Genotype-by-Environment Interactions

  • P. M. Priyadarshan


A phenotype is the function of a genotype, the environment and the differential response of genotypes to different environments. This is known as genotype-by-environment (G × E) interaction. G × E is a statistical decomposition of variance and provides a measure of the relative performance of genotypes grown under different environments. These interactions were managed and analysed by the plant breeders during the history of crop domestication, crop improvement and dispersal.


Statistical models for assessing G × E interactions Genotypes and environments Basic ANOVA and regression models Multiplicative models AMMI analysis Pattern analysis GGE biplot Measures of yield stability Software 



Additive main effect and multiplicative model


Best linear unbiased prediction


Completely multiplicative model


Factor analytic multiplicative mixed model


Factorial regression

G × E

Genotype × environment interaction


Genotype regression model


Linear regression

M × E

Marker × environment interaction


Multi-environment trial


Non-crossover interaction


Principal component analysis


Partial least square regression


Quantitative trait locus

Q × E

QTL × environmental interaction


Shifted multiplicative model


Singular value decomposition


Sites regression model


Target population of environments

Further Reading

  1. Annicchiarico P (1992) Cultivar adaptation and recommendation from alfalfa trials in northern Italy. J Genet Breed 46:269–278Google Scholar
  2. Annicchiarico P (1997) STABSAS: a SAS computer programme for stability analysis. Ital J Agron 1:7–9Google Scholar
  3. Annicchiarico P (1997a) Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica 94:53–62CrossRefGoogle Scholar
  4. Annicchiarico P (1997b) Additive main effects and multiplicative interaction (AMMI) of genotype-location interaction in variety trials repeated over years. Theor Appl Genet 94:1072–1077CrossRefGoogle Scholar
  5. Annicchiarico P (2002) Defining adaptation strategies and yield stability targets in breeding programmes. In: Kang MS (ed) Quantitative genetics, genomics, and plant breeding. CABI, Wallingford, pp 365–383Google Scholar
  6. Cooper M, DeLacy IH, Basford KE (1996) Relationships among analytical methods used to study genotypic adaptation in multi-environment trials. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CABI, Wallingford, pp 193–224Google Scholar
  7. Cornelius PL, Crossa J, Seyedsadr MS (1996) Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. In: Kang MS, Gauch HG (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 199–234Google Scholar
  8. Des Marais DL, Hernandez KM, Juenger TE (2013) Genotype-by-environment interaction and plasticity: exploring genomic responses of plants to the abiotic environment. Annu Rev Ecol Evol Syst 44:5–29CrossRefGoogle Scholar
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  12. Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol.
  13. Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228CrossRefGoogle Scholar
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  15. Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000a) Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci 40:596–605CrossRefGoogle Scholar
  16. Yan W (2014) Crop variety trials: data management and analysis. Wiley/Blackwell, HobokenCrossRefGoogle Scholar
  17. Yan W, Kang MS (2003) GGE Biplot analysis: a graphical tool for breeders, geneticists and agronomists. CRC Press, Boca RatonGoogle Scholar
  18. Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000b) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • P. M. Priyadarshan
    • 1
  1. 1.Erstwhile Deputy DirectorRubber Research Institute of IndiaKottayamIndia

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