Literature DB >> 25403536

Epistasis analysis using information theory.

Jason H Moore1, Ting Hu.   

Abstract

Here we introduce entropy-based measures derived from information theory for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the methods and highlight some of the modifications that have greatly improved its power for genetic analysis. We end with a few published studies of complex human diseases that have used these measures.

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Year:  2015        PMID： 25403536     DOI： 10.1007/978-1-4939-2155-3_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN： 1064-3745


  9 in total

1.  Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity.

Authors:  Rishika De; Ting Hu; Jason H Moore; Diane Gilbert-Diamond
Journal:  BioData Min       Date:  2015-12-29       Impact factor: 2.522

2.  Bayesian reversible-jump for epistasis analysis in genomic studies.

Authors:  Marcio Balestre; Claudio Lopes de Souza
Journal:  BMC Genomics       Date:  2016-12-09       Impact factor: 3.969

Review 3.  Transferring entropy to the realm of GxG interactions.

Authors:  Paola G Ferrario; Inke R König
Journal:  Brief Bioinform       Date:  2018-01-01       Impact factor: 11.622

4.  An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma.

Authors:  J Magaña; M G Contreras; K L Keys; O Risse-Adams; P C Goddard; A M Zeiger; A C Y Mak; J R Elhawary; L A Samedy-Bates; E Lee; N Thakur; D Hu; C Eng; S Salazar; S Huntsman; T Hu; E G Burchard; M J White
Journal:  BioData Min       Date:  2020-07-03       Impact factor: 2.522

Review 5.  How to increase our belief in discovered statistical interactions via large-scale association studies?

Authors:  K Van Steen; J H Moore
Journal:  Hum Genet       Date:  2019-03-06       Impact factor: 4.132

Review 6.  Entropy, or Information, Unifies Ecology and Evolution and Beyond.

Authors:  William Bruce Sherwin
Journal:  Entropy (Basel)       Date:  2018-09-21       Impact factor: 2.524

7.  Information Theory in Computational Biology: Where We Stand Today.

Authors:  Pritam Chanda; Eduardo Costa; Jie Hu; Shravan Sukumar; John Van Hemert; Rasna Walia
Journal:  Entropy (Basel)       Date:  2020-06-06       Impact factor: 2.524

8.  Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.

Authors:  Jason H Moore; Peter C Andrews; Randal S Olson; Sarah E Carlson; Curt R Larock; Mario J Bulhoes; James P O'Connor; Ellen M Greytak; Steven L Armentrout
Journal:  BioData Min       Date:  2017-05-30       Impact factor: 2.522

Review 9.  Predicting Physical Appearance from DNA Data-Towards Genomic Solutions.

Authors:  Ewelina Pośpiech; Paweł Teisseyre; Jan Mielniczuk; Wojciech Branicki
Journal:  Genes (Basel)       Date:  2022-01-10       Impact factor: 4.096
  9 in total

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