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This article is part of the supplement: Selected articles from the Ninth Asia Pacific Bioinformatics Conference (APBC 2011)

Open Access Research

Gene-gene interaction filtering with ensemble of filters

Pengyi Yang123*, Joshua WK Ho13, Yee Hwa Yang2 and Bing B Zhou14*

Author Affiliations

1 School of Information Technologies, University of Sydney, NSW 2006, Australia

2 School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia

3 National ICT Australia, Australian Technology Park, Eveleigh, NSW 2015, Australia

4 Centre for Distributed and High Performance Computing, University of Sydney, NSW 2006, Australia

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BMC Bioinformatics 2011, 12(Suppl 1):S10  doi:10.1186/1471-2105-12-S1-S10

Published: 15 February 2011

Abstract

Background

Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the ‘unstable’ results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance.

Results

We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.