ArticleJournal of the American Medical Informatics Association : JAMIA
An information-gain approach to detecting three-way epistatic interactions in genetic association studies.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.
- Transferring entropy to the realm of GxG interactions.Briefings in bioinformatics · 2018Pooled it
- A new score with superior stroke risk prediction in atrial fibrillation: entropy-based information gain approaches in a large nationwide cohort.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2025Review
- Review on GPU accelerated methods for genome-wide SNP-SNP interactions.Molecular genetics and genomics : MGG · 2024Review
- Synergistic Epistasis and Systems Biology Approaches to Uncover a Pharmacogenomic Map Linked to Pain, Anti-Inflammatory and Immunomodulating Agents (PAIma) in a Healthy Cohort.Cellular and molecular neurobiology · 2024Article
- Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies.BioData mining · 2024Article
- Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information.BMC bioinformatics · 2023Article
- NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution.Bioinformatics advances · 2023Article
- Article
- Epistatic genetic interactions govern morphogenesis during sexual reproduction and infection in a global human fungal pathogen.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes.Biology · 2021Article
- Optimized permutation testing for information theoretic measures of multi-gene interactions.BMC bioinformatics · 2021Article
- A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions.BioData mining · 2021Article
- A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data.IEEE open journal of engineering in medicine and biology · 2021Article
- Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence.PLoS genetics · 2021Article
- A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits.G3 (Bethesda, Md.) · 2020Article
- Genetic interactions among Pto-miR319 family members and their targets influence growth and wood properties in Populus tomentosa.Molecular genetics and genomics : MGG · 2020Article
- Information Theory in Computational Biology: Where We Stand Today.Entropy (Basel, Switzerland) · 2020Article
- Detection of epistasis between ACTN3 and SNAP-25 with an insight towards gymnastic aptitude identification.PloS one · 2020Article
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- Entropy, or Information, Unifies Ecology and Evolution and Beyond.Entropy (Basel, Switzerland) · 2018Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 4 countries.
Funding
Abstract
backgroundEpistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.
objectivesIn this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.
methodsSuch a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.
resultsOur method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.
conclusionOur study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.
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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.