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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Gene-Environment Interactions

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...

You might also read

Related Articles

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

Sort by

Same author

Performance of large language models as an information resource on functional hypothalamic amenorrhea for patients and healthcare professionals.

Frontiers in artificial intelligence·2026

Same author

Differences in overall survival for invasive epithelial ovarian cancer by race and ethnicity: results from the Ovarian Cancer Association Consortium.

British journal of cancer·2026

Same author

Sex, Not Age, Predicts Weight Loss Outcomes With Tirzepatide: A Retrospective Analysis.

Obesity (Silver Spring, Md.)·2026

Same author

United States Menopausal Hormone Therapy Usage Trends: An Observational Study.

Mayo Clinic proceedings·2026

Same author

Revisiting the modifiable areal unit problem in the era of exposome-wide association studies: Assessing the performance of the CDC/ATSDR social vulnerability index at privacy-protecting spatial scales.

Environmental research·2026

Same author

Vision for Exposomics-Scale Investigation: A Heuristic for Exposure-Wide Association Studies (ExWAS).

Environmental science & technology·2026

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: Jun 6, 2026

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

Grammatical evolution decision trees for detecting gene-gene interactions.

Alison A Motsinger-Reif¹, Sushamna Deodhar, Stacey J Winham

¹Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA 27695. motsinger@stat.ncsu.edu.

|November 20, 2010

Summary

This summary is machine-generated.

Grammatical Evolution Decision Trees (GEDT) effectively identify gene-gene interactions for complex diseases. This novel method enhances genetic association studies by detecting epistatic models with high statistical power.

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

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Related Experiment Videos

Last Updated: Jun 6, 2026

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

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

Human genetics
Computational biology
Statistical genetics

Background:

Complex diseases arise from genetic and environmental factors, including gene-gene interactions (epistasis).
Identifying epistatic models is challenging due to the need for statistical modeling and variable selection.
Advancing genotyping technologies increase the number of potential genetic variables, amplifying analytical challenges.

Purpose of the Study:

To introduce and evaluate Grammatical Evolution Decision Trees (GEDT) for detecting gene-gene interactions.
To address the limitations of traditional decision trees in identifying epistatic effects.
To provide a novel computational approach for genetic association studies.

Main Methods:

Utilized grammatical evolution to build decision trees capable of detecting gene-gene interactions.
Developed and applied the Grammatical Evolution Decision Trees (GEDT) method and software.
Evaluated GEDT performance on simulated data against traditional decision trees and random search.

Main Results:

GEDT demonstrated high power in detecting genetic risk models, including those with moderate effect sizes.
The method successfully identified gene-gene interactions, both with and without significant main effects.
GEDT outperformed traditional decision tree algorithms and random search in detecting purely epistatic interactions.

Conclusions:

Grammatical Evolution Decision Trees (GEDT) represent a promising new approach for genetic association studies.
GEDT shows significant potential for identifying complex gene-gene interactions.
The method is currently in early development but shows strong performance in simulations.