Using genomics to enhance selection of novel traits in North American dairy cattle

Authors: CHESNAIS, JACQUES - Semex Alliance; Cooper, Tabatha; Wiggans, George; SARGOLZAEI, MEHDI - Semex Alliance; PRYCE, JENNIE - Canadian Dairy Network; MIGLIOR, FILIPPO - University Of Guelph

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2015
Publication Date: 3/1/2016
Citation: Chesnais, J., Cooper, T.A., Wiggans, G.R., Sargolzaei, M., Pryce, J., Miglior, F. 2016. Using genomics to enhance selection of novel traits in North American dairy cattle. Journal of Dairy Science. 99(3):2413-2427.

Interpretive Summary: Genomics offers new opportunities for the effective selection of novel traits. For traits such as mastitis resistance, hoof health, or the prediction of milk composition from mid-infrared data enough records are available to carry out genomic evaluations. For traits that are more difficult to measure or expensive to collect, such as individual feed intake or immune response, the development of a cow reference population is the most effective approach for such evaluations. The reliability of genomic predictions depends primarily on the size of the reference population and on trait heritability. The reliability of genomic selection was estimated for various traits using reference populations of 2,000 to 10,000 Canadian born Holstein cows, or 5,000 to 20,000 US born Holstein cows, which had been genotyped with a panel of 6,000 SNP or higher. Prediction accuracies ranged from 0.21 to 0.78 depending on the trait and the size of the cow reference population. In many instances, the accuracy of genomic selection for novel traits can be increased through the use of indicator traits. As an example, protein yield, cow size and mid-infrared data can be used as indicator traits for feed efficiency. Using these traits in conjunction with 5,000 cow records for dry matter intake increased the reliability of genomic predictions for young animals from 0.20 to 0.50. Genomics can provide evaluations of adequate accuracy for selection.

Technical Abstract: Genomics offers new opportunities for the effective selection of novel traits. For traits such as mastitis resistance, hoof health, or the prediction of milk composition from mid-infrared (MIR) data, for example, enough records are usually available to carry out genomic evaluations using sire genotypes and the phenotypes of their daughters. For traits that are more difficult to measure or expensive to collect, such as individual feed intake or immune response, the development of a cow reference population is the most effective approach. The reliability of the resulting genomic predictions depends primarily on the size of the reference population and on trait heritability, as shown by Daetwyler et al. (2008). In order to provide an empirical check of these theoretical estimates of reliability, the reliability of genomic selection was estimated for various traits using reference populations of 2,000 to 10,000 Canadian born Holstein cows, or 5,000 to 20,000 US born Holstein cows, which had been genotyped with a panel of 6,000 SNP or higher. All genotypes were imputed to 50K. The effects of SNP were estimated from cow records only, after excluding the dams of validation bulls. Bulls first proven in 2013 and 2014 were then used to carry out a validation and estimate the accuracy of genomic selection based on these SNP effects. Prediction accuracies obtained this way ranged from 0.21 to 0.78 depending on the trait and the size of the cow reference population. They were generally higher than prediction accuracies derived from the Daetwyler formula, which ranged from 0.03 to 0.65 when assuming an effective population size of 100, and from 0.06 to 0.80 when assuming an effective population size of 50. The difference was highest for low heritability traits and smaller population sizes. In many instances, the accuracy of genomic selection for novel traits can be increased through the use of indicator traits. As an example, protein yield, cow size and MIR data were used as indicator traits for feed efficiency. Using these traits in conjunction with 5,000 cow records for dry matter intake increased the reliability of genomic predictions for young animals from 0.20 to 0.50.