Email updates

Keep up to date with the latest news and content from BioData Mining and BioMed Central.

Open Access Methodology

Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

Hongying Dai1*, Richard J Charnigo3, Mara L Becker2, J Steven Leeder2 and Alison A Motsinger-Reif4

Author Affiliations

1 Research Development and Clinical Investigation, Children’s Mercy Hospital, Kansas City, MO, 64108, USA

2 Division of Clinical Pharmacology and Medical Toxicology, Department of Pediatrics, Children’s Mercy Hospital, Kansas City, MO, 64108, USA

3 Department of Statistics, University of Kentucky, Lexington, KY, 40506, USA

4 Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC, 27695, USA

For all author emails, please log on.

BioData Mining 2013, 6:1  doi:10.1186/1756-0381-6-1

Published: 8 January 2013

Abstract

Background

Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account.

Results

We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX) that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy.

Conclusions

The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi) are proposed to detect multiple GxG interactions.

Keywords:
A-MDR; Epistasis enriched risk score; Epistasis enriched gene network; pRR; pOR; pChi