Accuracy of genomic breeding values in multi-breed dairy cattle populations

Hayes, Ben J., Bowman, Phillip J., Chamberlain, Amanda C., Verbyla, Klara and Goddard, Mike E. (2009) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 41 321-329. doi:10.1186/1297-9686-41-51

Author Hayes, Ben J.
Bowman, Phillip J.
Chamberlain, Amanda C.
Verbyla, Klara
Goddard, Mike E.
Title Accuracy of genomic breeding values in multi-breed dairy cattle populations
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
Publication date 2009-11-24
Year available 1992
Sub-type Article (original research)
DOI 10.1186/1297-9686-41-51
Open Access Status DOI
Volume 41
Start page 321
End page 329
Total pages 9
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.

Methods: Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES-SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.

Results: When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.

Conclusion: Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.
Keyword Agriculture, Dairy & Animal Science
Veterinary Sciences
Veterinary Sciences
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
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