ArticleComputational and mathematical methods in medicine2022
Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 2 citations in OpenAlex.
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- Genome-wide epistasis analysis reveals significant epistatic signals associated with Parkinson's disease risk.Brain : a journal of neurology · 2025Article
- Detecting Trivariate Associations in High-Dimensional Datasets.Sensors (Basel, Switzerland) · 2022Article
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6 authors at 3 institutions in 1 country.
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Abstract
Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.
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