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The genetics of environmental variation of dry matter grain yield in maize


Dry matter grain yield per plot from three genetically homogeneous single-cross maize hybrids were analysed to investigate whether environmental variance depends on genotype. Three genotypes were tested at 20 locations in 3 years. The data were analysed using a non-parametric approach and fully parametric Bayesian models. Both analyses reveal effects of genotype on environmental variation. The Bayesian analyses indicate that genotype by location–year interactions are the most important effects acting at the level of the mean. The best-fitting Bayesian model is one postulating genotype by location–year interactions acting on the mean and main effects of genotype and of location–year on the variance. Despite the detection of genotypic effects acting on the variance, location–year effects constitute the biggest relative source of variance heterogeneity.

Corresponding author
*Corresponding author: Daniel Sorensen. Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830 Tjele, Denmark. E-mail:
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Ansel, J., Bottin, H., Rodriguez-Beltran, C., Damon, C., Nagarajan, M., Fehrmann, S., François, J. & Yvert, G. (2008). Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genetics 4. doi:10.1371/journal.pgen.1000049.
Bartlett, M. S. (1937). Properties of sufficiency and statistical tests. Proceedings of the Royal Statistical Society, A 160, 268282.
Box, G. E. P. & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society Series B 26, 211252.
Cotes, J. M., Crossa, J., Sanches, A. & Cornelius, P. L. (2006). A Bayesian approach for assessing the stability of genotypes. Crop Science 46, 26542665.
Edwards, J. W. & Jannink, J. L. (2006). Bayesian modeling of heterogeneous error and genotype×environment interaction variances. Crop Science 2006, 820833.
Falconer, D. S. & Mackay, T. F. C. (1996). Introduction to Quantitative Genetics. Longman.
Gelfand, A. E. (1996). Model determination using sampling-based methods. In Markov Chain Monte Carlo in Practice ( ed. Gilks, W. R., Richardson, S. & Spiegelhalter, D. J.), pp. 145161. London: Chapman and Hall.
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (2004). Bayesian Data Analysis. London: Chapman and Hall.
Hill, W. G. & Mulder, H. A. (2010). Genetic analysis of environmental variation. Genetics Research 92, 381395.
Hozo, S. P., Djulbegovic, B. & Hozo, I. (2005). Estimating the mean and variance from the median, range, and the size of a sample. BMC Medical Research Methodology 5. doi:10.1186/1471-2288-5-13.
Jinks, J. L. & Pooni, H. S. (1988). The genetic basis of environmental sensitivity. In Proceedings of the Second International Conference on Quantitative Genetics. North Carolina State University, NC, USA: Sinauer Associates, Inc., pp. 505522.
Mackay, T. F. C. & Lyman, R. F. (2005). Drosophila bristles and the nature of quantitative genetic variation. Philosophical Transactions of the Royal Society, B 360, 15131527.
Mood, A. M., Graybill, F. A. & Boes, D. C. (1974). Introduction to the Theory of Statistics. New York: McGraw-Hill.
Ordas, B., Malvar, R. A. & Hill, W. G. (2008). Genetic variation and quantitative trait loci associated developmental stability and the environmental correlation between traits in maize. Genetical Research 90, 385395.
Piepho, H. P. (1999). Stability analysis using the SAS system. Agronomy Journal 91, 154160.
Sorensen, D. & Waagepetersen, R. (2003). Normal linear models with genetically structured residual variance heterogeneity: a case study. Genetical Research 82, 207222.
Yang, Y., Christensen, O. F. & Sorensen, D. (2011). Analysis of a genetically structured variance heterogeneity model using the Box–Cox transformation. Genetics Research 93, 3346.
Zhang, X. S. & Hill, W. G. (2005). Evolution of the environmental component of phenotypic variance: Stabilizing selection in changing environments and the cost of homogeneity. Evolution 59, 12371244.
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Genetics Research
  • ISSN: 0016-6723
  • EISSN: 1469-5073
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