Transferring Entropy to the Realm of GxG Interactions

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2016 Oct 23

PMID 27769993

Citations 7

Authors

Paola G Ferrario

Inke R Konig

Affiliations

Soon will be listed here.

Abstract

Genome-wide association studies are moving to genome-wide interaction studies, as the genetic background of many diseases appears to be more complex than previously supposed. Thus, many statistical approaches have been proposed to detect gene-gene (GxG) interactions, among them numerous information theory-based methods, inspired by the concept of entropy. These are suggested as particularly powerful and, because of their nonlinearity, as better able to capture nonlinear relationships between genetic variants and/or variables. However, the introduced entropy-based estimators differ to a surprising extent in their construction and even with respect to the basic definition of interactions. Also, not every entropy-based measure for interaction is accompanied by a proper statistical test. To shed light on this, a systematic review of the literature is presented answering the following questions: (1) How are GxG interactions defined within the framework of information theory? (2) Which entropy-based test statistics are available? (3) Which underlying distribution do the test statistics follow? (4) What are the given strengths and limitations of these test statistics?

Citing Articles

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Optimized permutation testing for information theoretic measures of multi-gene interactions.

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