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This article in CS

  1. Vol. 47 No. 1, p. 311-320
    Received: Sept 6, 2006

    * Corresponding author(s): j.crossa@cgiar.org
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Modeling Additive × Environment and Additive × Additive × Environment Using Genetic Covariances of Relatives of Wheat Genotypes

  1. Juan Burgueñoa,
  2. José Crossa *a,
  3. Paul L. Corneliusb,
  4. Richard Trethowanc,
  5. Graham McLarend and
  6. Anitha Krishnamacharid
  1. a Biometrics and Statistics Unit of the Crop Informatics Laboratory (CRIL), International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, México, D.F., México
    b Dep. of Plant and Soil Sciences and Dep. of Statistics, Univ. of Kentucky, Lexington, KY 40546-0312, USA
    c Plant Breeding Institute, University of Sydney, PMB 11, Camden NSW 2570, Australia
    d Biometrics and Bioinformatics Unit of the Crop Informatics Laboratory (CRIL), International Rice Research Institute (IRRI), DAPO Box 7777, Manila, the Philippines


In self-pollinated species, the variance–covariance matrix of breeding values of the genetic strains evaluated in multienvironment trials (MET) can be partitioned into additive effects, additive × additive effects, and their interaction with environments. The additive relationship matrix A can be used to derive the additive × additive genetic variance–covariance relationships among strains, Ã. This study shows how to separate total genetic effects into additive and additive × additive and how to model the additive × environment interaction and additive × additive × environment interaction by incorporating variance–covariance structures constructed as the Kronecker product of a factor-analytic model across sites and the additive (A) and additive × additive relationships (Ã), between strains. Two CIMMYT international trials were used for illustration. Results show that partitioning the total genotypic effects into additive and additive × additive and their interactions with environments is useful for identifying wheat (Triticum aestivum L.) lines with high additive effects (to be used in crossing programs) as well as high overall production. Some lines and environments had high positive additive × environment interaction patterns, whereas other lines and environments showed a different additive × additive × environment interaction pattern.

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