy cattle breeding programs.
Breeding values were estimated for individual trees and their parents.
