Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

2000 ◽  
Vol 100 (2) ◽  
pp. 285-298 ◽  
Author(s):  
M. Brancourt-Hulmel ◽  
J. -B. Denis ◽  
C. Lecomte
Keyword(s):  
Winter Wheat ◽  
Environment Interaction ◽  
Environmental Covariates ◽  
Factorial Regression ◽  
Genotype Environment Interaction ◽  
Probe Genotypes

METHODS FOR EVALUATING THE GENOTYPE-ENVIRONMENT INTERACTION IN BREEDING WINTER WHEAT FOR PRODUCTIVITY

2020 ◽  
Vol 15 (1) ◽  
pp. 56-64
Author(s):  
Irina Manukyan ◽  
◽  
Madina Basieva ◽  
Elena Miroshnikova ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Environment Interaction ◽  
Genotype Environment Interaction

scholarly journals Genotype × environment interaction and stability of grain yield and selected quality traits in winter wheat in Central Asia

2015 ◽  
Vol 39 ◽  
pp. 920-929 ◽  
Author(s):  
Shakhnoza KHAZRATKULOVA ◽  
Ram C. SHARMA ◽  
Amir AMANOV ◽  
Zokhid ZIYADULLAEV ◽  
Oybek AMANOV ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Central Asia ◽  
Environment Interaction ◽  
Quality Traits ◽  
Genotype Environment Interaction

scholarly journals Genotype × Environment Interaction in Psychiatric Genetics: Deep Truth or Thin Ice?

2017 ◽  
Vol 20 (3) ◽  
pp. 187-196 ◽  
Author(s):  
Lindon Eaves
Keyword(s):  
Genetic Model ◽  
Clinical Symptoms ◽  
Theory Of Measurement ◽  
Risk Scores ◽  
Environment Interaction ◽  
Psychiatric Genetics ◽  
Environmental Covariates ◽  
Genotype Environment Interaction ◽  
Sum Scores ◽  
Method Of Assessment

Background:There continues to be significant investment in the detection of genotype × environment interaction (G × E) in psychiatric genetics. The implications of the method of assessment for the genetic analysis of psychiatric disorders are examined for simulated twin data on symptom scores and environmental covariates.Methods: Additive and independent genetic and environmental risks were simulated for 10,000 monozygotic (MZ) and 10,000 dizygotic (DZ) twin pairs and the ‘subjects’ administered typical simulated checklists of clinical symptoms and environmental factors. A variety of standard tests for G × E were applied to the simulated additive risk scores, sum scores derived from the checklists and transformed sum scores.Results:All analyses revealed no evidence for G × E for latent risk but marked evidence for G × E and other effects of modulation in the sum scores. These effects were all removed by transformation. An integrated genetic and psychometric model, accounting for both the causes of latent liability and a theory of measurement, was fitted to a sample of the simulated sum-score data and showed that there was no significant modulation of the parameters of the genetic model by environmental covariates (i.e., no G × E).Conclusions:Claims to detect G × E based on analytical methods that ignore the theory of measurement must be subjected to greater scrutiny prior to publication.

Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models

2005 ◽  
Vol 56 (9) ◽  
pp. 883 ◽  
Author(s):  
Fred A. van Eeuwijk ◽  
Marcos Malosetti ◽  
Xinyou Yin ◽  
Paul C. Struik ◽  
Piet Stam
Keyword(s):  
Statistical Models ◽  
Regression Models ◽  
Physiological Model ◽  
Environment Interaction ◽  
Response Curves ◽  
Physiological Models ◽  
Factorial Regression ◽  
Multiple Quantitative Trait Locus ◽  
Bilinear Models ◽  
Genotype Environment Interaction

To study the performance of genotypes under different growing conditions, plant breeders evaluate their germplasm in multi-environment trials. These trials produce genotype × environment data. We present statistical models for the analysis of such data that differ in the extent to which additional genetic, physiological, and environmental information is incorporated into the model formulation. The simplest model in our exposition is the additive 2-way analysis of variance model, without genotype × environment interaction, and with parameters whose interpretation depends strongly on the set of included genotypes and environments. The most complicated model is a synthesis of a multiple quantitative trait locus (QTL) model and an eco-physiological model to describe a collection of genotypic response curves. Between those extremes, we discuss linear-bilinear models, whose parameters can only indirectly be related to genetic and physiological information, and factorial regression models that allow direct incorporation of explicit genetic, physiological, and environmental covariables on the levels of the genotypic and environmental factors. Factorial regression models are also very suitable for the modelling of QTL main effects and QTL × environment interaction. Our conclusion is that statistical and physiological models can be fruitfully combined for the study of genotype × environment interaction.

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