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Open Access Methodology article

Artificial neural networks modeling gene-environment interaction

Frauke Günther1, Iris Pigeot1 and Karin Bammann12*

Author affiliations

1 BIPS - Institute for Epidemiology and Prevention Research GmbH, Bremen 28359, Achterstraße 30, Germany

2 University of Bremen, Institute of Public Health and Nursing Science (IPP), Bremen 28359, Grazer Straße 4, Germany

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Citation and License

BMC Genetics 2012, 13:37  doi:10.1186/1471-2156-13-37

Published: 14 May 2012



Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only.


In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor.


Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

Gene-environment interaction; Multilayer perceptron; MLP; Neural network; Pattern recognition; Simulation study