Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 387748
Title Statistical Analysis of Genotype by Environment Data
Author(s) Romagosa, I.; Eeuwijk, F.A. van; Thomas, W.T.B.
Source In: Handbook for Plant Breeding. Vol. 3. Cereals. Pt. 2 / Carena, M.J., Springer Science + Business Media - ISBN 9780387722979 - p. 1 - 41.
DOI https://doi.org/10.1007/978-0-387-72297-9_10
Department(s) Biometris (WU MAT)
PE&RC
Publication type Peer reviewed book chapter
Publication year 2009
Abstract We introduce in this chapter a series of linear and bilinear models for the study of genotype by environment interaction (GE) and adaptation. These models increasingly incorporate available genetic, physiological, and environmental information for modelling genotype by environment interaction (GE). They are based on analyses of variance and regression and can be formulated in most standard statistical packages. We use the data of a series of trials for 65 barley genotypes (G) grown in 12 environments (E) for illustration and interpretation of the output of such analyses. We aim at identifying key environmental covariables to explain differential phenotypic responses as well as to estimate genotypic sensitivities to these covariables. Using genetic covariables in the form of molecular markers, we partition genotypic main effect terms and GE terms into main effects for quantitative trait loci (QTL) and QTL by environment interaction (QTL.E). The QTL.E estimates can be further regressed on environmental covariables to target differential QTL expression potentially related to environmental factors. We believe that the statistical models that describe GE in direct association to genetic, physiological, and environmental information provide insight in GE and facilitate the development and deployment of new breeding strategies
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