Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22513
Title: Genomic prediction from observed and imputed high-density ovine genotypes
Contributor(s): Moghaddar, Nasir (author); Swan, Andrew (author); Van Der Werf, Julius H (author)orcid 
Publication Date: 2017
Open Access: Yes
DOI: 10.1186/s12711-017-0315-4
Handle Link: https://hdl.handle.net/1959.11/22513
Open Access Link: http://dx.doi.org/10.1186/s12711-017-0315-4
Abstract: Background: Genomic prediction using high-density (HD) marker genotypes is expected to lead to higher prediction accuracy, particularly for more heterogeneous multi-breed and crossbred populations such as those in sheep and beef cattle, due to providing stronger linkage disequilibrium between single nucleotide polymorphisms and quantitative trait loci controlling a trait. The objective of this study was to evaluate a possible improvement in genomic prediction accuracy of production traits in Australian sheep breeds based on HD genotypes (600k, both observed and imputed) compared to prediction based on 50k marker genotypes. In particular, we compared improvement in prediction accuracy of animals that are more distantly related to the reference population and across sheep breeds. Methods: Genomic best linear unbiased prediction (GBLUP) and a Bayesian approach (BayesR) were used as prediction methods using whole or subsets of a large multi-breed/crossbred sheep reference set. Empirical prediction accuracy was evaluated for purebred Merino, Border Leicester, Poll Dorset and White Suffolk sire breeds according to the Pearson correlation coefficient between genomic estimated breeding values and breeding values estimated based on a progeny test in a separate dataset. Results: Results showed a small absolute improvement (0.0 to 8.0% and on average 2.2% across all traits) in prediction accuracy of purebred animals from HD genotypes when prediction was based on the whole dataset. Greater improvement in prediction accuracy (1.0 to 12.0% and on average 5.2%) was observed for animals that were genetically lowly related to the reference set while it ranged from 0.0 to 5.0% for across-breed prediction. On average, no significant advantage was observed with BayesR compared to GBLUP.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, 49(1), p. 1-10
Publisher: BioMed Central Ltd
Place of Publication: United Kingdom
ISSN: 1297-9686
0999-193X
Field of Research (FOR): 070201 Animal Breeding
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
School of Environmental and Rural Science

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