Improving the Accuracy of Genomic Prediction of Milk Fat

  • Melanie K. Hayr (Iowa State University)
  • Mahdi Saatchi (Iowa State University)
  • Dave Johnson (Livestock Improvement Corporation)
  • Dorian J. Garrick (Iowa State University)


Four statistical models were considered to quantify any advantage of including the genotype of known causative mutations when calculating direct genomic values. Data included 50k genotypes from 5,661 Holstein Friesian cows and 2,287 bulls. This study showed that when a known QTL for milk traits, DGAT1, was fit as a fixed class or fixed covariate in genomic prediction, an increase in accuracy was seen compared to fitting it as either a random covariate or relying on linked 50k markers fit as random covariates. The regression coefficients of genomic prediction on phenotype were near one for all estimates, indicating no major bias was in the estimates. These results suggest it is beneficial to the accuracy of prediction to include information from known major QTL in genomic analyses.

Keywords: Animal Science

How to Cite:

Hayr, M. K., Saatchi, M., Johnson, D. & Garrick, D. J., (2005) “Improving the Accuracy of Genomic Prediction of Milk Fat”, Iowa State University Animal Industry Report 11(1). doi: https://doi.org/10.31274/ans_air-180814-1155

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Published on
01 Jan 2005
Peer Reviewed