ArticlePloS one2014
Discovering pair-wise genetic interactions: an information theory-based approach.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
- Article
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Optimized permutation testing for information theoretic measures of multi-gene interactions.BMC bioinformatics · 2021Article
- Article
- Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application.BMC genomics · 2018Article
- The Information Content of Discrete Functions and Their Application in Genetic Data Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Discovering Pair-wise Synergies in Microarray Data.Scientific reports · 2016Article
- Informative gene selection and the direct classification of tumors based on relative simplicity.BMC bioinformatics · 2016Article
- Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.
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Registered trials
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