Improving Accuracy of Genomic Prediction in Holstein Friesians

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


Three statistical models were considered to assess the advantage of including information of known causative mutations when estimating genomic breeding values. Data included phenotypic records and 50k genotypes from 5,661 Holstein Friesian cows. This study showed that when aknown causative mutation for milk traits, DGAT1, was fit as a fixed effect in genomic prediction, an increase in accuracy was seen compared to fitting it as either a random effect or not explicitly fitting it and relying on linked markers fitted as random effects. The regression coefficients of genomic prediction on phenotype were near one for all estimates, indicating that no major bias was present in the estimates. These results suggest that, when calculatinggenomic predictions, it is beneficial to include information from known major genes in the analysis to increase the accuracy of prediction.

Keywords: ASL R2784

How to Cite:

Hayr, M. K., Saatchi, M., Johnson, D. & Garrick, D. J., (2013) “Improving Accuracy of Genomic Prediction in Holstein Friesians”, Iowa State University Animal Industry Report 10(1). doi: https://doi.org/10.31274/ans_air-180814-717

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Published on
01 Jan 2013
Peer Reviewed