Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/12519
Title: The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes
Contributor(s): Clark, Sam A  (author)orcid ; Hickey, J (author); Daetwyler, H D (author); Van Der Werf, Julius H  (author)orcid 
Publication Date: 2012
Open Access: Yes
DOI: 10.1186/1297-9686-44-4Open Access Link
Handle Link: https://hdl.handle.net/1959.11/12519
Abstract: Background: The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. Methods: Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. Results: The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, v.44, p. 1-9
Publisher: BioMed Central Ltd
Place of Publication: United Kingdom
ISSN: 0999-193X
1297-9686
Field of Research (FOR): 060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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