Neural networks for modeling gene-gene interactions in association studies.
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.
Günther
Frauke
F
University of Bremen, Bremen Institute for Prevention Research and Social Medicine, Linzer Strasse 10, 28359 Bremen, Germany. guenther@bips.uni-bremen.de
Wawro
Nina
N
Bammann
Karin
K
D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D016015:Logistic Models; D008957:Models, Genetic; D016571:Neural Networks, Computer