title: Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds.
abstract: This study compared the accuracy of genome-enabled prediction models using individual single nucleotide polymorphisms (SNP) or haplotype blocks as covariates when using either a single breed or a combined population of Nordic Red cattle. The main objective was to compare predictions of breeding values of complex traits using a combined training population with haplotype blocks, with predictions using a single breed as training population and individual SNP as predictors. To compare the prediction reliabilities, bootstrap samples were taken from the test data set. With the bootstrapped samples of prediction reliabilities, we built and graphed confidence ellipses to allow comparisons. Finally, measures of statistical distances were used to calculate the gain in predictive ability. Our analyses are innovative in the context of assessment of predictive models, allowing a better understanding of prediction reliabilities and providing a statistical basis to effectively calibrate whether one prediction scenario is indeed more accurate than another. An ANOVA indicated that use of haplotype blocks produced significant gains mainly when Bayesian mixture models were used but not when Bayesian BLUP was fitted to the data. Furthermore, when haplotype blocks were used to train prediction models in a combined Nordic Red cattle population, we obtained up to a statistically significant 5.5% average gain in prediction accuracy, over predictions using individual SNP and training the model with a single breed.
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark. Electronic address: beatriz.cuyabano@mbg.au.dk.
authors.forename: B C D
authors.initials: BC
authors.lastname: Cuyabano
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark. Electronic address: beatriz.cuyabano@mbg.au.dk.
authors.forename: G
authors.initials: G
authors.lastname: Su
authors.affiliation: Department of Animal Sciences, University of Wisconsin, Madison 53706.
authors.forename: G J M
authors.initials: GJ
authors.lastname: Rosa
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark.
authors.forename: M S
authors.initials: MS
authors.lastname: Lund
authors.affiliation: Department of Animal Sciences, University of Wisconsin, Madison 53706.
authors.forename: D
authors.initials: D
authors.lastname: Gianola
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D005260:Female; D014644:Genetic Variation; D016678:Genome; D006239:Haplotypes; D008297:Male; D020641:Polymorphism, Single Nucleotide
keywords: Nordic Red cattle; bootstrap analysis; haplotype block; multi-breed genomic prediction