Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 425858
Title Breeding Value Estimation for Environmental Sensitivity on a Large Dairy Cattle Data Set
Author(s) Ronnegard, L.; Fikse, W.F.; Mulder, H.A.; Strandberg, E.
Source In: Proceedings of the 2011 Interbull meeting, 26-29 August 2011, Stavanger, Norway. - - p. 110 - 113.
Event 2011 Interbull meeting, Stavanger, Norway, 2011-08-27/2011-08-29
Department(s) LR - Backoffice
Publication type Contribution in proceedings
Publication year 2011
Abstract Animal robustness, or environmental sensitivity, may be studied through individual differences in re-sidual variance. These differences appear to be heritable, and there is therefore a need to fit models having breeding values explaining differences in residual variance. The aim of this report is to study whether breeding value estimation for environmental sensitivity (vEBV) can be performed on a large dairy cattle data set having around 1.6 million records. Two traits were analyzed separately, somatic cell score and milk yield. Estimation of variance components, ordinary breeding values and vEBVs was performed using standard variance component estimation software (ASReml), applying the me-thodology for double hierarchical generalized linear models. Converged estimates were obtained by running ASReml iteratively 20 times, which took less than 10 days on a Linux server. The genetic co-efficients of variation for environmental variance were 0.45 and 0.52, for somatic cell score and milk yield, respectively, which indicate a substantial genetic variance for environmental variance. This study shows that estimation of variance components, EBVs and vEBVs, is feasible for large dairy cat-tle data sets using standard variance component estimation software.
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