Effects of Different Methods and Genomic Relationship Matrices on Reliabilities of Genomic Selection in Dairy Cattle

Abstract

Since genomic prediction is widely used in dairy cattle, we aimed to evaluate the performance of pedigree based (ABLUP), SNP based (GBLUP) and single-step GBLUP (ss-GBLUP) methods with different sets of information in terms of reliability of genomic prediction. Four different methods were evaluated: (Method 1) ABLUP with all available phenotypes and pedigree; (Method 2) GBLUP with SNP genotypes and phenotypes of genotyped cows; (Method 3) single-step GBLUP with SNP genotypes, phenotypes of genotyped cows and all pedigree and (Method 4) single-step GBLUP with SNP genotypes, all phenotypes of both genotyped and nongenotyped cows and all pedigree. SNP based methods also used different genomic relationship matrices (GRMs) formed by different approaches: vanRaden, Astle, Yang and Endelman. The simulated dataset replicates a common dairy cattle population. A significant increase in reliability of prediction was observed in ss-GBLUP with all phenotypes and pedigree beside genotyped cows. This increase was apparent for both first lactation milk yield (LMY) and milk fat percentage (Fat%). Combining all available information with ss-GBLUP gave about 1.6 and 1.2 times higher reliabilities for LMY and Fat%, respectively, compared to those obtained from the other three methods.