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Analysis of Genotype × Environment Interaction (G×E) Using SAS Programming
Author(s) -
Dia Mahendra,
Wehner Todd C.,
Arellano Consuelo
Publication year - 2016
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2016.02.0085
Subject(s) - univariate , statistics , stability (learning theory) , bootstrapping (finance) , statistic , descriptive statistics , mathematics , multivariate statistics , computer science , econometrics , machine learning
Agronomy Journa l • Volume 108 , I s sue 5 • 2016 Genotype ́ environment interaction (G ́E) refers to the modifi cation of genetic factors by environmental factors and to the role of genetic factors in determining the performance of genotypes in diff erent environments. A G ́E can occur for quantitative traits of economic importance and is oft en studied in plant and animal breeding, genetic epidemiology, pharmacogenomics, and conservation biology research. Th e traits include reproductive fi tness, longevity, height, weight, yield, and disease resistance. Selection of superior genotypes in target environments is an important objective of plant breeding programs. A target environment is a production environment used by growers. To identify superior genotypes across multiple environments, plant breeders conduct trials across locations and years, especially during the fi nal stages of cultivar development. A G ́E is said to exist when diff erences in genotype performance across environments result in a scale shift or a rank shift . Performance of genotypes can vary greatly across environments because of the eff ect of the environment on trait expression. Th erefore, cultivars with high and stable performance are of value. Because it is impossible to test genotypes in all target environments, plant breeders do indirect selection using their own multiple-environment trials or test environments. Genotype ́ environment interaction reduces the predictability of the performance of genotypes in target environments based on genotype performance in test environments. An important factor in plant breeding is the selection of suitable test locations because it accounts for G ́E and maximizes gain from selection (Yan et al., 2011). An effi cient test location is discriminating and is representative of the target environments for the cultivars to be released. Discriminating locations can detect diff erences among genotypes with only a few replications. Representative locations will make it likely that the genotypes selected will perform well in the target environments (Yan et al., 2011). Analysis of variance (ANOVA) is useful in determining the existence, size, and signifi cance of G ́E. To determine G ́E Analysis of Genotype ́ Environment Interaction (G ́E) Using SAS Programming