My Account: Log In | Join | Renew
Search
Author
Title
Vol.
Issue
Year
1st Page

Abstract

 

This article in CS

  1. Vol. 45 No. 3, p. 1004-1016
     
    Received: Feb 6, 2004
    Published: May, 2005


    * Corresponding author(s): wyan@ggebiplot.com
    yanw@agr.gc.ca
 View
 Download
 Alerts
 Permissions
 Share

doi:10.2135/cropsci2004.0076

An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction

  1. Weikai Yan * and
  2. Nicholas A. Tinker
  1. Eastern Cereal and Oilseed Research Center, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, Ontario, Canada, K1A 0C6

Abstract

Multienvironment trials (MET) generate two types of two-way data: genotype × environment data for a target trait and genotype × trait data in individual or across environments. These data can be visually analyzed by a GGE biplot and a genotype × trait biplot, respectively. This paper describes a third type of biplot, the covariate-effect biplot, and illustrates its tandem use with the other biplots to achieve a fuller understanding of MET data. The covariate-effect biplot is generated on the basis of an explanatory trait × environment two-way table consisting of correlation coefficients between the target trait (e.g., yield) and each of the other traits in each of the environments. This biplot displays the yield-trait relations in individual environments and addresses whether and how the genotype × environment interactions (GE) for yield can be explored by indirect selection for the other traits. These other traits are treated as genetic covariables and can be replaced by other genetic covariables such as genetic markers, QTL, or genes. The biplot methodology was demonstrated by MET data of barley (Hordeum vulgare L.) conducted across North America. Both the GGE biplot and the covariate-effect biplot showed that the environments fell into two (eastern vs. western) megaenvironments. The covariate-effect pattern explained 81% of the GGE pattern, suggesting that the GE pattern for yield can be effectively explored by indirect selection for these traits. Specifically, barley yield can be improved by selecting for larger kernel weight, earlier heading, and better lodging resistance in the eastern megaenvironment. In contrast, the yield–trait relationship in the western megaenvironment was highly variable, and yield improvement can be achieved only by selecting for yield per se across environments. We suggest that the GGE biplot, the genotype × trait biplot, and the covariate-effect biplot be used jointly to better understand and more fully explore MET data.

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © 2005. Crop Science Society of AmericaCrop Science Society of America