The effect of alternative permutation testing strategies on the performance of multifactor dimensionality reduction.

ABSTRACT:

Background

Multifactor Dimensionality Reduction (MDR) is a novel method developed to detect gene-gene interactions in case-control association analysis by exhaustively searching multi-locus combinations. While the end-goal of analysis is hypothesis generation, significance testing is employed to indicate statistical interest in a resulting model. Because the underlying distribution for the null hypothesis of no association is unknown, non-parametric permutation testing is used. Lately, there has been more emphasis on selecting all statistically significant models at the end of MDR analysis in order to avoid missing a true signal. This approach opens up questions about the permutation testing procedure. Traditionally omnibus permutation testing is used, where one permutation distrib

SUBMITTER: Motsinger-Reif AA 

PROVIDER: S-EPMC2631601 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature