|JENKO, JANEZ - Roslin Institute|
|COOPER, TABATHA - Former ARS Employee|
|EAGLEN, S.A.E. - Roslin Institute|
|DE L. LUFF, WILLIAM - Collaborator|
|BICHARD, MICHAEL - Collaborator|
|PONG-WONG, RICARDO - Roslin Institute|
|WOOLLIAMS, JOHN - Roslin Institute|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/21/2016
Publication Date: 1/1/2017
Citation: Jenko, J., Wiggans, G.R., Cooper, T.A., Eaglen, S., De L. Luff, W.G., Bichard, M., Pong-Wong, R., Woolliams, J.A. 2017. Cow genotyping strategies for genomic selection in small dairy cattle population. Journal of Dairy Science. 100(1):439-452.
Interpretive Summary: The benefit of using cow genotypes for building training sets in small dairy populations was examined using the Guernsey breed. Adding genotypes from a single cohort of cows improved the accuracy of prediction substantially over a training set of 200 bulls alone. For this population the genetic correlation for bulls and cows in the training set was <1. Strategies to improve the cost effectiveness of genotyping can also be beneficial. Genotyping all cows always gave the greatest accuracy, however, genotyping only half, divergently selected, recovered substantial information and was better than genotyping the same number when randomly or directionally-selected.
Technical Abstract: This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds there are few sires with progeny records and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using four different methods: (i) pedigree BLUP, (ii) genomic BLUP using only bulls, (iii) univariate genomic BLUP using bulls and cows, and (iv) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism (SNP) effects increased the correlation between genomic EBV and phenotypes compared to using only bulls by 0.163 ± 0.022 for milk yield, 0.111 ± 0.021 for fat yield, and 0.113 ± 0.018 for protein yield, a drop of 0.014 ± 0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. There was only very small change in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows but when only half of the cows were genotyped, a divergent selection strategy was better compared to the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds.