ArticleAmerican journal of human genetics2007
Information-theoretic metrics for visualizing gene-environment interactions.
Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
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- Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts.Scientific reports · 2019Article
- Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.Briefings in bioinformatics · 2018Review
- Entropy, or Information, Unifies Ecology and Evolution and Beyond.Entropy (Basel, Switzerland) · 2018Review
- CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.BMC bioinformatics · 2016Article
- An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.BioMed research international · 2015Article
- Practical aspects of genome-wide association interaction analysis.Human genetics · 2014Review
- Synergy, redundancy, and multivariate information measures: an experimentalist's perspective.Journal of computational neuroscience · 2014Review
- IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis.BMC medical genomics · 2014Article
- SYMPHONY, an information-theoretic method for gene-gene and gene-environment interaction analysis of disease syndromes.Heredity · 2013Article
- Challenges and opportunities in genome-wide environmental interaction (GWEI) studies.Human genetics · 2012Review
- Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction.PloS one · 2012Article
- Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases.Genetic epidemiology · 2011Article
- Modeling of environmental and genetic interactions with AMBROSIA, an information-theoretic model synthesis method.Heredity · 2011Article
- SNPsyn: detection and exploration of SNP-SNP interactions.Nucleic acids research · 2011Article
- Uncovering hidden variance: pair-wise SNP analysis accounts for additional variance in nicotine dependence.Human genetics · 2011Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The purpose of our work was to develop heuristics for visualizing and interpreting gene-environment interactions (GEIs) and to assess the dependence of candidate visualization metrics on biological and study-design factors. Two information-theoretic metrics, the k-way interaction information (KWII) and the total correlation information (TCI), were investigated. The effectiveness of the KWII and TCI to detect GEIs in a diverse range of simulated data sets and a Crohn disease data set was assessed. The sensitivity of the KWII and TCI spectra to biological and study-design variables was determined. Head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibrium test (PDT) methods were obtained. The KWII and TCI spectra, which are graphical summaries of the KWII and TCI for each subset of environmental and genotype variables, were found to detect each known GEI in the simulated data sets. The patterns in the KWII and TCI spectra were informative for factors such as case-control misassignment, locus heterogeneity, allele frequencies, and linkage disequilibrium. The KWII and TCI spectra were found to have excellent sensitivity for identifying the key disease-associated genetic variations in the Crohn disease data set. In head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results from visual interpretation of the KWII and TCI spectra performed satisfactorily. The KWII and TCI are promising metrics for visualizing GEIs. They are capable of detecting interactions among numerous single-nucleotide polymorphisms and environmental variables for a diverse range of GEI models.
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