title: Transferring entropy to the realm of GxG interactions.
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?
authors: Paola G
authors: PG
authors: Ferrario
authors: Inke R
authors: IR
authors: König
mesh_terms: D019277:Entropy; D004843:Epistasis, Genetic; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D006801:Humans; D007257:Information Theory; D008957:Models, Genetic; D015233:Models, Statistical
keywords: entropy; estimation; genetic interactions; information theory