Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

Epistasis

Epistasis

In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Statistical knockoffs improve biomarker discovery from transcriptomic data.

Briefings in bioinformatics·2026

Same author

Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings.

GigaScience·2026

Same author

Machine learning in Alzheimer's disease genetics.

Nature communications·2025

Same author

Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification.

Communications medicine·2025

Same author

Noninvasive Multicancer Detection Using DNA Hypomethylation of LINE-1 Retrotransposons.

Clinical cancer research : an official journal of the American Association for Cancer Research·2024

Same author

Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.

Journal of chemical information and modeling·2024

Same journal

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores For Single-Molecule Sensing.

GigaScience·2026

Same journal

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

GigaScience·2026

Same journal

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

GigaScience·2026

Same journal

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026

Same journal

ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models.

GigaScience·2026

Same journal

ClusterGraph: a new tool for visualisation and compression of multidimensional data.

GigaScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 4, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Interpretable network-guided epistasis detection.

Diane Duroux¹, Héctor Climente-González^2,3,4,5, Chloé-Agathe Azencott^2,3,4

¹BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liège, 4000 Liège, Belgium, 11 Liège 4000, Belgium.

|February 8, 2022

Summary

This summary is machine-generated.

This study introduces a novel network-based method for detecting gene-gene epistatic interactions, crucial for understanding complex diseases. The approach enhances statistical power and biological interpretability in genome-wide association studies.

Keywords:

biology-informed analysis epistasis network gene-gene interaction inflammatory bowel disease systems biology

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Related Experiment Videos

Last Updated: Oct 4, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Epistatic interactions between genes are vital for complex disease etiology.
Genome-wide association studies (GWAS) face statistical challenges in detecting these interactions.
Current methods struggle with mapping gene interactions to single-nucleotide polymorphism (SNP) interaction hypotheses.

Purpose of the Study:

To develop and evaluate a multi-step protocol for epistasis detection using gene-gene co-function networks.
To reduce the number of statistical tests and control for type I errors in GWAS.
To provide interpretable gene interaction insights for complex diseases.

Main Methods:

A network-based approach utilizing gene-gene co-function networks.
Comparison of three SNP-gene mapping strategies: positional overlap, expression quantitative trait loci (eQTLs), and 3D structure proximity.
Application of the adaptive truncated product method for non-parametric gene pair score computation.

Main Results:

The protocol was applied to a GWAS dataset for inflammatory bowel disease (IBD).
Different protocol configurations yielded distinct results, suggesting diverse IBD mechanisms.
Results showed overlap with known IBD characteristics and outperformed conventional non-network approaches.

Conclusions:

Incorporating prior biological knowledge via networks enhances epistasis detection in GWAS.
The proposed method offers a powerful strategy for discovering gene interactions underlying complex diseases.
This network-guided approach has the potential for significant additional discoveries in genetic association studies.