Journal
ANIMAL GENETICS
Volume 53, Issue 3, Pages 466-469Publisher
WILEY
DOI: 10.1111/age.13186
Keywords
genetic architecture; genomic prediction; optimal omega; RFI; SNP
Funding
- earmarked fund for the modern agro-industry technology research system [CARS-41]
- National Nonprofit Institute Research Grant [Y2020PT02]
- Agricultural Breed Project of Shandong Province [2020LZGC013]
- Agricultural Science and Technology Innovation Program [ASTIP--IAS04, CAAS-ZDRW202005]
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This study compared the GBLUP model and GA-BLUP model with previously identified associated SNPs. The GA-BLUP model with nine RFI-associated SNPs showed improved prediction accuracy compared to the GBLUP model.
Residual feed intake (RFI) is a measure of the feed efficiency of animals. Previous studies have identified SNPs associated with RFI. The objective of this study was to compare the GBLUP model with the GA-BLUP model including previously identified associated SNPs. The nine associated SNPs were obtained from the genome-wide association study on a discovery population as preselection information. These models were analysed using ASREML software using a 5-fold cross-validation method on a validation population. With the genetic architecture (GA) matrix used, which was conducted with the nine RFI-associated SNPs, the prediction accuracy of RFI was improved compared with the original GBLUP model. The calculated optimal omega was 0.981 for RFI, which is in line with the optimal range from 0.9 to 1.0 in the gradient test. The prediction accuracy increased by 2% in the GA-BLUP model with omega being 0.981 compared with the GBLUP model. In conclusion, the GA-BLUP with the nine RFI-associated SNPs and an optimal omega can improve the prediction accuracy for a specific trait compared with GBLUP.
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