ArticleBMC medical genomics2020
Modified entropy-based procedure detects gene-gene-interactions in unconventional genetic models.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- DNA Sequence Perplexity Reveals Evolutionarily Conserved Patterns in cis-Regulatory Regions Across Diverse Species.Biochemical genetics · 2026Article
- A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.Interdisciplinary sciences, computational life sciences · 2024Article
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Roles of interacting stress-related genes in lifespan regulation: insights for translating experimental findings to humans.Journal of translational genetics and genomics · 2021Article
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2 authors.
Funding
Abstract
backgroundSince it is assumed that genetic interactions play an important role in understanding the mechanisms of complex diseases, different statistical approaches have been suggested in recent years for this task. One interesting approach is the entropy-based IGENT method by Kwon et al. that promises an efficient detection of main effects and interaction effects simultaneously. However, a modification is required if the aim is to only detect interaction effects.
methodsBased on the IGENT method, we present a modification that leads to a conditional mutual information based approach under the condition of linkage equilibrium. The modified estimator is investigated in a comprehensive simulation based on five genetic interaction models and applied to real data from the genome-wide association study by the North American Rheumatoid Arthritis Consortium (NARAC).
resultsThe presented modification of IGENT controls the type I error in all simulated constellations. Furthermore, it provides high power for detecting pure interactions specifically on unconventional genetic models both in simulation and real data.
conclusionsThe proposed method uses the IGENT software, which is free available, simple and fast, and detects pure interactions on unconventional genetic models. Our results demonstrate that this modification is an attractive complement to established analysis methods.
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