Using information interaction to discover epistatic effects in complex diseases.

ABSTRACT: It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show stronger effects when considered together than when considered individually, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of information interaction to discover pairwise epistatic effects related with complex diseases. We start by showing that traditional approaches such as classification methods or greedy feature selection methods (such as the Fleuret method) do not perform well on this problem. We then compare our information interaction method with BEAM and SNPHarvester in artificial datasets simulating epistatic interactions and show that our method is more powerf

SUBMITTER: Anunciacao O 

PROVIDER: S-EPMC3806769 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature