%A Malosetti,Marcos
%A Ribaut,Jean-Marcel
%A van Eeuwijk,Fred A
%D 2013
%J Frontiers in Physiology
%C
%F
%G English
%K adaptation,Genotype by environment interaction,Multi-environment trials,QTL by environment interaction,QTL mapping methodology,REML
%Q
%R 10.3389/fphys.2013.00044
%W
%L
%N 44
%M
%P
%7
%8 2013-March-12
%9 Methods
%+ Dr Marcos Malosetti,Wageningen University,Biometris,Wageningen,Netherlands,marcos.malosetti@wur.nl
%#
%! Statistical models for genotype and QTL-by-environment interaction
%*
%<
%T The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis
%U https://www.frontiersin.org/article/10.3389/fphys.2013.00044
%V 4
%0 JOURNAL ARTICLE
%@ 1664-042X
%X Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”