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Increasing the Accuracy of Genomic Predictions for RFI in Dairy Cattle through Using Genomic Information from Beef Breeds

Monday, August 18, 2014: 5:45 PM
Stanley Park Ballroom (The Westin Bayshore)
Majid Khansefid , Dairy Futures Cooperative Research Centre (CRC), Melbourne, Australia
Jennie E. Pryce , Biosciences Research Division, Department of Environment and Primary Industries, Victoria, Australia
Sunduimijid Bolormaa , CRC for Sheep Industry Innovation, Armidale, Australia
Stephen P Miller , Centre for Genetic Improvement of Livestock - University of Guelph, Guelph, ON, Canada
Zhiquan Wang , University of Alberta, Edmonton, AB, Canada
Changxi Li , University of Alberta, Edmonton, AB, Canada
Michael E. Goddard , The Department of Environment and Primary Industries, Bundoora, Australia
Recorded Presentation (flv)
Abstract Text:

The GBLUP method of calculating GEBVs assumes SNPs contribute equally to variation in the trait. However, in reality it is likely that some SNPs have bigger effects than predicted under this infinitesimal model. The accuracy of GEBVs could potentially be improved by giving more weight to relevant SNPs in constructing the genomic relationship matrix (GRM). The aim of this study was to improve the accuracy of GEBVs for residual feed intake (RFI) in Holsteins by identifying SNPs with a greater effect on RFI than average in beef cattle. A genome wide association study in beef cattle found 1,876 SNPs significantly (p<0.001) associated with RFI in beef cattle. When the additional variance explained by these SNPs was used in the construction of the GRM, the accuracy of GEBVs in a validation sample of Holsteins increased from 0.33 to 0.39.

Keywords:

Genomic prediction accuracy

Residual feed intake

See more of: Symposium: Genetics of Trait Complexes - Feed Intake and Efficiency
See more of: Genetics of Trait Complexes: Feed intake and efficiency
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