Power of data mining methods to detect genetic associations and interactions.

ABSTRACT:

Background

Genetic association studies, thus far, have focused on the analysis of individual main effects of SNP markers. Nonetheless, there is a clear need for modeling epistasis or gene-gene interactions to better understand the biologic basis of existing associations. Tree-based methods have been widely studied as tools for building prediction models based on complex variable interactions. An understanding of the power of such methods for the discovery of genetic associations in the presence of complex interactions is of great importance. Here, we systematically evaluate the power of three leading algorithms: random forests (RF), Monte Carlo logic regression (MCLR), and multifactor dimensionality reduction (MDR).

Methods

We use the algorithm-specific variable importance m

SUBMITTER: Molinaro AM 

PROVIDER: S-EPMC3222116 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature