title: Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.
abstract: One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction.
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.|Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
authors.forename: X
authors.initials: X
authors.lastname: Wu
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.
authors.forename: M S
authors.initials: MS
authors.lastname: Lund
authors.affiliation: Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
authors.forename: D
authors.initials: D
authors.lastname: Sun
authors.affiliation: Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
authors.forename: Q
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.
authors.forename: G
authors.initials: G
authors.lastname: Su
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002417:Cattle; D023281:Genomics / Q000379:methods*; D016014:Linear Models; D000069550:Machine Learning; D015203:Reproducibility of Results
keywords: Bayesian mixture model; genomic BLUP model; genomic relationship