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The Plant Genome Abstract - Original Research

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models

 

This article in TPG

  1. Vol. 9 No. 3
    unlockOPEN ACCESS
     
    Received: Mar 07, 2016
    Accepted: July 22, 2016
    Published: September 22, 2016


    * Corresponding author(s): j.crossa@cgiar.org
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doi:10.3835/plantgenome2016.03.0024
  1. Jaime Cuevasa,
  2. José Crossa *b,
  3. Víctor Soberanisa,
  4. Sergio Pérez-Elizaldec,
  5. Paulino Pérez-Rodríguezc,
  6. Gustavo de los Camposd,
  7. O. A. Montesinos-Lópezb and
  8. Juan Burgueñob
  1. a Universidad de Quintana Roo, Chetumal, Quintana Roo, México
    b Biometrics and Statistics Unit of the International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México DF, México
    c Colegio de Postgraduados, CP 56230, Montecillos, Edo. de México, México
    d Dep. of Epidemiology & Biostatistics, Michigan State Univ., 909 Fee Road, Room B601, East Lansing, MI 48824, USA

Abstract

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat (Triticum aestivum L.) and maize (Zea mays L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.

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