A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

TLDR

We extend the two‑class multifactor dimensionality reduction (MDR) algorithm to detect and characterize epistatic SNP‑SNP interactions for quantitative traits. The Quantitative MDR (QMDR) method modifies MDR’s constructive induction to use a T‑test, replaces balanced accuracy with a T‑test statistic for model scoring, and is validated by simulation and applied to genetic data from the PREVEND study.

Abstract

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.