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We included four fixed effects: year (categorical), bolting month (categorical), environment (i.e. lab or field categorical), plant size (continuous, based on prebolting rosette leaf length), and two crossed effects: bolting month X year and environment X year.
Power calculation showed that allele frequencies, and characteristics of environment (e.g., measurement level, and in the case of a categorical environment, the frequencies of the environmental conditions) affect the power to detect G × E interaction.
Assuming independence between environment and genotype in the population of controls, Begg and Zhang (3), Piegorsch et al. (2), Umbach and Weinberg (4), and Yang et al. (5) showed that efficient estimates of interaction for categorical environment and binary genotype variables can be obtained via logistic regression in a case-only analysis.
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Their extensions to multiple categorical risk factors (for example, three genotypes and multiple categorical environments) are cumbersome.
Furthermore, G × E interactions often exist between categorical environments and are often analysed with character state or multivariate models [ 2].
Adaptation of these scripts for the modeling of more than two siblings, or categorical environments with multiple levels is quite straightforward.
The categorical ANOVA including genotype, environment, and G × E as fixed factors, identified 1,606 genes demonstrating a significant interaction between copper environment and genotype (G × E; P < 0.001).
Habitat, a categorical factor describing the environment where the organisms are usually found, was divided into five branches: aquatic, host-associated, terrestrial, specialized (extremophiles) and multiple (same species found in many different environments).
Albert et al. (6), Armstrong (7), and Cheng (8) explored extensions to continuous environment and categorical genotype variables using both logistic and multinomial regression.
Convergent evidence supporting cultural diversity has been found in scene perception [10], [11], description [10] and categorization [12], showing that Westerners focus analytically on salient objects and use categorical rules when organizing their environment.
In these models the gene and environment variables (both categorical) will be used in the model as predictor variables in addition to a variable that represents the interaction between the gene and environment variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com