, Volume 161, Issue 1–2, pp 209–228 | Cite as

BLUP for phenotypic selection in plant breeding and variety testing

  • H. P. Piepho
  • J. Möhring
  • A. E. Melchinger
  • A. Büchse


Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non-genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance–covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedigree information is often included via the so-called numerator relationship matrix \(({\user2{A}})\), we stress that it is frequently straightforward to exploit the same information by a simple mixed model without explicit reference to the \({\user2{A}}\)-matrix.


Mixed model Breeding value Pedigree Genetic effect Genotypic value 



We thank J. Léon and A.M. Bauer for critically reading an earler draft of this paper. Thanks are due to Tobias Schrag for providing the maize data. We are grateful to T. Calinski for helpful discussions on the spectral decomposition of variance-covariance matrices. J. Möhring was supported within the Breeding and Informatics (BRAIN) project of the Genome Analysis of the Plant Biological System (GABI) initiative ( We thank all breeders of GABI-BRAIN who have provided data and information on their breeding programmes. Two anonymous referees are thanked for very helpful comments.


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© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • H. P. Piepho
    • 1
  • J. Möhring
    • 1
  • A. E. Melchinger
    • 1
  • A. Büchse
    • 1
  1. 1.Bioinformatics UnitUniversity of HohenheimStuttgartGermany

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