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This article in CS

  1. Vol. 45 No. 3, p. 1151-1159
    Received: July 2, 2004

    * Corresponding author(s): piepho@uni-hohenheim.de
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Best Linear Unbiased Prediction of Cultivar Effects for Subdivided Target Regions

  1. H. P. Piepho * and
  2. J. Möhring
  1. Bioinformatics Unit, Univ. of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany


Breeding for local adaptation may be economically viable providing there is sufficient genotype × subregion interaction. If the targeted subregion is part of a larger region covered by a testing network, information from neighboring subregions can be exploited to gain more precise estimates for the targeted subregion. For balanced data, the simplest approach is to use genotypic mean estimates for the whole target region, and this has often been shown to yield better predictions than simple means per subregion. A disadvantage of this approach is that it gives equal weight to all neighboring subregions and the targeted subregion, thus ignoring potential heterogeneity in information content. The objective of the present paper is to propose a method that allows a weighted combination of data from several subregions and to compare that method to other estimators. The proposed method is based on best linear unbiased prediction, which employs a weighted mean of subregion means. It follows from the theory of mixed models that the resulting estimator is optimal under the assumed model, minimizing prediction errors and maximizing the expected gain from selection. Using published variance component estimates, we found the resulting predictions to be superior to other approaches. We also show that the estimator is beneficial when selecting for global adaptation.

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