ArticleBMC genomics2009
Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits.
Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
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- Identification of the associations between genes and quantitative traits using entropy-based kernel density estimation.Genomics & informatics · 2022Article
- Optimized permutation testing for information theoretic measures of multi-gene interactions.BMC bioinformatics · 2021Article
- Information Theory in Computational Biology: Where We Stand Today.Entropy (Basel, Switzerland) · 2020Article
- ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction.BMC medical genomics · 2019Article
- Entropy, or Information, Unifies Ecology and Evolution and Beyond.Entropy (Basel, Switzerland) · 2018Review
- Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification.BMC bioinformatics · 2018Article
- CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.BMC bioinformatics · 2016Article
- Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure.BioMed research international · 2015Article
- An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.BioMed research international · 2015Article
- Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model.PloS one · 2015Article
- SYMPHONY, an information-theoretic method for gene-gene and gene-environment interaction analysis of disease syndromes.Heredity · 2013Article
- A modified entropy-based approach for identifying gene-gene interactions in case-control study.PloS one · 2013Article
- Challenges and opportunities in genome-wide environmental interaction (GWEI) studies.Human genetics · 2012Review
- Dissecting the genetic architecture of host-pathogen specificity.PLoS pathogens · 2010Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the k-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).
resultsThe KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.
conclusionThe KWII and PAI are promising metrics for analyzing the GEI of QT.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.