## 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?

Originalsprache | Englisch |
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Zeitschrift | Briefings in bioinformatics |

Jahrgang | 19 |

Ausgabenummer | 1 |

Seiten (von - bis) | 136-147 |

Seitenumfang | 12 |

ISSN | 1467-5463 |

DOIs | |

Publikationsstatus | Veröffentlicht - 01.01.2018 |

## Strategische Forschungsbereiche und Zentren

- Querschnittsbereich: Medizinische Genetik