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Impact of relationships between test and training animals and among training animals on reliability of genomic prediction
Author(s) -
Wu X.,
Lund M.S.,
Sun D.,
Zhang Q.,
Su G.
Publication year - 2015
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12165
Subject(s) - reliability (semiconductor) , test (biology) , training (meteorology) , population , training set , bayesian probability , statistics , biology , best linear unbiased prediction , set (abstract data type) , test set , genomic selection , computer science , genetics , artificial intelligence , mathematics , selection (genetic algorithm) , medicine , geography , ecology , genotype , gene , single nucleotide polymorphism , environmental health , power (physics) , quantum mechanics , programming language , physics , meteorology
Summary 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 E uro G enomics data and a group of N ordic H olstein 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.