Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Friday, July 05, 2013

A simple extension of Multifactor Dimensionality Reduction (MDR) for detecting epistasis effects on quantitative traits

Our Multifactor Dimensionality Reduction (MDR) method and software was developed for detecting and characterizing epistasis or gene-gene interactions in population-based studies. We present here a simple and computationally-efficient extension of MDR that can be used to detect epistasis effects on quantitative traits. This approach is implemented in version 3.0 of our open-source MDR software package.

Gui J, Moore JH, Williams SM, Andrews P, Hillege HL, van der Harst P, Navis G, Van Gilst WH, Asselbergs FW, Gilbert-Diamond D. A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits. PLoS One. 2013 Jun 21;8(6):e66545. [PubMed] [PLoS One] [PDF]


We present an extension of the two-class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of a quantitative trait. The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR's constructive induction algorithm to use a T-test. QMDR replaces the balanced accuracy metric with a T-test statistic as the score to determine the best interaction model. We used a simulation to identify the empirical distribution of QMDR's testing score. We then applied QMDR to genetic data from the ongoing prospective Prevention of Renal and Vascular End-Stage Disease (PREVEND) study.