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Modelling expectation and variance for genotype by environment data
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  • Original Article
  • Published: 01 August 1997

Modelling expectation and variance for genotype by environment data

  • Jean-Baptiste Denis1,
  • Hans-Peter Piepho2 &
  • Fred A van Eeuwijk3 

Heredity volume 79, pages 162–171 (1997)Cite this article

  • 1958 Accesses

  • 59 Citations

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Abstract

An integration of two types of models for the analysis of genotype by environment interaction is presented. On the one hand, the expectation of G × E interaction is frequently modelled by regression models; on the other hand, for deviations from these regressions, either separate stability parameters are defined or extra components of variance are introduced. A class of mixed models is described that contains facilities for modelling expectation by regression and, in addition, has extensive possibilities for dealing with heteroscedasticity. Practical aspects of the use of these mixed models are illustrated on a data set involving sugar yield in beet.

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Author information

Authors and Affiliations

  1. Laboratoire de Biométrie, INRA, Route de Saint-Cyr, Versailles, F-78026, France

    Jean-Baptiste Denis

  2. Faculty of Agricultural and Environmental Sciences, University of Kassel, Witzenhausen, D-37213, Germany

    Hans-Peter Piepho

  3. DLO-Center for Plant Breeding and Reproduction Research, CPRO-DLO, PO Box 16, Wageningen, NL-6700AA, The Netherlands

    Fred A van Eeuwijk

Authors
  1. Jean-Baptiste Denis
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  2. Hans-Peter Piepho
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  3. Fred A van Eeuwijk
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Corresponding author

Correspondence to Jean-Baptiste Denis.

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Cite this article

Denis, JB., Piepho, HP. & van Eeuwijk, F. Modelling expectation and variance for genotype by environment data. Heredity 79, 162–171 (1997). https://doi.org/10.1038/hdy.1997.139

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  • Received: 05 August 1996

  • Issue date: 01 August 1997

  • DOI: https://doi.org/10.1038/hdy.1997.139

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Keywords

  • covariate
  • factorial regression
  • genotype × environment interaction
  • heteroscedasticity
  • interaction
  • mixed model

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