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Cereals pp 291-331 | Cite as

Statistical Analyses of Genotype by Environment Data

  • Ignacio Romagosa
  • Fred A. van Eeuwijk
  • William T.B. Thomas
Chapter
Part of the Handbook of Plant Breeding book series (HBPB, volume 3)

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

Keywords

Quantitative Trait Locus Quantitative Trait Locus Effect Marker Trait Association Bilinear Model Bilinear Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Contribution of the other partners of the EU FP5 INCO-MED project ‘Mapping Adaptation of Barley to Droughted Environments’ in assembling this data set is highly appreciated, namely, Salvatore Ceccarelli and Stefania Grando from ICARDA, Syria; Michele Stanca from the Istituto Sperimentale per la Cerealicoltura, Italy; José Luis Molina-Cano and Alexander Pswarayi from the Centre UdL-IRTA, Spain; Tanner Akar from the Central Research for Field Crops, Turkey; Adnan Al-Yassin from NCARTT, Jordan; Abdelkader Benbelkacem from ITGC, Algeria; Mohammed Karrou and Hassan Ouabbou from INRA, Morocco; Nicola Pecchioni and Enrico Francia from the Università di Modena e Reggio Emilia, Italy; Wafaa Choumane from Tishreen University, Latakia, Syria; and Jordi Bort and José Luis Araus from the University of Barcelona. We also want to express our gratitude to Jordi Comadran, Joanne Russell from SCRI for providing the marker data used for association mapping and to Christine Hackett from BioSS for fruitful statistical discussions. The above work was funded by the European Union-INCO-MED program (ICA3-CT2002-10026). The Centre UdL-IRTA acknowledges partial funding from grant AGL2005-07195-C02-02 from the Spanish Ministry of Science and Education.

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Copyright information

© Springer Science + Business Media, LLC 2009

Authors and Affiliations

  • Ignacio Romagosa
    • 1
  • Fred A. van Eeuwijk
  • William T.B. Thomas
  1. 1.Centre UdL-IRTAUniversity of LleidaSpain

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