%0 Journal Article
%T The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes
%A Samuel A Clark
%A John M Hickey
%A Hans D Daetwyler
%A Julius HJ van der Werf
%J Genetics Selection Evolution
%D 2012
%I BioMed Central
%R 10.1186/1297-9686-44-4
%X Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.Genomic selection (GS) is a method that uses genomic information to estimate breeding values and rank selection candidates in livestock breeding programs. It has become widely used in some livestock industries e.g. dairy cattle and pig improvement programs. Initial studies on genomic evaluation have suggested that GS predicts the effects of markers in linkage disequilibrium (LD) with quantitative trait loci (QTL). This implies that accurate predicti
%U http://www.gsejournal.org/content/44/1/4