Open Access
Genet. Sel. Evol.
Volume 39, Number 3, May-June 2007
Page(s) 225 - 247
Published online 14 April 2007
Genet. Sel. Evol. 39 (2007) 225-247
DOI: 10.1051/gse:2007001

Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application

Larry A. Kuehn, Ronald M. Lewis and David R. Notter

Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA

(Received 6 June 2006; accepted 6 January 2007; published online 14 April 2007)

Abstract - Comparing predicted breeding values (BV) among animals in different management units (e.g. flocks, herds) is challenging if units have different genetic means. Unbiased estimates of differences in BV may be obtained by assigning base animals to genetic groups according to their unit of origin, but units must be connected to estimate group effects. If many small groups exist, error of BV prediction may be increased. Alternatively, genetic groups can be excluded from the statistical model, which may bias BV predictions. If adequate genetic connections exist among units, bias is reduced. Several measures of connectedness have been proposed, but their relationships to potential bias in BV predictions are not well defined. This study compares alternative strategies to connect small units and assesses the ability of different connectedness statistics to quantify potential bias in BV prediction. Connections established using common sires across units were most effective in reducing bias. The coefficient of determination of the mean difference in predicted BV was a perfect indicator of potential bias remaining when comparing individuals in separate units. However, this measure is difficult to calculate; correlated measures such as prediction errors of differences in unit means and correlations among prediction errors are suggested as practical alternatives.

Key words: connectedness / genetic evaluation / bias / genetic groups / breeding value

Correspondence and reprints: Larry.Kuehn@ARS.USDA.GOV

© INRA, EDP Sciences 2007