Comparison of genomic predictions using medium-density (~54,000) and high-density (~777) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations

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This study investigated genomic prediction using medium density (54k) and high density marker panels (777k), based on data from Nordic Holstein and Red dairy cattle (RDC). The Holstein data comprised 4,539 progeny-tested bulls, and the RDC data 4,403 progeny-tested bulls. The data were divided into reference data and test data using 2001-10-01 as a cut-off date (birth date of the bulls). This resulted in about 25% genotyped bulls in the Holstein test data, and 20% in the RDC test data. For each breed, three datasets of markers were used to predict breeding values: 1) 54k dataset with missing genotypes, 2) 54k dataset where missing genotypes were imputed, and 3) imputed high density (HD) marker dataset which was created by imputing the 54k data to HD data based on 557 bulls genotyped using a 777k SNP chip in Holstein, and 706 bulls in RDC. Based on the three marker datasets, direct genomic breeding values (DGV) for protein, fertility and udder health were predicted using a genomic BLUP model (GBLUP) and a Bayesian mixture model with two normal distributions. Reliability of DGV was measured as squared correlations between de-regressed proofs (DRP) and DGV corrected for reliability of DRP. Unbiasedness was assessed by regression of DRP on DGV, based on the bulls in the test datasets. Averaged over the three traits, reliability of DGV based on the HD markers was 0.5% higher than that based on the 54k data in Holstein, and 1.0% higher in RDC. In addition, the HD markers led to an improvement of unbiasedness of DGV. The Bayesian mixture model led to 0.5% higher reliability than the GBLUP model in Holstein, but not in RDC. Imputing missing genotypes in the 54k marker data did not improve genomic predictions for most of the traits.
TidsskriftJournal of Dairy Science
Sider (fra-til)4657-4665
Antal sider9
StatusUdgivet - 2012

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