title: Genomic predictions based on a joint reference population for the Nordic Red cattle breeds.
abstract: The main aim of this study was to compare accuracies of imputation and genomic predictions based on single and joint reference populations for Norwegian Red (NRF) and a composite breed (DFS) consisting of Danish Red, Finnish Ayrshire, and Swedish Red. The single nucleotide polymorphism (SNP) data for NRF consisted of 2 data sets: one including 25,000 markers (NRF25K) and the other including 50,000 markers (NRF50K). The NRF25K data set had 2,572 bulls, and the NRF50K data set had 1,128 bulls. Four hundred forty-two bulls were genotyped in both data sets (double-genotyped bulls). The DFS data set (DSF50K) included 50,000 markers of 13,472 individuals, of which around 4,700 were progeny-tested bulls. The NRF25K data set was imputed to 50,000 density using the software Beagle. The average error rate for the imputation of NRF25K decreased slightly from 0.023 to 0.021, and the correlation between observed and imputed genotypes changed from 0.935 to 0.936 when comparing the NRF50K reference and the NRF50K-DFS50K joint reference imputations. A genomic BLUP (GBLUP) model and a Bayesian 4-component mixture model were used to predict genomic breeding values for the NRF and DFS bulls based on the single and joint NRF and DFS reference populations. In the multiple population predictions, accuracies of genomic breeding values increased for the 3 production traits (milk, fat, and protein yields) for both NRF and DFS. Accuracies increased by 6 and 1.3 percentage points, on average, for the NRF and DFS bulls, respectively, using the GBLUP model, and by 9.3 and 1.3 percentage points, on average, using the Bayesian 4-component mixture model. However, accuracies for health or reproduction traits did not increase from the multiple population predictions. Among the 3 DFS populations, Swedish Red gained most in accuracies from the multiple population predictions, presumably because Swedish Red has a closer genetic relationship with NRF than Danish Red and Finnish Ayrshire. The Bayesian 4-component mixture model performed better than the GBLUP model for most production traits for both NRF and DFS, whereas no advantage was found for health or reproduction traits. In general, combining NRF and DFS reference populations was useful in genomic predictions for both the NRF and DFS bulls.
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
authors.forename: L
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Box 5003, 1432 Ås, Norway. Electronic address: bjorg.heringstad@nmbu.no.
authors.forename: B
authors.initials: B
authors.lastname: Heringstad
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark. Electronic address: guosheng.su@agrsci.dk.
authors.forename: G
authors.initials: G
authors.lastname: Su
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
authors.forename: B
authors.initials: B
authors.lastname: Guldbrandtsen
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Box 5003, 1432 Ås, Norway.
authors.forename: T H E
authors.initials: TH
authors.lastname: Meuwissen
authors.affiliation: Geno Breeding and AI Association, 1432 Ås, Norway.
authors.forename: M
authors.initials: M
authors.lastname: Svendsen
authors.affiliation: Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Box 5003, 1432 Ås, Norway.
authors.forename: H
authors.initials: H
authors.lastname: Grove
authors.affiliation: Danish Agriculture Advisory Service, DK-8200 Aarhus N, Denmark.
authors.forename: U S
authors.initials: US
authors.lastname: Nielsen
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
authors.forename: M S
authors.initials: MS
authors.lastname: Lund
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D030541:Databases, Genetic; D004041:Dietary Fats / Q000032:analysis; D005260:Female; D005387:Finland; D005819:Genetic Markers; D016678:Genome; D023281:Genomics / Q000379:methods*; D005838:Genotype; D060005:Genotyping Techniques; D007774:Lactation; D008297:Male; D008892:Milk / Q000378:metabolism; D008894:Milk Proteins / Q000032:analysis; D008957:Models, Genetic; D009664:Norway; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012098:Reproduction; D013548:Sweden
chemical_list: D004041:Dietary Fats; D005819:Genetic Markers; D008894:Milk Proteins
keywords: Bayesian 4-component mixture model; genomic BLUP; imputation; multiple population genomic prediction