Studying crossover genotype × environment interaction using linear-bilinear models and mixed models
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In agriculture and plant breeding the most important genotype × environment interaction pattern is crossover genotype × environment interaction. Procedures using linearbilinear models are used to find disjoint subsets of sites or genotypes with negligible crossover genotype × environment interaction. In terms of correlations, these subsets of sites or genotypes should be such that pairs of sites have nearly perfect positive genetic correlations. Perfect positive genetic correlation between sites is a sufficient, but not a necessary, condition for a non-COI pattern. The main objective of this study was to use the mixed model theory to confirm that the subsets of sites and genotypes formed by using the linear-bilinear models have negligible crossover genotype × environment interaction. If there is no significant crossover interaction in a subset of sites or genotypes, then the mixed model should be able to confirm this by nonrejection of the hypothesis that the covariance structure has perfect genetic correlation. Mixed model analysis of results from two multisite cultivar trials confirmed the validity of the procedures using linear-bilinear model methods for clustering sites and genotypes into noncrossover genotype × environment interaction subsets.
Key WordsBiadditive models Genetic correlations Shifted multiplicative model Sites regression model Variance-covariance matrix
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- Abdalla, O. S., Crossa, J., and Cornelius, P. L. (1997), “Results and Biological Interpretation of Shifted Multiplicative Model Clustering of Durum Wheat Cultivars and Testing Sites,” Crop Science, 37, 88–97.Google Scholar
- Baker, R. J. (1990), “Crossover Genotype-Environmental Interaction in Spring Wheat,” in Genotype-By-Environment Interaction and Plant Breeding, ed. M.S. Kang, Baton Rouge, LA: Lousiana State University, pp. 42–51.Google Scholar
- Cornelius, P. L., and Crossa, J. (1999), “Prediction Assessment of Shrinkage Estimators of Multiplicative Models for Multi-environment Cultivar Trials,” Crop Science, 39, 998–1009.Google Scholar
- Cornelius, P. L., Crossa, J., and Seyedsadr, M. S. (1996), “Statistical Tests and Estimators of Multiplicative Models for Genotype-by-Environment Interaction,” in Genotype-by-Environment Interaction, eds. M. S. Kang and H. G. Gauch, Boca Raton, FL: CRC Press, pp. 199–234.Google Scholar
- Crossa, J., and Cornelius, P. L. (1993), “Recent Developments in Multiplicative Models for Cultivar Trials,” in International Crop Science I, eds. D. R. Buxton, R. Shibles, R. A. Forsberg, B. L. Blad, K. H. Asay, G. M. Paulsen, and R. F. Wilson, Madison, WI: CSSA, pp. 571–577.Google Scholar
- Crossa, J., Cornelius, P. L., and Seyedsadr, M. S. (1996), “Using the Shifted MultiplicativeModel Cluster Methods for Crossover Genotype-by-Environment Interaction,” in Genotype-by-Environment Interaction, eds. M.S. Kang and H.G. Gauch, Boca Raton, FL: CRC Press, pp. 175–198.Google Scholar
- SAS Institute, Inc. (1999), SAS/STAT User’s Guide, Version 8, Cary, NC: SAS Institute Inc.Google Scholar