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?

Original languageEnglish
JournalBriefings in bioinformatics
Volume19
Issue number1
Pages (from-to)136-147
Number of pages12
ISSN1467-5463
DOIs
Publication statusPublished - 01.01.2018

Research Areas and Centers

  • Research Area: Medical Genetics

Fingerprint

Dive into the research topics of 'Transferring entropy to the realm of GxG interactions'. Together they form a unique fingerprint.

Cite this