Statistical models for studying and understanding genotype × environment interaction in an era of climate change and increased genetic information.

2011 ◽  
pp. 263-283 ◽  
Author(s):  
J. Crossa ◽  
J. Burgueño ◽  
M. Vargas
Keyword(s):  
Climate Change ◽  
Statistical Models ◽  
Genetic Information ◽  
Environment Interaction ◽  
Genotype Environment Interaction

Comparative Analysis of Statistical Models for Evaluating Genotype × Environment Interaction in Rainfed Safflower

2017 ◽  
Vol 6 (4) ◽  
pp. 455-465 ◽  
Author(s):  
Khoshnood Alizadeh ◽  
Reza Mohammadi ◽  
Abdollah Shariati ◽  
Masoud Eskandari
Keyword(s):  
Comparative Analysis ◽  
Statistical Models ◽  
Environment Interaction ◽  
Genotype Environment Interaction

scholarly journals Climate change has increased genotype-environment interactions in wheat breeding

2020 ◽  
Author(s):  
Wei Xiong ◽  
Matthew Reynolds ◽  
Jose Crossa ◽  
Thomas Payne ◽  
Urs Schulthess ◽  
...  
Keyword(s):  
Climate Change ◽  
Genetic Model ◽  
Climatic Factors ◽  
Wheat Breeding ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Quantitative Genetic ◽  
Quantitative Genetic Model ◽  
Yield Responses ◽  
Wheat Lines

Abstract The International Maize and Wheat Improvement Center (CIMMYT) develops and distributes annually elite wheat lines as international trials worldwide to assess their performance in different environments and utilization by partners for use in breeding or release as varieties. However, as elsewhere, the collaborator test sites where trials are evaluated have experienced climate change, with implications for how adapted wheat genotypes are bred. Using a standard quantitative genetic model and archived datasets for four global spring wheat trials, we show that the genotype-environment-interaction (GEI) has increased by up to 500% over recent decades. Notably crossover has increased over time, a critical indicator of changes in the ranking of cultivar performance in different environments. Climatic factors explain over 70% of the year-to-year variability in GEI and crossover interactions for yield. Examining yield responses of all genotypes in all trial environments from 1985 to 2017 reveals that climate change has increased GEI by ~ 49% and ranking change by ~38%. Genetic improvement of wheat targeted to high-yielding environments has exacerbated this increase, but the performance of new wheat germplasm developed to withstand heat and drought stress is more adapted and stable, offsetting the increase in ranking changes due to the warmer climate.

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.

Linear, bilinear, and linear-bilinear fixed and mixed models for analyzing genotype × environment interaction in plant breeding and agronomy

2010 ◽  
Vol 90 (5) ◽  
pp. 561-574 ◽  
Author(s):  
J. Crossa ◽  
M. Vargas ◽  
A K Joshi
Keyword(s):  
Plant Breeding ◽  
Least Squares ◽  
Statistical Models ◽  
Fixed Effects ◽  
Linear Models ◽  
Environment Interaction ◽  
Bilinear Models ◽  
Genotype Environment Interaction ◽  
Effect Model ◽  
Value Decomposition

The purpose of this manuscript is to review various statistical models for analyzing genotype × environment interaction (GE). The objective is to present parsimonious approaches other than the standard analysis of variance of the two-way effect model. Some fixed effects linear-bilinear models such as the sites regression model (SREG) are discussed, and a mixed effects counterpart such as the factorial analytic (FA) model is explained. The role of these linear-bilinear models for assessing crossover interaction (COI) is explained. One class of linear models, namely factorial regression (FR) models, and one class of bilinear models, namely partial least squares (PLS) regression, allows incorporating external environmental and genotypic covariables directly into the model. Examples illustrating the use of various statistical models for analyzing GE in the context of plant breeding and agronomy are given. Key words: Least squares, singular value decomposition, environmental and genotypic covariables

scholarly journals Some aspects of adaptation and adaptability of barley in European conditions.

1985 ◽  
Vol 33 (3) ◽  
pp. 195-213
Author(s):  
L. Jestin
Keyword(s):  
Statistical Models ◽  
Spring Barley ◽  
Winter Barley ◽  
General View ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
Statistical Procedures ◽  
Genotype Environment Interaction ◽  
Nw Europe ◽  
Recent Extension

After a review of different approaches found in the literature to problems of adaptation and adaptability of barley, attention is paid to the ecophysiological reasons which may explain the recent extension of winter barley cultivation in NW Europe. A brief account is given of cooperative trials carried out in Europe to define spring barley varietal adaptability ("ESBAN" and "JESBT" trials). A general view of current statistical procedures to analyse adaptability and genotype environment interaction patterns is presented. Some indications are given of the use that the breeder can make of ecophysiological methodology and statistical models in breeding barley for wider adaptation. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.)

Phyton ◽  
2010 ◽  
Vol 79 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Kandus M ◽  
D Almorza ◽  
R Boggio Ronceros ◽  
JC Salerno
Keyword(s):  
Zea Mays ◽  
Statistical Models ◽  
Zea Mays L ◽  
Environment Interaction ◽  
Genotype Environment Interaction

Genotype-Environment Interaction in Calf Production. II. Live Measurements and Carcass Traits

1973 ◽  
Vol 36 (3) ◽  
pp. 471-475 ◽  
Author(s):  
T. R. Batra ◽  
W. R. Usborne ◽  
D. G. Grieve ◽  
E. B. Burnside
Keyword(s):  
Carcass Traits ◽  
Environment Interaction ◽  
Genotype Environment Interaction
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