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...

Behavioral Genetics and Its Designs

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

You might also read

Related Articles

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

Sort by

Same author

Leveraging automated machine learning to benchmark, deconstruct, and compare frailty indices for predicting adverse spinal surgery outcomes.

Scientific reports·2026

Same author

Beyond P-values: A Multi-Metric Framework for Robust Feature Selection and Predictive Modeling.

bioRxiv : the preprint server for biology·2025

Same author

The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.

Patterns (New York, N.Y.)·2025

Same author

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society·2025

Same author

A review of feature selection strategies utilizing graph data structures and Knowledge Graphs.

Briefings in bioinformatics·2024

Same author

PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies.

BioData mining·2024

Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026

Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026

Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026

Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026

Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026

Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 8, 2025

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

Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis.

Zhendong Sha¹, Philip J Freda², Priyanka Bhandary²

¹School of Computing, Queen's University, 557 Goodwin Hall, 21-25 Union St, Kingston, K7L 2N8, Ontario, Canada.

|December 28, 2024

Summary

This summary is machine-generated.

The exclusive-or (XOR) epistatic model reveals more genetic interactions than the traditional Cartesian model, uncovering higher-order epistasis and novel biological functions in rats. Network science enhances the study of complex genetic architectures.

Keywords:

Community detection Epistasis Higher-order interactions Interaction model Network analysis Network science XOR

More Related Videos

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: May 8, 2025

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

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Genetics and Systems Biology
Network Science in Genomics

Background:

Epistasis significantly influences complex traits, but traditional models like the Cartesian epistatic model may miss many genetic interactions.
The exclusive-or (XOR) epistatic model shows potential for detecting a wider range of interactions and identifying biologically relevant functions.

Purpose of the Study:

To investigate if the XOR epistatic model generates distinct network structures compared to the Cartesian model.
To apply network science to analyze genetic interactions underlying body mass index (BMI) in rats.

Main Methods:

Comparative network analysis of XOR and Cartesian epistatic models in rat BMI data.
Community-based enrichment analysis and motif analysis.
Evaluation of linkage disequilibrium (LD)-based edge pruning effects.
Network permutation analysis to validate network properties.

Main Results:

XOR and Cartesian models exhibit distinct network topologies.
The XOR model enhances detection of interactions within network communities, aiding in identifying novel trait-related functions.
XOR networks reveal triangle motifs, suggesting higher-order epistasis. LD-based pruning can fragment networks.
Permutation analysis confirmed distinct structural properties of derived networks.

Conclusions:

The XOR model effectively uncovers biological associations and higher-order epistasis.
Community-based and motif-based analyses are valuable for discovering epistatic interactions.
Network science is crucial for advancing epistasis research and understanding complex genetic architectures.