|LI, BINGJIE - Oak Ridge Institute For Science And Education (ORISE)
|GUDUK, ELIF - Council On Dairy Cattle Breeding
|O'CONNELL, JEFFREY - University Of Maryland School Of Medicine
|VANDEHAAR, MIKE - Michigan State University
|TEMPELMAN, ROBERT - Michigan State University
|WEIGEL, KENT - University Of Wisconsin
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2019
Publication Date: 3/1/2020
Citation: Li, B., Van Raden, P.M., Guduk, E., O'Connell, J.R., Null, D.J., Connor, E.E., VandeHaar, M.J., Tempelman, R.J., Weigel, K.A., Cole, J.B. 2020. Genomic prediction of residual feed intake in US Holstein dairy cattle. Journal of Dairy Science. 103(3):2477-2486. https://doi.org/10.3168/jds.2019-17332.
Interpretive Summary: We estimated genomic breeding values (GEBV) for residual feed intake (RFI) of U.S. Holstein dairy cattle. Feed accounts for much of the cost of milk production, so breeding cows that require less feed can help improve the profitability and sustainability of dairy operations. A new RFI model, two genomic prediction methods, and two types of genotype chips (60k and high-density) were used to estimate GEBV and assess prediction reliability. The reliability of prediction was generally low, but the addition of genomic information increased reliability compared to using only pedigree information. Continued collection of feed intake data is necessary to ensure that successive generations have higher reliabilities. Focusing RFI data collection on daughters of elite bulls that will have greatest genetic contribution to the next generation will produce more gains and profit.
Technical Abstract: Genomic selection is an important tool to introduce feed efficiency into dairy cattle breeding. The goals of the current research are to estimate genomic breeding values (GEBV) of Residual feed intake (RFI) and to assess the prediction reliability for RFI in the U.S. Holstein population. The RFI data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the U.S., and were pre-adjusted to remove phenotypic correlations with milk energy, metabolic body weight, body weight change and for several environmental effects. In the current analyses, genomic predicted transmitting abilities (GPTA) of milk energy and of body weight composite (BWC) were included into RFI model to further remove the genetic correlations that remained between RFI and these energy sinks. In the first part of the analyses, a national genomic evaluation for RFI was conducted including 1.6 million genotyped Holsteins and 60 million ungenotyped Holsteins, using a standard multi-step genomic evaluation method and 60,671 SNP list. In the second part of the study, a single-step genomic prediction method was applied to estimate GEBV of RFI for all cows with phenotypes, 5,252 elite young bulls, 4,029 young heifers, as well as their ancestors in the pedigree, using a high-density (HD) genotype chip. Theoretical prediction reliability was calculated for all the studied animals in the single-step genomic prediction by direct inversion of the mixed model equations. Genomic prediction reliabilities for RFI averaged 34% for all phenotyped animals and 13% for all 1.6 million genotyped animals. Including genomic information increased the prediction reliabilities for RFI compared to using only pedigree information. All bulls had low reliabilities, and averaged to only 16% for the top 100 net merit progeny-tested bulls. Analyses using single-step genomic prediction and HD genotypes gave similar results to what were obtained from the national evaluation. The average theoretical reliability for RFI was 0.18 among the elite young bulls under 5 years old, being lower in the younger generations of elite bulls compared to older bulls. To conclude, the size of the reference population and its relationship to the predicted population remain as the limiting factors in the genomic prediction for RFI. Continued collection of feed intake data is necessary so that reliabilities are maintained due to close relationships of phenotyped animals with breeding stock. Considering the currently low prediction reliability and high cost of data collection, focusing RFI data collection on daughters of elite bulls that will have the greatest genetic contribution to the next generation will give more gains and profit.