Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population

Background

A single-step blending approach allows genomic prediction using information ofgenotyped and non-genotyped animals simultaneously. However, the combinedrelationship matrix in a single-step method may need to be adjusted becausemarker-based and pedigree-based relationship matrices may not be on the samescale. The same may apply when a GBLUP model includes both genomic breeding valuesand residual polygenic effects. The objective of this study was to comparesingle-step blending methods and GBLUP methods with and without adjustment of thegenomic relationship matrix for genomic prediction of 16 traits in the NordicHolstein population.

Methods

The data consisted of de-regressed proofs (DRP) for 5 214 genotyped and 9 374non-genotyped bulls. The bulls were divided into a training and a validationpopulation by birth date, October 1, 2001. Five approaches for genomic predictionwere used: 1) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3)an adjusted GBLUP method with a polygenic effect, 4) a single-step blendingmethod, and 5) an adjusted single-step blending method. In the adjusted GBLUP andsingle-step methods, the genomic relationship matrix was adjusted for thedifference of scale between the genomic and the pedigree relationship matrices. Aset of weights on the pedigree relationship matrix (ranging from 0.05 to 0.40) wasused to build the combined relationship matrix in the single-step blending methodand the GBLUP method with a polygenetic effect.

Results

Averaged over the 16 traits, reliabilities of genomic breeding values predictedusing the GBLUP method with a polygenic effect (relative weight of 0.20) were 0.3%higher than reliabilities from the simple GBLUP method (without a polygeniceffect). The adjusted single-step blending and original single-step blendingmethods (relative weight of 0.20) had average reliabilities that were 2.1% and1.8% higher than the simple GBLUP method, respectively. In addition, the GBLUPmethod with a polygenic effect led to less bias of genomic predictions than thesimple GBLUP method, and both single-step blending methods yielded less bias ofpredictions than all GBLUP methods.

Conclusions

The single-step blending method is an appealing approach for practical genomicprediction in dairy cattle. Genomic prediction from the single-step blendingmethod can be improved by adjusting the scale of the genomic relationshipmatrix.