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A simple method for genomic selection of moderately sized dairy cattle populations

Published online by Cambridge University Press:  26 September 2011

J. I. Weller*
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel
M. Ron
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel
G. Glick
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel Department of Ruminant Science, The Robert H. Smith Faculty of Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
A. Shirak
Affiliation:
Department of Ruminant Science, Institute of Animal Sciences, A.R.O., The Volcani Center, Bet Dagan 50250, Israel
Y. Zeron
Affiliation:
Department of Ruminant Science, Sion – Israeli Company for Artificial Insemination & Breeding Ltd, M. P. Shikmim 79800, Israel
E. Ezra
Affiliation:
Department of Ruminant Science, Israel Cattle Breeders Association, Caesarea Industrial Park 38900, Israel
*
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Abstract

An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a ‘cow model,’ estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.

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Full Paper
Copyright
Copyright © The Animal Consortium 2011

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