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Open Access Highly Accessed Methodology article

Neural networks for modeling gene-gene interactions in association studies

Frauke Günther*, Nina Wawro and Karin Bammann

Author affiliations

University of Bremen, Bremen Institute for Prevention Research and Social Medicine (BIPS), Linzer Straße 10, 28359 Bremen, Germany

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Citation and License

BMC Genetics 2009, 10:87  doi:10.1186/1471-2156-10-87

Published: 23 December 2009

Abstract

Background

Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.

Results

The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.

Conclusions

Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.